Abstract
A wireless sensor network (WSN) is an intellect-sustainable network that comprises multiple spatially distributed sensor nodes and several sink nodes that collect data from sensors. WSNs remain an active research area in the literature due to challenging factors such as the selection of sensor location according to a given premise, finding optimal routing algorithm, and ensuring energy efficiency and consumption. Minimizing energy and prolonging the network lifetime in the WSNs are the focus of this research work. In the literature, a clustering approach is used in grouping sensor nodes into clusters and is seen as an effective technique used in optimizing energy consumption in WSNs. Hence, in this paper, we put forward a novel clustering-based approach by amalgamating the Gaussian elimination method with the Distributed Energy-Efficient Clustering to produce DEEC_Gaussian (DEEC_Gaus) to stabilize energy efficiency optimization in WSNs. We took the advantages of DEEC and Gaussian elimination algorithms to resolve energy efficiency problems in WSNs. DEEC presents attributes such as increased heterogeneity performance level, clustering stability in operation, and energy efficiency which helps to prolong network lifetime while the Gaussian elimination algorithm added an additional advantage to improve and optimize energy efficiency, to aggregate packets of operations performed in the network lifestyle of energy efficiency in WSNs. The simulations were carried out using MATLAB software with 1000 to 1500 nodes. The performance of the proposed work was compared with state-of-the-art algorithms such as DEEC, DDEEC, and EDEEC_E. The simulated results presented show that the proposed DEEC-Gauss outperformed the three other conventional algorithms in terms of network lifetime, first node dead, tenth node dead, alive nodes, and the overall timing of the packets received at the base station. The results showed that the proposed hyper-heuristic heterogeneous multisensor DEEC-Gauss algorithm presented an average percentage of 3.0% improvement for the tenth node dead (TND) and further improvement of 4.8% for the first node dead (FND). When the performance was compared to the state-of-the-art algorithms in larger networks, the overall delivery was greatly improved and optimized.
Highlights
Wireless sensor networks (WSNs) are networks that comprise multihop communication systems that are a bunch of battery-powered sensor nodes that are used to effectively monitor the processing unit and storage unit for collating and analyzing information with the use of their sensor modules [1,2,3,4]
Literature abounds with research works on state-of-theart algorithms, namely, Distributed Energy-Efficient Clustering Extended (DEEC_E), Developed Distributed Energy-Efficient Clustering Extends (DDEEC_E), and Distributed Energy-Efficient Clustering (DEEC) with normal, advance, and supernode classifications. is research study addresses a gap in this research area as it produced the results for larger wireless sensor networks (WSNs) of over 1000 nodes, namely, 1000, 1100, 1200, 1300, 1400, and 1500 sensor nodes for various initial energy levels, namely, 0.5, 0.6, 0.7, and 0.8 joules of energy that has not been seen before using the novel DEEC_Gausian algorithm
Nehra et al in [20] suggested Improved Distributed Energy-Efficient Clustering (I-DEEC) by distributing network nodes between two layers of normal and advanced hexagons for considering the distance between the base station within the same area. e sum of the ratio of the distance to the nodes and residual energy is calculated with the possibility of sensor nodes to nominate as the cluster head for revamps of DEEC protocols within the network lifetime, throughput, and the percentage area coverage
Summary
Wireless sensor networks (WSNs) are networks that comprise multihop communication systems that are a bunch of battery-powered sensor nodes that are used to effectively monitor the processing unit and storage unit for collating and analyzing information with the use of their sensor modules [1,2,3,4]. Is research study addresses a gap in this research area as it produced the results for larger wireless sensor networks (WSNs) of over 1000 nodes, namely, 1000, 1100, 1200, 1300, 1400, and 1500 sensor nodes for various initial energy levels, namely, 0.5, 0.6, 0.7, and 0.8 joules of energy that has not been seen before using the novel DEEC_Gausian algorithm. (i) To provide an effective energy efficiency optimization (ii) To carry out an extensive simulation of the proposed heterogeneous DEEC-Gauss algorithm in MATLAB (iii) To evaluate and compare the proposed algorithm with classical state-of-the-art algorithms using popular performance criteria: network lifetime of the algorithm, tenth node dead, alive node, and the overall timing of the packet received at the base station metrics e rest of the paper is presented as follows: Section 2 sheds more light on the related works.
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