A coverage-preserving node scheduling scheme for large wireless sensor networks

Tian, Di; Georganas, N.D.

doi:10.1145/570738.570744

Cited by 940 publications

(652 citation statements)

References 11 publications

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“…The authors have shown that they can significantly improve the coverage quality of the randomized scheduling. In [32], a similar scheduling scheme is proposed. The effectiveness of this scheme in terms of energy has been established by using the simulation tool NS-2.…”

Section: Related Workmentioning

confidence: 99%

Formal Analysis of a Scheduling Algorithm for Wireless Sensor Networks

Elleuch

Hasan

Tahar

et al. 2011

Formal Methods and Software Engineering

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In wireless sensor networks (WSNs), scheduling of the sensors is considered to be the most effective energy conservation mechanism. The random and unpredictable deployment of sensors in many WSNs in the open fields makes the sensor scheduling problem very challenging and randomized scheduling algorithms are thus used. The performance of these algorithms is usually analyzed using simulation techniques, which do not offer 100% accurate results. In this paper, we overcome this limitation by using higher-order-logic theorem proving to formally analyze the coverage-based random scheduling; a variant of the random scheduling algorithm designed for wireless sensor networks. Particularly, we aim at formalizing this coverage-based random scheduling within the probabilistic framework developed in the HOL theorem prover. We also formally reason about the expected values of coverage intensity, the upper bound on the total number of disjoint subsets, given expected coverage intensity, the lower bound on the total number of nodes and the average detection delay inside the network.

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Section: Related Workmentioning

confidence: 99%

Formal Analysis of a Scheduling Algorithm for Wireless Sensor Networks

Elleuch

Hasan

Tahar

et al. 2011

Formal Methods and Software Engineering

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“…The coverage problems can be classified in the following types (Cardei and Wu, 2006): 1 area coverage (Carle and Simplot, 2004;Tian and Georganas, 2002;Wang et al, 2003;Wu and Yang, 2004;Zhang and Hou, 2004), where the objective is to cover an area 2 point coverage (Cardei and Du, 2005;Cheng et al, 2005), where the objective is to cover a set of targets and 3 coverage problems that have the objective to determine the maximal support/breach path that traverses a sensor field (Meguerdichian et al, 2001). …”

Section: State-of-the Artmentioning

confidence: 99%

“…Set formation is done based on the problem requirements such as energy-efficiency, area monitoring, connectivity, etc. Different techniques have been proposed in the literature (Carle and Simplot, 2004;Tian and Georganas, 2002;Wang et al, 2003;Wu and Yang, 2004;Zhang and Hou, 2004) for determining the eligibility rule, that is, to select which sensors will be active in the next round. Wu and Yang (2004) addressed area coverage when sensors can adjust their sensing ranges.…”

Section: State-of-the Artmentioning

confidence: 99%

Improving network lifetime using sensors with adjustable sensing ranges

Cardei¹,

Wu²,

Lu³

2006

IJSNET

111

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This paper addresses the target coverage problem in wireless sensor networks with adjustable sensing range. Communication and sensing consume energy, therefore efficient power management can extend network lifetime. In this paper, we consider a large number of sensors with adjustable sensing range that are randomly deployed to monitor a number of targets. Since targets are redundantly covered by multiple sensors, to conserve energy resources, sensors can be organised in sets, activated successively. In this paper, we address the Adjustable Range Set Covers (AR-SC) problem that has as its objective finding a maximum number of set covers and the ranges associated with each sensor, such that each sensor set covers all the targets. A sensor can participate in multiple sensor sets, but the sum of the energy spent in each set is constrained by the initial energy resources. In this paper, we mathematically model solutions to this problem and design heuristics that efficiently compute the sets. Simulation results are presented to verify our approaches.

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“…Most of them rely on location information. A pruning method was proposed in [21], where a sensor can switch to sleep mode, if its sensing area is covered by the sensing areas of its neighbors. As the calculation of sensing area coverage becomes tedious, some simplifications have been used.…”

Section: Related Workmentioning

confidence: 99%

“…1) Targets form a contiguous region and the objective is to select a subset of sensors to cover the region [21]. Typical solutions involve geometry properties based on the positions of sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

On Connected Multiple Point Coverage in Wireless Sensor Networks

Yang

Dai

Cardei

et al. 2006

Int J Wireless Inf Networks

124

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Abstract-We consider a wireless sensor network consisting of a set of sensors deployed randomly. A point in the monitored area is covered if it is within the sensing range of a sensor. In some applications, when the network is sufficiently dense, area coverage can be approximated by guaranteeing point coverage. In this case, all the points of wireless devices could be used to represent the whole area, and the working sensors are supposed to cover all the sensors. Many applications related to security and reliability require guaranteed k-coverage of the area at all times. In this paper, we formalize the k-(Connected) Coverage Set (k-CCS/k-CS) problems, develop a linear programming algorithm, and design two non-global solutions for them. Some theoretical analysis is also provided followed by simulation results.Index Terms-Coverage problem, linear programming, localized algorithms, reliability, wireless sensor networks.

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A coverage-preserving node scheduling scheme for large wireless sensor networks

Cited by 940 publications

References 11 publications

Formal Analysis of a Scheduling Algorithm for Wireless Sensor Networks

Formal Analysis of a Scheduling Algorithm for Wireless Sensor Networks

Improving network lifetime using sensors with adjustable sensing ranges

On Connected Multiple Point Coverage in Wireless Sensor Networks

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