A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms and Open Problems

TL;DR

This survey reviews multi-objective optimization techniques applied to wireless sensor networks, highlighting objectives, algorithms, recent studies, and open challenges to guide future research in balancing conflicting network performance metrics.

Contribution

It provides a comprehensive overview of MOO approaches in WSNs, including mathematical, heuristic, and advanced techniques, and discusses open problems for future exploration.

Findings

Summarizes key optimization objectives in WSNs.

Reviews prevalent MOO algorithms and techniques.

Identifies open research challenges in the field.

Abstract

Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling trade-offs amongst the various conflicting optimization criteria, such as the network's energy dissipation, packet-loss rate, coverage and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming based scalarization methods, the family of heuristics/metaheuristics based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.