Wireless sensor networks (WSN) is composed of a large number of small nodes with limited functionality.
The most important issue in this type of networks is energy constraints. In this area several researches have
been done from which clustering is one of the most effective solutions. The goal of clustering is to divide
network into sections each of which has a cluster head (CH). The task of cluster heads collection, data
aggregation and transmission to the base station is undertaken. In this paper, we introduce a new approach
for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal
fitness function, which aims to extend network lifetime. The parameters used in this algorithm are residual
energy density, the distance from the base station, intra-cluster distance from the cluster head. Simulation
results show that the proposed method is more effective compared to protocols such as (LEACH, CHEF,
PSO-MV) in terms of network lifetime and energy consumption.
This presentation is all about the wireless sensor networks, how they collect data using aggregation, and how they evaluate or calculate the parameters
WIRELESS SENSOR NETWORK CLUSTERING USING PARTICLES SWARM OPTIMIZATION FOR RED...IJMIT JOURNAL
Wireless sensor networks (WSN) is composed of a large number of small nodes with limited functionality. The most important issue in this type of networks is energy constraints. In this area several researches have been done from which clustering is one of the most effective solutions. The goal of clustering is to divide network into sections each of which has a cluster head (CH). The task of cluster heads collection, data aggregation and transmission to the base station is undertaken. In this paper, we introduce a new approach for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal fitness function, which aims to extend network lifetime. The parameters used in this algorithm are residual energy density, the distance from the base station, intra-cluster distance from the cluster head. Simulation results show that the proposed method is more effective compared to protocols such as (LEACH, CHEF, PSO-MV) in terms of network lifetime and energy consumption.
This presentation is all about the wireless sensor networks, how they collect data using aggregation, and how they evaluate or calculate the parameters
WIRELESS SENSOR NETWORK CLUSTERING USING PARTICLES SWARM OPTIMIZATION FOR RED...IJMIT JOURNAL
Wireless sensor networks (WSN) is composed of a large number of small nodes with limited functionality. The most important issue in this type of networks is energy constraints. In this area several researches have been done from which clustering is one of the most effective solutions. The goal of clustering is to divide network into sections each of which has a cluster head (CH). The task of cluster heads collection, data aggregation and transmission to the base station is undertaken. In this paper, we introduce a new approach for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal fitness function, which aims to extend network lifetime. The parameters used in this algorithm are residual energy density, the distance from the base station, intra-cluster distance from the cluster head. Simulation results show that the proposed method is more effective compared to protocols such as (LEACH, CHEF, PSO-MV) in terms of network lifetime and energy consumption.
SIMULATION BASED ANALYSIS OF CLUSTER-BASED PROTOCOL IN WIRELESS SENSOR NETWORKijngnjournal
The modern growth in fabricate energy efficient Wireless Sensor Network is liberal a novel way to
systematize WSN in applications like surveillance, industrial monitoring, traffic monitoring, habitat
monitoring, cropping monitoring, crowd including etc. The rising use of these networks is making
engineers evolve novel and efficient ideas in this field. A group of research in data routing, data density
and in network aggregation has been proposed in recent years. The energy consumption is the main
apprehension in the wireless sensor network. There are many protocols in wireless sensor network to
diminish the energy consumption and to put in to the network lifetime. Among a range of types of
techniques, clustering is the most efficient technique to diminish the energy expenditure of network. In
this effort, LEACH protocol has been second-hand for clustering in which cluster heads are nominated on
the basis of distance and energy. The LEACH protocol is been implemented in a simulated environment
and analyze their performance graphically.
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.
Optimized Cluster Establishment and Cluster-Head Selection Approach in WSNIJCNCJournal
In recent years, limited resources of user products and energy-saving are recognized as the major challenges of Wireless Sensor Networks (WSNs). Clustering is a practical technique that can reduce all energy consumption and provide stability of workload that causes a larger difference in energy depletion among other nodes and cluster heads (CHs). In addition, clustering is the solution of energy-efficient for maximizing the network longevity and improvising energy efficiency. In this paper, a novel OCE-CHS (Optimized Cluster Establishment and Cluster-Head Selection) approach for sensor nodes is represented to improvise the packet success ratio and reduce the average energy-dissipation. The main contribution of this paper is categorized into two processes, first, the clustering algorithm is improvised that periodically chooses the optimal set of the CHs according to the speed of the average node and average-node energy. This is considerably distinguished from node-based clustering that utilizes a distributed clustering algorithm to choose CHs based on the speed of the current node and remaining node energy. Second, more than one factor is assumed for the detached node to join the optimal cluster. In the result section, we discuss our clustering protocols implementation of optimal CH-selection to evade the death of SNs, maximizing throughput, and further improvise the network lifetime by minimizing energy consumption.
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.
Various Clustering Techniques in Wireless Sensor NetworkEditor IJCATR
This document describes the various clustering techniques used in wireless sensor networks. Wireless sensor networks are
having vast applications in all fields which utilize sensor nodes. Clustering techniques are required so that sensor networks can
communicate in most efficient way.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
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.
INCREASING WIRELESS SENSOR NETWORKS LIFETIME WITH NEW METHODijwmn
One of the most important issues in Wireless Sensor Networks (WSNs) is severe energy restrictions. As the
performance of Sensor Networks is strongly dependence to the network lifetime, researchers seek a way to
use node energy supply effectively and increasing network lifetime. As a consequence, it is crucial to use
routing algorithms result in decrease energy consumption and better bandwidth utilization. The purpose of
this paper is to increase Wireless Sensor Networks lifetime using LEACH-algorithm. So before clustering
Network environment, it is divided into two virtual layers (using distance between sensor nodes and base
station) and then regarding to sensors position in each of two layers, residual energy of sensor and
distance from base station is used in clustering. In this article, we compare proposed algorithm with wellknown LEACH and ELEACH algorithms in homogenous environment (with equal energy for all sensors)
and heterogeneous one (energy of half of sensors get doubled), also for static and dynamic situation of base
station. Results show that our proposed algorithm delivers improved performance.
Data Centric Approach Based Protocol using Evolutionary Approach in WSNijsrd.com
The evolution of wireless communication and circuit technology has enabled the development of an infrastructure consists of sensing, computation and communication units that makes administrator capable to observe and react to a phenomena in a particular environment. In a Wireless Sensor Network (WSN), nodes are scattered densely in a large area. Sensor nodes can communicate with the sink node directly or through other nodes. Data transmission is the major issue in WSN. Each node has limited energy which is used in transmitting and receiving the data. Various routing protocols have been proposed to save the energy during the transmission of data. data centric approach based routing protocol which efficiently propagates information between sensor nodes in an energy constrained mode. This paper proposes a data centric routing Using evolutionary apporoach in WSN.The main objective of this protocol with evolutionary apporoach is to use artificial intelligence, to reduce the energy consumption by the nodes in transmitting and receiving the data. Implementation of Basic SEP, intelligence cluster routing and proposed protocols will be done using MATLAB.
SIMULATION BASED ANALYSIS OF CLUSTER-BASED PROTOCOL IN WIRELESS SENSOR NETWORKijngnjournal
The modern growth in fabricate energy efficient Wireless Sensor Network is liberal a novel way to
systematize WSN in applications like surveillance, industrial monitoring, traffic monitoring, habitat
monitoring, cropping monitoring, crowd including etc. The rising use of these networks is making
engineers evolve novel and efficient ideas in this field. A group of research in data routing, data density
and in network aggregation has been proposed in recent years. The energy consumption is the main
apprehension in the wireless sensor network. There are many protocols in wireless sensor network to
diminish the energy consumption and to put in to the network lifetime. Among a range of types of
techniques, clustering is the most efficient technique to diminish the energy expenditure of network. In
this effort, LEACH protocol has been second-hand for clustering in which cluster heads are nominated on
the basis of distance and energy. The LEACH protocol is been implemented in a simulated environment
and analyze their performance graphically.
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.
Optimized Cluster Establishment and Cluster-Head Selection Approach in WSNIJCNCJournal
In recent years, limited resources of user products and energy-saving are recognized as the major challenges of Wireless Sensor Networks (WSNs). Clustering is a practical technique that can reduce all energy consumption and provide stability of workload that causes a larger difference in energy depletion among other nodes and cluster heads (CHs). In addition, clustering is the solution of energy-efficient for maximizing the network longevity and improvising energy efficiency. In this paper, a novel OCE-CHS (Optimized Cluster Establishment and Cluster-Head Selection) approach for sensor nodes is represented to improvise the packet success ratio and reduce the average energy-dissipation. The main contribution of this paper is categorized into two processes, first, the clustering algorithm is improvised that periodically chooses the optimal set of the CHs according to the speed of the average node and average-node energy. This is considerably distinguished from node-based clustering that utilizes a distributed clustering algorithm to choose CHs based on the speed of the current node and remaining node energy. Second, more than one factor is assumed for the detached node to join the optimal cluster. In the result section, we discuss our clustering protocols implementation of optimal CH-selection to evade the death of SNs, maximizing throughput, and further improvise the network lifetime by minimizing energy consumption.
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.
Various Clustering Techniques in Wireless Sensor NetworkEditor IJCATR
This document describes the various clustering techniques used in wireless sensor networks. Wireless sensor networks are
having vast applications in all fields which utilize sensor nodes. Clustering techniques are required so that sensor networks can
communicate in most efficient way.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
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.
INCREASING WIRELESS SENSOR NETWORKS LIFETIME WITH NEW METHODijwmn
One of the most important issues in Wireless Sensor Networks (WSNs) is severe energy restrictions. As the
performance of Sensor Networks is strongly dependence to the network lifetime, researchers seek a way to
use node energy supply effectively and increasing network lifetime. As a consequence, it is crucial to use
routing algorithms result in decrease energy consumption and better bandwidth utilization. The purpose of
this paper is to increase Wireless Sensor Networks lifetime using LEACH-algorithm. So before clustering
Network environment, it is divided into two virtual layers (using distance between sensor nodes and base
station) and then regarding to sensors position in each of two layers, residual energy of sensor and
distance from base station is used in clustering. In this article, we compare proposed algorithm with wellknown LEACH and ELEACH algorithms in homogenous environment (with equal energy for all sensors)
and heterogeneous one (energy of half of sensors get doubled), also for static and dynamic situation of base
station. Results show that our proposed algorithm delivers improved performance.
Data Centric Approach Based Protocol using Evolutionary Approach in WSNijsrd.com
The evolution of wireless communication and circuit technology has enabled the development of an infrastructure consists of sensing, computation and communication units that makes administrator capable to observe and react to a phenomena in a particular environment. In a Wireless Sensor Network (WSN), nodes are scattered densely in a large area. Sensor nodes can communicate with the sink node directly or through other nodes. Data transmission is the major issue in WSN. Each node has limited energy which is used in transmitting and receiving the data. Various routing protocols have been proposed to save the energy during the transmission of data. data centric approach based routing protocol which efficiently propagates information between sensor nodes in an energy constrained mode. This paper proposes a data centric routing Using evolutionary apporoach in WSN.The main objective of this protocol with evolutionary apporoach is to use artificial intelligence, to reduce the energy consumption by the nodes in transmitting and receiving the data. Implementation of Basic SEP, intelligence cluster routing and proposed protocols will be done using MATLAB.
Enhancing energy efficient dynamic load balanced clustering protocol using Dy...IJTET Journal
Mobile Ad hoc Network (MANET) is a kind of self configuring and self describing wireless ad hoc networks. MANET has characteristics of topology dynamics due to factors such as energy conservation and node movement that leads to dynamic load balanced clustering problem (DLBCP). It is necessary to have an effective clustering algorithm for adapting the topology change. Generally, Clustering is mainly used to reduce the topology size. In this, we used load balance and energy metric in GA to solve the DLBCP. It is important to select the energy efficient cluster head for maintaining the cluster structure and balance the load effectively. Elitism based Immigrants Genetic algorithm (EIGA) and Memory Enhanced Genetic Algorithm (MEGA) are used to solve DLBCP. These schemes will select the optimal cluster head by considering the parameters includes distance and energy. We used EIGA to maintain the diversity level of the population and memory scheme (MEGA) to store the old environments into the memory. It promises the energy efficiency for the entire cluster structure to increase the lifetime of the network. The experimental results show that the proposed schemes increases the network life time and reduces the energy consumption.
EFFECT OF DUTY CYCLE ON ENERGY CONSUMPTION IN WIRELESS SENSOR NETWORKSIJCNC
Most studies define a common duty cycle value throughout the wireless sensor networks (WSNs) to
achieve synchronization among the nodes. On the other hand, a few studies proposed adaptation of the
duty cycle according to uniform traffic conditions to decrease the energy consumption and latency. In
this paper, the lifetime of the nodes based on overall energy consumption are estimated and the effect of
duty cycle on expected energy consumption is studied. The proposed scheme is compared with a standard
scheme and is shown to perform significantly better for sufficient node density.
A Review of Network Layer Attacks and Countermeasures in WSNiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
More details: (blog: http://sandyclassic.wordpress.com ,
linkedin: https://www.linkedin.com/in/sandepsharma )
2 New Routing algorithm for ad-hoc routing wireless sensor network, mathematical modelling for wireless sensor network 4 models for over all system and 2 models for energy measurement of wireless sensor network
Fault Node Recovery Algorithm for a Wireless Sensor NetworkYogesh Vk
The WSN is made of nodes from a few to several hundred, where each node is connected to one or several sensors.
The basic components of a node are
o Sensor and actuator - an interface to the physical world designed to sense the environmental parameters like pressure and temperature.
o Controller - is to control different modes of operation for processing of data
o Memory - storage for programming data.
o Communication - a device like antenna for sending and receiving data over a wireless channel.
o Power Supply- supply of energy for smooth operation of a node like battery.
Similar to Wireless sensor networks, clustering, Energy efficient protocols, Particles Swarm optimization algorithm, Centralized algorithmsWireless sensor network clustering using particles swarm optimization for reducing energy consumption
INCREASE THE LIFETIME OF WIRELESS SENSOR NETWORKS USING HIERARCHICAL CLUSTERI...ijwmn
Wireless sensor networks consist of hundreds or thousands of nodes with limited energy. Since the life time
of each sensor is equivalent to the battery life, the energy issue is considered as a major challenge.
Clustering has been proposed as a strategy to extend the lifetime of wireless sensor networks. Cluster size,
number of Cluster head per cluster and the selection of cluster head are considered as important factors in
clustering. In this research by studying LEACH algorithm and optimized algorithms of this protocol and by
evaluating the strengths and weaknesses, a new algorithm based on hierarchical clustering to increase the
lifetime of the sensor network is proposed. In this study, with a special mechanism the environment of
network is layered and the optimal number of cluster head in each layer is selected and then recruit for the
formation of clusters in the same layer by controlling the topology of the clusters is done independently.
Then the data is sent through the by cluster heads through the multi- stage to the main station. Simulation
results show that the above mentioned method increases the life time about 70% compared to the LEACH.
INCREASE THE LIFETIME OF WIRELESS SENSOR NETWORKS USING HIERARCHICAL CLUSTERI...ijwmn
Wireless sensor networks consist of hundreds or thousands of nodes with limited energy. Since the life time
of each sensor is equivalent to the battery life, the energy issue is considered as a major challenge.
Clustering has been proposed as a strategy to extend the lifetime of wireless sensor networks. Cluster size,
number of Cluster head per cluster and the selection of cluster head are considered as important factors in
clustering. In this research by studying LEACH algorithm and optimized algorithms of this protocol and by
evaluating the strengths and weaknesses, a new algorithm based on hierarchical clustering to increase the
lifetime of the sensor network is proposed. In this study, with a special mechanism the environment of
network is layered and the optimal number of cluster head in each layer is selected and then recruit for the
formation of clusters in the same layer by controlling the topology of the clusters is done independently.
Then the data is sent through the by cluster heads through the multi- stage to the main station. Simulation
results show that the above mentioned method increases the life time about 70% compared to the LEACH.
INCREASE THE LIFETIME OF WIRELESS SENSOR NETWORKS USING HIERARCHICAL CLUSTERI...ijwmn
Wireless sensor networks consist of hundreds or thousands of nodes with limited energy. Since the life time
of each sensor is equivalent to the battery life, the energy issue is considered as a major challenge.
Clustering has been proposed as a strategy to extend the lifetime of wireless sensor networks. Cluster size,
number of Cluster head per cluster and the selection of cluster head are considered as important factors in
clustering. In this research by studying LEACH algorithm and optimized algorithms of this protocol and by
evaluating the strengths and weaknesses, a new algorithm based on hierarchical clustering to increase the
lifetime of the sensor network is proposed. In this study, with a special mechanism the environment of
network is layered and the optimal number of cluster head in each layer is selected and then recruit for the
formation of clusters in the same layer by controlling the topology of the clusters is done independently.
Then the data is sent through the by cluster heads through the multi- stage to the main station. Simulation
results show that the above mentioned method increases the life time about 70% compared to the LEACH.
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.
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.
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.
In The present study with the objective of cutting down energy consumption and persistence of
network coverage, we have offered a novel algorithm based on clustering algorithms and multihop routing.To achieve this goal, first, we layer the network environment based on the size of the
network.We will identify the optimal number of cluster heads and every cluster head based on
the mechanism of topology control will start to accept members.Likewise, we set the first layer
as gate layer and subsequently identifying the gate’s nodes, we’d turn away half of the sensors
and then stop using energy and the remaining nodes in this layer will join the gate’s nodes
because they hold a critical part in bettering the functioning of the system. Cluster heads off
following layers send the information to cluster heads in the above layer until sent data will be
sent to gate’s nodes and finally will be sent to sink. We have tested the proposed algorithm in
two situations 1) when the sink is off and 2)when a sink is on and simulation data shows that
proposed algorithm has better performance in terms of the life span of a network than LEACH
and ELEACH protocols.
INCREASING WIRELESS SENSOR NETWORKS LIFETIME WITH NEW METHODijwmn
One of the most important issues in Wireless Sensor Networks (WSNs) is severe energy restrictions. As the
performance of Sensor Networks is strongly dependence to the network lifetime, researchers seek a way to
use node energy supply effectively and increasing network lifetime. As a consequence, it is crucial to use
routing algorithms result in decrease energy consumption and better bandwidth utilization. The purpose of
this paper is to increase Wireless Sensor Networks lifetime using LEACH-algorithm. So before clustering
Network environment, it is divided into two virtual layers (using distance between sensor nodes and base
station) and then regarding to sensors position in each of two layers, residual energy of sensor and
distance from base station is used in clustering. In this article, we compare proposed algorithm with wellknown LEACH and ELEACH algorithms in homogenous environment (with equal energy for all sensors)
and heterogeneous one (energy of half of sensors get doubled), also for static and dynamic situation of base
station. Results show that our proposed algorithm delivers improved performance.
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.
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.
DATA GATHERING ALGORITHMS FOR WIRELESS SENSOR NETWORKS: A SURVEYijasuc
Recent developments in processor, memory and radio technology have enabled wireless sensor networks
which are deployed to collect useful information from an area of interest. The sensed data must be
gathered and transmitted to a base station where it is further processed for end-user queries. Since the
network consists of low-cost nodes with limited battery power, power efficient methods must be employed
for data gathering and aggregation in order to achieve long network lifetimes. In an environment where in
a round of communication each of the sensor nodes has data to send to a base station, it is important to
minimize the total energy consumed by the system in a round so that the system lifetime is maximized. With
the use of data fusion and aggregation techniques, while minimizing the total energy per round, if power
consumption per node can be balanced as well, a near optimal data gathering and routing scheme can be
achieved in terms of network lifetime. Several application specific sensor network data gathering protocols
have been proposed in research literatures. However, most of the proposed algorithms have been some
attention to the related network lifetime and saving energy are two critical issues for wireless sensor
networks. In this paper we have explored general network lifetime in wireless sensor networks and made an
extensive study to categorize available data gathering techniques and analyze possible network lifetime on
them.
Data gathering in wireless sensor networks using intermediate nodesIJCNCJournal
Energy consumption is an essential concern to Wireless Sensor Networks (WSNs).The major cause of the energy consumption in WSNs is due to the data aggregation. A data aggregation is a process of collecting data from sensor nodes and transmitting these data to the sink node or base station. An effective way to perform such a task is accomplished by using clustering. In clustering, nodes are grouped into clusters where a number of nodes, called cluster heads, are responsible for gathering data from other nodes, aggregate them and transmit them to the Base Station (BS).
In this paper we produce a new algorithm which focused on reducing the transmission bath between sensor nodes and cluster heads. A proper utilization and reserving of the available power resources is achieved with this technique compared to the well-known LEACH_C algorithm.
Simulation Based Analysis of Cluster-Based Protocol in Wireless Sensor Networkjosephjonse
The modern growth in fabricate energy efficient Wireless Sensor Network is liberal a novel way to systematize WSN in applications like surveillance, industrial monitoring, traffic monitoring, habitat monitoring, cropping monitoring, crowd including etc. The rising use of these networks is making engineers evolve novel and efficient ideas in this field. A group of research in data routing, data density and in network aggregation has been proposed in recent years. The energy consumption is the main apprehension in the wireless sensor network. There are many protocols in wireless sensor network to diminish the energy consumption and to put in to the network lifetime. Among a range of types of techniques, clustering is the most efficient technique to diminish the energy expenditure of network. In this effort, LEACH protocol has been second-hand for clustering in which cluster heads are nominated on the basis of distance and energy. The LEACH protocol is been implemented in a simulated environment and analyze their performance graphically.
Implementation of energy efficient coverage aware routing protocol for wirele...ijfcstjournal
In recent years, wireless sensor network have been used in many application such as disaster reservation,
agriculture, environmental observation and forecasting .Coverage preservation and energy consumption
are two most important issues in wireless sensor networks. To increase the network lifetime, we propose an
energy efficient coverage aware routing protocol for wireless sensor network for randomly deployed sensor
nodes. Some of the routing protocol is based on energy efficiency and some are based on coverage aware.
The proposed routing protocol is based on both the issues i.e. coverage and energy, in which we first find
the k-mean i.e. the degree of coverage, so that we can use this in the selection of cluster heads in wireless
sensor network by using Genetic Algorithm for increasing network lifetime and coverage. For cluster head
selection each node evaluates its k-mean and energy by internal function which used as fitness function in
genetic algorithm. The proposed algorithm “Implementation of energy efficient coverage aware routing
protocol for Wireless Sensor Network” is designed for homogeneous wireless sensor network. Simulations
results show that proposed algorithm increases the network lifetime by reduce the energy consumption and
preserve coverage. Simulation is done with MATLAB and a comparison of algorithm with benchmark
algorithms is also performed.
This paper considers a heterogeneous network of energy constrained sensors deployed over a region. Each
Normal sensor node in a network is systematically gathering and transmitting sensed data to the clusterhead,
and then cluster head sending data to a base station (via intermediate cluster- heads). This paper
focuses on reducing the energy consumption and hence improving lifetime of wireless sensor Networks.
Clustering sensor node is an effective topology for the energy constrained networks. So energy saving
algorithm has been developed in which clusters are formed considering a subset of high energy nodes as a
cluster-head and another subset of powerful nodes is ask to go to sleep. When Cluster heads deplete their
energy another subset of nodes becomes active and acts as a cluster head. Proposed approach is
implemented in MATLAB, Simulation results shows that it can prolong the network lifetime than LEACH
protocol, and achieves better performance than the existing clustering algorithms such as LEACH.
Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Eff...CSCJournals
The objective of this paper is to develop a mechanism to increase the lifetime of homogeneous wireless sensor networks (WSNs) through minimizing long range communication, efficient data delivery and energy balancing. Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. In cluster based architecture, the role of aggregator node is very crucial because of extra processing and long range communication. Once the aggregator node becomes non functional, it affects the whole cluster. We introduced a candidate cluster head node on the basis of node density. We proposed a modified cluster based WSN architecture by introducing a server node (SN) that is rich in terms of resources. This server node (SN) takes the responsibility of transmitting data to the base station over longer distances from the cluster head. We proposed cluster head selection algorithm based on residual energy, distance, reliability and degree of mobility. The proposed method can save overall energy consumption and extend the lifetime of the sensor network and also addresses robustness against even/uneven node deployment.
A QoI Based Energy Efficient Clustering for Dense Wireless Sensor Networkijassn
In a wireless sensor network Quality of Information (QoI), Energy Efficiency, Redundant data avoidance,
congestion control are the important metrics that affect the performance of wireless sensor network. As
many approaches were proposed to increase the performance of a wireless sensor network among them
clustering is one of the efficient approaches in sensor network. Many clustering algorithms concentrate
mainly on power Optimization like FSCH, LEACH, and EELBCRP. There is necessity of the above
metrics in wireless sensor network where nodes are densely deployed in a given network area. As the nodes
are deployed densely there is maximum possibility of nodes appear in the sensing region of other nodes. So
there exists an option that nodes have to send the information that is already reached the base station by its
own cluster members or by members of other clusters. This mechanism will affect the QoI, Energy factor
and congestion control of the wireless sensor networks. Even though clustering uses TDMA (Time Division
Multiple Access) for avoiding congestion control for intra clustering data transmission, but it may fail in
some critical situation. This paper proposed a energy efficient clustering which avoid data redundancy in a
dense sensor network until the network becomes sparse and hence uses the TDMA efficiently during high
density of the nodes.
Designing an Energy Efficient Clustering in Heterogeneous Wireless Sensor Net...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical issue that degrades the network performance. Recharging and providing security to the sensor devices is very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an important and suitable approach to increase energy efficiency and transmitting secured data which in turn enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC) works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all the cluster heads are formed at a time and selected on rotation based on considering the highest energy of the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access (CSMA), a contention window based protocol is used at the MAC layer for collision detection and to provide channel access prioritization to HWSN of different traffic classes with reduction in End to End delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the cluster head for transmission without depleting the energy. Simulation parameters of the proposed system such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing system.
DESIGNING AN ENERGY EFFICIENT CLUSTERING IN HETEROGENEOUS WIRELESS SENSOR NET...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical
issue that degrades the network performance. Recharging and providing security to the sensor devices is
very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an
important and suitable approach to increase energy efficiency and transmitting secured data which in turn
enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC)
works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the
rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the
network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on
the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all
the cluster heads are formed at a time and selected on rotation based on considering the highest energy of
the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum
flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access
(CSMA), a contention window based protocol is used at the MAC layer for collision detection and to
provide channel access prioritization to HWSN of different traffic classes with reduction in End to End
delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the
cluster head for transmission without depleting the energy. Simulation parameters of the proposed system
such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing
system.
Similar to Wireless sensor networks, clustering, Energy efficient protocols, Particles Swarm optimization algorithm, Centralized algorithmsWireless sensor network clustering using particles swarm optimization for reducing energy consumption (20)
MULTIMODAL COURSE DESIGN AND IMPLEMENTATION USING LEML AND LMS FOR INSTRUCTIO...IJMIT JOURNAL
Traditionally, teaching has been centered around classroom delivery. However, the onslaught of the
COVID-19 pandemic has cultivated usage of technology, teaching, and learning methodologies for course
delivery. We investigate and describe different modes of course delivery that maintain the integrity of
teaching and learning. This paper answers to the research questions: 1) What course delivery method our
academic institutions use and why? 2) How can instructors validate the guidelines of the institutions? 3)
How courses should be taught to provide student learning outcomes? Using the Learning Environment
Modeling Language (LEML), we investigate the design and implementation of courses for delivery in the
following environments: face-to-face, online synchronous, asynchronous, hybrid, and hyflex. A good
course design and implementation are key components of instructional alignment. Furthermore, we
demonstrate how to design, implement, and deliver courses in synchronous, asynchronous, and hybrid
modes and describe our proposed enhancements to LEML.
Novel R&D Capabilities as a Response to ESG Risks-Lessons From Amazon’s Fusio...IJMIT JOURNAL
Environmental, Social, and Governance (ESG) management is essential for transforming corporate
financial performance-oriented business strategies into Finance (F) + ESG optimization strategies to
achieve the Sustainable Development Goals (SDGs).
In this trend, the rise of ESG risks has divided firms into two categories. Former incorporates a growthmindset that creates a passion for learning, and urges it to improve itself by endeavoring Research and
development (R&D) -driven challenges, while the other category, characterized by risk aversion, avoids
challenging highly uncertain R&D activities and seeks more manageable endeavors.
This duality underscores the complexity of corporate R&D strategies in addressing ESG risks and
necessitates the development of novel R&D capabilities for corporate R&D transformation strategies
towards F + ESG optimization.
International Journal of Managing Information Technology (IJMIT) ** WJCI IndexedIJMIT JOURNAL
The International Journal of Managing Information Technology (IJMIT) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of the strategic application of information technology (IT) in organizations. The journal focuses on innovative ideas and best practices in using IT to advance organizations – for-profit, non-profit, and governmental. The goal of this journal is to bring together researchers and practitioners from academia, government, and industry to focus on understanding both how to use IT to support the strategy and goals of the organization and to employ IT in new ways to foster greater collaboration, communication, and information sharing both within the organization and with its stakeholders. The International Journal of Managing Information Technology seeks to establish new collaborations, new best practices, and new theories in these areas.
International Journal of Managing Information Technology (IJMIT) ** WJCI IndexedIJMIT JOURNAL
The International Journal of Managing Information Technology (IJMIT) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of the strategic application of information technology (IT) in organizations. The journal focuses on innovative ideas and best practices in using IT to advance organizations – for-profit, non-profit, and governmental. The goal of this journal is to bring together researchers and practitioners from academia, government, and industry to focus on understanding both how to use IT to support the strategy and goals of the organization and to employ IT in new ways to foster greater collaboration, communication, and information sharing both within the organization and with its stakeholders. The International Journal of Managing Information Technology seeks to establish new collaborations, new best practices, and new theories in these areas.
NOVEL R & D CAPABILITIES AS A RESPONSE TO ESG RISKS- LESSONS FROM AMAZON’S FU...IJMIT JOURNAL
Environmental, Social, and Governance (ESG) management is essential for transforming corporate
financial performance-oriented business strategies into Finance (F) + ESG optimization strategies to
achieve the Sustainable Development Goals (SDGs).
In this trend, the rise of ESG risks has divided firms into two categories. Former incorporates a growthmindset that creates a passion for learning, and urges it to improve itself by endeavoring Research and
development (R&D) -driven challenges, while the other category, characterized by risk aversion, avoids
challenging highly uncertain R&D activities and seeks more manageable endeavors.
This duality underscores the complexity of corporate R&D strategies in addressing ESG risks and
necessitates the development of novel R&D capabilities for corporate R&D transformation strategies
towards F + ESG optimization.
Building on this premise, this paper conducts an empirical analysis, utilizing reliable firms data on ESG
risk and brand value, with a focus on 100 global R&D leader firms. It analyzes R&D and actions for ESG
risk mitigation, and assesses the development of new functions that fulfill F + ESG optimization through
R&D. The analysis also highlights the significance of network externality effects, with a specific focus on
Amazon, a leading R&D company, providing insights into the direction for transforming R&D strategies
towards F + ESG optimization.
The dynamics of stakeholder engagement in F + ESG optimization are indicated with the example of
amazon's activities. Through the analysis, it became evident that Amazon's capacity encompassing growth
and scalability, specifically its ability to grow and expand, is accelerating high-level research and
development by gaining the trust of stakeholders in the "synergy through R&D-driven ESG risk
mitigation."
Finally, as examples of these initiatives, the paper discussed the Climate Pledge led by Amazon and the
transformation of Japan's management system.
A REVIEW OF STOCK TREND PREDICTION WITH COMBINATION OF EFFECTIVE MULTI TECHNI...IJMIT JOURNAL
It is important for investors to understand stock trends and market conditions before trading stocks. Both
these capabilities are very important for an investor in order to obtain maximized profit and minimized
losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends
and market conditions. Technical analysis helps to understand stock prices behavior with regards to past
trends, the signals given by indicators and the major turning points of the market price. This paper reviews
the stock trend predictions with a combination of the effective multi technical indicator strategy to increase
investment performance by taking into account the global performance and the proposed combination of
effective multi technical indicator strategy model.
INTRUSION DETECTION SYSTEM USING CUSTOMIZED RULES FOR SNORTIJMIT JOURNAL
These days the security provided by the computer systems is a big issue as it always has the threats of
cyber-attacks like IP address spoofing, Denial of Service (DOS), token impersonation, etc. The security
provided by the blue team operations tends to be costly if done in large firms as a large number of systems
need to be protected against these attacks. This leads these firms to turn to less costly security
configurations like IDS Suricata and IDS Snort. The main theme of the project is to improve the services
provided by Snort which is a tool used in creating a vague defense against cyber-attacks like DDOS
attacks which are done on both physical and network layers. These attacks in turn result in loss of
extremely important data. The rules defined in this project will result in monitoring traffic, analyzing it,
and taking appropriate action to not only stop the attack but also locate its source IP address. This whole
process uses different tools other than Snort like Wireshark, Wazuh and Splunk. The product of this will
result in not only the detection of the attack but also the source IP address of the machine on which the
attack is initiated and completed. The end product of this research will result in sets of default rules for the
Snort tool which will not only be able to provide better security than its previous versions but also be able
to provide the user with the IP address of the attacker or the person conducting the attack. The system
involves the integration of Wazuh with Snort tool in order to make it more efficient than IDS Suricata
which is another intrusion detection system capable of detecting all these types of attacks as mentioned.
Splunk is another tool used in this project which increases the firewall efficiency to pass the no. of bits to
be scanned and the no. of bits scanned successfully. Wazuh is used in this system as it is the best choice for
traffic monitoring and incident response than any other of its alternatives in the market. Since this system
is used in firms which are known to handle big amounts of data and for this purpose, we use Splunk tool as
it is very efficient in handling big amounts of data. Wireshark is used in this system in order to give the IDS
automation in its capability to capture and report the malicious packets found during the network scan. All
of this gives the IDS a capability of a low budget automated threat detection system. This paper gives
complete guidelines for authors submitting papers for the AIRCC Journals.
Artificial Intelligence (AI) has rapidly become a critical technology for businesses seeking to improve
efficiency and profitability. One area where AI is proving particularly impactful is in service operations
management, where it is used to create AI-powered service operations (AIServiceOps) that deliver highvalue services to customers. AIServiceOps involve the use of AI to automate and optimize various business
processes, such as customer service, sales, marketing, and supply chain management. The rapid
development of Artificial Intelligence has prompted many changes in the field of Information Technology
(IT) Service Operations. IT Service Operations are driven by AI, i.e., AIServiceOps. AI has empowered
new vitality and addressed many challenges in IT Service Operations. However, there is a literature gap on
the Business Value Impact of Artificial intelligence (AI) Powered IT Service Operations. It can help IT
build optimized business resilience by creating value in complex and ever-changing environments as
product organizations move faster than IT can handle. So, this research paper examines how AIServiceOps
creates business value and sustainability, basically how AIServiceOps makes the IT staff liberation from a
low-level, repetitive workout and traditional IT practices for a continuously optimized process. One of the
research objectives is to compare Traditional IT Service Operations with AIServiceOPs. This paper
provides the basis for how enterprises can evaluate AIServiceOps and consider it a digital transformation
tool. The paper presents a case study of a company that implemented AI-powered service operations
(AIServiceOps) and analyzes the resulting business outcomes. The study shows that AIServiceOps can
significantly improve service delivery, reduce response times, and increase customer satisfaction.
Furthermore, it demonstrates how AIServiceOps can deliver substantial cost savings, such as reducing
labor costs and minimizing downtime.
MEDIATING AND MODERATING FACTORS AFFECTING READINESS TO IOT APPLICATIONS: THE...IJMIT JOURNAL
Although IOT seems to be the upcoming trend, it is still in its infancy; especially in the banking industry.
There is a clear gap in literature, as only few studies identify factors affecting readiness to IOT
applications in banks in general, and almost negligible investigations on mediating and moderating
factors. Accordingly, this research aims to investigate the main factors that affect employees’ readiness to
IOT applications, while highlighting the mediating and moderating factors in the Egyptian banking sector.
The importance of Egypt stems from its high population and steady steps taken towards technology
adoption. 479 valid questionnaires were distributed over HR employees in banks. Data collected was
statistically analysed using Regression and SEM. Results showed a significant impact of ‘Security’,
‘Networking’, ‘Software Development’ and ‘Regulations’ on ‘readiness to IOT applications. Thus, the
readiness acceptance level is high‘Security’ and ‘User Intention’ were proven to mediate the relationship
between research variables and readiness to IOT applications, and only a partial moderation role was
proven for ‘Efficiency’. The study contributes to increasing literature on IOT applications in general, and
fills a gap on the Egyptian banking context in particular. Finally, it provides decision makers at banks with
useful guidelines on how to optimally promote IOT applications among employees.
EFFECTIVELY CONNECT ACQUIRED TECHNOLOGY TO INNOVATION OVER A LONG PERIODIJMIT JOURNAL
IT (Information and Communication Technology) companies are facing the dilemma of decreasing
productivity despite increasing research and development efforts. M&A (Merger and Acquisition) is being
considered as a breakthrough solution. From existing research, it has been pointed out that M&A leads to
the emergence of new innovations. Purpose of this study was to discuss the efficient ways of acquisition and
to resolve the dilemma of productivity decline by clarifying how the technology obtained through M&A
leads to the creation of new innovations. Hypothesis 1 was that the technology acquired through M&A is
utilized for innovation creation, Hypothesis 2 was that the acquired technology is utilized over a long
period of time, and Hypothesis 3 was that a long-term utilization has a positive impact on corporate
performance. The results, using sports prosthetics as a case study and using patents as a proxy variable,
confirmed all the hypotheses set. We have revealed that long-term utilization of technology obtained
through M&A is effective for creating new innovations.
International Journal of Managing Information Technology (IJMIT) ** WJCI IndexedIJMIT JOURNAL
The International Journal of Managing Information Technology (IJMIT) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of the strategic application of information technology (IT) in organizations. The journal focuses on innovative ideas and best practices in using IT to advance organizations – for-profit, non-profit, and governmental. The goal of this journal is to bring together researchers and practitioners from academia, government, and industry to focus on understanding both how to use IT to support the strategy and goals of the organization and to employ IT in new ways to foster greater collaboration, communication, and information sharing both within the organization and with its stakeholders. The International Journal of Managing Information Technology seeks to establish new collaborations, new best practices, and new theories in these areas.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of information technology and management
4th International Conference on Cloud, Big Data and IoT (CBIoT 2023)IJMIT JOURNAL
4th International Conference on Cloud, Big Data and IoT (CBIoT 2023) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Cloud, Big Data and IoT. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Cloud, Big Data and IoT.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in Cloud, Big Data and IoT.
TRANSFORMING SERVICE OPERATIONS WITH AI: A CASE FOR BUSINESS VALUEIJMIT JOURNAL
Artificial Intelligence (AI) has rapidly become a critical technology for businesses seeking to improve
efficiency and profitability. One area where AI is proving particularly impactful is in service operations
management, where it is used to create AI-powered service operations (AIServiceOps) that deliver highvalue services to customers. AIServiceOps involve the use of AI to automate and optimize various business
processes, such as customer service, sales, marketing, and supply chain management. The rapid
development of Artificial Intelligence has prompted many changes in the field of Information Technology
(IT) Service Operations. IT Service Operations are driven by AI, i.e., AIServiceOps. AI has empowered
new vitality and addressed many challenges in IT Service Operations. However, there is a literature gap on
the Business Value Impact of Artificial intelligence (AI) Powered IT Service Operations. It can help IT
build optimized business resilience by creating value in complex and ever-changing environments as
product organizations move faster than IT can handle. So, this research paper examines how AIServiceOps
creates business value and sustainability, basically how AIServiceOps makes the IT staff liberation from a
low-level, repetitive workout and traditional IT practices for a continuously optimized process. One of the
research objectives is to compare Traditional IT Service Operations with AIServiceOPs. This paper
provides the basis for how enterprises can evaluate AIServiceOps and consider it a digital transformation
tool. The paper presents a case study of a company that implemented AI-powered service operations
(AIServiceOps) and analyzes the resulting business outcomes. The study shows that AIServiceOps can
significantly improve service delivery, reduce response times, and increase customer satisfaction.
Furthermore, it demonstrates how AIServiceOps can deliver substantial cost savings, such as reducing
labor costs and minimizing downtime.
DESIGNING A FRAMEWORK FOR ENHANCING THE ONLINE KNOWLEDGE-SHARING BEHAVIOR OF ...IJMIT JOURNAL
The main objective of this paper is to identify the factors that influence academic staff's digital knowledgesharing behaviors in Ethiopian higher education. A structural equation model was used to validate the
research framework using survey data from 210 respondents. The collected data has been analyzed using
Smart PLS software. The results of the study show that trust, self-motivation, and altruism are positively
related to attitude. Contrary to our expectations, knowledge technology negatively affects attitude.
However, reward systems and empowerment by leaders are significantly associated with knowledgesharing intentions.Knowledge-sharing intention, in turn, was significantly related to digital knowledgesharing behavior. The contributions of this study are twofold. The framework may serve as a roadmap for
future researchers and managers considering their strategy to enhance digital knowledge sharing in HEI.
The findings will benefit academic staff and university administrations.The study will also help academic
staff enhance their knowledge-sharing practices.
BUILDING RELIABLE CLOUD SYSTEMS THROUGH CHAOS ENGINEERINGIJMIT JOURNAL
Cloud computing systems need to be reliable so that they can be accessed and used for computing at any
given point in time. The complex nature of cloud systems is the motivation to conduct research in novel
ways of ensuring that cloud systems are built with reliability in mind. In building cloud systems, it is
expected that the cloud system will be able to deal with high demands and unexpected events that affect the
reliability and performance of the system.
In this paper, chaos engineering is considered a heuristic method that can be used to build reliable cloud
systems. Chaos engineering is aimed at exposing weaknesses in systems that are in production. Chaos
engineering will help identify system weaknesses and strengths when a system is exposed to unexpected
knocks and shocks while it is in production.
Chaos engineering allows system developers and administrators to get insights into how the cloud system
will behave when it is exposed to unexpected occurrences.
A REVIEW OF STOCK TREND PREDICTION WITH COMBINATION OF EFFECTIVE MULTI TECHNI...IJMIT JOURNAL
It is important for investors to understand stock trends and market conditions before trading stocks. Both
these capabilities are very important for an investor in order to obtain maximized profit and minimized
losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends
and market conditions. Technical analysis helps to understand stock prices behavior with regards to past
trends, the signals given by indicators and the major turning points of the market price. This paper reviews
the stock trend predictions with a combination of the effective multi technical indicator strategy to increase
investment performance by taking into account the global performance and the proposed combination of
effective multi technical indicator strategy model.
NETWORK MEDIA ATTENTION AND GREEN TECHNOLOGY INNOVATIONIJMIT JOURNAL
This paper will provide a novel empirical study for the relationship between network media attention and
green technology innovation and examine how network media attention can ease financing constraints. It
collected data from listed companies in China's heavy pollution industry and performed rigorous
regression analysis, in order to innovatively explore the environmental governance functions of the media.
It found that network media attention significantly promotes green technology innovation. By analyzing the
inner mechanism further, it found that network media attention can promote green innovation by easing
financing constraints. Besides, network media attention has a significant positive impact on green invention
patents while not affecting green utility model patents.
INCLUSIVE ENTREPRENEURSHIP IN HANDLING COMPETING INSTITUTIONAL LOGICS FOR DHI...IJMIT JOURNAL
Information System (IS) research advocates employing collaborative and loose coupling strategies to address contradictory issues to address diversified actors’ interests than the prescriptive and unilateral Information Technology (IT) governance mechanisms’, yet it is rarely depicting how managers employ these strategies in Health Information System (HIS) implementation, particularly in a resource-constrained setting where IS implementation activities have highly relied on multiple international organizations resources. This study explored how managers in resource-constrained settings employ collaborative IT governance mechanisms in the case of District Health Information System 2 (DHIS2) adoption with an interpretative case study approach and the institutional logic concept. The institutional logic concept was used to identify the major actors’ logics underpinning the DHIS2 adoption. The study depicted the importance of high-level officials' distance from the dominant systemic logic to consider new alternative, and to employ inclusive IT governance mechanisms which separated resource from the system that facilitated stakeholders’ collaboration in DHIS2 adoption based on their capacity and interest.
DEEP LEARNING APPROACH FOR EVENT MONITORING SYSTEMIJMIT JOURNAL
With an increasing number of extreme events and complexity, more alarms are being used to monitor
control rooms. Operators in the control rooms need to monitor and analyze these alarms to take suitable
actions to ensure the system’s stability and security. Security is the biggest concern in the modern world. It
is important to have a rigid surveillance that should guarantee protection from any sought of hazard.
Considering security, Closed Circuit TV (CCTV) cameras are being utilized for reconnaissance, but these
CCTV cameras require a person for supervision. As a human being, there can be a possibility to be tired
off in supervision at any point of time. So, we need a system to detect automatically. Thus, we came up with
a solution using YOLO V5. We have taken a data set and used robo-flow framework to enhance the existing
images into numerous variations where it will create a copy of grey scale image, a copy of its rotation and
a copy of its blurred version which will be used to get an enlarged data set. This work mainly focuses on
providing a secure environment using CCTV live footage as a source to detect the weapons. Using YOLO
algorithm, it divides an image from the video into grid system and each grid detects an object within itself
MULTIMODAL COURSE DESIGN AND IMPLEMENTATION USING LEML AND LMS FOR INSTRUCTIO...IJMIT JOURNAL
Traditionally, teaching has been centered around classroom delivery. However, the onslaught of the
COVID-19 pandemic has cultivated usage of technology, teaching, and learning methodologies for course
delivery. We investigate and describe different modes of course delivery that maintain the integrity of
teaching and learning. This paper answers to the research questions: 1) What course delivery method our
academic institutions use and why? 2) How can instructors validate the guidelines of the institutions? 3)
How courses should be taught to provide student learning outcomes? Using the Learning Environment
Modeling Language (LEML), we investigate the design and implementation of courses for delivery in the
following environments: face-to-face, online synchronous, asynchronous, hybrid, and hyflex. A good
course design and implementation are key components of instructional alignment. Furthermore, we
demonstrate how to design, implement, and deliver courses in synchronous, asynchronous, and hybrid
modes and describe our proposed enhancements to LEML.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
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Orchestrator execution result
Defect reporting
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Wireless sensor networks, clustering, Energy efficient protocols, Particles Swarm optimization algorithm, Centralized algorithmsWireless sensor network clustering using particles swarm optimization for reducing energy consumption
1. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
WIRELESS SENSOR NETWORK CLUSTERING USING
PARTICLES SWARM OPTIMIZATION FOR REDUCING
ENERGY CONSUMPTION
Amin Rostami1and Mohammad Hossin Mottar2
1Department of Computer Engineering, Ferdows Branch, Islamic Azad University,
Ferdows , Iran.
2Department of Computer Engineering, Mashhad Branch, Islamic Azad
ABSTRACT
Wireless sensor networks (WSN) is composed of a large number of small nodes with limited functionality.
The most important issue in this type of networks is energy constraints. In this area several researches have
been done from which clustering is one of the most effective solutions. The goal of clustering is to divide
network into sections each of which has a cluster head (CH). The task of cluster heads collection, data
aggregation and transmission to the base station is undertaken. In this paper, we introduce a new approach
for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal
fitness function, which aims to extend network lifetime. The parameters used in this algorithm are residual
energy density, the distance from the base station, intra-cluster distance from the cluster head. Simulation
results show that the proposed method is more effective compared to protocols such as (LEACH, CHEF,
PSO-MV) in terms of network lifetime and energy consumption.
Keywords
Wireless sensor networks, clustering, Energy efficient protocols, Particles Swarm optimization algorithm,
Centralized algorithms.
1. Introduction
Wireless Sensor Networks (WSN),as one of the most important technologies of the 21 century is
discussed in this paper. These networks include many number of very small sensor nodes that are
used for collection and peripheral information processing [1].Unlike ad hoc networks that may at
the first glance very similar to the sensor networks, the nodes in the sensor network usually lack
unique addresses and which is important for information collection by the sensors. Also due to
lack of access to nodes after their disperse process, the network nodes are virtually useless and
will die after that the available energy is over. So the energy consumption issue and optimization
is one of the challenges raised in these networks. In recent years, many works have been done in
this case [6]. Distinction between traditional telecommunication networks such as cellular -
systems and mobile ad hoc networks with WSN is that the networks have unique features such as:
node density deployment, the lack of reliability of the sensor nodes and severe restrictions on
energy computing and memory [3]. The applications that have already been proposed for sensor
networks and are added day to day,can be refer to examples such as; routing in broad
geographical environments, security system, control on large structures, control on patients with
critical condition, control on environmental parameters in areas where human presence in the mis
dangerous and so on [2]. In fact, Sensor networks is the accumulation of a large number of sensor
nodes scattered in the environment. And each is autonomous and collaborated with other groups
DOI : 10.5121/ijmit.2014.6401 1
2. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
to follow a particular purpose. The nodes are close together and each node can communicate with
other node and their data will be put to another node. Finally, under ambient conditions, the data
is reported to a central node. Techniques and practices used in such networks highly depend on
the nature of network usage and the network topology, atmospheric and environmental
conditions, limitations and factors affecting network performance parameters and cost. So, today
in the world wide valid universities and computer and electronics research centers, and especially
Telecommunications, WSN, is a very attractive and popular field of research[4]. Many
suggestions and researches are provided on various topics in this field.
The main goal of these efforts is to provide solutions with simple control method and low cost
which finally lead to accountability toourneeds and can be persistent against limitations such as:
bandwidth, energy, environmental interference,feeding and so on.
Also these solutions would be in accordance to our wishes and desires including the data
transmission overload reduction, survivability and high lifetime and low cost. One of the
challenges raised in the context of sensor networks is how to cluster nodes in the network since
these networks be efficient in terms of available energy and processing resources [6].
Clustering sensor networks, is an effective technique to increase the scalability and survivability
nodes, the main goal of clustering is to divide network to a set of individual and limited nodes
that can be easy controlled. by apply clustering can routing table size, repeats end messages
reiteration and energy consumption is reduced and enhance the network lifetime, until the nodes
their data transfer to the shortest distance of associated cluster heads[9].
Indeed, proposed algorithm is a centralized algorithm. That all of the main decisions performed in
base station. Such as: clustering and cluster head election. This algorithm make close itself to
optimal mood in every time of performance, automatically and if occur erratic event in network,
algorithm have this capability that decrease the effects of this problem to least quantity in whole
of network, automatically and during shortest time. Also, because collecting data is centralized
and do periodic, this algorithm is very suitable for when supervision and fined monitoring is
needed by wireless sensor network.
2
Rest of the paper is organized as follows:
2.Related Works
3.Network model
4. The proposed method
5. Simulation
6. Conclusion
2. RELATED WORKS
LEACH1protocol [7] is the oldest part of the clustering algorithms in WSN which is a
hierarchical method in which the routing method uses a single step. Its main objective is to
maximize network lifetime, and distribute energy consumption across all network nodes. Nodes
per cluster Received Information sent to cluster heads and The task of cluster heads is transmitted
data to the base station. All data processing duties such as integration and collection is performed
locally by cluster head. LEACH runs during several steps with two phases.
1
Low Energy Adaptive Clustering Hierarchy
3. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
1. Set up phase
2. Pitch phase
In the setup phase, each node decides the cluster heads or not. each node create a random number
between 0 and 1, that is, the probability of being selected as the cluster heads. If the probability p
of node n is less than threshold T(n), the node n is a cluster head for current phase r. In which
T(n)is calculated as follow:
3
=
1 − ∗
120. 1
Where G is the set of sensor nodes that are not in 1/P cluster heads of the final round. So in the
first round 0, each node of has the same probability P for clustering head., The amount of energy
consumption during the network lifetime is effective and the algorithm is not efficient in terms of
energy consumption.
In CHEF2protocol [5] cluster head election mechanism uses fuzzy logic. CHEF algorithm
obtains chance from two fuzzy set namely: residual energy of node and total distance between a
node with other nodes that are located at a radius r. CHEF can overcome the defects of LEACH.
This means that sensor node has more energy and has more chance of being cluster heads. the
main advantages of CHEF can be summarized as follows:
1. Amount of computational overhead is minimized by using fuzzy logic.
Cluster heads are elected locally, that reduces the overhead associated with centralized processes.
CHEF algorithm disadvantages can include:
1-CHEF algorithm employs few parameters in the election of cluster heads.
2-And the number of selected cluster heads greatly depends on the value of :
In the TL-LEACH3protocol [11]-It is an improvement in the basic LEACH algorithm.
The algorithm is performed in three steps:
1. CH selecting
2. Clusters formation
3. Data transmission
The algorithm uses two level cluster heads (primary and secondary).
Secondary cluster heads with other members which are located in one cluster communicate and
after data aggregation transmit them to primary cluster heads and primary cluster heads route data
to base station. The data which is sent to base station reduce energy consumption significantly but
there is a lot of overhead in the election of primary and secondary cluster heads which affects the
network lifetime.
EEHC4Protocol [13] is a randomly distributed clustering algorithm. The EEHC algorithm
assumes that communication environment is controversy and also there is an error-free
environment. This algorithm is based on a two-stage clustering.
2
Cluster Head Election mechanism using Fuzzy logic
3
A Two -Levels Hierarchy for Low-Energy Adaptive Clustering Hierarchy
4
Energy-efficient hierarchical clustering
121. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
1. Single-level clustering
2. Multi-level clustering
Sensor nodes are cluster heads in hierarchical architecture. Cluster heads consume more energy
than the other sensor nodes because, cluster heads have more work to do.
Hence, it is possible that the cluster heads energy is over faster than the other sensors. EEHC
algorithm can be run periodically for load balancing or start to work when the cluster heads
energy levels falls below a certain threshold. In Genetic Algorithm for Energy Efficient Clusters
in Wireless Sensor Networks [12] fitness function calculation depends on the distance between
cluster heads nodes and base station and the purpose of applying genetic algorithms is to elect the
most appropriate cluster heads. It is a centralized method in which members of each cluster and
sending time are determined. Each cluster head directly sends information to base station.
Although this algorithm performs better than LEACH, but there is not a significant improvement,
because the computation of the fitness function is complex.
In the method of GA- MPI5,It is proposed [14]to divide the network into several clusters, but
rather than sending data from nodes to cluster heads, they use a number of mobile agents in each
cluster. The mobile agents perform the task of data collection from nodes, data aggregation
sending them to the base station.
4
In this method, chromosomes are divided in two arrays:
1. Group array
2. Ordinal array
Group array includes a number of members of each cluster and ordinal array includes the nodes
that belong to each cluster. Crossover only alters the nodes in the same group and the mutation
alters the number of nodes in consistent group. In this method the purpose of using genetic
algorithms is to calculate the optimal number of mobile agents and embellishment of clusters and
is also network measurement using standard delay and does not consider the energy consumption.
Simulations show that the sensor nodes reduce energy consumption by using mobile agents [20].
In the GFCM6protocol [21] which has actually improved FCM protocol for network clustering
with combining genetic algorithm and fuzzy methods. Genetic algorithm is used to improve FCM
method in clustering and the best cluster heads election. This method has the effectiveness in
energy consumption balance of the network, which increases the network lifetime and reduces
the energy consumption. Authors of [18] use PSO to create cluster in WSN and optimal clusters
are sorted by fitness function based on within cluster distance. In this proposal, residual energy of
nodes is ignored in the calculations, and fitness function is considered as follows:
d
%
$
! + #$
$
'
()
' (2)
Where dij is distance between node i and cluster heads j. Dj is the distance between cluster heads j
and base station and nj is the number of nodes in each cluster. In this way, inertia coefficient
varies and the acceleration is constant. In [19] clustering wireless sensor networks based on
improved discrete particle swarm optimization is proposed which tries to solve clustering
inequality problem. In this method the number of nodes in each cluster is identical and the fitness
function is only based on communication distance. In addition, PSO inertia coefficient is
considered variable to distinguish between particles. Then the obtained cluster heads of PSO
algorithm are checked in terms of energy levels and if their residual energy is less than a
threshold value, the nearest node which its energy is higher than the threshold value will be
5
Genetic algorithm based multiple MAs itinerary planning
6
Genetic Fuzzy C-means algorithm
122. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
replaced. This method is compared with the LEACH show better results, although the total
residual energy of nodes and lifetime are not considered.
PSO-MV7protocol [15] as well is based on PSO method, and since energy consumption in
cluster heads is higher than other nodes, the purpose of the approach is energy balance. In the
PSO-MV method, the 2 nodes are selected as cluster heads that is a node as main cluster heads
(MCH) and other as (VCH) and the tasks between the nodes can be categorized, where the MCH
is responsible for data collecting and transmission and VCH is responsible for inter-cluster
communications or intra –cluster communications to BS. PSO-MV clustering algorithm is based
on Routing of clusters included generation steps and transmission of data. Initialized for cluster
head this is If the node of cluster heads candidate according to the set of nodes into clusters and
threshold energy that shows to E is chosen as follow:
E =+ ,/. /0
' (3)
The simulation results show that date of node death in PSO–MV take more than clearly PSO, but
the weakness of this algorithm is in selection the number of cluster heads optimum.
The EECS8protocol [16] focuses to solve the problem of clusters distance from BS. In fact, the
cluster which is far from the base station needs more energy consumption for data transmission.
For this reason ,of dynamic size to cluster is determined due to the distance of the cluster from
base stations. This algorithm makes a more uniform distribution of energy in the whole network
which leads to increasing network lifetime.
5
3. Network Model
• The nodes can use power control to adjust transmission power that is dependent on the
distance to the receiver.
• The Sensor nodes are stationary.
• For each node a unique identity is assigned
• The nodes are aware of self-position. (via GPS)
• The nodes are homogeneous network, it means that have identical processing potency.
• The nodes have limited energy and after disperse there is no battery charging capabilities.
• Each node has an initial value which is Emax and BS has no restrictions on energy,
memory and communication.
• Links are symmetric that is two nodes v1 and v2 can use the same transmission power to
connect.
3.1. PSO clustering problem
Two main problem of clustering using PSO method is the convergence to local optimal and slow
convergence velocity, which is tried to be solved by using two ideas of chaos theory and
acceleration strategy . In the formula of updating velocity of the cluster centers that is mentioned
in the eq. (4) updating is done for each particle for relocating the particle to the new position,
from the best answer for each particle (Pbest) and the best global solution so far (gbest) . In
7
Particle Swarm Optimization balanced energy consumption
8
Energy-Efficient Cluster-Head Selection
123. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
which W Inertia coefficient rate tends to previous velocity of the particle, c1ratestends to the best
local position of the particle, and c2trendsto the best global position of the particle.
In eq. (5) replacing cr instead of rr improves PSO algorithm as given:
102
(5)
(6)
6
345 = 6 102
782 + 9 6 : 6 ;02 − =02
782 + 9! 6 ! 6 02 − =02
782 (4)
345 = 6 102
102
782 + 9 6 9: 6 ;02 − =02
782 + 9! 6 9: 6 02 =02
782
?@A B 6 ?@ 6 ?@
In eq.(6), Crrandom value is created for each round independentlybetween 0and1.which
substitutes bothr1andr2,and parameter k is the number of predicted clusters. Using the chaos
theory in PSO population generation will result in more diverse of the algorithm.
Figure1. Chaos map [17]
As can be see in Figure 1. To achieve more optimal particle swarm optimization algorithm, chaos
theory is applied And in other change to increase the rate of convergence used acceleration
strategy therefore in this mode a number of the population which are the best toward the target
move not all population that it increases the rate of convergence [17].
4. Proposed Method
Our proposed algorithm is composed of two clustering and data transmission phases
4.1. Clustering Phase
In clustering phase, the particles are generated randomly. Then the best points are selected as the
cluster heads and other nodes which are located near each cluster head becomes the member of
the cluster and then fitness function is calculated for every cluster heads. If the fitness function is
better than global best it is substituted. This process is donefor1000generation.Theneach node
prepares a control message that contains identity and value of its residual energy and sends it
directly to the base station .The base station which receives the information performs clustering
operation.
124. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
7
4.2. Proposed Validation index
As previously mentioned, the clustering is more desirable in which intra-cluster density is higher
and in another word, the clusters are more cohesive and inter-cluster density is lower. Based on
this principle, in the proposed method to estimate the optimal number of clusters. The first Select
the number of clusters. Also to measure rate of clusters separation the different distance between
cluster than total center of data set for the number of clusters considered, and then calculated the
ratio between two, since the clustering is more desirable .The clusters are more compact and
farther apart So, for the number of clusters where the index is maximum the clustering is more
desirable and the optimal number of clusters is achieved. Validation index is
composedoftwoparts,F1andF2:
1CDE maxI
229. ( 7 )
Whatever the amount of the above criterion is greater clustering is better. eq.(8) denotes the F1
index and Figure 3 illustrates the cluster dispersion and density of nodes in each cluster:
F 03@4:6!
L03@:M6! (8)
Inter: inter-cluster distance for which farther is better.
Intra: intra-cluster distance for which closer is better.
Figure2.Performance of the proposed index
Eq. (9),(10) denotes the intra and inter cluster separation:
(9)
In Eq. (9) the total distance between nodes in each cluster and its cluster head ia calculated in
which c is the number of clusters, N is the number of nodes, Xj is the cluster head and Xi denotes
the distance of the nodes from its relative cluster head. The intra cluster separation is shown in the
following equation:
(10)
To calculate the inter clusters separation ,the distance between the centers of the clusters and the
center of total data set is calculated. For cluster range specified the amount of this index
calculate and show in chart.
230. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
In the conditions in which the slope of the curve is sharper the estimate of the number of clusters
is more accurate. Then with local search around the slope the optimal number of clusters can be
reached. Eq. (11) Explains how to calculate F2.
F2= cluster heads degree+( residual energy62) / centrality + distance to base station (11)
Residual energy : because of the rest energy effect in being cluster heads is more effective we
considered double its coefficients.
Cluster scale: the number of inter-cluster nodes divided by the total number of network nodes.
Moreover in the above relationship (centrality)is obtained as follows:
8
centerality
V WXY%
Z
[[
(12)
In which, !is the sum of squared distances of nodes to cluster heads. It is assumed that each
node is aware of its position, and can calculate its distance from the base station.
F2 associated per experimental cluster heads to obtain and then its totalfor12experimental cluster
heads is summed. Using 2 coefficient for energy is due to that in discussion of election the rest
energy of cluster heads than other parameters have more important and is more effective and for a
reason we are considered double its coefficient. In F1 formula without use of value coefficient, F1
than F2 was too small and invalidity could not significant effect so, we used -
morecoefficientsthatcouldbalancebetweenF1 andF2effect is created.
.As can be seen in this experiment, when the number of clusters change from 2 to16 the slope of
our validity index change dramatically. Now with local search around the intervals, the exact
number of clusters can be achieved.
0 5 10 15 20 25
400
350
300
250
200
150
100
50
0
Figure3. The proposed validity index when of the number of clusters change
4.3 Data transmission phase
After cluster formation and cluster heads election of each cluster;data can be transmitted by the
normal nodes to corresponding cluster heads. In this phase, each normal node is connected to the
nearest cluster head. Cluster heads are assigned with the implementation of a TDMA schedule to
each cluster member. Each node in the allocated interval sends its data to cluster head in the form
231. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
of data message. The cluster heads
receiving all messages from cluster
computed.
Fig
Energy model of the proposed protocol
illustrated in Fig.4. Energy consumed
calculated as:
,]^ _ `
^a ,48bB + Ba
^a ,48bB + B
Energy consumption per K bit of data
ERx(k,d) = Erx_elc(k) = k. Eelc
In the above equations, Eelc, denoted the
number of bits is denoted as k
amplification energy is Etx_amp
that transfer factor change in it.
as follows:
,cd efa a
aggregate and transmit data towards base
station after
member nodes. Then the energy consumption of all
Figure4. Energy consumption model [7]
is the same as the energy model proposed
nodes is
in [7]and
per bit of data transfer on the distance d in Equation(13
,]9 B + ,]ghiB
a jk:0ll
245. is calculated as follows:
(1)
(1)
(15)
, energy of sending/receiving message size
13) is
in terms of
k, the distance between the transmitter and receiver is d
friss
e
d, the
and amplifier factor and d crossover is threshold distance
energy consumption of data gathering cluster heads is
(16)
calculated
9
246. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
10
Figure 5. Data transmission phase flowchart
5. Simulation
The algorithm is simulated using MATLAB software. The parameters used are noted in the table
1. j
andj are electronic energies, and EDA is the energy needed for data aggregation at
cluster heads.
Table1.simulation parameters
Parameter Value
j 10 pJ/bit/m2
j
0.0013 pJ/bit/m4
Eelec 50/nJ/bit
EDA 5/nJ/bit/signal
Initial energy per node 0.5 j
Data packet size 4000 bit
Control packet size 200 bit
247. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
The first step of our purpose approach is that using PSO to find the most optimal points in area
and then the closest node to it are consider as cluster heads. we have a100 × 100 area with
100nodesrandomlydispersedand also the base station is put on the 50 × 50 coordinates. The
number of particle and the velocity is calculated with respect to the area size .Initially, minimum
particles together work is equal 20-bit and The velocity initially is equal 4.whichwascomparative
and by increase the number of current nodes in the environment are changed.
The most influential parameters in the calculation of PSO are values that must be consider for
c1, c2, w , which in more papers are considered asc1 = c2 = 2 and w = 1 . But to find more
accurate values due to their significant impact on the problem solution, we evaluated all possible
values between different intervals. After1000generations,with the cooperationof20particles
together, as you can see in Figure 6thebest value for the parameter is equal to
c1=c2=0.5andw=0.007.
11
0.4
0.6
GlobalBest's Values
X: 0.5
Y: 0.007
Z: 89.6
0.8
1
0.2
0.4
0.6
0.8
1
W
C1,C2
Figure6. The parameter values c1,c2,w
0.2
90
85
80
75
70
Fitness
88
86
84
82
80
78
76
74
72
70
As the result of the random motion, the particles may be out of the environment that are required
to move back into environment. We apply the support vector machine(SVM)supervised learning
method to return the particle into the environment [22].
Figure7: represents a range of educational particles
In Fig 7, there is a Xi particles outside the range that with i(st v) is returned into the
environment where compared to moving the particle on the border better results will be achieved.
The next important issue is the value that should be considered for alpha. When the node energy
is less than alpha value the cluster head is replacement. To consider the optimal alpha value all
248. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
values between 0 and 1 with the distance of the0.1 are considered. After running four times and
averaging ,the best alpha value for 100 nodes is equal to 0.8 and for 200nodes is equal to
0.4whichcan be seen in Figure8.
12
100 Nodes
200 Nodes
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
3000
2500
2000
1500
Alpha
Figure 8. Alpha value in two cases of100nodesand200nodes
First Node Dies (Round)
Using the formula(11) and run it at 5000rpm for the best number of clusters is equal to 12
That at the end of the first node runs in 2959 round and half of them in 3682 round is dead.
As you can see in Figure9 that cluster heads are suitably dispersed. A point that should be noted is
that the nodes that are close to the base station and its distance to the nearest cluster heads is less,
transmit data directly to the base station and reduce energy consumption considerably.
0 10 20 30 40 50 60 70 80 90 100
100
90
80
70
60
50
40
30
20
10
0
x
Figure9.The number of nodes associated to each cluster
y
We compared the proposed algorithm with the LEACH, CHEF, PSO-MV, GFCM algorithms
which results are as follows. Figure 10shows the rate of dead nodes and network lifetime after
implementing the proposed protocols which is higher compared to LEACH, CHEF, PSO-MV,
GFCM protocols, which increases networks lifetime.
249. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
LEACH
CHEF
PSO-MV
GFCM
Proposed Algorithm
0 10 20 30 40 50 60 70 80 90 100
4500
4000
3500
3000
2500
2000
1500
1000
500
The number of dead nodes
Round
Figure 10. Comparing the proposed algorithm with four efficient algorithms, namely LEACH,
13
CHEF, PSO-MV, GFCM in terms of the number of dead nodes
As shown in Figure 10 the first node in the LEACH algorithm dies at 790th round and the last
dies at 1420th round, while using the proposed algorithm the first node dies in round2959 and the
last node dies in the 4150the round which is due to the selection of the best possible cluster heads.
Figure 11 denoted the energy consumed by LEACH, CHEF, PSO-MV, GFCM protocols and the
proposed algorithm in which the proposed protocol has significantly lower total energy
consumption than the other protocols.
leach
CHEF
pso-mv
GFCM
Proposed Algorithm
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
50
45
40
35
30
25
20
15
10
5
0
Round
Dissipated energy
Figure 11. Comparing the proposed algorithm with four efficient algorithms, namely LEACH, CHEF, PSO-MV,
GFCM in terms of total energy consumption
As you can see in Figure11. the slope of the proposed algorithm is softer and suitable than that of
the LEACH algorithm which lead to slower energy discharge. Therefore in LEACH 5% of
energy is lost in the 98th round and total energy is finished inthe1238th round While using the
proposed algorithm, 5% of energy is lost inthe233rd round and total energy is finished in the
3971stround, which increases of network lifetime.
6.CONCLUSIONS
In this paper, we introduce a new approach for sensor network clustering using Particle Swarm
Optimization (PSO) algorithm. The parameters which are used in the algorithm are residual
250. International Journal of Managing Information Technology (IJMIT) Vol.6, No.4, November 2014
energy, density, distance from the base station, intra-cluster distance and cluster heads distance
from each other. Our goal was to propose a new cost function to select the best cluster heads that
combine the various criteria affecting the energy efficiency of cluster heads and cluster heads
rotation among the nodes. Also, using the proposed algorithm the network coverage is evaluated
and compared with some previous methods which have proved better performance and improved
network lifetime and energy consumption.
14
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