Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Quasi Oppositional Glowworm Swarm Optimization Algorithm for Energy Efficient Clustering in Wireless Sensor Networks

2020 ◽  
Vol 17 (12) ◽  
pp. 5447-5456
Author(s):  
R. M. Alamelu ◽  
K. Prabu
Keyword(s):  
Energy Efficient ◽  
Cluster Head ◽  
Residual Energy ◽  
Link Quality ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Node Degree ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Energy Efficient Clustering

Wireless sensor network (WSN) becomes popular due to its applicability in distinct application areas like healthcare, military, search and rescue operations, etc. In WSN, the sensor nodes undergo deployment in massive number which operates autonomously in harsh environment. Because of limited resources and battery operated sensor nodes, energy efficiency is considered as a main design issue. To achieve, clustering is one of the effective technique which organizes the set of nodes into clusters and cluster head (CH) selection takes place. This paper presents a new Quasi Oppositional Glowworm Swarm Optimization (QOGSO) algorithm for energy efficient clustering in WSN. The proposed QOGSO algorithm is intended to elect the CHs among the sensor nodes using a set of parameters namely residual energy, communication cost, link quality, node degree and node marginality. The QOGSO algorithm incorporates quasi oppositional based learning (QOBL) concept to improvise the convergence rate of GSO technique. The QOGSO algorithm effectively selects the CHs and organizes clusters for minimized energy dissipation and maximum network lifetime. The performance of the QOGSO algorithm has been evaluated and the results are assessed interms of distinct evaluation parameters.

scholarly journals Designing an Energy Efficient Clustering in Heterogeneous Wireless Sensor Network

2021 ◽  
Vol 13 (1) ◽  
pp. 75-92
Author(s):  
Lakshmi M ◽  
Prashanth C R
Keyword(s):  
Energy Efficient ◽  
Cluster Head ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Delivery Ratio ◽  
Mac Layer ◽  
Optimum Energy ◽  
Heterogeneous Wireless Sensor Network ◽  
Energy Efficient Clustering ◽  
Secured Data

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.

scholarly journals Energy-Efficient Clustering Multi-Hop Routing Protocol in a UWSN

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 627
Author(s):  
Nhat-Tien Nguyen ◽  
Thien T. T. Le ◽  
Huy-Hung Nguyen ◽  
Miroslav Voznak
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Depth Level ◽  
Large Numbers ◽  
Energy Efficient Clustering

Underwater wireless sensor networks are currently seeing broad research in various applications for human benefits. Large numbers of sensor nodes are being deployed in rivers and oceans to monitor the underwater environment. In the paper, we propose an energy-efficient clustering multi-hop routing protocol (EECMR) which can balance the energy consumption of these nodes and increase their network lifetime. The network area is divided into layers with regard to the depth level. The data sensed by the nodes are transmitted to a sink via a multi-hop routing path. The cluster head is selected according to the depth of the node and its residual energy. To transmit data from the node to the sink, the cluster head aggregates the data packet of all cluster members and then forwards them to the upper layer of the sink node. The simulation results show that EECMR is effective in terms of network lifetime and the nodes’ energy consumption.

scholarly journals An Efficient Proposed Approach of Dynamic Clustering for Target Tracing in Wireless Sensor Networks

2018 ◽  
Vol 7 (2) ◽  
pp. 48-51
Author(s):  
Manas Kumar Ray ◽  
Gitanjali Roy
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Mean Value ◽  
Fixed Number ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Dynamic Clustering ◽  
Dynamic Cluster ◽  
Movable Object

This paper proposes and evaluates decentralized dynamic clustering algorithm for tracing a movable target. Here firstly we proposed dynamic K mean clustering algorithm. In this algorithm a fixed number of sensor nodes is choose and then cluster is created. When the cluster is created then a cluster head (CH) is active. This active CH sensor nodes will create new cluster and that new cluster is also formed a new mean value of cluster head. But, the newly created cluster is only active when a moving objected is trace. According from the position of cluster head, few sensor nodes is active, where as few sensor nodes are inactive. According from the CH nodes newly cluster is created. So, creation of dynamic cluster is less energy efficient and stability of cluster will more than static cluster with sensor nodes. On the other hand, movable object tracing sensor nodes are familiar with energy utilization of sensor nodes. Here we proposed an energy efficient target tracing approach which follow network stability as well as energy saving. As we use dynamic clustering technique, so optimization of energy each sensor nodes with cluster head is maximum. So all the sensors with cluster head sensor nodes will continue more time for object tracing. In simulation result we show that our proposed dynamic K mean clustering algorithm is more accurate and more stable.

scholarly journals Clustering Algorithm for Energy- Efficient Wireless Sensor Network

2021 ◽  
Vol 12 (5) ◽  
pp. 877-884
Author(s):  
Asha Rawat, Dr. Mukesh Kalla
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Packet Transmission ◽  
Wireless Sensor ◽  
Energy Efficient Clustering

Wireless networks data aggregation allows in-network processing, reduces packet transmission and data redundancy, and thus helps extend wireless sensor systems to the full duration of their lives. There have been many ways of dividing the network into clusters, collecting information from nodes and adding it to the base station, to extend wireless sensor network life. Certain cluster algorithms consider the residual energy of the nodes when selecting clusterheads and others regularly rotate the selection head of the cluster. However, we seldom investigate the network density or local distance. In this report we present an energy-efficient clustering algorithm that selects the best cluster heads of the system after dividing the network into clusters. The cluster head selection depends on the distance between the base station nodes and the remaining power of this approach.Each node's residual energy is compared to the node count. Our results show that the solution proposed more efficiently extends the life of the wireless sensor network. The algorithm prolongs the life and effectiveness of the Wireless Sensor Network.

scholarly journals A Modified Energy-Efficient Clustering with Splitting and Merging for Wireless Sensor Networks using Cluster-Head Handover Mechanism

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Abdoulie M.S. Tekanyi ◽  
Jinadu A Braimoh ◽  
Buba G Bajoga
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Modified Model ◽  
Handover Mechanism ◽  
Energy Efficient Clustering

Energy efficiency is one of the most important challenges for Wireless Sensor Networks (WSNs). This is due to the fact that sensor nodes have limited energy capacity. Therefore, the energy of sensor nodes has to be efficiently managed to provide longer lifetime for the network. To reduce energy consumption in WSNs, a modified Energy Efficient Clustering with Splitting and Merging (EECSM) for WSNs using Cluster-Head Handover Mechanism was implemented in this research. The modified model used information of the residual energy of sensor nodes to select backup Cluster Heads (CHs) while maintaining a suitable CH handover threshold to minimize energy consumption in the network. The backup CHs take over the responsibilities of the CHs once the handover threshold is reached. The modified model was validated in terms of network lifetime and residual energy ratio with EECSM using MATLAB R2013a. Average improvements of 7.5% and 50.7% were achieved for the network lifetime and residual energy ratio respectively which indicates a significant reduction in energy consumption of the network nodes. Keywords— Clustering, Energy-Efficiency, Handover, Lifetime, Wireless Sensor Network

An Energy Efficient Clustering Algrithm Based on DEEC Protocol and K-mean Method

2016 ◽  
Vol 3 (1) ◽  
pp. 143
Author(s):  
Elahmadi Cheikh ◽  
Chakkor Saad ◽  
Baghori Mostafa ◽  
Hajraoui Abderrahmane
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Effective Technique ◽  
New Approach ◽  
Main Research ◽  
Energy Efficient Clustering ◽  
Research Challenge ◽  
Heterogeneous Clustering

<p>In WSN the sensor nodes are usually powered by batteries and thus have very limited lifetime if no power management is performed. Because of this major limitation, energy-efficient techniques are main research challenge in this context to solve it. Dividing the network in clusters is an effective technique to achieve this goal. This algorithm is based on creating virtual sub-groups of sensor nodes in order to minimize routing calculations and to reduce the size of cluster head data aggregation. Nowadays, a lot of heterogeneous clustering protocols for WSN are created. Nevertheless, these protocols need to find the optimal clusters formation in the network that conserve CHs and theirs member nodes energy consumption. A new approach is proposed combining between an efficient clustering algorithm K-means and our proposed DEEC.  This approach has been employed to enhance DEEC protocol performances. Numerical simulation proves that the improved protocol entitled KM-DEEC achieves a satisfactory results compared to others DEEC protocol versions.</p>

Developing Multi-Tier Network Design for Effective Energy Consumption of Cluster Head Selection in WSN

2016 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Wan Isni Sofiah Wan Din ◽  
Saadiah Yahya ◽  
Mohd Nasir Taib ◽  
Ahmad Ihsan Mohd Yassin ◽  
Razulaimi Razali
Keyword(s):  
Sensor Network ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Cluster Head Selection ◽  
Energy Usage ◽  
Network Cluster ◽  
Fuzzy Logic Approach ◽  
The Stability

Clustering in Wireless Sensor Network (WSN) is one of the methods to minimize the energy usage of sensor network. The design of sensor network itself can prolong the lifetime of network. Cluster head in each cluster is an important part in clustering to ensure the lifetime of each sensor node can be preserved as it acts as an intermediary node between the other sensors. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. Thus, this paper presents an improvement of clustering algorithm for two-tier network as we named it as Multi-Tier Algorithm (MAP). For the cluster head selection, fuzzy logic approach has been used which it can minimize the energy usage of sensor nodes hence maximize the network lifetime. MAP clustering approach used in this paper covers the average of 100Mx100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. It is concluded that, MAP dominant the lifetime of WSN compared to LEACH and SEP protocols. For the future work, the stability of this algorithm can be verified in detailed via different data and energy. 

scholarly journals An Efficient Leader Node Selection for Movable Nodes in WSN using PSO Technique

2019 ◽  
Vol 8 (6) ◽  
pp. 5072-5078
Keyword(s):  
Energy Efficient ◽  
Network Performance ◽  
Clustering Algorithm ◽  
Group Formation ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Delivery Ratio ◽  
Efficient Prediction ◽  
The Stability ◽  
Effective Group

Wireless Sensor Networks (WSN) is a group of sensor devices, which are used to sense the surroundings. The network performance is still an issue in the WSN and an efficient protocol is introduced such as LEACH. To improve the stability, LEACH with fuzzy descriptors is used in preceding research. However the existing has drawback with effective group formation in heterogeneous WSN and also it is not achieved the Super Leader Node (SLH). To overcome the above mentioned issues, the proposed system enhances the approach which is used for increasing the energy consumption, packet delivery ratio, and bandwidth and network lifetime. The proposed paper contains three phases such as grouping formation, Leader Node (LN) selection, SLN selection with three main objectives:(i) to acquire Energy-Efficient Prediction Clustering Algorithm (EEPCA) in heterogeneous WSN for grouping formation (ii)To design Low Energy Adaptive Clustering Hierarchy- Expected Residual Energy (LEACH-ERE) protocol for LN selection.(iii)To optimize the SCH selection by Particle Swarm Optimization (PSO) based fuzzy approach. The clustering formation is done by Energy-Efficient Prediction Clustering Algorithm (EEPCA) in heterogeneous WSN. It is used to calculate the sensor nodes which have shortest distance between each node. The LEACH-ERE protocol was proposed to form a Leader Node (LN) and all the nodes has to communicate with sink through LN only. New SLN is elected based on distance from the sink and battery power of the node.

scholarly journals Energy Efficient and Reliable Routing Algorithm for Wireless Sensors Networks

2020 ◽  
Vol 10 (5) ◽  
pp. 1885 ◽  
Author(s):  
Liangrui Tang ◽  
Zhilin Lu ◽  
Bing Fan
Keyword(s):  
Energy Efficient ◽  
Routing Algorithm ◽  
Evidence Theory ◽  
Fusion Rule ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Entropy Weight ◽  
Reliable Routing ◽  
Index Value

In energy-constrained wireless sensor networks, low energy utilization and unbalanced energy distribution are seriously affecting the operation of the network. Therefore, efficient and reasonable routing algorithms are needed to achieve higher Quality of Service (QoS). For the Dempster–Shafer (DS) evidence theory, it can fuse multiple attributes of sensor nodes with reasonable theoretical deduction and has low demand for prior knowledge. Based on the above, we propose an energy efficient and reliable routing algorithm based on DS evidence theory (DS-EERA). First, DS-EERA establishes three attribute indexes as the evidence under considering the neighboring nodes’ residual energy, traffic, the closeness of its path to the shortest path, etc. Then we adopt the entropy weight method to objectively determine the weight of three indexes. After establishing the basic probability assignment (BPA) function, the fusion rule of DS evidence theory is applied to fuse the BPA function of each index value to select the next hop. Finally, each node in the network transmits data through this routing strategy. Theoretical analysis and simulation results show that DS-EERA is promising, which can effectively prolong the network lifetime. Meanwhile, it can also reach a lower packet loss rate and improve the reliability of data transmission.

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