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.
WEIGHTED DYNAMIC DISTRIBUTED CLUSTERING PROTOCOL FOR HETEROGENEOUS WIRELESS S...ijwmn
In wireless sensor networks (WSN), conserving energy and increasing lifetime of the network are a critical issue that has been addressed by substantial research works. The clustering technique has been proven particularly energy-efficient in WSN. The nodes form groups (clusters) that include one cluster head and member clusters. Cluster heads (CHs) are able to process, filter, gather the data sent by sensors
belonging to their cluster and send it to the base station. Many routing protocols which have been proposed are based on heterogeneity and use the clustering scheme such as SEP and DEEC. In this paper we introduce a new approach called WDDC in which cluster heads are chosen on the basis
of probability of ratio of residual energy and average energy of the network. It also takes into consideration distances between nodes and the base station to favor near nodes with more energy to be cluster heads. Furthermore, WDDC is dynamic; it divides network lifetime in two zones in which it changes its behavior. Simulation results show that our approach performs better than the other distributed clustering protocols such as SEP and DEEC in terms of energy efficiency and lifetime of the network.
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.
Extending the longevity, is a significant job to be accomplished by these sensor networks. The traditional routing protocols could not be applied here, due to its nodes powered by batteries. Nodes are often clustered in to non-overlapping clusters, so as to provide energy efficiency. A concise overview on clustering processes, within wireless sensor networks is given in this paper. But it is difficult to replace the deceased batteries of the sensor nodes. A distinctive sensor node consumes much of its energy during wireless communication. This research work suggests the development of a hierarchical distributed clustering mechanism, which gives improved performance over the existing clustering algorithm LEACH. The two hiding concepts behind the proposed scheme are the hierarchical distributed clustering mechanism and the concept of threshold. Energy utilization is significantly reduced, thereby greatly prolonging the lifetime of the sensor nodes.
NEW APPROACH TO IMPROVING LIFETIME IN HETEROGENEOUS WIRELESS SENSOR NETWORKS ...chokrio
The major challenge for wireless sensor networks is energy consumption minimization. Wireless transmission consumes much more of energy. In the clustered network, a few nodes become cluster heads which causes the energetic heterogeneity. Therefore the behavior of the sensor network becomes very unstable. Hence, the need to apply the balancing of energy consumption across all nodes of the heterogeneous network is very important to prevent the death of those nodes and thereafter increase the
lifetime of the network. DEEC (Distributed Energy Efficient Clustering) is one of routing protocols
designed to extend the stability time of the network by reducing energy consumption. A disadvantage of
DEEC, which doesn’t takes into account the cluster size and the density of nodes in this cluster to elect the
cluster heads. When multiple cluster heads are randomly selected within a small area, a big extra energy
loss occurs. The amount of lost energy is approximately proportional to the number of cluster heads in this
area. In this paper, we propose to improve DEEC by a modified energy efficient algorithm for choosing
cluster heads that exclude a number of low energy levels nodes due to their distribution density and their
dimensions area. We show by simulation in MATLAB that the proposed approach increases the number of
received messages and prolong the lifetime of the network compared to DEEC. We conclude by studying
the parameters of heterogeneity that proposed technique provides a longer stability period which increases
by increasing the number of nodes which are excluded from the cluster head selection.
QUAD TREE BASED STATIC MULTI HOP LEACH ENERGY EFFICIENT ROUTING PROTOCOL: A N...IJCNCJournal
This research work propounds a simple graph theory semblance Divide and Conquer Quad tree based Multi-hop Static Leach (DCQMS-Leach) energy efficient routing protocol for wireless sensor networks. The pivotal theme of this research work is to demonstrate how divide and conquer plays a pivotal role in a multi-hop static leach energy efficient routing protocol. This research work motivates, enforces, reckons the DCQMS-Leach energy efficient routing protocol in wireless sensor networks using Mat lab simulator.This research work also computes the performance concepts of DCQMS-Leach routing protocol using various performance metrics such as Packet Drop Rate (PDR), Throughput, and End to End Delay (EED) by comparing and contrasting alive nodes with number of nodes, number of each packets sent to the cluster heads with rounds, number of cluster heads with rounds, number of packets forwarded to the base station with rounds and finally dead nodes with number of rounds. In order to curtail energy consumption this research work proffers a routing methodology such as DCQMS-Leach in energy efficient wireless,sensor routing protocol. The recommended DCQMS-Leach overcomes the in adequacies of all other different leach protocols suggested by the previous researchers.
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.
An Integrated Distributed Clustering Algorithm for Large Scale WSN...................................................1
S. R. Boselin Prabhu, S. Sophia, S. Arthi and K. Vetriselvi
An Efficient Connection between Statistical Software and Database Management System ................... 1
Sunghae Jun
Pragmatic Approach to Component Based Software Metrics Based on Static Methods ......................... 1
S. Sagayaraj and M. Poovizhi
SDI System with Scalable Filtering of XML Documents for Mobile Clients ............................................... 1
Yi Yi Myint and Hninn Aye Thant
An Easy yet Effective Method for Detecting Spatial Domain LSB Steganography .................................... 1
Minati Mishra and Flt. Lt. Dr. M. C. Adhikary
Minimizing the Time of Detection of Large (Probably) Prime Numbers ................................................... 1
Dragan Vidakovic, Dusko Parezanovic and Zoran Vucetic
Design of ATL Rules for TransformingUML 2 Sequence Diagrams into Petri Nets..................................... 1
Elkamel Merah, Nabil Messaoudi, Dalal Bardou and Allaoua Chaoui
WEIGHTED DYNAMIC DISTRIBUTED CLUSTERING PROTOCOL FOR HETEROGENEOUS WIRELESS S...ijwmn
In wireless sensor networks (WSN), conserving energy and increasing lifetime of the network are a critical issue that has been addressed by substantial research works. The clustering technique has been proven particularly energy-efficient in WSN. The nodes form groups (clusters) that include one cluster head and member clusters. Cluster heads (CHs) are able to process, filter, gather the data sent by sensors
belonging to their cluster and send it to the base station. Many routing protocols which have been proposed are based on heterogeneity and use the clustering scheme such as SEP and DEEC. In this paper we introduce a new approach called WDDC in which cluster heads are chosen on the basis
of probability of ratio of residual energy and average energy of the network. It also takes into consideration distances between nodes and the base station to favor near nodes with more energy to be cluster heads. Furthermore, WDDC is dynamic; it divides network lifetime in two zones in which it changes its behavior. Simulation results show that our approach performs better than the other distributed clustering protocols such as SEP and DEEC in terms of energy efficiency and lifetime of the network.
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.
Extending the longevity, is a significant job to be accomplished by these sensor networks. The traditional routing protocols could not be applied here, due to its nodes powered by batteries. Nodes are often clustered in to non-overlapping clusters, so as to provide energy efficiency. A concise overview on clustering processes, within wireless sensor networks is given in this paper. But it is difficult to replace the deceased batteries of the sensor nodes. A distinctive sensor node consumes much of its energy during wireless communication. This research work suggests the development of a hierarchical distributed clustering mechanism, which gives improved performance over the existing clustering algorithm LEACH. The two hiding concepts behind the proposed scheme are the hierarchical distributed clustering mechanism and the concept of threshold. Energy utilization is significantly reduced, thereby greatly prolonging the lifetime of the sensor nodes.
NEW APPROACH TO IMPROVING LIFETIME IN HETEROGENEOUS WIRELESS SENSOR NETWORKS ...chokrio
The major challenge for wireless sensor networks is energy consumption minimization. Wireless transmission consumes much more of energy. In the clustered network, a few nodes become cluster heads which causes the energetic heterogeneity. Therefore the behavior of the sensor network becomes very unstable. Hence, the need to apply the balancing of energy consumption across all nodes of the heterogeneous network is very important to prevent the death of those nodes and thereafter increase the
lifetime of the network. DEEC (Distributed Energy Efficient Clustering) is one of routing protocols
designed to extend the stability time of the network by reducing energy consumption. A disadvantage of
DEEC, which doesn’t takes into account the cluster size and the density of nodes in this cluster to elect the
cluster heads. When multiple cluster heads are randomly selected within a small area, a big extra energy
loss occurs. The amount of lost energy is approximately proportional to the number of cluster heads in this
area. In this paper, we propose to improve DEEC by a modified energy efficient algorithm for choosing
cluster heads that exclude a number of low energy levels nodes due to their distribution density and their
dimensions area. We show by simulation in MATLAB that the proposed approach increases the number of
received messages and prolong the lifetime of the network compared to DEEC. We conclude by studying
the parameters of heterogeneity that proposed technique provides a longer stability period which increases
by increasing the number of nodes which are excluded from the cluster head selection.
QUAD TREE BASED STATIC MULTI HOP LEACH ENERGY EFFICIENT ROUTING PROTOCOL: A N...IJCNCJournal
This research work propounds a simple graph theory semblance Divide and Conquer Quad tree based Multi-hop Static Leach (DCQMS-Leach) energy efficient routing protocol for wireless sensor networks. The pivotal theme of this research work is to demonstrate how divide and conquer plays a pivotal role in a multi-hop static leach energy efficient routing protocol. This research work motivates, enforces, reckons the DCQMS-Leach energy efficient routing protocol in wireless sensor networks using Mat lab simulator.This research work also computes the performance concepts of DCQMS-Leach routing protocol using various performance metrics such as Packet Drop Rate (PDR), Throughput, and End to End Delay (EED) by comparing and contrasting alive nodes with number of nodes, number of each packets sent to the cluster heads with rounds, number of cluster heads with rounds, number of packets forwarded to the base station with rounds and finally dead nodes with number of rounds. In order to curtail energy consumption this research work proffers a routing methodology such as DCQMS-Leach in energy efficient wireless,sensor routing protocol. The recommended DCQMS-Leach overcomes the in adequacies of all other different leach protocols suggested by the previous researchers.
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.
An Integrated Distributed Clustering Algorithm for Large Scale WSN...................................................1
S. R. Boselin Prabhu, S. Sophia, S. Arthi and K. Vetriselvi
An Efficient Connection between Statistical Software and Database Management System ................... 1
Sunghae Jun
Pragmatic Approach to Component Based Software Metrics Based on Static Methods ......................... 1
S. Sagayaraj and M. Poovizhi
SDI System with Scalable Filtering of XML Documents for Mobile Clients ............................................... 1
Yi Yi Myint and Hninn Aye Thant
An Easy yet Effective Method for Detecting Spatial Domain LSB Steganography .................................... 1
Minati Mishra and Flt. Lt. Dr. M. C. Adhikary
Minimizing the Time of Detection of Large (Probably) Prime Numbers ................................................... 1
Dragan Vidakovic, Dusko Parezanovic and Zoran Vucetic
Design of ATL Rules for TransformingUML 2 Sequence Diagrams into Petri Nets..................................... 1
Elkamel Merah, Nabil Messaoudi, Dalal Bardou and Allaoua Chaoui
Evaluate the performance of K-Means and the fuzzy C-Means algorithms to forma...IJECEIAES
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.
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.
Collaborative Re-Localization Method in Mobile Wireless Sensor Network Based ...CSCJournals
Localization in Mobile Wireless Sensor Networks (WSNs), particularly in areas like surveillance applications, necessitates triggering re-localization in different time periods in order to maintain accurate positioning. Further, the re-localization process should be designed for time and energy efficiency in these resource constrained networks. In this paper, an energy and time efficient algorithm is proposed to determine the optimum number of localized nodes that collaborate in the re-localization process. Four different movement methods (Random Waypoint Pattern, Modified Random Waypoint pattern, Brownian motion and Levy walk) are applied to model node movement. In order to perform re-localization, a server/head/anchor node activates the optimal number of localized nodes in each island/cluster. A Markov Decision Process (MDP) based algorithm is proposed to find the optimal policy to select those nodes in better condition to cooperate in the re-localization process. The simulation shows that the proposed MDP algorithm decreases the energy consumption in the WSN between 0.6% and 32%.
International Journal of Advanced Smart Sensor Network Systems ( IJASSN )ijassn
With the availability of low cost, short range sensor technology along with advances in wireless networking, sensor networks has become a hot topic of discussion. The International Journal of Advanced Smart Sensor Network Systems is an open access peer-reviewed journal which focuses on applied research and applications of sensor networks. While sensor networks provide ample opportunities to provide various services, its effective deployment in large scale is still challenging due to various factors. This journal provides a forum that impacts the development of high performance computing solutions to problems arising due to the complexities of sensor network systems. It also acts as a path to exchange novel ideas about impacts of sensor networks research.
Sierpinski carpet fractal monopole antenna for ultra-wideband applications IJECEIAES
Microstrip antenna is broadly used in the modern communication system due to its significant features such as light weight, inexpensive, low profile, and ease of integration with radio frequency devices. The fractal shape is applied in antenna geometry to obtain the ultra-wideband antennas. In this paper, the sierpinski carpet fractal monopole antenna (SCFMA) is developed for base case, first iteration and second iteration to obtain the wideband based on its space filling and self-similar characteristics. The dimension of the monopole patch size is optimized to minimize the overall dimension of the fractal antenna. Moreover, the optimized planar structure is proposed using the microstrip line feed. The monopole antenna is mounted on the FR4 substrate with the thickness of 1.6 mm with loss tangent of 0.02 and relative permittivity of 4.4. The performance of this SCFMA is analyzed in terms of area, bandwidth, return loss, voltage standing wave ratio, radiation pattern and gain. The proposed fractal antenna achieves three different bandwidth ranges such as 2.6-4.0 GHz, 2.5-4.3 GHz and 2.4-4.4 GHz for base case, first and second iteration respectively. The proposed SCFMA is compared with existing fractal antennas to prove the efficiency of the SCFMA design. The area of the SCFMA is 25×20 푚푚 2 , which is less when compared to the existing fractal antennas.
Design and implementation of grid based clustering in WSN using dynamic sink ...journalBEEI
A wireless sensor networks (WSNs) play a significant application, especially in the monitored remoting environmental, which enables by the availability of sensors which are cheaper, smaller, and intelligent. The equipment of such sensors be with wireless interfaces, which a communication with other sensors occurs for creating a network, that contains many distributed nodes. The closest nodes to the sink are exploited at an enormous traffic load while the data from the whole regions are forwarded between them to reach the sink. This result in exhausting their energy quickly and partitioning the network. This is solved by changing the sink node position in Grid based clustering technique, which considers the optimal method for this purpose. A simulation with MATLAB can be applied for grid based clustering technique to evaluate the performance of WSN. The expected results deal with outperforms in throughput, reducing energy consumption and increasing residual energy, in addition to prolong the network lifetime of the sensor network.
Sensing and Sharing Schemes for Spectral Efficiency of Cognitive Radios IJECEIAES
Increase in data traffic, number of users and their requirements laid to a necessity of more bandwidth. Cognitive radio is one of the emerging technology which addresses the spectrum scarcity issue. In this work we study the advantage of having collaboration between cognitive enabled small cell network and primary macrocell. Different from the existing works at spectrum sensing stage we are applying enhanced spectrum sensing to avoid probability of false alarms and missed detections which has impact on spec tral efficiency. Later power control optimization for secondary users known as Hybrid spectrum sharing is used for further improvement of spectral efficiency. Furthermore, the failed packets of Primary users are taken care by high ranked relays which in turn decreases the average Primary user packet delay by 20% when compared between assisted Secondary user method and non-assisted Secondary user method.
A New Method for Reducing Energy Consumption in Wireless Sensor Networks usin...Editor IJCATR
Nowadays, wireless sensor networks, clustering protocol based on the neighboring nodes into separate clusters and fault
tolerance for each cluster exists for sensors to send information to the base station, to gain the best performance in terms of increased
longevity and maintain tolerance than with other routing methods. However, most clustering protocols proposed so far, only
geographical proximity (neighboring) cluster formation is considered as a parameter. In this study, a new clustering protocol and fault
tolerance based on the fuzzy algorithms are able to clustering nodes in sensor networks based on fuzzy logic and fault tolerance. This
protocol uses clustering sensor nodes and fault tolerance exist in the network to reduce energy consumption, so that faulty sensors
from neighboring nodes are used to cover the errors, work based on the most criteria overlay neighbor sensors with defective sensors,
distance neighbor sensors from fault sensor and distance neighbor sensors from central station is done. Superior performance of the
protocol can be seen in terms of increasing the network lifetime and maintain the best network tolerance in comparison with previous
protocols such as LEACH in the simulation results.
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.
Block diagonalization precoding and power allocation for clustering small-cel...journalBEEI
The clustering network is a solution to improve data-rate transmission in small-cells. In this case, clustering small-cells (CSCs) adopt a multiple antennas concept. The multiple antennas are used to maximize the downlink data-rate transmission at the users, but it requires precoding techniques to minimize interference among CSC users. This paper proposes a block diagonalization (BD) as a precoding technique for minimizing interference among CSC users. The performance of the BD precoding implemented on the clustering network under various numbers of small-cells. The CSC also implements a water-filling power allocation (PA-CoopWF) to distribute the available transmission power along with the CSCs antennas. To show the performance, our paper simulates two types of precoding techniques; those are the proposed BD and minimum mean square error (MMSE) in CSCs. Based on the receiver user parts under the overlapping coordination of CSCs, our method based on the BD precoding achieves considerably higher data-rate transmission compared to the MMSE precoding, especially on larger clusters. The simulation also shows that by implementing CSC with the BD in short-range distances and higher numbers of antennas, it promotes better data-rate performances compared to the MMSE precoding by 2.75 times at distance 100m and 67% at 50 antennas.
ENERGY EFFICIENT ROUTING ALGORITHM FOR MAXIMIZING THE MINIMUM LIFETIME OF WIR...ijasuc
In wireless sensor network, devices or nodes are generally battery powered devices. These nodes have
limited amount of initial energy that are consumed at different rates, depending on the power level. The
lifetime of the network is defined as the time until the first node fails (or runs out of battery). In this paper
different type of energy efficient routing algorithms are discussed and approach of these algorithms is to
maximize the minimum lifetime of wireless sensor network. Special attention has been devoted for
algorithms formulate the routing problem as a linear programming problem, which uses the optimal flow
path for data transmission and gives the optimum results. Advantages, limitations as well as comparative
study of these algorithms are also discussed in this paper.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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 Efficient Approach for Data Gathering and Sharing with Inter Node Communi...cscpconf
In today’s era Wireless sensor networks (WSNs) have emerged as a solution for a wide range of
applications. Most of the traditional WSN architectures consist of static nodes which are densely deployed
over a sensing area. Recently, several WSN architectures based on mobile elements (MEs) have been
proposed. Most of them exploit mobility to address the problem of data collection in WSNs. The common
drawback among them is to data sharing between interconnected nodes. In this paper we propose an
Efficient Approach for Data Gathering and Sharing with Inter Node Communication in Mobile-Sink. Our
algorithm is divided into seven parts: Registration Phase, Authentication Phase, Request and Reply Phase,
Setup Phase, Setup Phase (NN), Data Gathering, and Forwarding to Sink. Our approach provides an
efficient way to handle data in between the intercommunication nodes. By the above approach we can
access the data from the node which is not in the list, by sharing the data from the node which is
approachable to the desired node. For accessing and sharing we need some security so that the data can
be shared between authenticated nodes. For this we use two way security approach one for the accessing
node and other for the sharing.
Energy Conservation in Wireless Sensor Networks Using Cluster-Based ApproachIJRES Journal
In a wireless networking environment, the network is comprised of sensor nodes and backbones are subsets of sensors or actuators that suffice for performing basic data communication operations. They are applied for energy efficient broadcasting. In a broadcasting (also known as data dissemination) task, a message is to be sent from one node, which could be a sink or an actuator, to all the sensors or all the actuators in the network. The goal is to minimize the number of rebroadcasts while attempting to deliver messages to all sensors or actuators. Neighbor detection and route discovery algorithms that consider a realistic physical layer are described. An adaptive broadcasting protocol without parameters suitable for delay tolerant networks is further discussed. In existing solutions for minimal energy broadcasting problem, nodes can adjust their transmission powers. Wireless Sensor Networks (WSNs) are sets of many sensors that gather data and collaborate together. So, the procedures of broadcast or multicast are more important than traditional point-to-point communication in computer network. This paper focuses on broadcasting in structured WSNs. In such a kind, the procedure of network communications is easier than in unstructured WSNs. Thus, it will make an overview of Multi Point Relay (MPR) to show its weakness. Then define a cluster-based architecture for WSNs which is constructed using MPR. Next, provide a new broadcast algorithm based on the previous cluster architecture called 3B (Backbone Based Broadcasting). By the end, an illustration of 3B shows that it minimizes the energy consumption for accomplishing broadcast compared to MPR.
Performance evaluation of data filtering approach in wireless sensor networks...ijmnct
Wireless Sensor Network is a field of research which is viable in every application area like security
services, patient care, traffic regulations, habitat monitoring and so on. The resource limitation of small
sized tiny nodes has always been an issue in wireless sensor networks. Various techniques for improving
network lifetime have been proposed in the past. Now the attention has been shifted towards heterogeneous
networks rather than having homogeneous sensor nodes in a network. The concept of partial mobility has
also been suggested for network longevity. In all the major proposals; clustering and data aggregation in
heterogeneous networks has played an integral role. This paper contributes towards a new concept of
clustering and data filtering in wireless sensor networks. In this paper we have compared voronoi based
ant systems with standard LEACH-C algorithm and MTWSW with TWSW algorithm. Both the techniques
have been applied in heterogeneous wireless sensor networks. This approach is applicable both for critical
as well as for non-critical applications in wireless sensor networks. Both the approaches presented in this
paper outperform LEACH-C and TWSW in terms of energy efficiency and shows promising results for
future work.
A Novel Weighted Clustering Based Approach for Improving the Wireless Sensor ...IJERA Editor
Great lifetime and reliability is the key aim of Wireless Sensor Network (WSN) design. As for prolonging
lifetime of this type of network, energy is the most important resource; all recent researches are focused on more
and more energy efficient techniques. Proposed work is Weighted Clustering Approach based on Weighted
Cluster Head Selection, which is highly energy efficient and reliable in mobile network scenario. Weight
calculation using different attributes of the nodes like SNR (Signal to Noise Ratio), Remaining Energy, Node
Degree, Mobility, and Buffer Length gives efficient Cluster Head (CH) on regular interval of time. CH rotation
helps in optimum utilization of energy available with all nodes; results in prolonged network lifetime.
Implementation is done using the NS2 network simulator and performance evaluation is carried out in terms of
PDR (Packet Delivery Ratio), End to End Delay, Throughput, and Energy Consumption. Demonstration of the
obtained results shows that proposed work is adaptable for improving the performance. In order to justify the
solution, the performance of proposed technique is compared with the performance of traditional approach. The
performance of proposed technique is found optimum as compared to the traditional techniques.
ENERGY-EFFICIENT MULTI-HOP ROUTING WITH UNEQUAL CLUSTERING APPROACH FOR WIREL...IJCNCJournal
The development of an energy-efficient routing protocol, capable of extending the life of the network, is one of the main constraints of wireless sensor networks (WSN). Research studies on WSN routing prove that clustering offers an effective approach to prolong the lifetime of a WSN, particularly when it is combined with multi-hop communication that can reduces energy costs by minimizing the distance between transmitter and receiver. Most clustering algorithms using multi-hop in data transmission encounter the hotspot problem. In this work, an Energy-efficient Multi-hop routing with Unequal Clustering approach (EMUC) is proposed, to create clusters of different sizes, which depend on the distance between the sensor node and the base station. Equilibrate the energy dissipation between the cluster heads is the purpose of this approach by adopting multi-hop communication to relay data to the base station. The implementation of multi-hop mode to transmit data to the base station reduces the energy cost of transmission over long distances. The effectiveness of this approach is validated through performed simulations, which prove that EMUC balances energy consumption between sensor nodes, mitigates the hotspots problem, saves more energy and significantly extends the network lifetime.
Energy efficient clustering in heterogeneousIJCNCJournal
Cluster head election is a key technique used to reduce energy consumption and enhancing the throughput
of wireless sensor networks. In this paper, a new energy efficient clustering (E2C) protocol for
heterogeneous wireless sensor networks is proposed. Cluster head is elected based on the predicted
residual energy of sensors, optimal probability of a sensor to become a cluster head, and its degree of
connectivity as the parameters. The probability threshold to compete for the role of cluster head is derived.
The probability threshold has been extended for multi-levels energy heterogeneity in the network. The
proposed E2C protocol is simulated in MATLAB. Results obtained in the simulationshowthat performance
of the proposed E2Cprotocol is betterthan stable election protocol (SEP), and distributed energy efficient
clustering (DEEC) protocol in terms of energy consumption, throughput, and network lifetime.
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.
Energy Aware Talented Clustering with Compressive Sensing (TCCS) for Wireless...IJCNCJournal
Wireless sensor networks (WSNs) are networks of sensor nodes that interact wirelessly to gather information about the surrounding environment. Nodes are often low-powered and dispersed in an ad hoc, decentralized manner. Although WSNs have gained in popularity, they still have several serious shortcomings, like limited battery life and bandwidth. In this paper, the cluster head (CH) selection, the Compressive Sensing (CS) theory, the Connection-based Decentralized Clustering (CDC), the relay node selection, and the Multi Objective Genetic Algorithm (MOGA)are all taken into account The initial stage provided a theoretical revision to the concepts of network construction, compressive sensing, and MOGA, which impacted the improvement of network lifetime. In the second stage developed a novel model such as Energy Aware Talented Clustering with Compressive Sensing (TCCS) for the sensor network. This approach considers increasing longevity but also raises the network's overall quality of service (QoS). In the analysis, the TCCS model is applied to both the centralized and distributed networks and compared with the existing methods. When compared to the previous methods, the simulation results show that the proposed work performs better in terms of the calculation of maximum packet delivery ratio of 93.93 percent, minimum energy consumption of 8.04J, maximum energy efficiency of 91.04 percent, maximum network throughput of 465.51kbps, minimum packet loss of 282 packets, and minimum delay of 63.82 msec.
Evaluate the performance of K-Means and the fuzzy C-Means algorithms to forma...IJECEIAES
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.
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.
Collaborative Re-Localization Method in Mobile Wireless Sensor Network Based ...CSCJournals
Localization in Mobile Wireless Sensor Networks (WSNs), particularly in areas like surveillance applications, necessitates triggering re-localization in different time periods in order to maintain accurate positioning. Further, the re-localization process should be designed for time and energy efficiency in these resource constrained networks. In this paper, an energy and time efficient algorithm is proposed to determine the optimum number of localized nodes that collaborate in the re-localization process. Four different movement methods (Random Waypoint Pattern, Modified Random Waypoint pattern, Brownian motion and Levy walk) are applied to model node movement. In order to perform re-localization, a server/head/anchor node activates the optimal number of localized nodes in each island/cluster. A Markov Decision Process (MDP) based algorithm is proposed to find the optimal policy to select those nodes in better condition to cooperate in the re-localization process. The simulation shows that the proposed MDP algorithm decreases the energy consumption in the WSN between 0.6% and 32%.
International Journal of Advanced Smart Sensor Network Systems ( IJASSN )ijassn
With the availability of low cost, short range sensor technology along with advances in wireless networking, sensor networks has become a hot topic of discussion. The International Journal of Advanced Smart Sensor Network Systems is an open access peer-reviewed journal which focuses on applied research and applications of sensor networks. While sensor networks provide ample opportunities to provide various services, its effective deployment in large scale is still challenging due to various factors. This journal provides a forum that impacts the development of high performance computing solutions to problems arising due to the complexities of sensor network systems. It also acts as a path to exchange novel ideas about impacts of sensor networks research.
Sierpinski carpet fractal monopole antenna for ultra-wideband applications IJECEIAES
Microstrip antenna is broadly used in the modern communication system due to its significant features such as light weight, inexpensive, low profile, and ease of integration with radio frequency devices. The fractal shape is applied in antenna geometry to obtain the ultra-wideband antennas. In this paper, the sierpinski carpet fractal monopole antenna (SCFMA) is developed for base case, first iteration and second iteration to obtain the wideband based on its space filling and self-similar characteristics. The dimension of the monopole patch size is optimized to minimize the overall dimension of the fractal antenna. Moreover, the optimized planar structure is proposed using the microstrip line feed. The monopole antenna is mounted on the FR4 substrate with the thickness of 1.6 mm with loss tangent of 0.02 and relative permittivity of 4.4. The performance of this SCFMA is analyzed in terms of area, bandwidth, return loss, voltage standing wave ratio, radiation pattern and gain. The proposed fractal antenna achieves three different bandwidth ranges such as 2.6-4.0 GHz, 2.5-4.3 GHz and 2.4-4.4 GHz for base case, first and second iteration respectively. The proposed SCFMA is compared with existing fractal antennas to prove the efficiency of the SCFMA design. The area of the SCFMA is 25×20 푚푚 2 , which is less when compared to the existing fractal antennas.
Design and implementation of grid based clustering in WSN using dynamic sink ...journalBEEI
A wireless sensor networks (WSNs) play a significant application, especially in the monitored remoting environmental, which enables by the availability of sensors which are cheaper, smaller, and intelligent. The equipment of such sensors be with wireless interfaces, which a communication with other sensors occurs for creating a network, that contains many distributed nodes. The closest nodes to the sink are exploited at an enormous traffic load while the data from the whole regions are forwarded between them to reach the sink. This result in exhausting their energy quickly and partitioning the network. This is solved by changing the sink node position in Grid based clustering technique, which considers the optimal method for this purpose. A simulation with MATLAB can be applied for grid based clustering technique to evaluate the performance of WSN. The expected results deal with outperforms in throughput, reducing energy consumption and increasing residual energy, in addition to prolong the network lifetime of the sensor network.
Sensing and Sharing Schemes for Spectral Efficiency of Cognitive Radios IJECEIAES
Increase in data traffic, number of users and their requirements laid to a necessity of more bandwidth. Cognitive radio is one of the emerging technology which addresses the spectrum scarcity issue. In this work we study the advantage of having collaboration between cognitive enabled small cell network and primary macrocell. Different from the existing works at spectrum sensing stage we are applying enhanced spectrum sensing to avoid probability of false alarms and missed detections which has impact on spec tral efficiency. Later power control optimization for secondary users known as Hybrid spectrum sharing is used for further improvement of spectral efficiency. Furthermore, the failed packets of Primary users are taken care by high ranked relays which in turn decreases the average Primary user packet delay by 20% when compared between assisted Secondary user method and non-assisted Secondary user method.
A New Method for Reducing Energy Consumption in Wireless Sensor Networks usin...Editor IJCATR
Nowadays, wireless sensor networks, clustering protocol based on the neighboring nodes into separate clusters and fault
tolerance for each cluster exists for sensors to send information to the base station, to gain the best performance in terms of increased
longevity and maintain tolerance than with other routing methods. However, most clustering protocols proposed so far, only
geographical proximity (neighboring) cluster formation is considered as a parameter. In this study, a new clustering protocol and fault
tolerance based on the fuzzy algorithms are able to clustering nodes in sensor networks based on fuzzy logic and fault tolerance. This
protocol uses clustering sensor nodes and fault tolerance exist in the network to reduce energy consumption, so that faulty sensors
from neighboring nodes are used to cover the errors, work based on the most criteria overlay neighbor sensors with defective sensors,
distance neighbor sensors from fault sensor and distance neighbor sensors from central station is done. Superior performance of the
protocol can be seen in terms of increasing the network lifetime and maintain the best network tolerance in comparison with previous
protocols such as LEACH in the simulation results.
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.
Block diagonalization precoding and power allocation for clustering small-cel...journalBEEI
The clustering network is a solution to improve data-rate transmission in small-cells. In this case, clustering small-cells (CSCs) adopt a multiple antennas concept. The multiple antennas are used to maximize the downlink data-rate transmission at the users, but it requires precoding techniques to minimize interference among CSC users. This paper proposes a block diagonalization (BD) as a precoding technique for minimizing interference among CSC users. The performance of the BD precoding implemented on the clustering network under various numbers of small-cells. The CSC also implements a water-filling power allocation (PA-CoopWF) to distribute the available transmission power along with the CSCs antennas. To show the performance, our paper simulates two types of precoding techniques; those are the proposed BD and minimum mean square error (MMSE) in CSCs. Based on the receiver user parts under the overlapping coordination of CSCs, our method based on the BD precoding achieves considerably higher data-rate transmission compared to the MMSE precoding, especially on larger clusters. The simulation also shows that by implementing CSC with the BD in short-range distances and higher numbers of antennas, it promotes better data-rate performances compared to the MMSE precoding by 2.75 times at distance 100m and 67% at 50 antennas.
ENERGY EFFICIENT ROUTING ALGORITHM FOR MAXIMIZING THE MINIMUM LIFETIME OF WIR...ijasuc
In wireless sensor network, devices or nodes are generally battery powered devices. These nodes have
limited amount of initial energy that are consumed at different rates, depending on the power level. The
lifetime of the network is defined as the time until the first node fails (or runs out of battery). In this paper
different type of energy efficient routing algorithms are discussed and approach of these algorithms is to
maximize the minimum lifetime of wireless sensor network. Special attention has been devoted for
algorithms formulate the routing problem as a linear programming problem, which uses the optimal flow
path for data transmission and gives the optimum results. Advantages, limitations as well as comparative
study of these algorithms are also discussed in this paper.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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 Efficient Approach for Data Gathering and Sharing with Inter Node Communi...cscpconf
In today’s era Wireless sensor networks (WSNs) have emerged as a solution for a wide range of
applications. Most of the traditional WSN architectures consist of static nodes which are densely deployed
over a sensing area. Recently, several WSN architectures based on mobile elements (MEs) have been
proposed. Most of them exploit mobility to address the problem of data collection in WSNs. The common
drawback among them is to data sharing between interconnected nodes. In this paper we propose an
Efficient Approach for Data Gathering and Sharing with Inter Node Communication in Mobile-Sink. Our
algorithm is divided into seven parts: Registration Phase, Authentication Phase, Request and Reply Phase,
Setup Phase, Setup Phase (NN), Data Gathering, and Forwarding to Sink. Our approach provides an
efficient way to handle data in between the intercommunication nodes. By the above approach we can
access the data from the node which is not in the list, by sharing the data from the node which is
approachable to the desired node. For accessing and sharing we need some security so that the data can
be shared between authenticated nodes. For this we use two way security approach one for the accessing
node and other for the sharing.
Energy Conservation in Wireless Sensor Networks Using Cluster-Based ApproachIJRES Journal
In a wireless networking environment, the network is comprised of sensor nodes and backbones are subsets of sensors or actuators that suffice for performing basic data communication operations. They are applied for energy efficient broadcasting. In a broadcasting (also known as data dissemination) task, a message is to be sent from one node, which could be a sink or an actuator, to all the sensors or all the actuators in the network. The goal is to minimize the number of rebroadcasts while attempting to deliver messages to all sensors or actuators. Neighbor detection and route discovery algorithms that consider a realistic physical layer are described. An adaptive broadcasting protocol without parameters suitable for delay tolerant networks is further discussed. In existing solutions for minimal energy broadcasting problem, nodes can adjust their transmission powers. Wireless Sensor Networks (WSNs) are sets of many sensors that gather data and collaborate together. So, the procedures of broadcast or multicast are more important than traditional point-to-point communication in computer network. This paper focuses on broadcasting in structured WSNs. In such a kind, the procedure of network communications is easier than in unstructured WSNs. Thus, it will make an overview of Multi Point Relay (MPR) to show its weakness. Then define a cluster-based architecture for WSNs which is constructed using MPR. Next, provide a new broadcast algorithm based on the previous cluster architecture called 3B (Backbone Based Broadcasting). By the end, an illustration of 3B shows that it minimizes the energy consumption for accomplishing broadcast compared to MPR.
Performance evaluation of data filtering approach in wireless sensor networks...ijmnct
Wireless Sensor Network is a field of research which is viable in every application area like security
services, patient care, traffic regulations, habitat monitoring and so on. The resource limitation of small
sized tiny nodes has always been an issue in wireless sensor networks. Various techniques for improving
network lifetime have been proposed in the past. Now the attention has been shifted towards heterogeneous
networks rather than having homogeneous sensor nodes in a network. The concept of partial mobility has
also been suggested for network longevity. In all the major proposals; clustering and data aggregation in
heterogeneous networks has played an integral role. This paper contributes towards a new concept of
clustering and data filtering in wireless sensor networks. In this paper we have compared voronoi based
ant systems with standard LEACH-C algorithm and MTWSW with TWSW algorithm. Both the techniques
have been applied in heterogeneous wireless sensor networks. This approach is applicable both for critical
as well as for non-critical applications in wireless sensor networks. Both the approaches presented in this
paper outperform LEACH-C and TWSW in terms of energy efficiency and shows promising results for
future work.
A Novel Weighted Clustering Based Approach for Improving the Wireless Sensor ...IJERA Editor
Great lifetime and reliability is the key aim of Wireless Sensor Network (WSN) design. As for prolonging
lifetime of this type of network, energy is the most important resource; all recent researches are focused on more
and more energy efficient techniques. Proposed work is Weighted Clustering Approach based on Weighted
Cluster Head Selection, which is highly energy efficient and reliable in mobile network scenario. Weight
calculation using different attributes of the nodes like SNR (Signal to Noise Ratio), Remaining Energy, Node
Degree, Mobility, and Buffer Length gives efficient Cluster Head (CH) on regular interval of time. CH rotation
helps in optimum utilization of energy available with all nodes; results in prolonged network lifetime.
Implementation is done using the NS2 network simulator and performance evaluation is carried out in terms of
PDR (Packet Delivery Ratio), End to End Delay, Throughput, and Energy Consumption. Demonstration of the
obtained results shows that proposed work is adaptable for improving the performance. In order to justify the
solution, the performance of proposed technique is compared with the performance of traditional approach. The
performance of proposed technique is found optimum as compared to the traditional techniques.
ENERGY-EFFICIENT MULTI-HOP ROUTING WITH UNEQUAL CLUSTERING APPROACH FOR WIREL...IJCNCJournal
The development of an energy-efficient routing protocol, capable of extending the life of the network, is one of the main constraints of wireless sensor networks (WSN). Research studies on WSN routing prove that clustering offers an effective approach to prolong the lifetime of a WSN, particularly when it is combined with multi-hop communication that can reduces energy costs by minimizing the distance between transmitter and receiver. Most clustering algorithms using multi-hop in data transmission encounter the hotspot problem. In this work, an Energy-efficient Multi-hop routing with Unequal Clustering approach (EMUC) is proposed, to create clusters of different sizes, which depend on the distance between the sensor node and the base station. Equilibrate the energy dissipation between the cluster heads is the purpose of this approach by adopting multi-hop communication to relay data to the base station. The implementation of multi-hop mode to transmit data to the base station reduces the energy cost of transmission over long distances. The effectiveness of this approach is validated through performed simulations, which prove that EMUC balances energy consumption between sensor nodes, mitigates the hotspots problem, saves more energy and significantly extends the network lifetime.
Energy efficient clustering in heterogeneousIJCNCJournal
Cluster head election is a key technique used to reduce energy consumption and enhancing the throughput
of wireless sensor networks. In this paper, a new energy efficient clustering (E2C) protocol for
heterogeneous wireless sensor networks is proposed. Cluster head is elected based on the predicted
residual energy of sensors, optimal probability of a sensor to become a cluster head, and its degree of
connectivity as the parameters. The probability threshold to compete for the role of cluster head is derived.
The probability threshold has been extended for multi-levels energy heterogeneity in the network. The
proposed E2C protocol is simulated in MATLAB. Results obtained in the simulationshowthat performance
of the proposed E2Cprotocol is betterthan stable election protocol (SEP), and distributed energy efficient
clustering (DEEC) protocol in terms of energy consumption, throughput, and network lifetime.
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.
Energy Aware Talented Clustering with Compressive Sensing (TCCS) for Wireless...IJCNCJournal
Wireless sensor networks (WSNs) are networks of sensor nodes that interact wirelessly to gather information about the surrounding environment. Nodes are often low-powered and dispersed in an ad hoc, decentralized manner. Although WSNs have gained in popularity, they still have several serious shortcomings, like limited battery life and bandwidth. In this paper, the cluster head (CH) selection, the Compressive Sensing (CS) theory, the Connection-based Decentralized Clustering (CDC), the relay node selection, and the Multi Objective Genetic Algorithm (MOGA)are all taken into account The initial stage provided a theoretical revision to the concepts of network construction, compressive sensing, and MOGA, which impacted the improvement of network lifetime. In the second stage developed a novel model such as Energy Aware Talented Clustering with Compressive Sensing (TCCS) for the sensor network. This approach considers increasing longevity but also raises the network's overall quality of service (QoS). In the analysis, the TCCS model is applied to both the centralized and distributed networks and compared with the existing methods. When compared to the previous methods, the simulation results show that the proposed work performs better in terms of the calculation of maximum packet delivery ratio of 93.93 percent, minimum energy consumption of 8.04J, maximum energy efficiency of 91.04 percent, maximum network throughput of 465.51kbps, minimum packet loss of 282 packets, and minimum delay of 63.82 msec.
ENERGY AWARE TALENTED CLUSTERING WITH COMPRESSIVE SENSING (TCCS) FOR WIRELESS...IJCNCJournal
Wireless sensor networks (WSNs) are networks of sensor nodes that interact wirelessly to gather
information about the surrounding environment. Nodes are often low-powered and dispersed in an ad hoc,
decentralized manner. Although WSNs have gained in popularity, they still have several serious
shortcomings, like limited battery life and bandwidth. In this paper, the cluster head (CH) selection, the
Compressive Sensing (CS) theory, the Connection-based Decentralized Clustering (CDC), the relay node
selection, and the Multi Objective Genetic Algorithm (MOGA)are all taken into account The initial stage
provided a theoretical revision to the concepts of network construction, compressive sensing, and MOGA,
which impacted the improvement of network lifetime. In the second stage developed a novel model such as
Energy Aware Talented Clustering with Compressive Sensing (TCCS) for the sensor network. This
approach considers increasing longevity but also raises the network's overall quality of service (QoS). In
the analysis, the TCCS model is applied to both the centralized and distributed networks and compared
with the existing methods. When compared to the previous methods, the simulation results show that the
proposed work performs better in terms of the calculation of maximum packet delivery ratio of 93.93
percent, minimum energy consumption of 8.04J, maximum energy efficiency of 91.04 percent, maximum
network throughput of 465.51kbps, minimum packet loss of 282 packets, and minimum delay of 63.82 msec.
ENERGY EFFICIENT AGGREGATION WITH DIVERGENT SINK PLACEMENT FOR WIRELESS SENSO...ijasuc
In WSN the data aggregation is a means for condensing the energy requirement by reducing number of
transmission by combining the data and sending the final required result to the base station. The lifetime
of the WSN can be improved by employing the aggregation techniques. During the process of aggregation
the numbers of transmission are reduced by combining the similar data from the nearby areas. By using
the clustering technique and aggregating the correlated data greatly minimize the energy consumed in
collecting and disseminating the data. In this work, we evaluate the performance of a novel energy
efficient cluster based aggregation protocol (EECAP) for WSN. The main focus in this proposed work is
to study the performance of our proposed aggregation protocol with divergent sink placements such as
when sink is at the centre of the sensing field, corner of the sensing field or at a location selected
randomly in the sensor field. We present experimental results by calculating the lifetime of network in
terms of number of sensing rounds using various parameters such as – average remaining energy of
nodes, number of dead nodes after the specified number of sensing rounds. Finally the performance of
various aggregation algorithms such as LEACH, SEP and our proposed aggregation protocol (EECAP)
are compared with divergent sink placements. The simulation results demonstrates that EECAP exhibits
good performance in terms of lifetime and the energy consumption of the wireless sensor networks and
which can be as equally compared with existing clustering protocols.
Bottleneck Detection Algorithm to Enhance Lifetime of WSNjosephjonse
In recent years, a wireless sensor network is gaining much more importance due to its immense contribution in numerous applications. Deployment of sensor nodes that would reduce computation, minimize cost and gaining high degree of network connectivity is an challenging task. Random deployment of sensor nodes causes the wireless sensor networks to face topological weaknesses such as communication bottlenecks, network partitions and sensing holes. These problems lead to uneven energy utilization, reduction in reliability of network and reduction in network lifetime. Bottleneck detection algorithm is proposed to identify bottleneck and minimal bottleneck zones in network. Additional sensor node deployment strategy is used in bottleneck detection algorithm to extend network lifetime. Random additional sensor node deployment and Targeted additional sensor node deployment are proposed to enhance network lifetime. Deployment strategies are compared with respect to network parameters such as throughput, packet delivery fraction and network lifetime.
BOTTLENECK DETECTION ALGORITHM TO ENHANCE LIFETIME OF WSNijngnjournal
In recent years, a wireless sensor network is gaining much more importance due to its immense
contribution in numerous applications. Deployment of sensor nodes that would reduce computation,
minimize cost and gaining high degree of network connectivity is an challenging task. Random deployment
of sensor nodes causes the wireless sensor networks to face topological weaknesses such as communication
bottlenecks, network partitions and sensing holes. These problems lead to uneven energy utilization,
reduction in reliability of network and reduction in network lifetime. Bottleneck detection algorithm is
proposed to identify bottleneck and minimal bottleneck zones in network. Additional sensor node
deployment strategy is used in bottleneck detection algorithm to extend network lifetime. Random
additional sensor node deployment and Targeted additional sensor node deployment are proposed to
enhance network lifetime. Deployment strategies are compared with respect to network parameters such as
throughput, packet delivery fraction and network lifetime.
Bottleneck Detection Algorithm to Enhance Lifetime of WSNjosephjonse
In recent years, a wireless sensor network is gaining much more importance due to its immense contribution in numerous applications. Deployment of sensor nodes that would reduce computation, minimize cost and gaining high degree of network connectivity is an challenging task. Random deployment of sensor nodes causes the wireless sensor networks to face topological weaknesses such as communication bottlenecks, network partitions and sensing holes. These problems lead to uneven energy utilization, reduction in reliability of network and reduction in network lifetime. Bottleneck detection algorithm is proposed to identify bottleneck and minimal bottleneck zones in network. Additional sensor node deployment strategy is used in bottleneck detection algorithm to extend network lifetime. Random additional sensor node deployment and Targeted additional sensor node deployment are proposed to enhance network lifetime. Deployment strategies are compared with respect to network parameters such as throughput, packet delivery fraction and network lifetime.
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.
Clustering provides an effective method for
extending the lifetime of a wireless sensor network. Current
clustering methods selecting cluster heads with more residual
energy, and rotating cluster heads periodically to distribute the
energy consumption among nodes in each cluster. However,
they rarely consider the hot spot problem in multi hop sensor
networks. When cluster heads forward their data to the base
station, the cluster heads closer to the base station are heavily
burdened with traffic and tend to die much faster. To mitigate
the hot spot problem, we propose a Novel Energy Efficient
Unequal Clustering Routing (NEEUC) protocol. It uses residual
energy and groupsthe nodesinto clusters of unequal layers
Clustering and data aggregation scheme in underwater wireless acoustic sensor...TELKOMNIKA JOURNAL
Underwater Wireless Acoustic Sensor Networks (UWASNs) are creating attentiveness in
researchers due to its wide area of applications. To extract the data from underwater and transmit to
watersurface, numerous clustering and data aggregation schemes are employed. The main objectives of
clustering and data aggregation schemes are to decrease the consumption of energy and prolong the
lifetime of the network. In this paper, we focus on initial clustering of sensor nodes based on their
geographical locations using fuzzy logic. The probability of degree of belongingness of a sensor node to its
cluster, along with number of clusters is analysed and discussed. Based on the energy and distance the
cluster head nodes are determined. Finally using using similarity function data aggregation is analysed and
discussed. The proposed scheme is simulated in MATLAB and compared with LEACH algorithm.
The simulation results indicate that the proposed scheme performs better in maximizing network lifetime
and minimizing energy consumption.
A multi-hop routing protocol for an energy-efficient in wireless sensor networkIJECEIAES
The low-energy adaptive clustering hierarchy (LEACH) protocol has been developed to be implemented in wireless sensor networks (WSNs) systems such as healthcare and military systems. LEACH protocol depends on clustering the employed sensors and electing one cluster head (CH) for each cluster. The CH nodes are changed periodically to evenly distribute the energy load among sensors. Updating the CH node requires electing different CH and re-clustering sensors. This process consumes sensors’ energy due to sending and receiving many broadcast and unicast messages thus reduces the network lifetime, which is regarded as a significant issue in LEACH. This research develops a new approach based on modifying the LEACH protocol to minimize the need of updating the cluster head. The proposal aims to extend the WSN’s lifetime by maintaining the sensor nodes’ energy. The suggested approach has been evaluated and shown remarkable efficiency in comparison with basic LEACH protocol and not-clustered protocol in terms of extending network lifetime and reducing the required sent messages in the network reflected by 15%, and, in addition, reducing the need to reformatting the clusters frequently and saving network resources.
Energy efficient data communication approach in wireless sensor networksijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based
configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor
node creates challenges for routing in WSNs. The research work presents the organized and detailed
description of energy conservation method for WSNs. In the proposed method reclustering and multihop
data transmission processes are utilized for data reporting to base station by sensor node. The accurate use
of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the
proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the
total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for
longer times inside the cluster. The result shows that the proposed clustering approach has higher stable
region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy
Adaptive Clustering Hierarchy (LEACH) for WSNs.
ENERGY EFFICIENT DATA COMMUNICATION APPROACH IN WIRELESS SENSOR NETWORKSijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor node creates challenges for routing in WSNs. The research work presents the organized and detailed description of energy conservation method for WSNs. In the proposed method reclustering and multihop data transmission processes are utilized for data reporting to base station by sensor node. The accurate use of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for longer times inside the cluster. The result shows that the proposed clustering approach has higher stable region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy Adaptive Clustering Hierarchy (LEACH) for WSNs.
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 Cluster-
based 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
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.
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.
CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...ijwmn
A wireless sensor network is composed of many sensor nodes, that have beengiven out in a
specific zoneandeach of them hadanability of collecting information from the environment and
sending collected data to the sink. The most significant issues in wireless sensor networks,
despite the recent progress is the trouble of the severe limitations of energy resources.Since that
in different applications of sensor nets, we could throw a static or mobile sink, then all aspects of
such networks should be planned with an awareness of energy.One of the most significant topics
related to these networks, is routing. One of the most widely used and efficient methods of
routing isa hierarchy (based on clustering) method.
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A NODE DEPLOYMENT MODEL WITH VARIABLE TRANSMISSION DISTANCE FOR WIRELESS SENSOR NETWORKS
1. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 12, No.4, August 2020
DOI:10.5121/ijwmn.2020.12403 37
A NODE DEPLOYMENT MODEL WITH VARIABLE
TRANSMISSION DISTANCE FOR WIRELESS SENSOR
NETWORKS
Fan Tiegang1
and Chen Junmin2
1
Key Laboratory of Machine Learning and Computational Intelligence, College of
Mathematics and Information Science, Hebei University, Baoding 071002, Hebei, China
2
College of Mathematics and Information Science ,Hebei University, Baoding, 071002,
Hebei, China
ABSTRACT
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.
KEYWORDS
Node Deployment, Optimization Model, Minimize Cost, Transmission Distance
1. INTRODUCTION
With the development of wireless communication and microelectronics technology, wireless
sensor networks have received more attention from researchers and many industries. It is used in
many practical applications, including battlefield surveillance, forest fire monitoring, crop
detection, environmental monitoring, medical insurance, industrial automation, etc. It changes the
way people sense the world to a large extent, and at the same time, it also changes humanity's
lifestyle. The location of the sensor and the network's operation mode are related to the regular
operation and performance of the network. Network deployment is one of the essential tasks for
wireless sensor networks.
Many node deployment methods have been proposed. They can be divided into static node
deployment method and movable node deployment method based on whether the node is
movable. The static deployment method includes deterministic deployment and random
deployment. The movable node deployment method includes centralized deployment and
distributed deployment. Different deployment methods focus on different network performance,
mainly network coverage, connectivity, time delay, network lifetime, energy efficiency, network
cost, etc. There is a close correlation between these metrics. Since sensors are generally powered
by batteries and the energy is limited, saving energy and extending network lifetime has become
an important research content for network node deployment.
2. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 12, No.4, August 2020
38
With the continuous expansion of WSNs detection area, more and more attention is paid to the
network cost. How to deploy the network to minimize the network cost while meeting
performance requirements has become an important research content. There are not many works
on network cost, and the network cost concerned mainly refers to the number of nodes. In
addition to the number of sensors, many factors affect the network cost, such as the area of the
detection area, energy efficiency, and network lifetime. As far as I know, there is no research to
discuss network cost based on the above factors. Our goal is to design a network structure and
network node deployment scheme that minimizes the cost of the network while meeting the given
performance requirements.
This paper gives an optimization model to minimize the network kcost, and proposes a
deployment method. To sum up, the main contributions are presented as follows.
(1). A "corona + cluster" network structure is proposed. The width and cluster size of each
corona is different.
(2). By analyzing the energy consumption and network cost, an optimization model is proposed
to minimizing cost per unit area.
(3). The deployment parameters are obtained by solving and improving the model, and the
deployment steps of the multi-sink network are given.
2. RELATED WORK
Effective deployment of Sensor Nodes(SNs) is a significant point of concern as the performance
and lifetime of any WSN primarily depend on it. Various models have been proposed by
researchers to deploy SNs in large-scale open regions. Network coverage and connectivity are
the fundamental concerns of node deployment. The paper [1] describes relevant deployment
strategies to meet coverage requirements. Network connectivity and coverage are closely related,
which mainly depends on the relationship between sensing distance and communication distance.
Paper [2][3] give relevant conclusions. In the large-scale field or the inaccessibility/harshness
field, random deployment becomes only one option. There are two classes random deployment
strategies, simple strategies, and compound strategies [4]. One of the main research contents of
random deployment is determining the distribution function of nodes to ensure network
connectivity and coverage. The paper[5] gave an analytical expression for estimating the average
minimum number of nodes for getting full connection of networks. The paper [6] proposed a
probabilistic approach to compute the covered area fraction at critical percolation for both SCPT
and NCPT problems. In [7], a novel framework was proposed for solving optimal deployment
problems for randomly deployed and clustered WSNs. The percolation theory is adopted to
analyze the degree of connectivity when the targeted degree of partial coverage is achieved.
Many researcher focus on the relocation of mobile sensors after random deployment. The paper
[8] proposed the Virtual Force Algorithm (VFA) for clustering-based networks. In this method,
the cluster head node calculates the coverage of the area and sends instructions to the cluster
members to move to the corresponding position accordingly. Paper [9] proposed a deployment
algorithm for nodes with limited mobility. Limited mobility means that the maximum distance a
node can move is limited. The paper [10] proposed a non-uniform deployment method that can
approximate energy balance consumption. It is assumed that all nodes are deployed in a circular
area, the sink is located at the center of the circle, the transmission distance of the sensing node is
the same, and the node's energy consumption includes data reception and transmission. Paper
[11] proposed a non-uniform deployment method based on unequal clusters to optimize the
3. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 12, No.4, August 2020
39
Energy-Consuming Rate. This method combines the advantages of unequal clusters and non-
uniform deployment and adopts a node sleep mechanism. It not only improves energy efficiency
but also balances node energy consumption and avoids the energy holes problem. The paper [12]
studied the problem of unbalanced energy consumption in a uniformly distributed network and
proposed two random relay node deployment methods. The first method aims to balance the
energy consumption rate of relay nodes, thereby extending the network's lifetime. This method
does not guarantee the connection with the sensor node. The second method considers lifetime
and connectivity. The article discusses both single-hop and multi-hop scenarios.
The paper [13] proposed a novel modeling solution capable of representing a wide variety of
scenarios, from totally random to planned stochastic node deployment, in heterogeneous sensor
networks. Using only about 2% of H-sensors and deploying nodes by using the P- and Q-models
to distribute the L-sensors around the H-sensors deployed with a repulsive model, they observed
essential characteristics of the network topology, such as low average path length and high
clustering coefficient.The paper [14] gave a survey about clustering and cluster-based multi-hop
routing protocols. Some parameters were given to evaluate the properties of the different
methods. The studied methods are classified into four categories: classical approaches, fuzzy-
based approaches, metaheuristic-based approaches, and hybrid metaheuristic- and fuzzy-based
approaches. In each category of the classification, criteria and parameters are presented according
to the type of methodology. The authors [15] gave a efficient coding techniques algorithm for
cluster-heads communication in wireless sensor networks to improve the Bit-error rate. The paper
[16] proposed a new cluster-based algorithm to minimize energy consumption in WSNs.
In the wireless sensor network, the uneven energy consumption of the nodes significantly
shortens the network lifetime and causes waste. An essential idea to solve this problem is to equip
nodes with different energy. Paper [17] studied the benefits of multi-level batteries for prolonging
the network's lifetime, showing that under the total energy budget, the network lifetime under the
multi-level battery allocation method is much longer than that under the same battery allocation.
Paper [18] further studied the battery distribution method. The problem can be described as:
There are m types of batteries, and the power and cost are known. Different types and numbers of
batteries can be packaged and distributed to the node. Assuming that there M battery packs,
determine the number of each type battery in each pack to maximize the network lifetime. In
order to balance the energy consumption of sensors, the mixed/hybrid transmission schemes
appeared in many papers [19]. Each sensor can adjust its transmission power level and alternates
between direct transmission mode (sending data directly to sink without using any relay node)
and hop-by-hop transmission mode (forwarding data to next-hop neighbors).
According to the energy consumption model of node data transmission, energy consumption can
be changed by adjusting the transmission distance. Some deployment methods have been
developed to balance the energy consumption of nodes by adjusting the nodes' data transmission
distance. The paper [20] discussed the problem of balancing the energy consumption of nodes by
changing the transmission distance, and provided an iteration-based method for determining the
transmission distance. This paper proposed an improved corona model with levels to investigate
the transmission range assignment strategy used to maximize the lifetime of wireless sensor
networks. The author proved the problem of searching optimal transmission range lists is a multi-
objective optimization problem, and that is also NP hard. The author [21] proposed a mixed
integer programming formulation, i.e., SPSRC, that combines all design issues in a single model.
The SPSRC finds the sensors' and sinks' optimal locations, active/standby periods of the sensors,
and the data transmission routes from each active sensor to its assigned sink.
4. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 12, No.4, August 2020
40
In this paper, we introduce a node deployment strategy based on variable transmission distance.
Based on the network structure of "corona + cluster," an optimization model to minimize network
cost is proposed.
3. NETWORK STRUCTURE
It is assumed that there is a circular detected area with a radius of R. The base station is located at
the center of the area. The sensor has a fixed sensing radius and adjustable data transmission
distance. The problem to be solved is how to deploy sensors to minimize network cost while
meeting relevant design requirements.
Figure 1. The network structure of “ corona + cluster”
The sensors can be placed uniformly by random throwing, and the density of the nodes can be
obtained on the sensing radius. The monitoring area is divided into several coronas by concentric
circles. The transmission distance of the sensor in the corona is equal to the corona's width where
it is located. As shown in Figure 1, ih
represents the ith corona, and the corresponding circle
radius is represented by ir
. The width of the ith corona 1i i ic r r
. The sensors in the same
corona form multiple clusters. The number of clusters is related to the radius of the corresponding
circle. The cluster head nodes are generated from the nodes in the cluster and are continuously
rotated. Sensors periodically sense the environment, generate data, and send the data to cluster
head nodes in the same cluster through a single hop. The cluster head nodes receive the cluster
node's data, integrate the cluster data, receive the data sent by the cluster head node of the
previous corona, and transmit the data to the cluster head node of the next corona.
Since the transmission distances of sensor nodes in different coronas are different, we use an
energy consumption model and introduce the following symbols:
0e
: The energy consumption of generating unit data,
1e
: The energy consumption of electrical devices receiving or sending unit data,
2e
: Amplification factor when sending data
5. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 12, No.4, August 2020
41
3e : The energy consumption of integrated unit data
The energy consumption formula for sending unit data is:
2
1 2+e e d
, where d is the sending
distance. The sensor’s energy consumption per unit time is:
2
0 1 1 2 3( ) ,E e x e y e e d z e n
where , , ,x y z n indicate the corresponding amount of data,respectively.
Since the energy consumption of cluster management and cluster head generation is far less than
that of data transmission, it can be ignored. Of course, it can also be set as a fixed value
independent of data volume, namely the fixed energy consumption per unit time, which has
almost no impact on our optimization model's solution.
The cost model of the sensor is a bE , where a is the hardware cost of sensor, b is the unit
energy cost, E is the energy value fixed at the node. It is known that the energy consumption of
each corona node is different. To effectively use the energy, we will adopt the method that
assembles different initial energy for the nodes in different coronas. In this way, the costs of
nodes in different coronas are different. The base station is powered by dedicated lines or solar
energy, and its cost is fixed.
Network lifetime refers to the minimum lifetime of a node
min{ }iT T , where iT
is the lifetime
of node i. In an ideal network, each node has the same lifetime. One of our design goals is that
all nodes have approximately the same lifetime. As a design index, network lifetime should meet
the application requirement. Network design lifetime is an important factor affecting network
cost.
4. OPTIMIZATION MODEL
4.1. Analysis and Model
Firstly, the sensor's energy consumption is analyzed. We divide the energy consumption into two
parts: (1) Intra-cluster energy consumption, including energy consumption of sensor, sensing the
environment and transmitting data to the cluster head node, and energy consumption of the
cluster head node that receives, integrates the data in the cluster and sends to next cluster head
node. (2)Inter-cluster energy consumption refers to the energy consumption when the cluster-
head node receives data from the previous cluster-head node and sends it to the next cluster head
node.
The number of nodes in the ith corona is iN
, then
2 2
1= )i i iN r r (
Where, is a node density that ensures to meet the coverage requirements of the monitoring area.
It has been proved in literature [22] that at least
2 i ir c clusters are needed to cover the ith
corona. We can calculate the proportion of cluster-head nodes to all nodes in the ith corona.
6. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 12, No.4, August 2020
42
2 2 2
1
2
2
=
) (2 )
i
i i
i
i i i i i
r
c r
p
r r c r c
( ,
The energy consumption per unit time of each cluster-head node in ith corona is:
2
_ 0 1 3 2 1
1 1 1
( 1) (e )ch i i
i i i
E e l e l e l ml c e
p p p
,
where l is the amount of data generated by a sensor per unit time. In the above formula, the first
term is the energy consumption for generating data, the second is the energy consumption of
receiving data from other nodes in the cluster, the third is the energy consumption of integrating
data, and the fourth is the consumption for sending the integrated data to the next cluster head
node, where m is the data compression factor. In many non-uniform clustering algorithms,
clusters far from the base station are larger than clusters close to the base station [11]. To ensure
sending data to the cluster head node of the next corona, transmission distance of the cluster head
node is set as the corona width.
The energy consumption per unit time of sensor in ith corona is:
2
_ 0 2 1(e )nch i iE e l l c e
,
The first term is the energy consumption for generating sensory data. The second is the energy
consumption of sending the data to the cluster head node. Based on the above two formulas, the
energy consumption of all nodes in ith corona per unit time can be obtained.
intra _ _ _
2 2
0 1 3 2 1 0 2 1
2
0 1 2 3
(1 )
(1 ) (e ) (1 ) (1 ) (e )
{ [2(1 ) ] (1 )e }
i i i ch i i i nch i
i i i i i i i i i i i i
i i i i
E N p E N p E
N p e l N p e l N e l N ml c e N p e l N p l c e
N l e p m e p m c e
The inter-cluster energy consumption per unit time of all cluster head nodes in ith corona is:
2 2 2 2 2
int _ 1 1 2
2 2 2
1 2
( ) ( ) ( )
= ( ) (2 )
er i i i i
i i
E R r mle R r ml e e c
R r ml e e c
In particular, since the cluster head nodes in the outermost corona does not receive data from
other coronas, int _ 0er kE
. The total energy consumption of all nodes in ith corona per unit time
is:
int _ int _i ra i er iE E E
,
It can be seen that the total energy consumption is different under different corona numbers and
corona widths.The average energy consumption per unit time of each node in ith corona is:
7. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 12, No.4, August 2020
43
i
i
i
E
E
N
,
Assuming that the network design lifetime is T , the average energy consumption of each node
for cluster management (including cluster head generation, information exchange in the cluster,
etc.) per unit time is a fixed value
*
E . The energy assembled by each node in the corona should
be:
*
_ ( )p i iE E E T
,
According to the previous cost model, the total network cost can be obtained as:
2 *
_
1
k
i p i
i
C a R b N E c
,
The first term of the above equation is the hardware cost of all sensors, the second term is the
cost of the total energy assembled on the sensors, and the third term
*
c is the cost of the base
station. It can be seen that the total cost is different under the different number of coronas and
corona widths. Therefore, it is hoped to find a suitable number of coronas and corona width to
minimize the total cost.Further, theCost Per Unit Area(CPUA) can be obtained :
2 *
_
1
2
*
*
2 2
1
1
k
i p i
i
k
i
i
C
CPUA
S
a R b N E c
R
c
a b E T Tb E
R R
The meaning of the above formula is also obvious. With the CPUA as the target function, the
following optimization model is obtained:
1 2
1
1 1
min
. .
, 2
i
i i i
k
CPUA
s t d c d
c r r i k
c r
r R
where, 1d
and 2d
is the lower and upper limits of node transmission distance. When the detection
region is given, namely R is the determined value, the objective function of the above model can
be replaced by the total network cost, and the optimal solution of the decision variable is the
same.
8. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 12, No.4, August 2020
44
When the relevant parameter values( , , , etc.) of the model are given, minimizing the unit
area cost CPUA (or total cost C) is equivalent to minimizing total energy consumption (the sum
of energy consumption except for cluster management). The above model can be simplified as:
1
1 2
1
1 1
min
. .
, 2
k
i
i
i
i i i
k
E
s t d c d
c r r i k
c r
r R
4.2. Solving the Model.
Given the relevant parameters, the above model can be solved. The values of the model
parameters are as follows in Table 1.
Table 1 Model parameters
Solve the model according to the following steps to determine the optimal number of coronas and
the width of each corona.
(1) Determine the upper and lower limits of the number of coronas according to the upper and
lower limits of the data transmission distance of the sensing node. Since , , , it can be
known that the number of coronas is between 3-10.
(2) Solve the model under each corona number respectively to get the optimal corona width
and minimum total energy consumption( or unit area cost) of each corona under the
corresponding number of coronas.
(3) Compare the total energy consumption(or unit area cost) between different corona
numbers, and take the corona number and corona width corresponding to the minimum
value to be the final result.
The solution of the model are shown in Table 9. It can be seen that when the number of coronas
is 6, the CPUA or total energy consumption is the lowest. The widths of the first corona to the
R T
Parameters Values Parameters Values
R 200m 0e 50nJ/bit
0.0318 1e 50nJ/bit
l 256bit/minute 2e 10pJ/bit/m2
m 0.1 3e 5nJ/bit
1d 20m 2d 80m
a 10$ b 2$
T 100000minutes *
c 200$
*
E 100nJ/minute
9. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 12, No.4, August 2020
45
sixth corona are: 58.5m, 42.3m, 33.9m, 25.3m, 20m, 20m, and the corona width decreases from
the inside to the outside. This result is significantly different from the results of many different
cluster algorithms[11]. In the EEUC algorithm, the cluster's size far away from the base station is
larger than that close to the base station. The main reason is that our deployment strategy
assembles different initial energies for the nodes.
Table 2 Energy consumption, cost and corona width under different number of coronas
k=3 k=4 k=5 k=6 k=7 k=8 k=9 k=10
Total
energy
consum
ption
22817.
15
21171.53
20598.7
7
20395.7
9
20517.97 20924.04 21573.38 22424.78
CPUA
0.6833
738
0.657183
0.64806
72
0.64483
67
0.646781
3
0.653244
1
0.663578
6
0.677129
1
h1 80 70.4 64.8 58.5 52.8 44.9 37.7 20
h2 64.9 51.5 48 42.3 38 32.1 22.3 20
h3 55.1 42.6 39 33.9 29.2 23 20 20
h4 35.5 27 25.3 20 20 20 20
h5 20 20 20 20 20 20
h6 20 20 20 20 20
h7 20 20 20 20
h8 20 20 20
h9 20 20
h10 20
4.3. Model Improvement
From the results of the previous model, it can be seen that the width of the outer corona is smaller
than the width of the inner corona. In the energy consumption analysis, we set the cluster head
node's data transmission distance as the width of its corona, which will lead to uneven energy
consumption for different corona's node. To solve this problem, we set the cluster head node's
data transmission distance in ith corona as the width of (i-1)th corona -1ic .
2
0 1 3 2 1 1
1 1 1
_
2
0 1 3 2 -1 1
1 1 1
( 1) (e ) , 1
1 1 1
( 1) (e ) , 2
ch i
i
i i i
e l e l e l ml c e i
p p p
E
e l e l e l ml c e i k
p p p
,
2 2 2
1 2 1
int _ 2 2 2
1 2 -1
( ) (2 ), 1
( ) (2 ), 2
i
er i
i i
R r ml e e c i
E
R r ml e e c i k
By replacing _ch iE and int _er iE in CPUA with the _ch iE , and int _er iE , we get the CPUA, and the
improved optimization model can be obtained:
10. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 12, No.4, August 2020
46
1 2
1
1
1 1
min
. .
, 2
, 2
i
i i i
i i
k
CPUA
s t d c d
c r r i k
c c i k
c r
r R
Constraint 1i ic c is added to the model to ensure that the corona width changes regularly.
Resolve the model, and the CPUA and total energy consumption under different corona numbers
are obtained . It can be seen that compared with the model solution before the improvement, the
optimal value has increased corresponding to the same corona number. This is because of the
increasing data transmission distance of cluster head nodes. By comparison, the cost is still the
lowest when the number of coronas is 6. The corresponding corona width from the first corona to
the sixth corona is 49.8m, 45.7m, 36.8m, 27.7m, 20m, 20m, respectively.
Table 3 Energy consumption and cost under different corona numbers (improved model)
k=3 k=4 k=5 k=6 k=7 k=8 k=9 k=10
Total
energy
consum
ption
23062.
72
21398.65
20798.4
6
20581.7
1
20677.48 21046.11 21634.69
22424.7
8
CPUA
0.6872
822
0.6607977
0.65124
53
0.64779
56
0.649319
9
0.655186
8
0.664554
3
0.67712
90
5. NODE DEPLOYMENT BASIC STEPS
By solving the optimization model, we get the relevant parameters value for node deployment:
(1) The number of coronas k and the width of each corona ic ;
(2) The number of sensors in each corona iN ;
(3) The proportion of cluster head nodes in each corona ip ; (4) The initial energy to be
assembled for each node in each corona _p iE .
The basic steps for node deployment are given below:
(1) According to the obtained corona width, the detected area is divided into coronas,
(2) Prepare s sensing nodes with initial energy f for each corona,
(3) Throw the corresponding nodes evenly on each corona,
11. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 12, No.4, August 2020
47
(4) Each corona generates cluster heads, forms clusters, and starts data collection and
transmission.
The fourth step above includes the generation and update mechanism of cluster heads and the
data routing method between cluster heads, which has an important impact on the normal
operation and energy consumption of the network. The previous optimization model does not
cover these contents, which becomes our main work in the next step.
6. CONCLUSIONS
A node deployment method with variable transmission distance is proposed to minimize the
network cost. We adopt the network structure based on "corona+cluster". The data transmission
distance of the sensor can be adjusted. Sensors located in different coronas form clusters of
different sizes. The nodes in different coronas are equipped with different initial energy. Based
on the analysis of node energy consumption and network cost, an optimization model to
minimize cost is proposed. The number of coronas and the optimal transmission distance of
nodes can be determined by solving the model. It is found that the node's transmission distance
gradually increases from the outer corona to the inner corona. For the energy consumption
balance of the nodes in the same corona, the transmission distance of the cluster head node is
revised, and the model is improved. On this basis, the steps of network node deployment are
proposed
7. DISCUSSION
The sensors used have various characteristics in this study, such as adjustable data transmission
distance and assembled with different initial energy. These characteristics appeared singly in the
existing studies only, and the application of these characteristics can effectively reduce the
network cost and improve the network performance. New features can be added in future work as
the technology evolves. According to the conclusion of this paper, multi-sink network should be
deployed for a large detected area. In this paper, the network lifetime is the ideal design lifetime,
in general, while the actual network lifetime will be less than it. The generation and updating
mechanism of clusters and the data routing algorithm have an important influence on network
lifetime. These two elements will be focused on in future work.
8. ACKNOWLEDGMENTS
The paper sincerely thank the anonymous reviewers and the associate editor for valuable and
constructive comments to improve the manuscript's quality and organization. This work is
supported by grants from the Science and Technology Research Projects of Higher
Schools(205020517502) in Hebei Province, China.
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AUTHORS
Fan Tiegang is an associate professor in the software engineering department of Hebei
University. His research interests include optimization model, wireless sensor
networks, and machine learning. He has published papers in journals and conferences
in the area of wireless communications and artificial intelligence, and has been
involved in projects with public funding.
Chen Junmin is an associate professor in the department of mathematics of Hebei
University. Her research interests include the optimization theory, the convergence of
algorithms for variational inequalities. She has published papers in journals and
conferences in the area of variational inequalities, and has been involved in one
national project and two provincial projects.