The clustering approach is considered as a vital method for wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving the energy and prolonging network lifetime which is totally dependent on the sensors battery, that is considered as a major challenge in the WSNs. Classification algorithms such as K-means (KM) and Fuzzy C-means (FCM), which are two of the most used algorithms in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces these algorithms to produce unbalanced clusters, which adversely affects the lifetime of the network. Based for our knowledge, there is no study has analyzed the performance of these algorithms in terms clusters construction in WSNs. In this study, we investigate in KM and FCM performance and which of them has better ability to construct balanced clusters, in order to enable the researchers to choose the appropriate algorithm for the purpose of improving network lifespan. In this study, we utilize new parameters to evaluate the performance of clusters formation in multi-scenarios. Simulation result shows that our FCM is more superior than KM by producing balanced clusters with the random distribution manner for sensor nodes.
Improved fuzzy c-means algorithm based on a novel mechanism for the formation...TELKOMNIKA JOURNAL
The clustering approach is considered as a vital method for many fields suchas machine learning, pattern recognition, image processing, information retrieval, data compression, computer graphics, and others.Similarly, it hasgreat significance in wireless sensor networks (WSNs) by organizing thesensor nodes into specific clusters. Consequently, saving energy and prolonging network lifetime, which is totally dependent on the sensor’s battery, that is considered asa major challenge in the WSNs. Fuzzyc-means (FCM) is one of classification algorithm, which is widely used in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces this algorithm to produce unbalanced clusters, which adversely affects the lifetime of the network.To overcome this problem, a new clustering method called FCM-CMhas been proposed by improving the FCM algorithm to form balanced clustersfor random nodes deployment. The improvement is conductedby integrating the FCM with a centralized mechanism(CM).The proposed method will be evaluated based on four new parameters. Simulation result shows that our proposed algorithm is more superior to FCM by producing balanced clustersin addition to increasing the balancing of the intra-distances of the clusters, which leads to energy conservation and prolonging network lifespan.
Energy efficiency has recently turned out to be primary issue in wireless sensor networks.
Sensor networks are battery powered, therefore become dead after a certain period of time. Thus,
improving the data dissipation in energy efficient way becomes more challenging problem in order to
improve the lifetime for sensor devices. The clustering and tree based data aggregation for sensor
networks can enhance the network lifetime of wireless sensor networks. Non-dominated Sorting Genetic
Algorithm (NSGA) -III based energy efficient clustering and tree based routing protocol is proposed.
Initially, clusters are formed on the basis of remaining energy, then, NSGA-III based data aggregation
will come in action to improve the inter-cluster data aggregation further. Extensive analysis demonstrates
that proposed protocol considerably enhances network lifetime over other techniques.
AN ENHANCED HYBRID ROUTING AND CLUSTERING TECHNIQUE FOR WIRELESS SENSOR NETWORKijwmn
Wireless Sensor Networks (WSN) have extensively deployed in a wide range of applications. However, WSN still faces several limitations in processing capabilities, memory, and power supply of sensor nodes. It is required to extend the lifetime of WSN. Mainly this is achieved by routing protocols choosing the best transmission path in-network with desired power conservation.This cause is developing a generic protocol framework for WSNa big challenge. This work proposed a new routing technique, described as Hybrid Routing-Clustering (HRC) model. This new approach takes advantage of clustering and routing procedures defined in K-Mean clustering and AODV routing, which constituted of three phases. This development aims to achieve enhanced power conservation rate in consequence network lifetime. An extensive evaluation methodology utilized to measure the performance of the proposed model in simulated scenarios.The results categorized in terms of the average amount of packet received and power conservation rate. The Hybrid Routing-Clustering (HRC) model was determined, showed enhanced results regarding both parameters. In the end, they are comparing these results with well-known routing and well-known clustering algorithms.
A NODE DEPLOYMENT MODEL WITH VARIABLE TRANSMISSION DISTANCE FOR WIRELESS SENS...ijwmn
The deployment of network nodes is essential to ensure the wireless sensor network's regular operation and affects the multiple network performance metrics, such as connectivity, coverage, lifetime, and cost. This paper focuses on the problem of minimizing network costs while meeting network requirements, and proposes a corona-based deployment method by using the variable transmission distance sensor. Based on the analysis of node energy consumption and network cost, an optimization model to minimize Cost Per Unit Area is given. The transmission distances and initial energy of the sensors are obtained by solving the model. The optimization model is improved to ensure the energy consumption balance of nodes in the same corona. Based on these parameters, the process of network node deployment is given. Deploying the
network through this method will greatly reduce network costs.
IMPROVEMENTS IN ROUTING ALGORITHMS TO ENHANCE LIFETIME OF WIRELESS SENSOR NET...IJCNCJournal
Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited
computation, communication, memory, and energy resources that are being used fora huge range of
applications. Clustering in WSNs is an effective way to minimize the energy consumption of sensor nodes.
In this paper improvements in various parameters are compared for three different routing algorithms.
First, it is started with Low Energy Adaptive Cluster Hierarchy (LEACH)which is a famed clustering
mechanism that elects a CH based on the probability model. Then, work describes a Fuzzy logic system
initiated CH selection algorithm for LEACH. Then Artificial Bee Colony (ABC)which is an optimisation
protocol owes its inspiration to the exploration behaviour of honey bees. In this study ABC optimization
algorithm is proposed for fuzzy rule selection. Then, the results of the three routing algorithms are
compared with respect to various parameters
AN OPTIMUM ENERGY CONSUMPTION HYBRID ALGORITHM FOR XLN STRATEGIC DESIGN IN WSN’SIJCNCJournal
In this paper, X-Layer protocol is originated which executes mobility error prediction (MEP) algorithm to calculate the remaining energy level of each node. This X-Layer protocol structure employs the mobility aware protocol that senses the mobility concerned to each node with the utilization of Ad-hoc On-Demand Distance Vector (AODV), which shares the information or data specific to the distance among individual nodes. With the help of this theory, the neighbour list will be updated only to those nodes which are mobile resulting in less energy consumption when compared to all (static/mobile) other nodes in the network. Apart from the MEP algorithm, clustering head (CH) election algorithm has also been specified to identify the relevant clusters whether they exists within the network region or not. Also clustering multi-hop routing (CMHR) algorithm was implemented in which the node can identify the cluster to which it belongs depending upon the distance from each cluster surrounding the node. Finally comprising the AODV routing protocol with the Two-Ray Ground method, we implement X-Layer protocol structure by considering MAC protocol in accordance to IEEE 802.15.4 to obtain the best results in energy consumption and also by reducing the energy wastage with respect to each node. The effective results had been illustrated through Network Simulator-II platform.
Enhancenig OLSR routing protocol using K-means clustering in MANETs IJECEIAES
The design of robust routing protocol schemes for MANETs is quite complex, due to the characteristics and structural constraints of this network. A numerous variety of protocol schemes have been proposed in literature. Most of them are based on traditional method of routing, which doesn’t guarantee basic levels of Qos, when the network becomes larger, denser and dynamic. To solve this problem we use one of the most popular methods named clustering. In this work we try to improve the Qos in MANETs. We propose an algorithm of clustering based in the new mobility metric and K-Means method to distribute the nodes into several clusters; it is implemented to standard OLSR protocol giving birth a new protocol named OLSR Kmeans-SDE. The simulations showed that the results obtained by OLSR Kmeans-SDE exceed those obtained by standard OLSR Kmeans and OLSR Kmed+ in terms of, traffic Control, delay and packet delivery ratio.
Improved fuzzy c-means algorithm based on a novel mechanism for the formation...TELKOMNIKA JOURNAL
The clustering approach is considered as a vital method for many fields suchas machine learning, pattern recognition, image processing, information retrieval, data compression, computer graphics, and others.Similarly, it hasgreat significance in wireless sensor networks (WSNs) by organizing thesensor nodes into specific clusters. Consequently, saving energy and prolonging network lifetime, which is totally dependent on the sensor’s battery, that is considered asa major challenge in the WSNs. Fuzzyc-means (FCM) is one of classification algorithm, which is widely used in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces this algorithm to produce unbalanced clusters, which adversely affects the lifetime of the network.To overcome this problem, a new clustering method called FCM-CMhas been proposed by improving the FCM algorithm to form balanced clustersfor random nodes deployment. The improvement is conductedby integrating the FCM with a centralized mechanism(CM).The proposed method will be evaluated based on four new parameters. Simulation result shows that our proposed algorithm is more superior to FCM by producing balanced clustersin addition to increasing the balancing of the intra-distances of the clusters, which leads to energy conservation and prolonging network lifespan.
Energy efficiency has recently turned out to be primary issue in wireless sensor networks.
Sensor networks are battery powered, therefore become dead after a certain period of time. Thus,
improving the data dissipation in energy efficient way becomes more challenging problem in order to
improve the lifetime for sensor devices. The clustering and tree based data aggregation for sensor
networks can enhance the network lifetime of wireless sensor networks. Non-dominated Sorting Genetic
Algorithm (NSGA) -III based energy efficient clustering and tree based routing protocol is proposed.
Initially, clusters are formed on the basis of remaining energy, then, NSGA-III based data aggregation
will come in action to improve the inter-cluster data aggregation further. Extensive analysis demonstrates
that proposed protocol considerably enhances network lifetime over other techniques.
AN ENHANCED HYBRID ROUTING AND CLUSTERING TECHNIQUE FOR WIRELESS SENSOR NETWORKijwmn
Wireless Sensor Networks (WSN) have extensively deployed in a wide range of applications. However, WSN still faces several limitations in processing capabilities, memory, and power supply of sensor nodes. It is required to extend the lifetime of WSN. Mainly this is achieved by routing protocols choosing the best transmission path in-network with desired power conservation.This cause is developing a generic protocol framework for WSNa big challenge. This work proposed a new routing technique, described as Hybrid Routing-Clustering (HRC) model. This new approach takes advantage of clustering and routing procedures defined in K-Mean clustering and AODV routing, which constituted of three phases. This development aims to achieve enhanced power conservation rate in consequence network lifetime. An extensive evaluation methodology utilized to measure the performance of the proposed model in simulated scenarios.The results categorized in terms of the average amount of packet received and power conservation rate. The Hybrid Routing-Clustering (HRC) model was determined, showed enhanced results regarding both parameters. In the end, they are comparing these results with well-known routing and well-known clustering algorithms.
A NODE DEPLOYMENT MODEL WITH VARIABLE TRANSMISSION DISTANCE FOR WIRELESS SENS...ijwmn
The deployment of network nodes is essential to ensure the wireless sensor network's regular operation and affects the multiple network performance metrics, such as connectivity, coverage, lifetime, and cost. This paper focuses on the problem of minimizing network costs while meeting network requirements, and proposes a corona-based deployment method by using the variable transmission distance sensor. Based on the analysis of node energy consumption and network cost, an optimization model to minimize Cost Per Unit Area is given. The transmission distances and initial energy of the sensors are obtained by solving the model. The optimization model is improved to ensure the energy consumption balance of nodes in the same corona. Based on these parameters, the process of network node deployment is given. Deploying the
network through this method will greatly reduce network costs.
IMPROVEMENTS IN ROUTING ALGORITHMS TO ENHANCE LIFETIME OF WIRELESS SENSOR NET...IJCNCJournal
Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited
computation, communication, memory, and energy resources that are being used fora huge range of
applications. Clustering in WSNs is an effective way to minimize the energy consumption of sensor nodes.
In this paper improvements in various parameters are compared for three different routing algorithms.
First, it is started with Low Energy Adaptive Cluster Hierarchy (LEACH)which is a famed clustering
mechanism that elects a CH based on the probability model. Then, work describes a Fuzzy logic system
initiated CH selection algorithm for LEACH. Then Artificial Bee Colony (ABC)which is an optimisation
protocol owes its inspiration to the exploration behaviour of honey bees. In this study ABC optimization
algorithm is proposed for fuzzy rule selection. Then, the results of the three routing algorithms are
compared with respect to various parameters
AN OPTIMUM ENERGY CONSUMPTION HYBRID ALGORITHM FOR XLN STRATEGIC DESIGN IN WSN’SIJCNCJournal
In this paper, X-Layer protocol is originated which executes mobility error prediction (MEP) algorithm to calculate the remaining energy level of each node. This X-Layer protocol structure employs the mobility aware protocol that senses the mobility concerned to each node with the utilization of Ad-hoc On-Demand Distance Vector (AODV), which shares the information or data specific to the distance among individual nodes. With the help of this theory, the neighbour list will be updated only to those nodes which are mobile resulting in less energy consumption when compared to all (static/mobile) other nodes in the network. Apart from the MEP algorithm, clustering head (CH) election algorithm has also been specified to identify the relevant clusters whether they exists within the network region or not. Also clustering multi-hop routing (CMHR) algorithm was implemented in which the node can identify the cluster to which it belongs depending upon the distance from each cluster surrounding the node. Finally comprising the AODV routing protocol with the Two-Ray Ground method, we implement X-Layer protocol structure by considering MAC protocol in accordance to IEEE 802.15.4 to obtain the best results in energy consumption and also by reducing the energy wastage with respect to each node. The effective results had been illustrated through Network Simulator-II platform.
Enhancenig OLSR routing protocol using K-means clustering in MANETs IJECEIAES
The design of robust routing protocol schemes for MANETs is quite complex, due to the characteristics and structural constraints of this network. A numerous variety of protocol schemes have been proposed in literature. Most of them are based on traditional method of routing, which doesn’t guarantee basic levels of Qos, when the network becomes larger, denser and dynamic. To solve this problem we use one of the most popular methods named clustering. In this work we try to improve the Qos in MANETs. We propose an algorithm of clustering based in the new mobility metric and K-Means method to distribute the nodes into several clusters; it is implemented to standard OLSR protocol giving birth a new protocol named OLSR Kmeans-SDE. The simulations showed that the results obtained by OLSR Kmeans-SDE exceed those obtained by standard OLSR Kmeans and OLSR Kmed+ in terms of, traffic Control, delay and packet delivery ratio.
Genetic-fuzzy based load balanced protocol for WSNsIJECEIAES
Recent advancement in wireless sensor networks primarily depends upon energy constraint. Clustering is the most effective energy-efficient technique to provide robust, fault-tolerant and also enhance network lifetime and coverage. Selection of optimal number of cluster heads and balancing the load of cluster heads are most challenging issues. Evolutionary based approach and soft computing approach are best suitable for counter the above problems rather than mathematical approach. In this paper we propose hybrid technique where Genetic algorithm is used for the selection of optimal number of cluster heads and their fitness value of chromosome to give optimal number of cluster head and minimizing the energy consumption is provided with the help of fuzzy logic approach. Finally cluster heads uses multi-hop routing based on A*(A-star) algorithm to send aggregated data to base station which additionally balance the load. Comparative study among LEACH, CHEF, LEACH-ERE, GAEEP shows that our proposed algorithm outperform in the area of total energy consumption with various rounds and network lifetime, number of node alive versus rounds and packet delivery or packet drop ratio over the rounds, also able to balances the load at cluster head.
Every cluster comprise of a leader which is known as cluster head. The cluster head will be chosen by the sensor nodes in the individual cluster or be pre-assigned by the user. The main advantages of clustering are the transmission of aggregated data to the base station, offers scalability for huge number of nodes and trims down energy consumption. Fundamentally, clustering could be classified into centralized clustering, distributed clustering and hybrid clustering. In centralized clustering, the cluster head is fixed. The rest of the nodes in the cluster act as member nodes. In distributed clustering, the cluster head is not fixed. The cluster head keeps on shifting form node to node within the cluster on the basis of some parameters. Hybrid clustering is the combination of both centralized clustering and distributed clustering mechanisms. This paper gives a brief overview on clustering process in wireless sensor networks. A research on the well evaluated distributed clustering algorithm Low Energy Adaptive Clustering Hierarchy (LEACH) and its followers are portrayed artistically. To overcome the drawbacks of these existing algorithms a hybrid distributed clustering model has been proposed for attaining energy efficiency to a larger scale.
Dynamic K-Means Algorithm for Optimized Routing in Mobile Ad Hoc Networks IJCSES Journal
In this paper, a dynamic K-means algorithm to improve the routing process in Mobile Ad-Hoc networks
(MANETs) is presented. Mobile ad-hoc networks are a collocation of mobile wireless nodes that can
operate without using focal access points, pre-existing infrastructures, or a centralized management point.
In MANETs, the quick motion of nodes modifies the topology of network. This feature of MANETS is lead
to various problems in the routing process such as increase of the overhead massages and inefficient
routing between nodes of network. A large variety of clustering methods have been developed for
establishing an efficient routing process in MANETs. Routing is one of the crucial topics which are having
significant impact on MANETs performance. The K-means algorithm is one of the effective clustering
methods aimed to reduce routing difficulties related to bandwidth, throughput and power consumption.
This paper proposed a new K-means clustering algorithm to find out optimal path from source node to
destinations node in MANETs. The main goal of proposed approach which is called the dynamic K-means
clustering methods is to solve the limitation of basic K-means method like permanent cluster head and fixed
cluster members. The experimental results demonstrate that using dynamic K-means scheme enhance the
performance of routing process in Mobile ad-hoc networks.
Abstract: Energy consumption is one of the constraints in Wireless Sensor Networks (WSNs). The routing protocols are the hot areas to address quality-of-service (QoS) related issues viz. Energy consumption, network lifetime, network scalability and packet overhead. In existing system a hybrid optimization based PEGASIS-DSR optimized routing protocol (PDORP) is presented which used cache and directional transmission concept of both proactive and reactive routing protocols. The performance of PDORP has been evaluated and the results indicated that it performs better in most significant parameters. The performance of the existing method is checked when it is evaluated and validated with the nodes which are highly dynamic in nature based on the application requirement. The current system finds the trusted nodes in the case of only static environment. To overcome the issue the proposed system is applied for dynamic WSN’s with the location frequently being changed. The PDORP-LC is applied with local caching (LC) to acquire the location information so that the path learning can be dynamic without depending on the fixed location. The proposed work is performing in dynamic environment with the dynamic derivation of trusted nodes.
Keywords: local caching (LC), Wireless Sensor Networks (WSNs), PEGASIS-DSR optimized routing protocol (PDORP).
Title: Energy Efficient Optimal Paths Using PDORP-LC
Author: ADARSH KUMAR B, BIBIN CHRISTOPHER, ISSAC SAJAN, AJ DEEPA
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
A Cluster-Based Routing Protocol and Fault Detection for Wireless Sensor NetworkIJCNCJournal
In Wireless Sensors Networks (WSN) based application, a large number of sensor devices must be deployed. Energy efficiency and network lifetime are the two most challenging issues in WSN. As a consequence, the main goal is to reduce the overall energy consumption using clustering protocols which have to ensure reliability and connectivity in large-scale WSN. This work presents a new clustering and routing algorithm based on the properties of the sensor networks. The main goal of this work is to extend the network lifetime via charge equilibration in the WSN. According to many errors with sensing devices and to have greater data accuracy, we use a quorum mechanism. The proposed algorithms are evaluated widely and the results are compared with related works. The experimental results show that the proposed algorithm provides an effective improvement in terms of energy consumption, data accuracy and network lifetime.
ADAPTIVE RANDOM SPATIAL BASED CHANNEL ESTIMATION (ARSCE) FOR MILLIMETER WAVE ...IJCNCJournal
Millimeter-wave and mMIMO communications are the most essential success systems for next-generation wireless sensor networks to have enormous amounts of accessible throughput and spectrum. Through installing huge antenna arrays at the base station and performing coherent transceiver processing, mMIMO is a potential technology for enhancing the bandwidth efficiency of wireless sensor networks. The use of mmWave frequencies for mMIMO systems solves the problem of high path-loss through offering greater antenna gains. In this work, we provide a design with a random spatial sample structure that incorporates a totally random step before the analogue is received. It contains a totally random step before the analogue received signals are sent into the digital component of the HBF receiver. Adaptive random spatial based channel estimation (ARSCE) is proposed for channel session measurement collection, and an analogue combiner with valves has been used to estimate the signals at each receiving antenna. The proposed optimization problem formulation attempts to discover the orientations and gains of wideband channel routes. In addition, our proposed model has compared to various state-of-art techniques while considering error minimization.
MULTICASTING BASED ENHANCED PROACTIVE SOURCE ROUTING IN MANETSIJCNCJournal
Mobile Ad-hoc Network (MANET) is an accumulation of movable nodes organizing a irregular topology without centralized administration. In a MANET, multicasting is a significant technique for utilizing data communication system. Multicasting based enhanced proactive source routing is proposed in this paper for Mobile Ad hoc Networks. It explains an innovative multicasting algorithm that considers the transmission energy and residual energy while forwarding the data packets. It improves the network throughput and raises the network lifetimes. Simulation analysis is carried in this proposed system and this method shows improved performance over the existing system.
Optimized Cluster Establishment and Cluster-Head Selection Approach in WSNIJCNCJournal
In recent years, limited resources of user products and energy-saving are recognized as the major challenges of Wireless Sensor Networks (WSNs). Clustering is a practical technique that can reduce all energy consumption and provide stability of workload that causes a larger difference in energy depletion among other nodes and cluster heads (CHs). In addition, clustering is the solution of energy-efficient for maximizing the network longevity and improvising energy efficiency. In this paper, a novel OCE-CHS (Optimized Cluster Establishment and Cluster-Head Selection) approach for sensor nodes is represented to improvise the packet success ratio and reduce the average energy-dissipation. The main contribution of this paper is categorized into two processes, first, the clustering algorithm is improvised that periodically chooses the optimal set of the CHs according to the speed of the average node and average-node energy. This is considerably distinguished from node-based clustering that utilizes a distributed clustering algorithm to choose CHs based on the speed of the current node and remaining node energy. Second, more than one factor is assumed for the detached node to join the optimal cluster. In the result section, we discuss our clustering protocols implementation of optimal CH-selection to evade the death of SNs, maximizing throughput, and further improvise the network lifetime by minimizing energy consumption.
The novel applications of sensor networks impose some requirements in wireless sensor network design. With the energy efficiency and lifetime awareness, the throughput and network delayalso required to support emerging applications of sensor networks. In this paper, we propose
throughput and network delay aware intra-cluster routing protocol. We introduce the back-up links in the intra-cluster communication path. The link throughput, communication delay, packet loss ratio, interference, residual energy and node distance are the considered factors in finding efficient path of data communication among the sensor nodes within the cluster. The
simulation result shows the higher throughput and lower average packet delay rate for the proposed routing protocol than the existing benchmarks. The proposed routing protocol also shows energy efficiency and lifetime awareness with better connectivity rate.
Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for He...chokrio
Ameliorating the lifetime in heterogeneous wireless sensor network is an important task because the sensor nodes are limited in the resource energy. The best way to improve a WSN lifetime is the clustering based algorithms in which each cluster is managed by a leader called Cluster Head. Each other node must communicate with this CH to send the data sensing. The nearest base station nodes must also send their data to their leaders, this causes a loss of energy. In this paper, we propose a new approach to ameliorate a threshold distributed energy efficient clustering protocol for heterogeneous wireless sensor networks by excluding closest nodes to the base station in the clustering process. We show by simulation in MATLAB that the proposed approach increases obviously the number of the received packet messages and prolongs the lifetime of the network compared to TDEEC protocol.
Energy efficient clustering and routing optimization model for maximizing lif...IJECEIAES
Recently, the wide adoption of WSNs (Wireless-Sensor-Networks) is been seen for provision non-real time and real-time application services such as intelligent transportation and health care monitoring, intelligent transportation etc. Provisioning these services requires energy-efficient WSN. The clustering technique is an efficient mechanism that plays a main role in reducing the energy consumption of WSN. However, the existing model is designed considering reducing energy- consumption of the sensor-device for the homogenous network. However, it incurs energy-overhead (EO) between cluster-head (CH). Further, maximizing coverage time is not considered by the existing clustering approach considering heterogeneous networks affecting lifetime performance. In order to overcome these research challenges, this work presents an energy efficient clustering and routing optimization (EECRO) model adopting cross-layer design for heterogeneous networks. The EECRO uses channel gain information from the physical layer and TDMA based communication is adopted for communication among both intra-cluster and inter-cluster communication. Further, clustering and routing optimization are presented to bring a good trade-off among minimizing the energy of CH, enhancing coverage time and maximizing the lifetime of sensor-network (SN). The experiments are conducted to estimate the performance of EECRO over the existing model. The significantperformance is attained by EECRO over the existing model in terms of minimizing routing and communication overhead and maximizing the lifetime of WSNs.
SECTOR TREE-BASED CLUSTERING FOR ENERGY EFFICIENT ROUTING PROTOCOL IN HETEROG...IJCNCJournal
One of the main challenges for researchers to build routing protocols is how to use energy efficiently to extend the lifespan of the whole wireless sensor networks (WSN) because sensor nodes have limited battery power resources. In this work, we propose a Sector Tree-Based clustering routing protocol (STB-EE) for Energy Efficiency to cope with this problem, where the entire network area is partitioned into dynamic sectors (clusters), which balance the number of alive nodes. The nodes in each sector only communicate with their nearest neighbour by constructing a minimum tree based on the Kruskal algorithm and using mixed distance from candidate node to base station (BS) and remaining energy of candidate nodes to determine which node will become the cluster head (CH) in each cluster? By calculating the duration of time in each round for suitability, STB-EE increases the number of data packets sent to the BS. Our simulation results show that the network lifespan using STB-EE can be improved by about 16% and 10% in comparison to power-efficient gathering in sensor information system (PEGASIS) and energy-efficient PEGASIS-based protocol (IEEPB), respectively.
QoS controlled capacity offload optimization in heterogeneous networksjournalBEEI
An efficient resource allocation mechanism in the physical layer of wireless networks ensures that resources such as bandwidth and power are used with high efficiency in spite of low delay and high edge user data rate. Microcells in the network are typically set with bias settings to artificially increase the Signal-to-Interference-Plus-Noise Ratio, thus encouraging users to offload to the microcell. However, the artificial bias settings are tedious and often suboptimal. This work presents a low complexity algorithm for maximization of network capacity with load balancing in a heterogeneous network without the need for bias setting. The small cells were deployed in a grid topology at a selected distance from macrocell to enhance network capacity through coverage overlap. User association and minimum user throughput were incorporated as constraints to enable closer simulation to real word Quality of Service requirements. The results showed that the proposed algorithm was able to maintain less than 10% user drop rate. The proposed algorithm can increase user confidence as well as maintain load balancing, maintain the scalability, and reduce power consumption of the wireless network.
Novel approach for hybrid MAC scheme for balanced energy and transmission in ...IJECEIAES
Hybrid medium access control (MAC) scheme is one of the prominent mechanisms to offer energy efficiency in wireless sensor network where the potential features for both contention-based and schedule-based approaches are mechanized. However, the review of existing hybrid MAC scheme shows many loopholes where mainly it is observed that there is too much inclusion of time-slotting or else there is an inclusion of sophisticated mechanism not meant for offering flexibility to sensor node towards extending its services for upcoming applications of it. Therefore, this manuscript introduces a novel hybrid MAC scheme which is meant for offering cost effective and simplified scheduling operation in order to balance the performance of energy efficiency along with data aggregation performance. The simulated outcome of the study shows that proposed system offers better energy consumption, better throughput, reduced memory consumption, and faster processing in contrast to existing hybrid MAC protocols.
GREEDY CLUSTER BASED ROUTING FOR WIRELESS SENSOR NETWORKSijcsit
In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications. Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station. But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station. GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for
balancing energy consumption. Experimental results show that GCR achieves significant energy savings and prolong network lifetime.
Cds based energy efficient topology control algorithm in wireless sensor net...eSAT Journals
Abstract Wireless Sensor Networks (WSNs) are a self organized network which consists of large number of sensor nodes that collects the data in a various environment [1, 2]. The sensors work on battery that have limited lifetime so it is a challenge to create an energy efficient network that can reduce the energy consumption and interference in the network graph and thereby extend the network lifetime [2]. For saving energy and extending network lifetime the topology is a well-known technique in WSNs and the widely used topology control strategy is the construction of Connected Dominating Set (CDS) [3, 4]. In this paper, we construct a CDS based energy efficient topology control algorithm i.e. GCDSTC for WSNs. The performance analysis includes the study of GCDSTC algorithm in terms of complexity and compares it with EBTC (Energy Balanced Topology Control) algorithm. The simulation results indicate that the GCDSTC algorithm reduce the energy consumption and interference in the network graph, in order to enhance the network lifetime. Keywords: Wireless Sensor Network (WSN), Connected Dominating Set (CDS), Topology Control (TC), etc.
Efficient energy, cost reduction, and QoS based routing protocol for wireless...IJECEIAES
Recent developments and widespread in wireless sensor network have led to many routing protocols, many of these protocols consider the efficiency of energy as the ultimate factor to maximize the WSN lifetime. The quality of Service (QoS) requirements for different applications of wireless sensor networks has posed additional challenges. Imaging and data transmission needs both QoS aware routing and energy to ensure the efficient use of sensors. In this paper, we propose an Efficient, Energy-Aware, Least Cost, (ECQSR) quality of service routing protocol for sensor networks which can run efficiently with best-effort traffic processing. The protocol aims to maximize the lifetime of the network out of balancing energy consumption across multiple nodes, by using the concept of service differentiation, finding lower cost by finding the shortest path using nearest neighbor algorithm (NN), also put certain constraints on the delay of the path for real-time data from where link cost that captures energy nodes reserve, energy of the transmission, error rate and other parameters. The results show that the proposed protocol improves the network lifetime and low power consumption.
Data Dissemination in Wireless Sensor Networks: A State-of-the Art SurveyCSCJournals
A wireless sensor network is a network of tiny nodes with wireless sensing capacity for data collection processing and further communicating with the Base Station this paper discusses the overall mechanism of data dissemination right from data collection at the sensor nodes, clustering of sensor nodes, data aggregation at the cluster heads and disseminating data to the Base Station the overall motive of the paper is to conserve energy so that lifetime of the network is extended this paper highlights the existing algorithms and open research gaps in efficient data dissemination.
An Energy Efficient Mobile Sink Based Mechanism for WSNs.pdfMohammad Siraj
Network lifetime and energy efficiency are crucial performance metrics used to evaluate
wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be
employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering
is to decrease the energy consumption of the network. In fact, the clustering technique will be
considered effective if the energy consumed by sensor nodes decreases after applying clustering,
however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this
paper, the energy consumption of nodes, before clustering, is considered to determine the optimal
cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of
cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent
problem which leads to a remarkable decrease in the network’s lifespan. This problem stems from
the asynchronous energy depletion of nodes located in different layers of the network.
In order to provide sensing services to low-powered IoT devices, wireless sensor networks (WSNs) organize specialized transducers into networks. Energy usage is one of the most important design concerns in WSN because it is very hard to replace or recharge the batteries in sensor nodes. For an energy-constrained network, the clustering technique is crucial in preserving battery life. By strategically selecting a cluster head (CH), a network's load can be balanced, resulting in decreased energy usage and extended system life. Although clustering has been predominantly used in the literature, the concept of chain-based clustering has not yet been explored. As a result, in this paper, we employ a chain-based clustering architecture for data dissemination in the network. Furthermore, for CH selection, we employ the coati optimisation algorithm, which was recently proposed and has demonstrated significant improvement over other optimization algorithms. In this method, the parameters considered for selecting the CH are energy, node density, distance, and the network’s average energy. The simulation results show tremendous improvement over the competitive cluster-based routing algorithms in the context of network lifetime, stability period (first node dead), transmission rate, and the network's power reserves.
Genetic-fuzzy based load balanced protocol for WSNsIJECEIAES
Recent advancement in wireless sensor networks primarily depends upon energy constraint. Clustering is the most effective energy-efficient technique to provide robust, fault-tolerant and also enhance network lifetime and coverage. Selection of optimal number of cluster heads and balancing the load of cluster heads are most challenging issues. Evolutionary based approach and soft computing approach are best suitable for counter the above problems rather than mathematical approach. In this paper we propose hybrid technique where Genetic algorithm is used for the selection of optimal number of cluster heads and their fitness value of chromosome to give optimal number of cluster head and minimizing the energy consumption is provided with the help of fuzzy logic approach. Finally cluster heads uses multi-hop routing based on A*(A-star) algorithm to send aggregated data to base station which additionally balance the load. Comparative study among LEACH, CHEF, LEACH-ERE, GAEEP shows that our proposed algorithm outperform in the area of total energy consumption with various rounds and network lifetime, number of node alive versus rounds and packet delivery or packet drop ratio over the rounds, also able to balances the load at cluster head.
Every cluster comprise of a leader which is known as cluster head. The cluster head will be chosen by the sensor nodes in the individual cluster or be pre-assigned by the user. The main advantages of clustering are the transmission of aggregated data to the base station, offers scalability for huge number of nodes and trims down energy consumption. Fundamentally, clustering could be classified into centralized clustering, distributed clustering and hybrid clustering. In centralized clustering, the cluster head is fixed. The rest of the nodes in the cluster act as member nodes. In distributed clustering, the cluster head is not fixed. The cluster head keeps on shifting form node to node within the cluster on the basis of some parameters. Hybrid clustering is the combination of both centralized clustering and distributed clustering mechanisms. This paper gives a brief overview on clustering process in wireless sensor networks. A research on the well evaluated distributed clustering algorithm Low Energy Adaptive Clustering Hierarchy (LEACH) and its followers are portrayed artistically. To overcome the drawbacks of these existing algorithms a hybrid distributed clustering model has been proposed for attaining energy efficiency to a larger scale.
Dynamic K-Means Algorithm for Optimized Routing in Mobile Ad Hoc Networks IJCSES Journal
In this paper, a dynamic K-means algorithm to improve the routing process in Mobile Ad-Hoc networks
(MANETs) is presented. Mobile ad-hoc networks are a collocation of mobile wireless nodes that can
operate without using focal access points, pre-existing infrastructures, or a centralized management point.
In MANETs, the quick motion of nodes modifies the topology of network. This feature of MANETS is lead
to various problems in the routing process such as increase of the overhead massages and inefficient
routing between nodes of network. A large variety of clustering methods have been developed for
establishing an efficient routing process in MANETs. Routing is one of the crucial topics which are having
significant impact on MANETs performance. The K-means algorithm is one of the effective clustering
methods aimed to reduce routing difficulties related to bandwidth, throughput and power consumption.
This paper proposed a new K-means clustering algorithm to find out optimal path from source node to
destinations node in MANETs. The main goal of proposed approach which is called the dynamic K-means
clustering methods is to solve the limitation of basic K-means method like permanent cluster head and fixed
cluster members. The experimental results demonstrate that using dynamic K-means scheme enhance the
performance of routing process in Mobile ad-hoc networks.
Abstract: Energy consumption is one of the constraints in Wireless Sensor Networks (WSNs). The routing protocols are the hot areas to address quality-of-service (QoS) related issues viz. Energy consumption, network lifetime, network scalability and packet overhead. In existing system a hybrid optimization based PEGASIS-DSR optimized routing protocol (PDORP) is presented which used cache and directional transmission concept of both proactive and reactive routing protocols. The performance of PDORP has been evaluated and the results indicated that it performs better in most significant parameters. The performance of the existing method is checked when it is evaluated and validated with the nodes which are highly dynamic in nature based on the application requirement. The current system finds the trusted nodes in the case of only static environment. To overcome the issue the proposed system is applied for dynamic WSN’s with the location frequently being changed. The PDORP-LC is applied with local caching (LC) to acquire the location information so that the path learning can be dynamic without depending on the fixed location. The proposed work is performing in dynamic environment with the dynamic derivation of trusted nodes.
Keywords: local caching (LC), Wireless Sensor Networks (WSNs), PEGASIS-DSR optimized routing protocol (PDORP).
Title: Energy Efficient Optimal Paths Using PDORP-LC
Author: ADARSH KUMAR B, BIBIN CHRISTOPHER, ISSAC SAJAN, AJ DEEPA
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
A Cluster-Based Routing Protocol and Fault Detection for Wireless Sensor NetworkIJCNCJournal
In Wireless Sensors Networks (WSN) based application, a large number of sensor devices must be deployed. Energy efficiency and network lifetime are the two most challenging issues in WSN. As a consequence, the main goal is to reduce the overall energy consumption using clustering protocols which have to ensure reliability and connectivity in large-scale WSN. This work presents a new clustering and routing algorithm based on the properties of the sensor networks. The main goal of this work is to extend the network lifetime via charge equilibration in the WSN. According to many errors with sensing devices and to have greater data accuracy, we use a quorum mechanism. The proposed algorithms are evaluated widely and the results are compared with related works. The experimental results show that the proposed algorithm provides an effective improvement in terms of energy consumption, data accuracy and network lifetime.
ADAPTIVE RANDOM SPATIAL BASED CHANNEL ESTIMATION (ARSCE) FOR MILLIMETER WAVE ...IJCNCJournal
Millimeter-wave and mMIMO communications are the most essential success systems for next-generation wireless sensor networks to have enormous amounts of accessible throughput and spectrum. Through installing huge antenna arrays at the base station and performing coherent transceiver processing, mMIMO is a potential technology for enhancing the bandwidth efficiency of wireless sensor networks. The use of mmWave frequencies for mMIMO systems solves the problem of high path-loss through offering greater antenna gains. In this work, we provide a design with a random spatial sample structure that incorporates a totally random step before the analogue is received. It contains a totally random step before the analogue received signals are sent into the digital component of the HBF receiver. Adaptive random spatial based channel estimation (ARSCE) is proposed for channel session measurement collection, and an analogue combiner with valves has been used to estimate the signals at each receiving antenna. The proposed optimization problem formulation attempts to discover the orientations and gains of wideband channel routes. In addition, our proposed model has compared to various state-of-art techniques while considering error minimization.
MULTICASTING BASED ENHANCED PROACTIVE SOURCE ROUTING IN MANETSIJCNCJournal
Mobile Ad-hoc Network (MANET) is an accumulation of movable nodes organizing a irregular topology without centralized administration. In a MANET, multicasting is a significant technique for utilizing data communication system. Multicasting based enhanced proactive source routing is proposed in this paper for Mobile Ad hoc Networks. It explains an innovative multicasting algorithm that considers the transmission energy and residual energy while forwarding the data packets. It improves the network throughput and raises the network lifetimes. Simulation analysis is carried in this proposed system and this method shows improved performance over the existing system.
Optimized Cluster Establishment and Cluster-Head Selection Approach in WSNIJCNCJournal
In recent years, limited resources of user products and energy-saving are recognized as the major challenges of Wireless Sensor Networks (WSNs). Clustering is a practical technique that can reduce all energy consumption and provide stability of workload that causes a larger difference in energy depletion among other nodes and cluster heads (CHs). In addition, clustering is the solution of energy-efficient for maximizing the network longevity and improvising energy efficiency. In this paper, a novel OCE-CHS (Optimized Cluster Establishment and Cluster-Head Selection) approach for sensor nodes is represented to improvise the packet success ratio and reduce the average energy-dissipation. The main contribution of this paper is categorized into two processes, first, the clustering algorithm is improvised that periodically chooses the optimal set of the CHs according to the speed of the average node and average-node energy. This is considerably distinguished from node-based clustering that utilizes a distributed clustering algorithm to choose CHs based on the speed of the current node and remaining node energy. Second, more than one factor is assumed for the detached node to join the optimal cluster. In the result section, we discuss our clustering protocols implementation of optimal CH-selection to evade the death of SNs, maximizing throughput, and further improvise the network lifetime by minimizing energy consumption.
The novel applications of sensor networks impose some requirements in wireless sensor network design. With the energy efficiency and lifetime awareness, the throughput and network delayalso required to support emerging applications of sensor networks. In this paper, we propose
throughput and network delay aware intra-cluster routing protocol. We introduce the back-up links in the intra-cluster communication path. The link throughput, communication delay, packet loss ratio, interference, residual energy and node distance are the considered factors in finding efficient path of data communication among the sensor nodes within the cluster. The
simulation result shows the higher throughput and lower average packet delay rate for the proposed routing protocol than the existing benchmarks. The proposed routing protocol also shows energy efficiency and lifetime awareness with better connectivity rate.
Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for He...chokrio
Ameliorating the lifetime in heterogeneous wireless sensor network is an important task because the sensor nodes are limited in the resource energy. The best way to improve a WSN lifetime is the clustering based algorithms in which each cluster is managed by a leader called Cluster Head. Each other node must communicate with this CH to send the data sensing. The nearest base station nodes must also send their data to their leaders, this causes a loss of energy. In this paper, we propose a new approach to ameliorate a threshold distributed energy efficient clustering protocol for heterogeneous wireless sensor networks by excluding closest nodes to the base station in the clustering process. We show by simulation in MATLAB that the proposed approach increases obviously the number of the received packet messages and prolongs the lifetime of the network compared to TDEEC protocol.
Energy efficient clustering and routing optimization model for maximizing lif...IJECEIAES
Recently, the wide adoption of WSNs (Wireless-Sensor-Networks) is been seen for provision non-real time and real-time application services such as intelligent transportation and health care monitoring, intelligent transportation etc. Provisioning these services requires energy-efficient WSN. The clustering technique is an efficient mechanism that plays a main role in reducing the energy consumption of WSN. However, the existing model is designed considering reducing energy- consumption of the sensor-device for the homogenous network. However, it incurs energy-overhead (EO) between cluster-head (CH). Further, maximizing coverage time is not considered by the existing clustering approach considering heterogeneous networks affecting lifetime performance. In order to overcome these research challenges, this work presents an energy efficient clustering and routing optimization (EECRO) model adopting cross-layer design for heterogeneous networks. The EECRO uses channel gain information from the physical layer and TDMA based communication is adopted for communication among both intra-cluster and inter-cluster communication. Further, clustering and routing optimization are presented to bring a good trade-off among minimizing the energy of CH, enhancing coverage time and maximizing the lifetime of sensor-network (SN). The experiments are conducted to estimate the performance of EECRO over the existing model. The significantperformance is attained by EECRO over the existing model in terms of minimizing routing and communication overhead and maximizing the lifetime of WSNs.
SECTOR TREE-BASED CLUSTERING FOR ENERGY EFFICIENT ROUTING PROTOCOL IN HETEROG...IJCNCJournal
One of the main challenges for researchers to build routing protocols is how to use energy efficiently to extend the lifespan of the whole wireless sensor networks (WSN) because sensor nodes have limited battery power resources. In this work, we propose a Sector Tree-Based clustering routing protocol (STB-EE) for Energy Efficiency to cope with this problem, where the entire network area is partitioned into dynamic sectors (clusters), which balance the number of alive nodes. The nodes in each sector only communicate with their nearest neighbour by constructing a minimum tree based on the Kruskal algorithm and using mixed distance from candidate node to base station (BS) and remaining energy of candidate nodes to determine which node will become the cluster head (CH) in each cluster? By calculating the duration of time in each round for suitability, STB-EE increases the number of data packets sent to the BS. Our simulation results show that the network lifespan using STB-EE can be improved by about 16% and 10% in comparison to power-efficient gathering in sensor information system (PEGASIS) and energy-efficient PEGASIS-based protocol (IEEPB), respectively.
QoS controlled capacity offload optimization in heterogeneous networksjournalBEEI
An efficient resource allocation mechanism in the physical layer of wireless networks ensures that resources such as bandwidth and power are used with high efficiency in spite of low delay and high edge user data rate. Microcells in the network are typically set with bias settings to artificially increase the Signal-to-Interference-Plus-Noise Ratio, thus encouraging users to offload to the microcell. However, the artificial bias settings are tedious and often suboptimal. This work presents a low complexity algorithm for maximization of network capacity with load balancing in a heterogeneous network without the need for bias setting. The small cells were deployed in a grid topology at a selected distance from macrocell to enhance network capacity through coverage overlap. User association and minimum user throughput were incorporated as constraints to enable closer simulation to real word Quality of Service requirements. The results showed that the proposed algorithm was able to maintain less than 10% user drop rate. The proposed algorithm can increase user confidence as well as maintain load balancing, maintain the scalability, and reduce power consumption of the wireless network.
Novel approach for hybrid MAC scheme for balanced energy and transmission in ...IJECEIAES
Hybrid medium access control (MAC) scheme is one of the prominent mechanisms to offer energy efficiency in wireless sensor network where the potential features for both contention-based and schedule-based approaches are mechanized. However, the review of existing hybrid MAC scheme shows many loopholes where mainly it is observed that there is too much inclusion of time-slotting or else there is an inclusion of sophisticated mechanism not meant for offering flexibility to sensor node towards extending its services for upcoming applications of it. Therefore, this manuscript introduces a novel hybrid MAC scheme which is meant for offering cost effective and simplified scheduling operation in order to balance the performance of energy efficiency along with data aggregation performance. The simulated outcome of the study shows that proposed system offers better energy consumption, better throughput, reduced memory consumption, and faster processing in contrast to existing hybrid MAC protocols.
GREEDY CLUSTER BASED ROUTING FOR WIRELESS SENSOR NETWORKSijcsit
In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications. Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station. But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station. GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for
balancing energy consumption. Experimental results show that GCR achieves significant energy savings and prolong network lifetime.
Cds based energy efficient topology control algorithm in wireless sensor net...eSAT Journals
Abstract Wireless Sensor Networks (WSNs) are a self organized network which consists of large number of sensor nodes that collects the data in a various environment [1, 2]. The sensors work on battery that have limited lifetime so it is a challenge to create an energy efficient network that can reduce the energy consumption and interference in the network graph and thereby extend the network lifetime [2]. For saving energy and extending network lifetime the topology is a well-known technique in WSNs and the widely used topology control strategy is the construction of Connected Dominating Set (CDS) [3, 4]. In this paper, we construct a CDS based energy efficient topology control algorithm i.e. GCDSTC for WSNs. The performance analysis includes the study of GCDSTC algorithm in terms of complexity and compares it with EBTC (Energy Balanced Topology Control) algorithm. The simulation results indicate that the GCDSTC algorithm reduce the energy consumption and interference in the network graph, in order to enhance the network lifetime. Keywords: Wireless Sensor Network (WSN), Connected Dominating Set (CDS), Topology Control (TC), etc.
Efficient energy, cost reduction, and QoS based routing protocol for wireless...IJECEIAES
Recent developments and widespread in wireless sensor network have led to many routing protocols, many of these protocols consider the efficiency of energy as the ultimate factor to maximize the WSN lifetime. The quality of Service (QoS) requirements for different applications of wireless sensor networks has posed additional challenges. Imaging and data transmission needs both QoS aware routing and energy to ensure the efficient use of sensors. In this paper, we propose an Efficient, Energy-Aware, Least Cost, (ECQSR) quality of service routing protocol for sensor networks which can run efficiently with best-effort traffic processing. The protocol aims to maximize the lifetime of the network out of balancing energy consumption across multiple nodes, by using the concept of service differentiation, finding lower cost by finding the shortest path using nearest neighbor algorithm (NN), also put certain constraints on the delay of the path for real-time data from where link cost that captures energy nodes reserve, energy of the transmission, error rate and other parameters. The results show that the proposed protocol improves the network lifetime and low power consumption.
Data Dissemination in Wireless Sensor Networks: A State-of-the Art SurveyCSCJournals
A wireless sensor network is a network of tiny nodes with wireless sensing capacity for data collection processing and further communicating with the Base Station this paper discusses the overall mechanism of data dissemination right from data collection at the sensor nodes, clustering of sensor nodes, data aggregation at the cluster heads and disseminating data to the Base Station the overall motive of the paper is to conserve energy so that lifetime of the network is extended this paper highlights the existing algorithms and open research gaps in efficient data dissemination.
An Energy Efficient Mobile Sink Based Mechanism for WSNs.pdfMohammad Siraj
Network lifetime and energy efficiency are crucial performance metrics used to evaluate
wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be
employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering
is to decrease the energy consumption of the network. In fact, the clustering technique will be
considered effective if the energy consumed by sensor nodes decreases after applying clustering,
however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this
paper, the energy consumption of nodes, before clustering, is considered to determine the optimal
cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of
cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent
problem which leads to a remarkable decrease in the network’s lifespan. This problem stems from
the asynchronous energy depletion of nodes located in different layers of the network.
In order to provide sensing services to low-powered IoT devices, wireless sensor networks (WSNs) organize specialized transducers into networks. Energy usage is one of the most important design concerns in WSN because it is very hard to replace or recharge the batteries in sensor nodes. For an energy-constrained network, the clustering technique is crucial in preserving battery life. By strategically selecting a cluster head (CH), a network's load can be balanced, resulting in decreased energy usage and extended system life. Although clustering has been predominantly used in the literature, the concept of chain-based clustering has not yet been explored. As a result, in this paper, we employ a chain-based clustering architecture for data dissemination in the network. Furthermore, for CH selection, we employ the coati optimisation algorithm, which was recently proposed and has demonstrated significant improvement over other optimization algorithms. In this method, the parameters considered for selecting the CH are energy, node density, distance, and the network’s average energy. The simulation results show tremendous improvement over the competitive cluster-based routing algorithms in the context of network lifetime, stability period (first node dead), transmission rate, and the network's power reserves.
AN OPTIMIZED WEIGHT BASED CLUSTERING ALGORITHM IN HETEROGENEOUS WIRELESS SENS...cscpconf
The last few years have seen an increased interest in the potential use of wireless sensor networks (WSNs) in various fields like disastermanagementbattle field surveillance, and border security surveillance. In such applications, a large number of sensor nodes are deployed, which are often unattended and work autonomously. The process of dividing the network into interconnected substructures is called clustering and the interconnected substructures are called clusters. The cluster head (CH) of each cluster act as a coordinator within the substructure. Each CH acts as a temporary base station within its zone or cluster. It also communicates with other CHs. Clustering is a key technique used to extend the lifetime of a sensor network by reducing energy consumption. It can also increase network scalability. Researchers in all fields of wireless sensor network believe that nodes are homogeneous, but
some nodes may be of different characteristics to prolong the lifetime of a WSN and its reliability. We have proposed an algorithm for better cluster head selection based on weights for different parameter that influence on energy consumption which includes distance from base station as a new parameter to reduce number of transmissions and reduce energy consumption by sensor nodes. Finally proposed algorithm compared with the WCA, IWCA algorithm in terms of number of clusters and energy consumption.
An energy efficient optimized cluster establishment methodology for sensor n...nooriasukmaningtyas
The compatibility of WSN is with various applications such as; healthcar eand environmental monitoring. Whereas nodes present in that network have limited ‘battery-life’ that cause difficulty to replace and recharge those batteries after deployment. Energy efficiency is a major problem in the present situation. In present, many algorithms based on energy efficiency have been introduced to improvise the conservation of energy in WSN. The LEACH algorithm improvises the network lifetime in comparison to direct transmission and multi-hop, but it has several limitations. The selection of CHs can be randomly done that doesn’t confirm the optimal solution, proper distribution and it lacks during complete network management. The centralized EE optimized cluster establishment approach (OCEA) for sensor nodes is proposed to decrease the average energy dissipation and provide significant improvement. The proposed EE WSN model with the sensor nodes is examined under a real-time scenario and it is compared with stateof-art techniques where it balances the energy consumption of the network and decreasing the cluster head number.
AN EXTENDED K-MEANS CLUSTER HEAD SELECTION ALGORITHM FOR EFFICIENT ENERGY CON...IJNSA Journal
Effective use of sensor nodes’ batteries in wireless sensor networks is critical since the batteries are difficult to recharge or replace. This is closely connected to the networks’ lifespan since once the battery is used up, the node is no longer useful. The entire network will not function if 60 to 80% of the nodes in it have completely depleted their energy. In order to minimize energy usage and sustain the network for a long time, many cluster head selection algorithms have been developed. However, the existing cluster head selection algorithms such as K-Means, particle swarm selection optimization (PSO), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy C-Means (FCM) cluster head election algorithm have not fully reduced the issue of energy usage in WSN. The objective of this paper was to develop an extended K Mean Cluster Head selection(CHS) algorithm that uses remaining energy, distance between node and base station, distance between nodes and neighbour nodes, node density, node degree Maximum Cluster size, received signal strength indicator (RSSI) and Signal to Noise Ratio. The algorithm developed was used to enhance the lifespan of WSNs. The performance of the simulated variants of LEACH routing protocols is measured and evaluated using the quantitative research methodology. Utilizing residual node energy, packet delivery ratio, throughput, network longevity, average energy usage, and the number of live and dead node, the suggested approach is contrasted to previous approaches. From the study we observed that the proposed approach outperforms existing actual LEACH, Mod-LEACH and TSILEACH approaches.
Coverage and Connectivity Aware Neural Network Based Energy Efficient Routing...graphhoc
There are many challenges when designing and deploying wireless sensor networks (WSNs). One of the key challenges is how to make full use of the limited energy to prolong the lifetime of the network, because energy is a valuable resource in WSNs. The status of energy consumption should be continuously monitored after network deployment. In this paper, we propose coverage and connectivity aware neural network based energy efficient routing in WSN with the objective of maximizing the network lifetime. In the proposed scheme, the problem is formulated as linear programming (LP) with coverage and connectivity aware constraints. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage and connectivity aware routing with data transmission. The proposed scheme is compared with existing schemes with respect to the parameters such as number of alive nodes, packet delivery fraction, and node residual energy. The simulation results show that the proposed scheme can be used in wide area of applications in WSNs.
Routing Optimization with Load Balancing: an Energy Efficient ApproachEswar Publications
The area of Wireless Sensor Network (WSN) is covered with considerable range of problems, where majority of research attempts were carried out to enhance the network lifetime of WSN. But very few of the studies have proved successful. This manuscript discusses about a structure for optimizing routing and load balancing that uses standard radio and energy model to perform energy optimization by introducing a novel routing agent. The routing agent is built within aggregator node and base station to perform self motivated reconfiguration in case of energy depletion. Compared with standard LEACH algorithm, the proposed technique has better energy efficiency within optimal data aggregation duration.
Mobile Agents based Energy Efficient Routing for Wireless Sensor NetworksEswar Publications
Energy Efficiency and prolonged network lifetime are few of the major concern areas. Energy consumption rated of sensor nodes can be reduced in various ways. Data aggregation, result sharing and filtration of aggregated data among sensor nodes deployed in the unattended regions have been few of the most researched areas in the field of wireless sensor networks. While data aggregation is concerned with minimizing the information transfer from source to sink to reduce network traffic and removing congestion in network, result sharing focuses on sharing of information among agents pertinent to the tasks at hand and filtration of aggregated data so as to remove redundant information. There exist various algorithms for data aggregation and filtration using different mobile agents. In this proposed work same mobile agent is used to perform both tasks data aggregation and data filtration. This approach advocates the sharing of resources and reducing the energy consumption level of sensor nodes.
An energy-efficient cluster head selection in wireless sensor network using g...TELKOMNIKA JOURNAL
Clustering is considered as one of the most prominent solutions to preserve theenergy in the wireless sensor networks. However, for optimal clustering, anenergy efficient cluster head selection is quite important. Improper selectionofcluster heads(CHs) consumes high energy compared to other sensor nodesdue to the transmission of data packets between the cluster members and thesink node. Thereby, it reduces the network lifetime and performance of thenetwork. In order to overcome the issues, we propose a novelcluster headselection approach usinggrey wolf optimization algorithm(GWO) namelyGWO-CH which considers the residual energy, intra-cluster and sink distance.In addition to that, we formulated an objective function and weight parametersfor anefficient cluster head selection and cluster formation. The proposedalgorithm is tested in different wireless sensor network scenarios by varyingthe number of sensor nodes and cluster heads. The observed results conveythat the proposed algorithm outperforms in terms of achieving better networkperformance compare to other algorithms.
A CLUSTER-BASED ROUTING PROTOCOL AND FAULT DETECTION FOR WIRELESS SENSOR NETWORKIJCNCJournal
In Wireless Sensors Networks (WSN) based application, a large number of sensor devices must be
deployed. Energy efficiency and network lifetime are the two most challenging issues in WSN. As a
consequence, the main goal is to reduce the overall energy consumption using clustering protocols which
have to ensure reliability and connectivity in large-scale WSN. This work presents a new clustering and
routing algorithm based on the properties of the sensor networks. The main goal of this work is to extend
the network lifetime via charge equilibration in the WSN. According to many errors with sensing devices
and to have greater data accuracy, we use a quorum mechanism. The proposed algorithms are evaluated
widely and the results are compared with related works. The experimental results show that the proposed
algorithm provides an effective improvement in terms of energy consumption, data accuracy and network
lifetime
Energy efficient intelligent routing in WSN using dominant genetic algorithmIJECEIAES
In the current era of wireless sensor network development, among the various challeng- ing issues, the life enhancement has obtained the prime interest. Reason is clear and straight: the battery operated sensors do have limited period of life hence to keep the network active as much as possible, life of network should be larger. To enhance the life of the network, at different level different approaches has been applied, broadly defining the proper scheduling of sensors and defining the energy efficient communication. In this paper heuristic based energy efficient communication approch has applied. A new development in the Genetic algorithm has presented and called as Dominant Genetic algorithm to determine the optimum energy efficient routing path between sensor nodes and to define the optimal energy efficient trajectory for mobile data gathering node. Dominancy of high fitness solution has included in the Genetic algorithm because of its natural existence. The proposed solution has applied the connection oriented crossover and mutation operator to maintain the feasibility of generated solution. The proposed solution has applied with various simulation experiments under two different scenarios: in first case energy efficient routes among the sensors have explored to deliver the information from source sensor to the sink node and in second case, energy efficient route among all local data hubs for mobile data gathering node has obtained. The proposed solution performances have been analyzed quantitatively and analytically. It has observed with various experimental results that proposed method not only has delivered the better solution but also has faster convergence and high level of reliability in compared to conventional form of Genetic algorithm.
MULTI-CLUSTER MULTI-CHANNEL SCHEDULING (MMS) ALGORITHM FOR MAXIMUM DATA COLLE...IJCNCJournal
Interference during data transmission can cause performance degradation like packet collisions in Wireless Sensor Networks (WSNs). While multi-channels available in IEEE 802.15.4 protocol standard WSN technology can be exploited to reduce interference, allocating channel and channel switching
algorithms can have a major impact on the performance of multi-channel communication. This paper presents an improved Fuzzy Logic based Cluster Formation and Cluster Head (CH) Selection algorithm with enhanced network lifetime for multi-cluster topology. The Multi-Cluster Multi-Channel Scheduling
(MMS) algorithm proposed in this paper improves the data collection by minimizing the maximum interference and collision. The presented work has developed Cluster formation and cluster head (CH) selection algorithm and Interference-free data communication by proper channel scheduled. The extensive
simulation and experimental outcomes prove that the proposed algorithm not only provides an interference-free transmission but also provides delay minimization and longevity of the network lifetime, which makes the presented algorithm suitable for energy-constrained wireless sensor networks.
Multi Objective Salp Swarm based Energy Efficient Routing Protocol for Hetero...IJCNCJournal
Routing is a persistent concern in wireless sensor networks (WSNs), as getting data from sources to destinations can be a tricky task. Challenges include safeguarding the data being transferred, ensuring network longevity, and preserving energy in harsh environmental conditions. Consequently, this study delves into the suitability of using multi-objective swarm optimization to route heterogeneous WSNs in the hope of mitigating these issues while boosting the speed and accuracy of data transmission. In order to achieve better performance in terms of load balancing and reducing energy expenditure, the MOSSA-BA algorithm was developed. This algorithm combines the Multi-Objective Salp Swarm Algorithm (MOSSA) with the exploiting strategy of the artificial bee colony (BA) in the neighbourhood of Salps. Inspired by the SEP and EDEEC protocols, the integrated solutions of MOSSA-BA were used to route two and three levels of heterogeneous networks. The embedded solutions provided outstanding performance in regards to FND, HND, LND, percentage of remaining energy, and the number of packages delivered to the base station. Compared to SEP, EDEEC, and other competitors based on MOSSA and a modified multi-objective particle swarm optimization (MOPSO), the MOSSA-BA-based protocols demonstrated energy-saving percentages of more than 34% in medium-sized areas of interest and over 22% in large-sized areas of detection.
Multi Objective Salp Swarm based Energy Efficient Routing Protocol for Hetero...IJCNCJournal
Routing is a persistent concern in wireless sensor networks (WSNs), as getting data from sources to destinations can be a tricky task. Challenges include safeguarding the data being transferred, ensuring network longevity, and preserving energy in harsh environmental conditions. Consequently, this study delves into the suitability of using multi-objective swarm optimization to route heterogeneous WSNs in the hope of mitigating these issues while boosting the speed and accuracy of data transmission. In order to achieve better performance in terms of load balancing and reducing energy expenditure, the MOSSA-BA algorithm was developed. This algorithm combines the Multi-Objective Salp Swarm Algorithm (MOSSA) with the exploiting strategy of the artificial bee colony (BA) in the neighbourhood of Salps. Inspired by the SEP and EDEEC protocols, the integrated solutions of MOSSA-BA were used to route two and three levels of heterogeneous networks. The embedded solutions provided outstanding performance in regards to FND, HND, LND, percentage of remaining energy, and the number of packages delivered to the base station. Compared to SEP, EDEEC, and other competitors based on MOSSA and a modified multi-objective particle swarm optimization (MOPSO), the MOSSA-BA-based protocols demonstrated energy-saving percentages of more than 34% in medium-sized areas of interest and over 22% in large-sized areas of detection.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.
Similar to Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 2, April 2020 : 1515 - 1523
1516
enabling less delay, where it is considered as an advantage for both the lifespan and the scalability of
a network [4, 5]. In general, clustering algorithms are signified as the compilation of unsupervised
classification methods that assigns objects into groups, or the partitioning of datasets into subsets known as
clusters. Through the utilization of suitable clustering algorithm, the formation of clusters with objects that
have the same features into the same cluster as opposed to objects in differing clusters is enabled.
This means, clustering entails the allocation of objects possessing certain similarities into the same cluster
according to their characteristics [6, 7]. One of the most important challenges faced by the clustering
approach in WSN is how to improve the cluster structure and construct a balanced size of clusters [8]. Cluster
size in our study refers to the quantity of member nodes in individual cluster. For this objective, several
approaches were used based on KM and FCM algorithms for better clusters formation. Due to the nature of
the random distribution of nodes in the monitoring area, at times these algorithms construct imbalanced
clusters size [8], In this situation, large and small size of clusters are produced, as shown in Figure 1.
Consequently, when the clusters sizes are not similar, the situation will lead to an imbalanced in the energy
consumption among the nodes, which will result in a reduction in the lifespan of the network. Although there
are many studies that have investigated on which of these algorithms is more superior for clustering process
in other fields [6, 7, 9–13]. However, based on our knowledge, there is none that investigate which algorithm
has a relatively better performance in terms forming a balanced size of clusters with the random distribution
manner for nodes in the monitoring area. This is what motivated us to do an analysis study to investigate
which of these algorithms has a better performance to form balanced clusters in the case of randomly node
distribution. This study can enable researchers to choose the appropriate algorithm in order to improve
network lifetime, where choosing an effective clustering method is the first issue that is faced during
the construction of the clusters in the WSNs [14].
(a) (b)
Figure 1. Formation an imbalanced clusters size by (a) KM and (b) FCM
We have simulated several scenarios based on three measured parameters which are; Variation
between clusters size, Standard deviation for Mean Square Error for intra-distances [15], and the ratio
between minimum cluster size and maximum cluster size in the network. The remainder of the current study
will be ensued by the ensuing sections; Section Two entails the related works. Additionally, in Section Three,
we will explain the clustering algorithms. In Section Four, the scenarios and evaluation will be explained.
Finally, Section 5 consists of the discussion and conclusion.
2. RELATED WORKS
Among the principle objectives in WSN is the effective clustering of the whole network, as it is able
to decrease the energy being consumed [14] and also is able to offer balanced energy consumption. Hence,
KM and FCM are the most utilized algorithms to realize this purpose. Initially, KM algorithm is used by
researchers to construct balanced clusters sizes, where it increases the distances between the clusters along
with the reduction distance inside the cluster, through the determination of the best cluster centroid. This is
due to the fact that it is easily understood, and easily enforced, in addition to its uncomplicated features
which resulted in this approach to be very popular. Another notable advantage of this method is that it does
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not require prior calculation of the distances between the two entities that would have otherwise effect an
extreme compromise in terms of operation duration and memory space. So, there are a few techniques which
are based on the K-means algorithm used in clustering [16]. Ray and De [8] recommended an Energy-
Efficient Clustering Protocol based on K-means midpoint algorithm (EECPK-means) for WSN.
The midpoint algorithm is utilized to enhance the initial selection of the cluster centroid process. Moreover,
the CH selection procedure will be optimized through the dependency on the residual energy as one of
the parameters in the selection method as well as the Euclidean distance that is already utilized in KM.
As shown previously by Mechta et al [17], they recommended Leach-CKM as an energy-effective method for
the optimal energy usage. In their study, the researchers presented a pair of varying differing algorithms,
which are the K-means algorithm for the purpose of clustering, and the routing protocol MTE for
the transmission of data within the networks.
Through Bholowalia and Kumar [18], an additional version of Leach protocol was recommended for
the balancing of energy usage by utilizing K-means and Elbow algorithm. The underlying concept of
the technique for the acquisition is executed entailing the utilization of the k-means algorithm, and in
ascertaining the optimum value “k,” the Elbow technique was utilized. Consequently, a new cluster system
for WSN was offered, with a dynamic system that automatically choose the amount of clusters.
From Sheta et. al. [19], in their work, they recommended a hybrid protocol to extend the network lifespan.
It utilized the k-means algorithms in clustering the nodes into groups. In addition, it incorporated the particle
swarm optimization technique, and utilized the genetic algorithms for CH candidate’s selection. Furthermore
Rezaei et. al. [20], proposed a Multi-Hop Routing Energy Efficient Scheme (MRRCE). In their work,
they improved the KM algorithm through the utilization of the Steiner Points (SPs) concept to enhance
the selection of initial cluster centroid. Where they considered the SPs as an alternative way for a random
selection of initial cluster centroid step in KM algorithm. The principle function of SPs is the fusing of each
node with its neighbors, and the creation of a grid among nodes, where the KM algorithm will then select
the initial cluster centroid based on this means rather than by random selection. Consequently, the CHs will
determine the best location for the initial CH. In addition to that, Elkamel and Cherif [16] proposed a new
technique in surmounting the energy usage issues. Their research aims were the incorporation of an enhanced
algorithm, that entails the K-means in the generation of balanced energy in the clusters, in the addition of
the utilization of the Gaussian elimination algorithm whilst the assignment of the CHs were being executed,
which will ensure the dissemination of energy consumption. In order to resolve the issue of ascertaining
the optimum quantity of groups, the researchers utilized the Davies Bouldin index to prolong the lifespan of
the network. A certain study by Razzaq and Shin [21] recommended a system that takes into account
K-means clustering during the clustering formation stage, and computes the weight function for
the procedure of choosing the CH. Furthermore, it takes into account an optimal fixed packet size in relations
to radio parameters and the state of the channel of of the transceiver. During the stage of the transference of
the data, it applies a multi-objective weight function as a connection cost by utilizing the conventional
Dijkstra algorithm.
Likewise, the FCM algorithm is widely used by researchers to construct balanced clusters size.
Since it has the ability to determine the cluster’s centroid such as KM, that helps in the optimization of
the clusters according to the minimization of the space between the sensor node and the cluster centroid [22].
Alia [23] a Decentralized Fuzzy Clustering Protocol, named DCFP was suggested. The construction
procedure of the framework for a particular WSNs is conducted one off at the starting of the protocol at
a base station, that persists in its unaltered state transcending the entirety of the lifespan of the the network.
At the beginning of the formation stage, a Fuzzy C-Means Algorithm is modified to assign the sensor nodes
to their optimum suitable clusters. During the CH-Election stage, the assignment of new CHs is executed
locally within individual cluster, in which instance, a new multi-criteria objective function is recommended
for the enhancement of the quality of assigned cluster heads. Furthermore Bouyer et al [24] suggested a new
method for minimizing energy consumption within the wireless sensor networks with hybrid LEACH
protocol and Fuzzy C-Means Algorithm. The Fuzzy C-means (FCM) algorithm is utilized in the optimization
of the number of the CHs and ascertaining their location and their allocation. The utilization of FCM in
WSNs assists in changing the LEACH protocol parameters during the implementation. Through Hadjila
et al [22] suggested a duo of algorithms utilizing a method which integrates the fFuzzy C-Means Algorithm
and the ant colony optimization in the construction of the clusters, and the management of the data
transference within the network. Firstly, Fuzzy C-Means clustering Algorithm is utilized in the formation of
a predetermined amount of clusters. Secondly, the Ant Colony Optimization (ACO) algorithm was applied in
the formation of a local minimal chain in individual clusters. In another research done by Kaushik [25]
a hybrid approach based on Fuzzy C-Means clustering and neural network was suggested. The benefits of
both methods, which are the Fuzzy C-Means clustering and neural network used to enable an energy
effective network that prolonged the network lifespan had been utilized by the researcher. The formation of
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the cluster is conducted through the utilization of FCM to construct evenly sized clusters within the network.
Furthermore, the determination of CH selection is executed through the neural network, by taking into
consideration the factors such as the proximity from the base station and the node energy. In their work,
Shokrollahi et al [26] introduced an energy-efficient clustering algorithm founded on the Fuzzy C-Means
Algorithm and genetic fuzzy system (ECAFG). Through the utilization of the FCM algorithm, the formation
of clusters are conducted, followed by the selection of the CHs through utilization of a genetic fuzzy system
(GFS). The formed clusters will continue to be unchanged, however the Cluster Heads are chosen at
the starting of every turn. The FCM algorithm constructs balanced static clusters to decrease the data
expenses, and disseminate the used energy amongst the clusters. Jain and Goel [27] proposed an Energy
Efficient Routing Algorithm using Fuzzy C-Means (EEA-FCM). In that particular work, the Fuzzy C-Means
clustering was utilized to form an optimal amount of static clusters. The notion of coherence was utilized to
remove surplus and unneeded data generation, and unnecessary transmission which averts unwarranted
energy wastage. The utilization of the Intra-cluster and inter-cluster gateways are to avert the nodes from
transferring data over an extensive length.
Many researchers examined the performance of KM and FCM by functioned a relative comparison
between these algorithms based on execution time and accuracy of these clustering algorithms in other
fields [3, 4, 10, 12, 13, 28, 29]. Based on [11] KM is superior in terms the total sum of distances for nodes
and time of execution in comparison to FCM when applied these algorithms on three intrusion datasets which
are: KDDCup99, NSLKDD, and GureKDD. But in segmentation, the image of Brain Tumourin the [30],
the error percentage value for image segmentation is lowest with FCM clustering and it outperforms KM.
However, none of the research studied demonstrate which of these algorithms has more ability to form
a balanced size of clusters under the random distribution manner of nodes in WSNs. This motivated us to
make this study. In the next section, the details with pros and cons for each algorithm will be presented.
3. CLUSTERING ALGORITHMS
3.1. K-means algorithm (KM)
It is one of the unsupervised clustering methods, which efficiently utilized to form spherical shapes
clusters. Stuart Lloyd was firstly researcher suggested this algorithm [31]. It divided points of data into
a specific number of clusters [32]. It mostly increases the distances between the clusters along with reducing
distance inside the cluster. The goal of this algorithm is sought to find the best cluster centroid when
diminishing the objective function based on a Squared-Error-Function (SEF). Using the K-means algorithm,
the clusters have a better formation where the average distance of each node of the cluster is minimized. It is
more efficient to balance the load of the network to distribute the nodes between clusters [16]. This algorithm
is very beneficial to construct the clusters for various applications of WSN [8]. The objective function of KM
is defined as:
𝐽 = ∑ ∑ 𝑑(𝑥𝑖 , 𝑥 𝑐)2𝑘
𝑗=1
𝑛
𝑖=1 i= 1, 2, ..., n j=1, 2, …, k (1)
Where 𝑑(𝑥𝑖 , 𝑥 𝑐)2
represent the Euclidean distance that used to determine the distance between node xij with
its cluster centroid cj, i refers to numbea r of nodes, and j refers to cluster number. The processes of this
algorithm are including the following phases [33]:
Phase 1: Locate the k centroids points in the space which is representing by the data set, where K is
a predefined number.
Phase 2: Allocate every point of data to the specific cluster, which has the nearest centroid distance.
Phase 3: Once all point of data has been clustered, re-determine the locations of the k centroids.
Phase 4: reiterate the Phase 2 and Phase 3 till no shown change in the location of centroids.
3.2. Fuzzy C-Means (FCM)
It is considered as one of the most efficient protocols [34], in many real situations, the fuzzy
clustering methods, dealing with uncertainty, fuzziness, and vagueness, fuzzy clustering is considering as
an effective clustering method. Among the fuzzy clustering methods, the fuzzy C-means (FCMs) algorithm
has been most widely used in the clustering processes [35]. The goal of FCM is to minimize the sum of
distances between the instances and the cluster centers [36]. In WSNs, the aim is to cluster N sensor nodes
into k distinguished clusters. The objective function of FCM for clustering in WSNs can be formulated
as follows:
𝐽 = ∑ ∑ 𝜇 𝑚𝑘
𝑗=1
𝑛
𝑖=1 𝑑(𝑥𝑖 , 𝑥 𝑐)2
, i= 1, 2, ..., n j=1, 2, …, k (2)
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𝜇𝑖𝑗 =
1
∑ (
𝑑(𝑥 𝑖,𝑐 𝑗)
𝑑(𝑥 𝑖,𝑐 𝑘)
)
2
𝑚−1𝑘
𝑗=1
(3)
𝜇𝑖𝑗∈ [0, 1]
𝐶𝑗 =
∑ (𝜇 𝑖𝑗) 𝑚 𝑥 𝑖
𝑛
1
∑ (𝜇 𝑖𝑗) 𝑚𝑛
1
(4)
Where 𝜇 is the membership of node i to cluster j, m is the value of fuzzifier is usually chosen as 2 in the most
of applications [7]. And Cj refers to cluster centroid. This function differs from KM with the use of weighted
squared errors instead of using squared errors only. These clustering algorithms still have some
disadvantages that hinder its function, Table 1 shown the disadvantages for both KM and FCM.
Table 1. Disadvantages of KM and FCM
KM FCM
1 The initial centroids are selected by the random way for the
input data set.
Same like KM
2 sensitivity to outliers points Same like KM
3 The number of clusters K is given as manually. The number of clusters K and the fuzzy weighted index (m)
is given as manually
4 No guarantee for K-means will converge into an optimal or
better solution.
It relapses into the local extreme point or saddle point easily
and the optimal solution cannot be obtained.
5 In the clusters formation, size of clusters is not considering Same like KM
4. SCENARIOS AND EVALUATION
We examined totally six scenarios for comparing KM and FCM using Matlab and excel.
Each scenario has different 50 observations i.e. different 50 distribution pattern (uniform random
distribution) for nodes in the monitoring area. Although, the study [8] relied on 7 observations in their
evaluation in order to decide whether the resulting clusters are balanced or not. However, to increase
the accuracy of our evaluation result, our study relied on different 50 observations.
To evaluate the performance for these algorithms in the formation of a balanced size of clusters,
we utilize three norms which are:
1. Standard deviation of Mean Square Error STD (MSE) for intra-distances: Which measures what
the difference in the homogeneity for the average of intra-distance for each cluster. This norm shows how
the average intra-distances of nodes to the cluster’s centroid are different from cluster to others. Where
the smaller the factor, the better. That mean, there is a uniformity of the intra-distances for clusters.
𝑆𝑇𝐷 (𝑀𝑆𝐸) = √
∑|𝑀𝑆𝐸(𝑗)−𝜇|2
𝑘
2
(5)
J=1, 2, …, k
Where 𝑆𝑇𝐷 (𝑀𝑆𝐸) is mean the standard deviation of Mean Square Error, k is the number of clusters, and
𝜇 is the Average of Mean Square Error for distances.
𝑀𝑆𝐸 (𝑗) = (1/𝑛) ∗ ∑ 𝐷(𝑥𝑖, 𝑥 𝑐)2𝑛
𝑖=1
, (6)
i=1, 2,…, n c=1, 2,…, k
The acronym MSE refers to the average of square intra-distances of nodes to the cluster’s centroid, n is
number of nodes in each cluster, and 𝐷(𝑥𝑖, 𝑥 𝑐)2
square intra distances for node (𝑥𝑖) to its cluster centroid (𝑥 𝑐)
in the cluster (c).
𝜇 =
∑ 𝑀𝑆𝐸𝑘
𝑗=1
𝑘
(7)
2. Variation for clusters size (V): Which measures the dissimilarity of the density of the nodes in the clusters
(number of member nodes in each cluster). Where the smaller the factor, the better. That mean, there is
a balance in clusters size.
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𝑉 =
∑ |S 𝑗−μ|2
𝑘
(8)
𝜇 =
∑ 𝑆 𝑗
𝑘
𝑗=1
𝑘
(9)
Where S𝑗 refers to cluster size (j) and 𝜇 refer to the mean of clusters size.
3. Clusters Size Range (CSR): Which measures the ratio of minimum cluster size to maximum cluster size.
So, the range of clusters size be limited within this range (CSR to 1), the narrower the range the better
(close to 1). That means no big difference in size between minimum cluster size to maximum cluster size.
CSR = min(
𝐶𝑆 𝑗
𝑚𝑎𝑥.𝐶𝑆
) (10)
Where CSj refers to clusters size and max.CS refers to the maximum cluster size in the network.
There are many parameters that utilized in the literature in WSN such as (residual energy, distance
to BS, velocity, and etc.). However from perspective of formation a balanced size of clusters in network,
only these novel parameters based on (intra-distance, nodes density of cluster, and clusters size) shown
whether it is a balanced cluster's size or not. These new parameters are considering as our main contribution
to this study.
In this study, we utilize these parameters together, where it is not sufficient to consider this network
has a balanced size of clusters more than others networks depending only on the density of the distribution of
the nodes within the clusters, regardless of the homogeneity to the average intra-distances in the clusters, vice
versa. In addition, is very essential to determine the range of clusters size, where the volumetric width shows
the difference in size between the largest and smallest cluster in the network. The movement in cluster sizes
is from CSR value to 1, CSR value the narrower (close to 1) the better. So, these three parameters used to
evaluate the performance of these algorithms, in terms which of these algorithms have the ability to produce
more balanced clusters compared to another with the random distribution manner for nodes in the monitoring
area for WSN.
In this study, we depended on the most frequent scenarios in the literature. We applied KM and
FCM for different 50 uniformly random distributions to divide nodes into five clusters for each scenario
when the BS location (x, y) is located outside network. Also, based on literature Squared Euclidean distance
norm was used as the distance measure in both KM and FCM algorithms, the remaining details of these
scenarios illustrated in Table 2.
Table 2. Details of scenarios
Number of scenarios Number of nodes Monitoring area (m)
1st scenario 100 200*200
2nd scenario 100 400*400
3rd scenario 100 500*500
4th scenario 200 200*200
5th scenario 200 400*400
6th scenario 200 500*500
5. EVALUATION AND RESULTS
Firstly, we evaluated the performance of KM and FCM to construct balanced clusters size based on
every parameter separately in each scenario. According STD (MSE) parameter, KM outperformed on FCM
19, 20, 10, 17, 22, and 19 observations (distributions) from 50 distributions in first scenario to sixth scenario,
respectively. Nonetheless FCM superior KM 31, 30, 40, 33, 28, and 31 observations from 50 observations for
each scenario from the first scenario to the sixth scenario, respectively. Which mean 62%, 60%, 80%, 66%,
56%, and 62% for each scenario from first scenario to sixth scenario, respectively. According to the CSR
parameter, KM outperformed on FCM 17, 16, 16, 9, 10, and 8 observations from 50 distributions for each
scenario from the first scenario to the sixth scenario, respectively. But FCM superior KM 32, 33, 30, 39, 37,
and 42 observations from 50 observations for each scenario from the first scenario to the sixth scenario,
respectively. Which mean 64%, 66%, 60%, 78%, 74%, and 84% for each scenario from first scenario to sixth
scenario, respectively. In addition, 1, 1, 4, 2, and 3 similar value of CSR for both KM and FCM in first,
second, third, fourth, and fifth, respectively.
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According to the variation parameter, KM outperformed on FCM 17, 17, 13, 10, 11, and 10
observations from 50 distributions in the first scenario to the sixth scenario, respectively. However, FCM
superior KM 33, 33, 35, 39, 38, and 40 observations from 50 observations for each scenario from the first
scenario to the sixth scenario, respectively. Which mean 66%, 66%, 70%, 78%, 76%, and 80% for each
scenario from first scenario to sixth scenario, respectively. In addition, 2, 1, and 1 similar value of variation
for both KM and FCM in the third, fourth, and fifth scenario, respectively. Figure 2 shows the result for each
parameter in different six scenarios.
Figure 2. The performance-based three parameters in different six scenarios
Secondly, we evaluate KM and FCM based on scenarios in terms which of these algorithms has
better performance based on these three together parameters (3 parameters) in the same observation or
distribution for all scenarios. The first scenario, KM is succeeded by 8 observations out of different 50
observations, while FCM is better than KM in 22 observations, where it achieved all together parameters in
22 observations out of different 50 observations. In the second scenario, although KM is succeeded by 8
observations out of different 50 observations, FCM is better than KM in 21 observations, where it achieved
all together parameters in 21 observations out of different 50 observations. In the third scenario, KM is
succeeded by 3 observations out of different 50 observations, whereas FCM is better than KM in 26
observations, where it achieved all together parameters in 26 observations out of different 50 observations. In
the fourth scenario, KM is succeeded by 3 observations out of different 50 observations, while FCM is better
than KM in 26 observations, where it achieved all together parameters in 26 observations out of different 50
observations. In the fifth scenario, KM is succeeded by 5 observations out of different 50 observations,
however, FCM is better than KM in 21 observations, where it achieved all together parameters in 21
observations out of different 50 observations. In the last scenario, KM is succeeded only in 1 observation out
of different 50 observations, while FCM is better than KM in 24 observations, where it achieved all together
parameters in 24 observations out of different 50 observations. Consequently, based on the three together
parameters, FCM is better than the KM in terms construct balanced clusters size as shown in Figure 3.
Figure 3. Formation of balanced clusters based on three together parameters
0
10
20
30
40
50
FCM KM FCM KM FCM KM
STD (MSE) CSR variation
1st scenario 2nd scenario 3rd scenario 4th scenario 5th scenario 6th scenario
22 21
26 26
21
24
8 8
3 3 5 1
50 50 50 50 50 50
0
20
40
60
First scenario Second scenario Third scenario Fourth scenario Fifth scenario Sixth scenario
FCM K-Means all distribution
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6. DISCUSSION AND CONCLUSION
In this study we evaluated the performance of KM and FCM in terms construct balanced clusters
size for random nodes deployment in the monitoring area in WSN. The evaluation was based on six
scenarios, each scenario has different 50 observation of nodes and the nodes in each observation have divided
into five clusters. Also, we utilized a Squared Euclidean distance norm as the distance measure in both KM
and FCM algorithms. Three new parameters associated with the size of the cluster has been used in this
evaluation, which is Variation between clusters size, Standard deviation for Mean Square Error for
intra-distances, and the ratio between minimum cluster size and maximum cluster size in the network. Based
on the result, FCM has better performance than KM to formation a balanced cluster's size based on these
parameters with the random distribution manner for sensor nodes in the monitoring area. Also, when
the number of nodes distributed is increasing along with the increase in the monitoring area, the performance
of FCM still relatively stable compared to KM, where the performance of KM decreased. Although FCM is
superior to KM, but still suffer from the effect of the random nodes deployment condition, where sometimes
form imbalanced clusters. This limitation requires to propose assist mechanism to overcome this problem,
this will be addressed in futurework. At the conclusion and based on the result, FCM is a better choice to
form a balanced cluster especially when the number of nodes distributed is high along with the big distance
of the monitoring area in random nodes deployment in the monitoring area for WSNs.
ACKNOWLEDGMENT
This work is supported by Universiti Teknikal Malaysia Melaka-Zamalah Scheme. So, the authors
would like to thank Zamalah Scheme for providing the facilities and financial support for this research.
REFERENCES
[1] E. C. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, “Wireless sensor networks: a survey,” Comput. Networks,
vol. 38, no. 4, pp. 393–422, 2002.
[2] A. A. Hassan, W. Shah, M. F. Iskandar, and M. N. Al-mhiqani, “Unequal Clustering Routing Algorithms in
Wireless Sensor Networks : A Comparative Study,” Journal of Advanced Research in Dynamical and Control
Systems, vol. 10, pp. 2142–2156, 2018.
[3] S. Al-Khammasi, D. Alhelal, and N. S. Ali, “Energy efficient cluster based routing protocol for dynamic and static
nodes in wireless sensor network,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 16, no. 5,
pp. 1974–1981, 2018.
[4] P. Maratha and P. Kapil, “A Comparative Study on Prominent Strategies of Cluster Head Selection in Wireless
Sensor Networks,” Integrated Intelligent Computing, Communication and Security, Springer Singapore, 2019.
[5] F. Liang, L. Zhang, and P. Sun, “Study on the Rough-set-based Clustering Algorithm for Sensor Networks,”
Bulletin of Electrical Engineering and Informatics (BEEI), vol. 3, no. 2, pp. 77–90, 2014.
[6] S. Al-Augby, S. Majewski, A. Majewska, and K. Nermend, “A Comparison Of K-Means And Fuzzy C-Means
Clustering Methods For A Sample Of Gulf Cooperation Council Stock Markets,” Folia Oeconomica Stetin.,
vol. 14, no. 2, pp. 19–36, 2014.
[7] Z. Cebeci and F. Yildiz, “Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster
Structures,” J. Agric. Informatics, vol. 6, no. 3, pp. 13–23, 2015.
[8] A. Ray and D. De, “Energy efficient clustering protocol based on K-means (EECPK-means)-midpoint algorithm
for enhanced network lifetime in wireless sensor network,” IET Wirel. Sens. Syst., vol. 6, no. 6, pp. 181–191, 2016.
[9] S. W. Guangul, “The effects of segmentation techniques in digital image based identification of ethiopian
paper currency,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 12, no. 3,
pp. 1106–1110, 2018.
[10] A. Kapoor, “A Comparative Study of K-Means , K-Means ++ and Fuzzy C- Means Clustering Algorithms,”
2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT),
pp. 1–6, 2017.
[11] S. K. Sahu and S. K. Jena, “A study of K-Means and C-Means clustering algorithms for intrusion detection product
development,” Int. J. Innov. Manag. Technol., vol. 5, no. 3, 2014.
[12] T. Singh and M. Mahajan, “Performance Comparison of Fuzzy C Means with Respect to Other Clustering
Algorithm,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4,
no. 5, pp. 89–93, 2014.
[13] A. Sheshasayee and P. Sharmila, “Comparative Study of Fuzzy C Means and K Means Algorithm for Requirements
Clustering,” Indian Journal of Science and Technology, vol. 7, no. 6, pp. 853–857, 2014.
[14] S. Dehghani and B. Barekatain, “An Enhanced Energy-Aware Cluster-Based Routing Algorithm in Wireless Sensor
Networks,” Wirel. Pers. Commun., vol. 98, no. 1, pp. 1605–1635, 2018.
[15] H. Karim, S. R. Niakan, and R. Safdari, “Comparison of neural network training algorithms for classification of
heart diseases,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 7, no. 4, pp. 185–189, 2018.
[16] R. ELkamel and A. Cherif, “Energy-efficient routing protocol to improve energy consumption in wireless sensors
networks,” Int. J. Commun. Syst., vol. 30, no. 17, pp. e3360, 2017.
9. Int J Elec & Comp Eng ISSN: 2088-8708
Evaluate the performance of K-Means and the fuzzy C-Means algorithms … (Ali Abdul-hussian Hassan)
1523
[17] D. Mechta, S. Harous, I. Alem, and D. Khebbab, “LEACH-CKM: Low Energy Adaptive Clustering Hierarchy
protocol with K-means and MTE,” in 2014 10th International Conference on Innovations in Information
Technology (IIT), pp. 99–103, 2014.
[18] K. Bholowalia P, “EBK-Means:Aclustering technique based on elbow method and k-means in WSN,” Int J Comput
Appl., vol. 105, no. 9, 2014.
[19] A. F. Sheta and B. Solaiman, “Evolving clustering algorithms for wireless sensor networks with various radiation
patterns to reduce energy consumption,” in 2015 Science and Information Conference (SAI), pp. 1037–1045, 2015.
[20] E. Rezaei, A. A. Baradaran, and A. Heydariyan, “Multi-hop Routing Algorithm Using Steiner Points for Reducing
Energy Consumption in Wireless Sensor Networks,” Wirel. Pers. Commun., vol. 86, no. 3, pp. 1557–1570, 2016.
[21] M. Razzaq and S. Shin, “Energy Efficient Dijkstra-Based Weighted Sum Minimization Routing Protocol for
WSN,” 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), Jun 2018.
[22] M. Hadjila et al., “A Hybrid Cluster and Chain-based Routing Protocol for Lifetime Improvement in WSN,”
International Conference on Wired/Wireless Internet Communications, 2015.
[23] O. M. D. Alia, “A decentralized fuzzy c-means-based energy-efficient routing protocol for wireless sensor
networks,” Sci. World J., vol. 2014, 2014.
[24] A. Bouyer, “A New Approach for Decreasing Energy in Wireless Sensor Networks with Hybrid LEACH Protocol
and Fuzzy C-Means Algorithm,” Int. J. Commun. Networks Distrib. Syst., Nov 2014.
[25] A. K. Kaushik, “A Hybrid Approach of Fuzzy C-means Clustering and Neural network to make Energy-Efficient
heterogeneous Wireless Sensor,” International Journal of Electrical and Computer Engineering, vol. 6, no. 2,
pp. 674, Apr 2016.
[26] Ayub Shokrollahi and Babak Mazloom-Nezhad Maybodi, “An Energy-Efficient Clustering Algorithm Using Fuzzy
C-Means and Genetic Fuzzy System for Wireless Sensor Network,” Journal of Circuits, Systems and Computers,
vol. 26, no. 1, pp. 1–22, 2017.
[27] A. Jain, “Energy Efficient Algorithm for Wireless Sensor Network using Fuzzy C-Means Clustering,” (IJACSA)
International Journal of Advanced Computer Science and Applications, vol. 9, no. 4, pp. 474–481, 2018.
[28] D. J. Bora, “A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm,”
International Journal of Emerging Trends & Technology in Computer Science, vol. 10, no. 2, pp. 108–113, 2014.
[29] Sanjay Kumar and Ghosh, Soumi, “Comparative analysis of k-means and fuzzy c-means algorithms,” Int. J. Adv.
Comput. Sci. Appl., vol. 4, no. 4, 2013.
[30] H. Hooda and O. P. Verma, “Brain Tumor Segmentation : A Performance Analysis using K-Means, Fuzzy
C-Means and Region Growing Algorithm,” International Conference on Advanced Communication Control and
Computing Technologies (ICACCCT), May 2014.
[31] S. P. Lloyd, “Least Squares Quantization in PCM,” IEEE Trans. Inf. Theory, vol. 28, no. 2, pp. 129–137, 1982.
[32] G. A. Wilkin and H. Xiuzhen, “K-means clustering algorithms: Implementation and comparison,” in Proceedings -
2nd International Multi-Symposiums on Computer and Computational Sciences, IMSCCS’07, 2007, pp. 133–136.
[33] T. M. Kodinariya, “Review on determining number of Cluster in K-Means Clustering,” International Journal of
Advance Research in Computer Science and Management Studies, vol. 1, no. 6, pp. 90–95, 2013.
[34] D. Thanh, L. Hoang, and V. Trong, “Novel fuzzy clustering scheme for 3D wireless sensor networks,” Appl. Soft
Comput. J., vol. 54, pp. 141–149, 2017.
[35] C. Lu, S. Xiao, and X. Gu, “Improving fuzzy C-means clustering algorithm based on a density-induced distance
measure,” The Journal of Engineering, Jan 2016.
[36] V. Krishnaswamy and S. K. S. Manvi, “Clustering and data aggregation scheme in underwater wireless acoustic
sensor network,” TELKOMNIKA (Telecommunication, Computing, Electronics and Control), vol. 17, no. 4,
pp. 1604–1614, 2019.