A scheduling algorithm for connected target coverage under probabilistic coverage model

Kim, Chan-Myung; Kim, Yonghwan; Kang, In Seok; Lee, Kang-whan; Han, Youn-Hee

doi:10.1109/icoin.2012.6164355

Cited by 7 publications

(11 citation statements)

References 14 publications

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“…In [ 45 ], both target coverage and connectivity were considered. They solved the Connected Target Coverage (CTC) problem under the probabilistic model.…”

Section: Related Workmentioning

confidence: 99%

“…In summary, [ 32 ] concentrates on the barrier coverage, while [ 43 , 44 , 45 ] belong to target coverage. The approach proposed by [ 32 , 44 , 45 ] is not applicable for us, since their work is designed for maximum lifetime, while we take account of minimum energy in this paper. The author in [ 43 ] has proposed an algorithm based on the genetic algorithm to minimize the costs of a network deployed for target coverage.…”

Section: Related Workmentioning

confidence: 99%

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Target Coverage in Wireless Sensor Networks with Probabilistic Sensors

Shan

Cheng

2016

Sensors

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Sensing coverage is a fundamental problem in wireless sensor networks (WSNs), which has attracted considerable attention. Conventional research on this topic focuses on the 0/1 coverage model, which is only a coarse approximation to the practical sensing model. In this paper, we study the target coverage problem, where the objective is to find the least number of sensor nodes in randomly-deployed WSNs based on the probabilistic sensing model. We analyze the joint detection probability of target with multiple sensors. Based on the theoretical analysis of the detection probability, we formulate the minimum ϵ-detection coverage problem. We prove that the minimum ϵ-detection coverage problem is NP-hard and present an approximation algorithm called the Probabilistic Sensor Coverage Algorithm (PSCA) with provable approximation ratios. To evaluate our design, we analyze the performance of PSCA theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm.

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“…In [ 45 ], both target coverage and connectivity were considered. They solved the Connected Target Coverage (CTC) problem under the probabilistic model.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Target Coverage in Wireless Sensor Networks with Probabilistic Sensors

Shan

Cheng

2016

Sensors

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“…The cost in [19,20] is high, since they fail to make sensors collaborate with each other. We firstly theoretically analyze the collaborative detection probability of a target by multiple sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Most studies are concerned with maximizing network lifetime (e.g., [ 19 , 20 , 21 ]) in the CTC problem as for sensors’ limited resources. The maximum lifetime CTC problem aims to schedule the activation of sensors to prolong network lifetime.…”

Section: Introductionmentioning

confidence: 99%

A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors

Shan

Cheng

et al. 2017

Sensors

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Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ-connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm.

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“…Chan-Myung Kim, Yong-hwan Kim and Kang-whan Lee et al defined the probabilistic model in which the probability that the sensor detects a target depends on distance with the target within the sensing range. They also proposed heuristic algorithm called CWGC-PM (Communication Weighted Greedy Cover-Probabilistic Model) to solve the CTC (Connected Target Coverage) problem [11].…”

Section: Introductionmentioning

confidence: 99%

Analysis of energy efficient connected target coverage algorithm for static and dynamic nodes in IWSNs

Mittal

Aggarwal

Kaur

2016

Int. J. Adv. Intell. Informatics

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Today breakthroughs in wireless technologies have greatly spurred the emergence of industrial wireless sensor networks (IWSNs).To facilitate the adaptation of IWSNs to industrial applications, concerns about networks full coverage and connectivity must be addressed to fulfill reliability and real time requirements. Although connected target coverage algorithms have been studied notice both limitations and applicability of various coverage areas from an industry viewpoint. In this paper is discuss the two energy efficiency connected target coverage (CTC) algorithms CWGC(Communication Weighted Greedy Cover) and OTTC(Overlapped Target and Connected Coverage) algorithm based on dynamic node to resolve the problem of Coverage improvement. This paper uses the simulation in MATLAB represent the performance of two CTC algorithms with Dynamic node to improve network lifetime and low energy consumption and quality of service. Compare the dynamic nodes results with static nodes results

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A scheduling algorithm for connected target coverage under probabilistic coverage model

Cited by 7 publications

References 14 publications

Target Coverage in Wireless Sensor Networks with Probabilistic Sensors

Target Coverage in Wireless Sensor Networks with Probabilistic Sensors

A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors

Analysis of energy efficient connected target coverage algorithm for static and dynamic nodes in IWSNs

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