Sensor Network Coverage Restoration

Kumar, Nitin; Gunopulos, Dimitrios; Kalogeraki, Vana

doi:10.1007/11502593_41

Cited by 11 publications

(2 citation statements)

References 17 publications

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“…For example, the problem of coverage restoration upon sensor failures [18] remains open. Similarly, the problem of sleep-wakeup [7], [14], [19], [30] which has traditionally assumed full coverage model or the barrier coverage model [20], also needs to be reinvestigated for this new model.…”

Section: Discussionmentioning

confidence: 99%

Trap Coverage: Allowing Coverage Holes of Bounded Diameter in Wireless Sensor Networks

Balister

Zheng²,

Kumar

et al. 2009

Ieee Infocom 2009

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Abstract-Tracking of movements such as that of people, animals, vehicles, or of phenomena such as fire, can be achieved by deploying a wireless sensor network. So far only prototype systems have been deployed and hence the issue of scale has not become critical. Real-life deployments, however, will be at large scale and achieving this scale will become prohibitively expensive if we require every point in the region to be covered (i.e., full coverage), as has been the case in prototype deployments.In this paper we therefore propose a new model of coverage, called Trap Coverage, that scales well with large deployment regions. A sensor network providing Trap Coverage guarantees that any moving object or phenomena can move at most a (known) displacement before it is guaranteed to be detected by the network, for any trajectory and speed. Applications aside, trap coverage generalizes the de-facto model of full coverage by allowing holes of a given maximum diameter (d). From a probabilistic analysis perspective, the trap coverage model explains the continuum between percolation (when coverage holes become finite) and full coverage (when coverage holes cease to exist).We take first steps toward establishing a strong foundation for this new model of coverage. We derive reliable, explicit estimates for the density needed to achieve trap coverage with a given diameter when sensors are deployed randomly. We show by simulation that our analytical predictions of density are quite accurate even for small networks. Next, we investigate optimal deterministic patterns for deployment. We show that for d ≤ 0.5552r, where r is the sensing range, the optimal deployment pattern is a triangular grid and for large d/r, the subdivided hexagonal grid is within 10% of optimal. Proving the exact optimal pattern appears to be an extremely difficult problem, related to several open problems in optimal plane packing. Finally, we propose polynomial-time algorithms to determine the level of trap coverage achieved once sensors are deployed on the ground.

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Section: Discussionmentioning

confidence: 99%

Trap Coverage: Allowing Coverage Holes of Bounded Diameter in Wireless Sensor Networks

Balister

Zheng²,

Kumar

et al. 2009

Ieee Infocom 2009

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“…The coverage restoration problem was modelled as a search problem within a decision tree (Al‐Mutairi and Habib, 2007). On the contrary, few researchers (Kumar et al , 2005; Yannis and Kalogeraki, 2007) tried to solve the coverage restoration problem, by generating new nodes to cover the uncovered area. Cardei et al (2005b) later on addressed the target coverage problem with varying sensing ranges.…”

Section: Related Workmentioning

confidence: 99%

Restoring coverage area for WSN through simulated annealing

Habib

Marimuthu²

2011

International Journal of Pervasive Computing and Communications

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PurposeContinuous exposure and over‐utilization of sensors in harsh environments can lead some sensors to fail, and thereby not covering the service area effectively and efficiently. The purpose of this paper is to propose a two‐level coverage restoration scheme for the failing sensors by the existing sensors deployed in the immediate neighborhood of the failing sensors. The restoration scheme extends the search process to the set of failed sensors' corner neighbors at a second stage, with non‐available immediate active neighboring sensors at its first stage. Thus, the coverage restoration scheme attempts to sustain a maximum area of coverage with failed sensors.Design/methodology/approachThe authors have considered a wireless sensor network (WSN), comprised of sensors deployed in a grid‐based arrangement in an inaccessible arena. The authors have formulated the coverage restoration problem as an optimization problem, to find the nearest and most apt neighbor sensors to reach solutions of maximizing the coverage area with failed sensors, while minimizing the energy consumption. Simulated annealing has been utilized as a search algorithm to find out the neighboring sensors with maximal energy in the vicinity of the failed node to cover its area.FindingsThe experimental results within the optimization algorithm have demonstrated that the restoration scheme shows a better trade‐off in maximizing the coverage area up to 90 per cent with a decrease of 26 per cent lifespan. The performance of the algorithm is further improved with extended search space including the corner neighbors in addition to the immediate neighbors.Practical implicationsThe proposed coverage restoration can be embedded within applications using WSN to restore the coverage and maintain its functionality with optimized energy consumption.Originality/valueThe paper employs a novel framework to restore the coverage of the failed sensors by doubling the sensing area of the neighborhood sensors, and it utilizes an optimization scheme to search for neighborhood sensors with maximal energy to extend the lifespan of WSN.

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Bi-directional Coverage in the Connectivity of Sensor Networks

Xia

2015

Internet of Vehicles - Safe and Intelligent Mobility

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Sensor Network Coverage Restoration

Cited by 11 publications

References 17 publications

Trap Coverage: Allowing Coverage Holes of Bounded Diameter in Wireless Sensor Networks

Trap Coverage: Allowing Coverage Holes of Bounded Diameter in Wireless Sensor Networks

Restoring coverage area for WSN through simulated annealing

Bi-directional Coverage in the Connectivity of Sensor Networks

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