A Multiutility Framework With Application for Studying Tradeoff Between Utility and Lifetime in Wireless Sensor Networks

Liao, Shengbin; Zhang, Qingfu

doi:10.1109/tvt.2014.2372793

Cited by 10 publications

(9 citation statements)

References 27 publications

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“…Eventually, a multirate-supportive MAC protocol was proposed for balancing the throughput versus energy consumption. Liao et al [257] generalized the NUM model to a multiutility framework using MOO and applied this framework to trade off the network utility against the lifetime in WSNs. An extended Lagrange duality method was proposed, which is capable of converging to a selected Pareto-optimal solution.…”

Section: Lifetime-versus-application-performance Trade-offsmentioning

confidence: 99%

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

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

383

202

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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.

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Section: Lifetime-versus-application-performance Trade-offsmentioning

confidence: 99%

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

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

383

202

View full text Add to dashboard Cite

show abstract

“…2 and store accordingly the surviving routes visited to the set S surv (i) , where i is the number of RNs constituting the visited route. 4…”

Section: Back-tracing-aided Quantum Pareto Optimizationmentioning

confidence: 99%

Quantum-Aided Multi-Objective Routing Optimization Using Back-Tracing-Aided Dynamic Programming

Alanis

Botsinis

Babar

et al. 2018

IEEE Trans. Veh. Technol.

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Pareto optimality is capable of striking the optimal trade-off amongst the diverse conflicting QoS requirements of routing in wireless multihop networks. However, this comes at the cost of increased complexity owing to searching through the extended multi-objective search-space. We will demonstrate that the powerful quantum-assisted dynamic programming optimization framework is capable of circumventing this problem. In this context, the so-called Evolutionary Quantum Pareto Optimization (EQPO) algorithm has been proposed, which is capable of identifying most of the optimal routes at a nearpolynomial complexity versus the number of nodes. As a benefit, we improve both the the EQPO algorithm by introducing a back-tracing process. We also demonstrate that the improved algorithm, namely the Back-Tracing-Aided EQPO (BTA-EQPO) algorithm, imposes a negligible complexity overhead, while substantially improving our performance metrics, namely the relative frequency of finding all Pareto-optimal solutions and the probability that the Pareto-optimal solutions are indeed part of the optimal Pareto front.

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“…MOEA/D is an easily-extensible but efficient evolutionary multiobjective algorithm framework. Several applications and extensions on MOEA/D have been made recently [33]- [35]. Zhang et al [36] considered a barrier coverage problem in multiobjective perspective.…”

Section: Related Workmentioning

confidence: 99%

Problem Specific MOEA/D for Barrier Coverage with Wireless Sensors

Zhang

Lee

et al. 2016

IEEE Trans. Cybern.

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Abstract-Barrier coverage with wireless sensors aims at detecting intruders who attempt to cross a specific area, where wireless sensors are distributed remotely at random. This paper considers limited-power sensors with adjustable ranges deployed along a linear domain to form a barrier to detect intruding incidents. We introduce three objectives to minimize: 1) total power consumption while satisfying full coverage; 2) the number of active sensors to improve the reliability; and 3) the active sensor nodes' maximum sensing range to maintain fairness. We refer to the problem as the tradeoff barrier coverage (TBC) problem. With the aim of obtaining a better tradeoff among the three objectives, we present a multiobjective optimization framework based on multiobjective evolutionary algorithm (MOEA)/D, which is called problem specific MOEA/D (PS-MOEA/D). Specifically, we define a 2-tuple encoding scheme and introduce a cover-shrink algorithm to produce feasible and relatively optimal solutions. Subsequently, we incorporate problem-specific knowledge into local search, which allows search procedures for neighboring subproblems collaborate each other. By considering the problem characteristics, we analyze the complexity and incorporate a strategy of computational resource allocation into our algorithm. We validate our approach by comparing with four competitors through several most-used metrics. The experimental results demonstrate that PS-MOEA/D is effective and outperforms the four competitors in all the cases, which indicates that our approach is promising in dealing with TBC.Index Terms-Barrier coverage, evolutionary algorithms, multiobjective optimization, wireless sensor networks (WSNs).

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A Multiutility Framework With Application for Studying Tradeoff Between Utility and Lifetime in Wireless Sensor Networks

Cited by 10 publications

References 27 publications

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

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

Quantum-Aided Multi-Objective Routing Optimization Using Back-Tracing-Aided Dynamic Programming

Problem Specific MOEA/D for Barrier Coverage with Wireless Sensors

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