Design of Energy Efficient WSN Using a Noble SMOWA Algorithm

Abstract

In this paper, the establishment of efficient Wireless Sensor Network (WSN) networks has been projected to minimize the consumption of energy using a new Self-adaptive Multi-Objective Weighted Approach (SMOWA) algorithm for solving a multi-objective problem. The Different WSN nodes deployment policies have been proposed and applied in this paper to design an efficient Wireless Sensor Network to minimize energy consumption. After that, the cluster head for each cluster has been selected with the help of the duty cycle. After configuring the WSN networks, the SMOWA algorithms have been developed to obtain the minimum energy consumption for the networks. Energy minimization, as well as the amount of day-saving, has been calculated for the different WSNs which has been configured through different deployment policies. The major finding of the research paper is to improve the durability of Wireless Sensor Network (i) applying different deployment strategies: (Random, S pattern and nautilus shell pattern), and (ii) using a new Meta-heuristic algorithm (SMOWA Algorithm). In this research, the lifetime of WSN has been increased to a significant level. To choose the best result set from all the obtained results set some constraints such as "equivalent distribution", "number of repetitions", "maximum amount energy storage by a node" has been set to an allowable range.

Improvement of Wireless Sensor Network Lifetime via Intelligent Clustering Under Uncertainty

“…Neighborhood: Let is a dataset with any two data points and . Neighborhood of any point can be defined as: (5) where denotes distance between data point and .…”

“…Due to its use in various sectors, such as agriculture, the military defence sector, home appliances, remote sensing, etc., wireless communication has several advantages. The WSNs are used for communication in several security-based applications, including those that monitor the environment, vehicle traffic, smart offices, and battlefield surveillance [4,5]. There aren't many WSN restrictions, such as memory units, limited-energy modules, and high-performance times.…”

Boosted backtracking search optimization with information exchange for photovoltaic system evaluation

“…Operation 4: Crossover. This stage produced the final form of test population V. A binary matrix map is first defined to guide the crossover direction, the size of which is N • D. The specific design of the map can be found in Civicioglu 72 and Chen et al 81 The crossover operation can then be guided according to Equation (19).…”

“…Zhang et al 18 proposed an enhanced biogeography‐based optimization method and used it to solve an image‐segmentation problem. Banerjee et al 19 utilized a self‐adaptive multiobjective weighting approach to solve a multiobjective problem, resulting in the design of an efficient wireless sensor network.…”