MOONGA: Multi-Objective Optimization of Wireless Network Approach Based on Genetic Algorithm

Abstract

In high-density wireless sensor networks, the quality of service in terms of sensing coverage, connectivity, lifetime, energy consumption and cost is closely linked to the position of the nodes in the network. Consequently, the placement of a large number of nodes while simultaneously optimizing several measurements is considered to be an NP-difficult problem. In this article, we propose a new approach to optimizing the problem of node placement. To achieve this objective, we started by studying the main approaches existing in the literature in order to identify their limits. In order to have accurate solutions, existing physical models are studied, improved, and validated with real measurements. Then, we proposed a new formulation of the deployment optimization problem as a constrained multi-objective optimization problem. This allowed us to develop an optimizer, based on the multi-objective genetic algorithm and the weighted sum optimization method, which we called MOONGA (multi-objective wireless network optimization using the genetic algorithm). This optimizer makes it possible to generate an optimal deployment according to the topology, the environment, the specifications of different applications and the preferences of the network designer users. The algorithms that we have developed and implemented within the framework of experiments carried out on test data in order to prove the effectiveness of our approach. The analysis of the results found confirm well the interest and the superiority of our proposed approach compared to main studied approaches.

Elite Adaptive Simulated Annealing Algorithm for Maximizing the Lifespan in LSWSNs

“…The comparison of algorithms is carried out under different number of sensors. In addition, all test cases are completed on a computer equipped with matlab2018a, and the applicability used in the algorithm is calculated according to formula (10).…”

“…In this paper, we propose an elite adaptive simulated algorithm (EASA) to settle the target coverage problem in LSWSNs. We first formulate our aim function as formula (10) to maximize the working life of sensor network under multiple constraints. To demonstrate the advantages of EASA, the target coverage problem was simulated.…”

“…Perform data simulation to judge the performance of EASA. Simulation results indicate the proposed algorithm can achieve a higher working life of LSWSNs over GA and PSO [8][9][10][11][12][13].…”

A Chaotic Parallel Artificial Fish Swarm Algorithm for Water Quality Monitoring Sensor Networks 3D Coverage Optimization

“…Most of the research on sensor coverage involves heuristic sensor coverage algorithms [4]. In previous studies, scholars have proposed many optimization algorithms to solve wireless sensor network (WSNs) problems, including genetic algorithm (GA) and particle swarm optimization (PSO) [5][6][7][8][9][10][11].…”

MiA-CODER: A Multi-Intelligent Agent-Enabled Reinforcement Learning for Accurate Coverage Hole Detection and Recovery in Unequal Cluster-Tree-Based QoSensing WSN

“…The QoSensing is directly proportional to the coverage rate, i.e., when the whole network is covered then the QoSensing is better in the network. One of the significant ways to achieve a better coverage rate is to deploy the sensor nodes in the optimal position where multiple objectives are to be satisfied for better coverage [1]. The target coverage sensors were found to overcome the coverage issues in which the sensors having poor coverage were eliminated and the sensors having contributing maximum coverage were considered [2].…”