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.
Highlights
The use of the Internet of Things (IoT) gave birth to smart systems operating in wireless sensor networks (WSNs) that require little human interaction to carry out operations as it engages machine-to-machine communication [1, 2]
The base station (BS) is a homogeneous station which is within and outside the sensing environment. These sensor nodes are aggregated between a time interval periodically, which is transferred to the cluster head
The simulation results is evidenced with the hyperheuristic energy model in comparison with the state-of-the-art benchmarked clustering algorithms
Summary
The use of the Internet of Things (IoT) gave birth to smart systems operating in wireless sensor networks (WSNs) that require little human interaction to carry out operations as it engages machine-to-machine communication [1, 2]. Different research works have focused on providing an effective WSN system for energy consumption, Journal of Sensors and many algorithms have been proposed to minimize GPS for the sensor node location identification. These include range free and range-based algorithms to estimate distance used and angles of connectivity for the information to travel between the known nodes and unknown sensor node locations [10, 11]. Performance metrics such as first node dead (FND), tenth node dead (TND), packets delivered to the base station, and processing time were used to ascertain the effectiveness and efficiency of the proposed algorithm.
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