Analysis of Binary and Discrete Grey Wolf Optimization Algorithms Applied for Enhancing Performance of Energy Efficient Low Energy Adaptive Clustering Hierarchy

2020 ◽  
Vol 17 (9) ◽  
pp. 3850-3859
Author(s):  
G. Devika ◽  
D. Ramesh ◽  
Asha Gowda Karegowda
Keyword(s):  
Life Span ◽  
Computational Intelligence ◽  
Energy Efficient ◽  
Cluster Head ◽  
Relay Node ◽  
Low Energy ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Adaptive Clustering ◽  
Selection Of

Wireless sensor networks (WSN) are a yield of advancement in information technology and the requirement of large-scale communication infrastructures. Routing of data via selected paths is a critical task in WSN as process need to be carried on under resource constraint situations. This route identification problem can be better handled by employing appropriate heuristic bio-inspired computational intelligence optimization method. The most frequently applied routing is hierarchical routing algorithm is Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm which has limitations in identifying energy efficient inter and intra route communication, identification of number of cluster head (CH), an eminent node to communicate to CH and Base Station (BS), selection of CH, and computing residual energy level, etc. Hence, researchers are focusing on boosting the capability of LEACH clustering algorithm by applying heuristic bio-inspired computational intelligence optimization methods. The proposed work is an attempt in this direction through applying heuristic bio-inspired Grey Wolf Optimization algorithm (GWO) for improving the performance of LEACH algorithm. In this paper, focus is given to increase the overall network time by adapting two modifications to conventional algorithms (i) selection of vice cluster head (VCH) in addition to CH (VCH node will replace the CH when CH when CH node goes down due to unexpected reasons as sensor node work under critical and uninterruptable environments and (ii) selection of intra and inter relay nodes (intra relay node will enhance the life span during CH data gathering and inter relay node will further enhance the life span of CH by acting as a mediator between CH an BS). The Spyder-py3 tool is used to simulate the proposed algorithms, LEACH Binary Grey Wolf search based Optimization (LEACH-BGWO) and LEACH Discrete Grey Wolf search based Optimization (LEACH-DGWO) protocols. The proposed work is compared with cluster based LEACH algorithm, chain based power-efficient gathering in sensor information systems (PEGASIS) algorithm, bio-inspired GWO and Genetic Algorithm data Aggregation (GADA) LEACH protocols. The results prove that both proposed algorithms outperformed other conventional algorithms in terms of prolonged network lifespan and increased throughput. Among proposed algorithms LEACH-BGWO outperformed LEACH-DGWO

scholarly journals Computational Intelligence based Clustering Algorithms for Wireless Sensor Networks: Trends and Possible Solutions

2021 ◽  
Vol 12 (2) ◽  
pp. 2992-3002
Author(s):  
Nitin Mittal
Keyword(s):  
Computational Intelligence ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Low Energy ◽  
Wireless Sensor ◽  
Clustering Methods ◽  
Radio Communication ◽  
Adaptive Clustering ◽  
Ch Selection ◽  
Selection Of

A wireless sensor network (WSN) is a state-of-the-art technology for radio communication. A WSN includes several sensors that are arbitrarily distributed in a particular region to detect and track physical characteristics that are hard for humans to observe, like temperature, humidity, and pressure. Because of the nature of WSNs, many issues may arise, including information routing, power consumption, clustering, and cluster head (CH) selection.  Although there are still some difficulties in the WSN, owing to its versatility and robustness, it has gained considerable attention among scientists and technologists despite the shortcomings. Various protocols were designed to solve these problems. Low energy adaptive clustering hierarchy (LEACH) is one of the significant hierarchical protocols used to reduce energy consumption in WSNs. This article provides an extensive analysis of LEACH-variant clustering protocols for WSNs. Recent research on Machine Learning, Computational Intelligence, and WSNs has highlighted the optimized WSN clustering algorithms. However, the selection of a suitable paradigm for a clustering solution continues an issue owing to the diversity of WSN applications. In this paper, a comprehensive review of suggested optimized clustering alternatives has been conducted and a comparison of these optimized clustering methods has been suggested based on various performance parameters. The centralized clustering approaches based on the Swarm Intelligence paradigm are observed to be more suitable for the applications in which low energy is required, high information delivery rate, or elevated scalability than algorithms that are based on the other paradigms described.

scholarly journals Hybrid WGWO: whale grey wolf optimization-based novel energy-efficient clustering for EH-WSNs

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Rajkumar Singh Rathore ◽  
Suman Sangwan ◽  
Shiv Prakash ◽  
Kabita Adhikari ◽  
Rupak Kharel ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Energy Efficient Clustering

scholarly journals Proficient Energy Efficient A2S Leach Technique in Wireless Sensor Networks

2020 ◽  
Vol 9 (5) ◽  
pp. 27-33
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Information Transmission ◽  
Energy Efficient ◽  
Low Energy ◽  
Wireless Sensor ◽  
Critical Test ◽  
Adaptive Clustering ◽  
Remote Sensor ◽  
The Ideal

In Wireless Sensor Networks (WSNs) lifetime of the system relies upon the vitality of the hubs, where vitality utilization is for the most part utilized for information transmission as opposed to detecting and preparing. A critical test in remote sensor systems is the ideal utilization of hub assets. Bunching of sensor hubs helps to utilize the hub vitality ideally and delay the lifetime of vitality compelled remote sensor arrange. Also, in WSN, the decision of steering convention assumes a significant job in using the vitality of hubs effectively. In this paper, another A2S LEACH (Active ↔Sleep - Low Energy Adaptive Clustering Hierarchy) directing strategy is proposed, which joins the two significant classes of various leveled conventions in particular bunch based methodology and chain based methodology. The proposed system is reproduced utilizing NS2 and the outcomes are examined. Reenactment results shows that the proposed A2S LEACH steering convention altogether diminishes vitality utilization and expands the all-out lifetime of the remote sensor organize when contrasted with the LEACH convention.

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