Coverage of communication-based sensor nodes deployed location and energy efficient clustering algorithm in WSN

2010 ◽  
Vol 21 (4) ◽  
pp. 698-704 ◽  
Author(s):  
Xiang Gao ◽  
Yintang Vanq ◽  
Duan Zhou
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Sensor Nodes ◽  
Energy Efficient Clustering

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.

scholarly journals Game Theory-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 478
Author(s):  
Xiao Yan ◽  
Cheng Huang ◽  
Jianyuan Gan ◽  
Xiaobei Wu
Keyword(s):  
Game Theory ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Sensor Node ◽  
Clustering Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Clustering

Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to prolong the network lifetime. This paper proposes a game theory-based energy-efficient clustering algorithm (GEC) for wireless sensor networks, where each sensor node is regarded as a player in the game. According to the length of idle listening time in the active state, the sensor node can adopt favorable strategies for itself, and then decide whether to sleep or not. In order to avoid the selfish behavior of sensor nodes, a penalty mechanism is introduced to force the sensor nodes to adopt cooperative strategies in future operations. The simulation results show that the use of game theory can effectively save the energy consumption of the sensor network and increase the amount of network data transmission, so as to achieve the purpose of prolonging the network lifetime.

scholarly journals GAECH: Genetic Algorithm Based Energy Efficient Clustering Hierarchy in Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
B. Baranidharan ◽  
B. Santhi
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Fitness Function ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
The Stability ◽  
Energy Efficient Clustering

Clustering the Wireless Sensor Networks (WSNs) is the major issue which determines the lifetime of the network. The parameters chosen for clustering should be appropriate to form the clusters according to the need of the applications. Some of the well-known clustering techniques in WSN are designed only to reduce overall energy consumption in the network and increase the network lifetime. These algorithms achieve increased lifetime, but at the cost of overloading individual sensor nodes. Load balancing among the nodes in the network is also equally important in achieving increased lifetime. First Node Die (FND), Half Node Die (HND), and Last Node Die (LND) are the different metrics for analysing lifetime of the network. In this paper, a new clustering algorithm, Genetic Algorithm based Energy efficient Clustering Hierarchy (GAECH) algorithm, is proposed to increase FND, HND, and LND with a novel fitness function. The fitness function in GAECH forms well-balanced clusters considering the core parameters of a cluster, which again increases both the stability period and lifetime of the network. The experimental results also clearly indicate better performance of GAECH over other algorithms in all the necessary aspects.

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>

An Energy-Balanced Cluster-Based Protocol for Wireless Sensor Networks

2013 ◽  
Vol 8 (3) ◽  
pp. 50-63 ◽  
Author(s):  
Eyad Taqieddin ◽  
Moad Mowafi ◽  
Fahed Awad ◽  
Omar Banimelhem ◽  
Hani Maher
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Clustering Protocol ◽  
Virtual Forces ◽  
Virtual Field ◽  
Energy Efficient Clustering

This paper proposes a novel energy-efficient clustering protocol for wireless sensor networks. It combines the benefits of using the k-means clustering algorithm with the, recently developed, LEACH with virtual forces (LEACH-VF) protocol. In this work, the k-means algorithm is employed to determine k centroids around which the clusters will be formed. After that, the virtual field force method is applied to these clusters to determine the most suitable positions for each node. The main target of such an approach is to improve the energy balance in the network and to extend the network lifetime. Simulation results show that the proposed protocol extends the time before the first node death, minimizes the variance of the average node energy, and reduces the distance that the sensor nodes travel within their respective clusters.

Enhanced Adaptive Distributed Energy-Efficient Clustering (EADEEC) for Wireless Sensor Networks

2020 ◽  
Vol 13 (2) ◽  
pp. 168-172
Author(s):  
Ravi Kumar Poluru ◽  
M. Praveen Kumar Reddy ◽  
Syed Muzamil Basha ◽  
Rizwan Patan ◽  
Suresh Kallam
Keyword(s):  
Network Lifetime ◽  
Routing Protocols ◽  
Energy Efficient ◽  
High Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Distributed Energy ◽  
Stability Period ◽  
Energy Efficient Clustering

Background:Recently Wireless Sensor Network (WSN) is a composed of a full number of arbitrarily dispensed energy-constrained sensor nodes. The sensor nodes help in sensing the data and then it will transmit it to sink. The Base station will produce a significant amount of energy while accessing the sensing data and transmitting data. High energy is required to move towards base station when sensing and transmitting data. WSN possesses significant challenges like saving energy and extending network lifetime. In WSN the most research goals in routing protocols such as robustness, energy efficiency, high reliability, network lifetime, fault tolerance, deployment of nodes and latency. Most of the routing protocols are based upon clustering has been proposed using heterogeneity. For optimizing energy consumption in WSN, a vital technique referred to as clustering.Methods:To improve the lifetime of network and stability we have proposed an Enhanced Adaptive Distributed Energy-Efficient Clustering (EADEEC).Results:In simulation results describes the protocol performs better regarding network lifetime and packet delivery capacity compared to EEDEC and DEEC algorithm. Stability period and network lifetime are improved in EADEEC compare to DEEC and EDEEC.Conclusion:The EADEEC is overall Lifetime of a cluster is improved to perform the network operation: Data transfer, Node Lifetime and stability period of the cluster. EADEEC protocol evidently tells that it improved the throughput, extended the lifetime of network, longevity, and stability compared with DEEC and EDEEC.

Energy Efficient Clustering and Routing Algorithm for WSN

2019 ◽  
Vol 12 ◽  
Author(s):  
Mohit Kumar ◽  
Sonu Mittal ◽  
Md. Amir Khusru Akhtar
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Hot Spot ◽  
Routing Algorithm ◽  
Relay Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Clustering ◽  
Hot Spot Problem

Background: This paper presents a novel Energy Efficient Clustering and Routing Algorithm (EECRA) for WSN. It is a clustering-based algorithm that minimizes energy dissipation in wireless sensor networks. The proposed algorithm takes into consideration energy conservation of the nodes through its inherent architecture and load balancing technique. In the proposed algorithm the role of inter-cluster transmission is not performed by gateways instead a chosen member node of respective cluster is responsible for data forwarding to another cluster or directly to the sink. Our algorithm eases out the load of the gateways by distributing the transmission load among chosen sensor node which acts as a relay node for inter-cluster communication for that round. Grievous simulations show that EECRA is better than PBCA and other algorithms in terms of energy consumption per round and network lifetime. Objective: The objective of this research lies in its inherent architecture and load balancing technique. The sole purpose of this clustering-based algorithm is that it minimizes energy dissipation in wireless sensor networks. Method: This algorithm is tested with 100 sensor nodes and 10 gateways deployed in the target area of 300m × 300m. The round assumed in this simulation is same as in LEACH. The performance metrics used for comparisons are (a) network lifetime of gateways and (b) energy consumption per round by gateways. Our algorithm gives superior result compared to LBC, EELBCA and PBCA. Fig 6 and Fig 7 shows the comparison between the algorithms. Results: The simulation was performed on MATLAB version R2012b. The performance of EECRA is compared with some existing algorithms like PBCA, EELBCA and LBCA. The comparative analysis shows that the proposed algorithm outperforms the other existing algorithms in terms of network lifetime and energy consumption. Conclusion: The novelty of this algorithm lies in the fact that the gateways are not responsible for inter-cluster forwarding, instead some sensor nodes are chosen in every cluster based on some cost function and they act as a relay node for data forwarding. Note the algorithm does not address the hot-spot problem. Our next endeavor will be to design an algorithm with consideration of hot-spot problem.

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