Wireless Sensor Network Optimization: Multi-Objective Paradigm

Abstract

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planning and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

K-Means clustering of optimized wireless network sensor using genetic algorithm

“…The best chromosome produced in last generation of the optimal resulted chromosome, which representing the solution for the problem of GA optimization [16]. Such that, the last generation shows final nodes locations and specific number of CH in the considered area of WSN [10].…”

“…As a results, the audience has chosen the optimization algorithm. The GA-based boost was impacted by diminishing energy-related aspects and increasing the accuracy of sensing areas [10]. Operating output (OE) and interaction energy (CE) are the two most fascinating resources characteristics, whereas sensor nodes per cluster nose error (SCE) and sensor systems out of range error are the two most interesting monitoring point equality parameters (SORE).…”

“…In single objective optimization the leading objective of the optimizer is to lminimize or maximize one goal under different requirements. while the multi objective optimization incorporates various targets are at the same time improved [10]. In many successful applications, GA is used with single or multi-objective modes of sensor network design that prompted to the improvement of a sundry other GA-based application explicit methodologies in WSN design [11] Figure 1.…”