An Evolutionary Multiobjective Sleep-Scheduling Scheme for Differentiated Coverage in Wireless Sensor Networks

Sengupta, Shamik; Das, Swagatam; Nasir, Md.; Vasilakos, Athanasios V.; Pedrycz, Witold

doi:10.1109/tsmcc.2012.2196996

Cited by 141 publications

(51 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As reported in [10], fuzzy dominance is incorporated into the original MOEA/D to solve the differentiated coverage problem. They aimed to obtain a better tradeoff among the coverage, energy consumption, and the network lifetime.…”

Section: Related Workmentioning

confidence: 99%

“…Traditional algorithms might not be applicable and only a few techniques address these objectives simultaneously. Multiobjective evolutionary algorithm (MOEA), which is a population based algorithm that naturally leads to Pareto optimal solutions, has been successfully applied to deal with MOPs in WSNs [10]- [12]. In recent years, several MOEAs have been proposed to give approximations on the Pareto optimal solutions (Pos).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Problem Specific MOEA/D for Barrier Coverage with Wireless Sensors

Zhang

Lee

et al. 2016

IEEE Trans. Cybern.

View full text Add to dashboard Cite

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).

show abstract

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Problem Specific MOEA/D for Barrier Coverage with Wireless Sensors

Zhang

Lee

et al. 2016

IEEE Trans. Cybern.

View full text Add to dashboard Cite

show abstract

“…Xiao et al [10] studied the underwater sensor networks according to the tight performance bounds of multihop fair access for MAC protocols. The papers [11][12][13] proposed a novel compressive data collection scheme. The measurement methods give our basis for measuring our methods.…”

Section: Introductionmentioning

confidence: 99%

An adaptive traffic MAC protocol based on correlation of nodes

Wang

2015

J Wireless Com Network

View full text Add to dashboard Cite

EA-MAC protocol is proposed on the basis of SMAC protocol to remedy the shortcomings of SMAC. In the EA-MAC, node correlation analysis algorithm and traffic adaptive duty cycle mechanism are added. Meanwhile, the residual energy is introduced into the existing traffic adaptive back-off mechanism to measure the saving efficiency. In the node correlation algorithm, all network nodes are divided into several areas by computing node correlation according to the collected information. The clustering mechanism is applied for choosing representative node in each area for transmitting data. This method can effectively reduce redundant nodes transmitting duplicate data. In traffic adaptive duty cycle mechanism, the duty cycle is regulated dynamically to decrease idle listening by comparing the threshold set with the flow value obtained from the predict flow model. In back-off mechanisms, by adjusting the value of contention window and back-off time, data collisions can be effectively avoided when network traffic is heavy. In addition, nodes with more remaining energy have priority to access the channel and have shorter back-off time, which can keep the balance of the whole network energy consumption and lengthen network lifetime. Simulation results show that the EA-MAC protocol has better energy saving, throughput, shorter delay performance, and low packet loss rate than that of SMAC protocol in dynamic traffic sensor networks.

show abstract

“…Recently, wireless sensor networks (WSNs) have got a new technological vision due to rapid development in wireless communication, energy supplies, network protocols, micro-sensor, pervasive technologies, and ubiquitous computing [1][2][3]. Existence of Internet with its enormous capabilities makes WSNs an important information resource for physical phenomena in addition to using their potential applications to construct more accurate models for servicing humanity in various aspects of life such as industrial control, security, smart home, e-health, and surveillance.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of WSN management system for QoS guarantee

Said

2015

J Wireless Com Network

View full text Add to dashboard Cite

As the wireless sensor networks (WSNs) continue to evolve, it becomes more and more important. Furthermore, no one can deny that quality of services (QoS) is still one of the most important areas in this domain. In this paper, a system for guaranteeing WSN QoS is proposed. The idea of this system is to use the previous individual solutions such as clustering, data prioritization, and data classification in addition to efficient techniques for network management. Simulation of this system is achieved using the network simulation package (NS2). Simulation results are given to demonstrate the effectiveness of the proposed system in terms of throughput, latency, lost packets, and sensor power consumption.

show abstract

An Evolutionary Multiobjective Sleep-Scheduling Scheme for Differentiated Coverage in Wireless Sensor Networks

Cited by 141 publications

References 17 publications

Problem Specific MOEA/D for Barrier Coverage with Wireless Sensors

Problem Specific MOEA/D for Barrier Coverage with Wireless Sensors

An adaptive traffic MAC protocol based on correlation of nodes

Performance evaluation of WSN management system for QoS guarantee

Contact Info

Product

Resources

About