A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems
By Zesong Fei, Bin Li, Shaoshi Yang, Chengwen Xing, Hongbin Chen, Lajos Hanzo. IEEE Communications Surveys and Tutorials, 2016
442 citations recorded by the source corpus.
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 tradeoffs 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.
Subfields: Computational Theory and Mathematics, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/037a611a2ee27998cdefccd49a4c151d1c69eb0e
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In Computational Theory and Mathematics
In Computer Networks and Communications
Citations in the corpus
- Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “2) Pareto-Optimal Future Wireless Networks: The challenging real-world optimization problems encountered in future wireless networks have to meet multiple objectives in order to arrive at an attractive solution [134].”