Multi Parameters Based Heterogeneous Clustering Algorithm for Energy Optimization in WSN

2019 ◽  
Author(s):  
Adarsh Kumar Yadav ◽  
Prince Rajpoot ◽  
Pappu Kumar ◽  
Kumkum Dubey ◽  
Shubham Harsh Singh ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Energy Optimization ◽  
Heterogeneous Clustering

scholarly journals An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
C. Vimalarani ◽  
R. Subramanian ◽  
S. N. Sivanandam
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Clustering Algorithm ◽  
Performance Metrics ◽  
Energy Optimization ◽  
Pso Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Target Area ◽  
Energy Conservation Measures

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

Multi-stage Clustering Algorithm for Energy Optimization in Wireless Sensor Networks

2019 ◽  
pp. 223-238 ◽  
Author(s):  
Israel Edem Agbehadji ◽  
Richard C. Millham ◽  
Simon James Fong ◽  
Jason J. Jung ◽  
Khac-Hoai Nam Bui ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Energy Optimization ◽  
Wireless Sensor ◽  
Multi Stage

scholarly journals Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks

2020 ◽  
Vol 16 (7) ◽  
pp. 155014772090877
Author(s):  
Israel Edem Agbehadji ◽  
Samuel Ofori Frimpong ◽  
Richard C Millham ◽  
Simon James Fong ◽  
Jason J Jung
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Network ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Energy Optimization ◽  
Base Station ◽  
Wireless Sensor ◽  
Optimal Time ◽  
Local Optimum

The current dispensation of big data analytics requires innovative ways of data capturing and transmission. One of the innovative approaches is the use of a sensor device. However, the challenge with a sensor network is how to balance the energy load of wireless sensor networks, which can be achieved by selecting sensor nodes with an adequate amount of energy from a cluster. The clustering technique is one of the approaches to solve this challenge because it optimizes energy in order to increase the lifetime of the sensor network. In this article, a novel bio-inspired clustering algorithm was proposed for a heterogeneous energy environment. The proposed algorithm (referred to as DEEC-KSA) was integrated with a distributed energy-efficient clustering algorithm to ensure efficient energy optimization and was evaluated through simulation and compared with benchmarked clustering algorithms. During the simulation, the dynamic nature of the proposed DEEC-KSA was observed using different parameters, which were expressed in percentages as 0.1%, 4.5%, 11.3%, and 34% while the percentage of the parameter for comparative algorithms was 10%. The simulation result showed that the performance of DEEC-KSA is efficient among the comparative clustering algorithms for energy optimization in terms of stability period, network lifetime, and network throughput. In addition, the proposed DEEC-KSA has the optimal time (in seconds) to send a higher number of packets to the base station successfully. The advantage of the proposed bio-inspired technique is that it utilizes random encircling and half-life period to quickly adapt to different rounds of iteration and jumps out of any local optimum that might not lead to an ideal cluster formation and better network performance.

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