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

arXiv: https://arxiv.org/abs/1609.04069 · HTML: https://mlsanity.com/paper/1609.04069/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2016.

## Built on by (most cited first, 9 of 9)

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- [Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization](https://mlsanity.com/paper/2110.15145/) (2021)
- [Context-aware Diversity Enhancement for Neural Multi-Objective Combinatorial Optimization](https://mlsanity.com/paper/2405.08604/) (2024)
- [Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization](https://mlsanity.com/paper/2307.03867/) (2023)
- [Scalable Min-Max Optimization via Primal-Dual Exact Pareto Optimization](https://mlsanity.com/paper/2504.02833/) (2025)
- [Explainability and Continual Learning meet Federated Learning at the Network Edge](https://mlsanity.com/paper/2504.08536/) (2025)
- [Adversarial Instance Generation and Robust Training for Neural Combinatorial Optimization with Multiple Objectives](https://mlsanity.com/paper/2601.01665/) (2026)

## Abstract (arXiv)

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.