An Innovative Hyperheuristic, Gaussian Clustering Scheme for Energy-Efficient Optimization in Wireless Sensor Networks

Abstract

Energy stability on sensor nodes in wireless sensor networks (WSNs) is always an important challenge, especially during data capturing and transmission of packets. The recent advancement in distributed clustering algorithms in the extant literature proposed for energy efficiency showed refinements in deployment of sensor nodes, network duration stability, and throughput of information data that are channelled to the base station. However, much scope still exists for energy improvements in a heterogeneous WSN environment. This research study uses the Gaussian elimination method merged with distributed energy efficient clustering (referred to as DEEC-Gauss) to ensure energy efficient optimization in the wireless environment. The rationale behind the use of the novel DEEC-Gauss clustering algorithm is that it fills the gap in the literature as researchers have not been able to use this scheme before to carry out energy-efficient optimization in WSNs with 100 nodes, between 1,000 and 5000 rounds and still achieve a fast time output. In this study, using simulation, the performance of highly developed clustering algorithms, namely, DEEC, EDEEC_E, and DDEEC, was compared to the proposed Gaussian Elimination Clustering Algorithm (DEEC-Gauss). The results show that the proposed DEEC-Gauss Algorithm gives an average percentage of 4.2% improvement for the first node dead (FND), a further 2.8% improvement for the tenth node dead (TND), and the overall time of delivery was increased and optimized when compared with other contemporary algorithms.

A comparative study on energy efficient clustering based on metaheuristic algorithms for WSN

“…Using PSO, the hybrid algorithm enhanced the global search behaviour of fireflies and obtained optimal CH placement. In [6] an optimized energy efficiency in the wireless environment by combining the Gaussian elimination approach with distributed energy-efficient clustering (DEEC-Gauss) was implemented. However, this model has a node localization issue that has to be solved [6].…”

An Adaptive Fuzzy C Means With Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field With IoT

“…Oluwasegun Julius Aroba et al, [20] have introduced a Gaussian elimination method merged with distributed energy efficient clustering (referred to as DEEC-Gauss) to guarantee energy efficient regulation in remote climates. And, reason behind the use of DEEC-Gauss group calculation was that with 100 centers in the WSNs, it fills the hole in the writing because analysts are reluctant to use this program before making energyefficient improvements somewhere in the range.…”

A Neighborhood Grid Clustering Algorithm for Solving Localization Problem in WSN Using Genetic Algorithm

“…e neighborhood grid cluster (NGC) can understand data clustering naturally and adapt by means of self-assertive shape information deftly. Along these lines, to optimize energy, clustering is utilized in a remote sensor network to build the system lifetime of the sensor node [12]. Clustering is thought of as a high-dimensional type of network shaping grid clustered in WSN. e essential thought of the framework for grid structures is to partition the detected region into equivalent estimated virtual squares where every grid is considered as a group with one cluster center (CC) in each cluster.…”