Complete optimal deployment patterns for full-coverage and k-connectivity (k≤6) wireless sensor networks

Bai, Xin; Xuan, Dong; Yun, Ziqiu; Lai, Ten-Hwang; Jia, Weijia

doi:10.1145/1374618.1374672

Cited by 132 publications

(79 citation statements)

References 8 publications

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“…After that, more and more researchers focus on putting the wireless sensor network into practice. Some paper discussed the problem in full coverage network mode [10][11][12]. The concept of barrier coverage was first introduced by Gage in the context of robot systems [13].…”

Section: Related Workmentioning

confidence: 99%

Energy-efficient border intrusion detection using wireless sensors network

Yang

et al. 2014

J Wireless Com Network

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Monitoring the illegal movement across national border effectively is a challenging problem. The emerging technology of wireless sensor network (WSN) is expected to provide a new way to realize energy-efficient border intrusion detection. We propose a scheme to measure and guarantee the coverage quality of WSN. We also design a new coverage model for detecting one-direction path. The simulation results shows that the new coverage model could not only detect the intrusion in border area well, but also extend the network lifetime in an efficient way.

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Section: Related Workmentioning

confidence: 99%

Energy-efficient border intrusion detection using wireless sensors network

Yang

et al. 2014

J Wireless Com Network

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“…In [12] and [13], the approaches for coverage are categorized in three groups: force based [29] [14], grid-based [8], [15], [16] and computational geometry based [17], [18] and [9]. All these approaches can be considered as a sub-group of the movement-assisted approach.…”

Section: Related Workmentioning

confidence: 99%

“…Unfortunately, this approach cannot guarantee full coverage and may introduce several sensing redundancies when the robot encounters obstacles. The approaches for coverage can be categorized into three groups: 1) force based [29], 2) grid-based [8] and 3) computational geometry based [9]. All the three categories can be considered as a subclass of the movement-assisted category.…”

Section: Introductionmentioning

confidence: 99%

Nodes self-deployment for coverage maximization in mobile robot networks using an evolving neural network

Costanzo

Loscrì

Natalizio

et al. 2012

Computer Communications

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. Nodes self-deployment for coverage maximization in mobile robot networks using an evolving neural network. Computer Communications, Elsevier, 2012, 35 (9) AbstractThere are many critical issues arising in Wireless Sensor and Robot Networks (WSRN). Based on the specific application, different objectives can be taken into account such as energy consumption, throughput, delay, coverage, etc. Also many schemes have been proposed in order to optimize a specific Quality of Service (QoS) parameter. With the focus on the self-organizing capabilities of nodes in WSRN, we propose a movement-assisted technique for nodes self-deployment. Specifically, we propose to use a neural network as a controller for nodes mobility and a genetic algorithm for the training of the neural network through reinforcement learning [27]. This kind of scheme is extremely adaptive, since it can be easily modified in order to consider different objective and/or QoS parameters. In fact, it is sufficient to consider a different input for the neural network to aim to a different objective. All things considered, we propose a new method for programming a WSRN and we will show practically how the technique works, when the coverage of the network is the QoS parameter to optimize. Simulation results show the flexibility and effectiveness of this approach even when the application scenario changes (e.g., by introducing physical obstacles).

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“…Our research can act as a complement to them by providing new network topologies as a carrier for their algorithms and protocols. In 2D WSNs, X. Bai et al in [18], [19], and [20] studied several optimal deployment patterns to achieve multiple connectivity and full coverage in 2D WSNs. The optimality of the proposed patterns in [20] is proved under the constraints of regularity.…”

Section: Related Workmentioning

confidence: 99%

“…In 2D WSNs, X. Bai et al in [18], [19], and [20] studied several optimal deployment patterns to achieve multiple connectivity and full coverage in 2D WSNs. The optimality of the proposed patterns in [20] is proved under the constraints of regularity. X. Bai et al have made an attempt to further the exploration of optimal lattice patterns into three dimensional space, and some results on high connectivity are presented in [31].…”

Section: Related Workmentioning

confidence: 99%

Low-connectivity and full-coverage three dimensional wireless sensor networks

Bai

Zhang

Xuan

et al. 2009

Proceedings of the Tenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

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Low-connectivity and full-coverage three dimensional Wireless Sensor Networks (WSNs) have many real-world applications. By low connectivity, we mean there are at least k disjoint paths between any two sensor nodes in a WSN, where k ≤ 4. In this paper, we design a set of patterns for these networks. In particular, we design and prove the optimality of 1-and 2-connectivity patterns under any value of the ratio of communication range rc over sensing range rs, among regular lattice deployment patterns. We further propose a set of patterns to achieve 3-and 4-connectivity patterns and investigate the evolutions among all the proposed low-connectivity patterns. Finally, we study the proposed patterns under several practical settings.

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Complete optimal deployment patterns for full-coverage and k-connectivity (k≤6) wireless sensor networks

Cited by 132 publications

References 8 publications

Energy-efficient border intrusion detection using wireless sensors network

Energy-efficient border intrusion detection using wireless sensors network

Nodes self-deployment for coverage maximization in mobile robot networks using an evolving neural network

Low-connectivity and full-coverage three dimensional wireless sensor networks

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