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

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.

38 pages, 17 figures, 11 tables, 289 references, accepted to appear on IEEE Communications Surveys & Tutorials, Sept. 2016

References in corpus (7)

• Wireless Sensor Network Virtualization: A Survey
• Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
• Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
• The vector linear program solver Bensolve -- notes on theoretical background
• Energy efficient OFDMA networks maintaining statistical QoS guarantees for delay-sensitive traffic
• Spectral and Energy Spectral Efficiency Optimization of Joint Transmit and Receive Beamforming Based Multi-Relay MIMO-OFDMA Cellular Networks
• Distributed Energy Spectral Efficiency Optimization for Partial/Full Interference Alignment in Multi-User Multi-Relay Multi-Cell MIMO Systems

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