Fuzzy random multi-objective optimization based routing for wireless sensor networks

Lu, Junling; Wang, Xiaoming; Zhang, Lichen; Zhao, Xueqing

doi:10.1007/s00500-013-1119-2

Cited by 24 publications

(12 citation statements)

References 15 publications

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“…Finally, an efficient distributed subgradient dual decomposition (SDD) algorithm was developed for striking an appealing trade-off. In [8], Lu et al formulated WSN routing as a fuzzy random multiobjective optimization (FRMOO) problem, which simultaneously considered the multiple objectives of delay, reliability, energy, delay jitter, the interference aspects and the energy balance of a path. They introduced a fuzzy random variable for characterizing the link delay, link reliability and the nodes' residual energy, with the objective of accurately reflecting the random characteristics in WSN routing.…”

Section: E Reliability-related Trade-offsmentioning

confidence: 99%

“…By adjusting the specific weight of each function, the algorithm adapts well to various services having different energy cost, delay and packet-loss rate requirements. This protocol was implemented using an advanced ACO algorithm that is based on a cloud model 8 .…”

Section: F Trade-offs Related To Other Metricsmentioning

confidence: 99%

“…In conventional WSN designs, typically the most salient performance metric is chosen as the optimization objective, while the remaining performance metrics are normally treated as the constraints of the optimization problem. Such singleobjective optimization approaches, however, may be unfair and unreasonable in real WSN applications, since it artificially over-emphasizes the importance of one of the metrics to the detriment of the rest [8]. Hence, a more realistic optimization is to simultaneously satisfy multiple objectives, such as the maximal energy efficiency, the shortest delay, the longest network lifetime, the highest reliability, and the most balanced distribution of the nodes' residual energy, or the trade-offs among the above objectives [9], [10].…”

Section: Introductionmentioning

confidence: 99%

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

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

382

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: E Reliability-related Trade-offsmentioning

confidence: 99%

Section: F Trade-offs Related To Other Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

382

202

View full text Add to dashboard Cite

show abstract

“…Li et al (2013) proposed a multiobjective train scheduling model by minimizing the energy and carbon emission cost as well as the total passenger time, and to represent the fuzzy nature of failure they used the linear and non-linear fuzzy membership functions. Further, multi-objective optimization techniques have been applied in reliability optimization problems (Garg and Sharma 2013), portfolio selection problems (Liu and Zhang 2013;KhaliliDamghani and Sadi-Nezhad 2013), designing fuzzy random routing algorithms for wireless sensor networks (Lu et al 2014) and solving method of several kinds of matrix games (Bector and Chandra 2005).…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization problem under fuzzy rule constraints using particle swarm optimization

Chakraborty

Guha

2015

Soft Comput

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In this paper, a fuzzy multi-objective programming problem is considered where functional relationships between decision variables and objective functions are not completely known to us. Due to uncertainty in real decision situations sometimes it is difficult to find the exact functional relationship between objectives and decision variables. It is assumed that information source from where some knowledge may be obtained about the objective functions consists of a block of fuzzy if-then rules. In such situations, the decision making is difficult and the presence of multiple objectives gives rise to multi-objective optimization problem under fuzzy rule constraints. In order to tackle the problem, appropriate fuzzy reasoning schemes are used to determine crisp functional relationship between the objective functions and the decision variables. Thus a multi-objective optimization problem is formulated from the original fuzzy rule-based multi-objective optimization model. In order to solve the resultant problem, a deterministic single-objective non-linear optimization problem is reformulated with the help of fuzzy optimization technique. Finally, PSO (Particle Swarm Optimization) algorithm is employed to solve the resultant singleobjective non-linear optimization model and the computation procedure is illustrated by means of numerical examples. Communicated by V. Loia.

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“…With direct data transmission between the cluster head and other network nodes, the single-hop routing does not require lots of information on the routing table [6] [7] or much node storage space for network expansion. In this case, the network scalability is positively proportional to the speed of the cluster head.…”

Section: Introductionmentioning

confidence: 99%

Routing Algorithm of Wireless Sensor Network and Robustness Analysis Based on Fuzzy Mathematics

et al. 2017

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Abstract-In order to reduce the energy consumption and enhance the robustness of wireless sensor network (WSN), this paper proposes a hierarchical clustering routing algorithm based on fuzzy mathematics (HCRAFM). To make a comprehensive analysis of WSN, it is also necessary to detect the robustness of the network. Facing the multiple random variables, the traditional robustness detection models assume that all nodes have the same weight, making it impossible to quantify the analysis indices or obtain accurate results. Thus, the fuzzy mathematics theory was introduced to the WSN robustness detection, forming a fuzzy comprehensive evaluation method. The simulation results show that the HCRAFM strikes a load balance between WSN nodes, extends the life cycle of each node, and prolongs the service life of the network. In addition, the proposed algorithm is proved to have sound robustness and strong applicability. Keywords-Routing Algorithms; Robustness Analysis; Wireless Sensor Network (WSN); Fuzzy Mathematics IntroductionIn wireless sensor network (WSN), the routing algorithm is responsible for setting up the path and mechanism of data transmission, realizing the dynamical update of network topology, and maintaining the information of network routing [1]. Therefore, the algorithm must be able to improve the energy efficiency of WSN nodes, and provide high quality services [2]. The traditional routing algorithms cannot satisfy the demand of WSN, which carries numerous different features from those of traditional networks. This calls for the design of a routing algorithm targeted at the demand of WSN [3]. The structure of a typical WSN is presented in Figure 1.

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Fuzzy random multi-objective optimization based routing for wireless sensor networks

Cited by 24 publications

References 15 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

Multi-objective optimization problem under fuzzy rule constraints using particle swarm optimization

Routing Algorithm of Wireless Sensor Network and Robustness Analysis Based on Fuzzy Mathematics

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