Coverage problems in wireless ad-hoc sensor networks

Meguerdichian, Saro; Koushanfar, Farinaz; Potkonjak, Miodrag; Srivastava, Mani

doi:10.1109/infcom.2001.916633

Cited by 1,260 publications

(840 citation statements)

References 13 publications

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“…Note that, even in this case, c is given by (22). As the energy spent in countering the propagation loss to communicate with the aircraft ( H k ) is much larger than the other energy terms ( r k , l, E f ), c 2 dominates over other c i (see (14) and (15)).…”

Section: Grid Deploymentmentioning

confidence: 98%

“…In [8], Chiasserini et al determine an optimum strategy to allocate sensor nodes to different clusters so as to improve the system lifetime. In [22], Meguerdichian et al study the coverage problem in a wireless sensor network by using Voronoi diagram tools. The authors propose an algorithm to determine a maximal breach path for a given sensor network topology.…”

Section: Related Workmentioning

confidence: 99%

“…Further, if we assume a propagation constant of k ¼ 2, then substituting for c from (22), and after some simplifications we obtain,…”

Section: Grid Deploymentmentioning

confidence: 99%

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A minimum cost heterogeneous sensor network with a lifetime constraint

Mhatre

Rosenberg

Kofman

et al. 2005

IEEE Trans. on Mobile Comput.

292

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Abstract-We consider a heterogeneous sensor network in which nodes are to be deployed over a unit area for the purpose of surveillance. An aircraft visits the area periodically and gathers data about the activity in the area from the sensor nodes. There are two types of nodes that are distributed over the area using two-dimensional homogeneous Poisson point processes; type 0 nodes with intensity (average number per unit area) 0 and battery energy E 0 ; and type 1 nodes with intensity 1 and battery energy E 1 . Type 0 nodes do the sensing while type 1 nodes act as the cluster heads besides doing the sensing. Nodes use multihopping to communicate with their closest cluster heads. We determine the optimum node intensities ( 0 , 1 ) and node energies (E 0 , E 1 ) that guarantee a lifetime of at least T units, while ensuring connectivity and coverage of the surveillance area with a high probability. We minimize the overall cost of the network under these constraints. Lifetime is defined as the number of successful data gathering trips (or cycles) that are possible until connectivity and/or coverage are lost. Conditions for a sharp cutoff are also taken into account, i.e., we ensure that almost all the nodes run out of energy at about the same time so that there is very little energy waste due to residual energy. We compare the results for random deployment with those of a grid deployment in which nodes are placed deterministically along grid points. We observe that in both cases 1 scales approximately as ffiffiffiffiffi 0 p. Our results can be directly extended to take into account unreliable nodes.

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Section: Grid Deploymentmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

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A minimum cost heterogeneous sensor network with a lifetime constraint

Mhatre

Rosenberg

Kofman

et al. 2005

IEEE Trans. on Mobile Comput.

292

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“…As pointed out in Meguerdichian et al (2001), the coverage concept is a measure of the Quality of Service (QoS) of the sensing function and is subject to a wide range of interpretations due to a large variety of sensors and applications. The goal is to have each location in the physical space of interest within the sensing range of at least one sensor.…”

Section: State-of-the Artmentioning

confidence: 99%

“…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%

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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Coverage and Connectivity Issues in Wireless Sensor Networks

Ghosh

Das

2005

Mobile, Wireless, and Sensor Networks

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Wireless sensor networks [33,34] have inspired tremendous research interest in since the mid-1990s. Advancement in wireless communication and microelectromechanical systems (MEMSs) have enabled the development of low-cost, lowpower, multifunctional, tiny sensor nodes that can sense the environment, perform data processing, and communicate with each other untethered over short distances. A typical wireless sensor network consists of thousands of sensor nodes, deployed either randomly or according to some predefined statistical distribution, over a geographic region of interest. A sensor node by itself has severe resource constraints, such as low battery power, limited signal processing, limited computation and communication capabilities, and a small amount of memory; hence it can sense only a limited portion of the environment. However, when a group of sensor nodes collaborate with each other, they can accomplish a much bigger task efficiently. One of the primary advantages of deploying a wireless sensor network is its low deployment cost and freedom from requiring a messy wired communication backbone, which is often infeasible or economically inconvenient.Wireless sensor networks ensure a wide range of applications [2], starting from security surveillance in military and battlefields, monitoring previously unobserved environmental phenomena, smart homes and offices, improved healthcare, industrial diagnosis, and many more. For instance, a sensor network can be deployed in a remote island for monitoring wildlife habitat and animal behavior [25], or near the crater of a volcano to measure temperature, pressure, and seismic activities. In many of these applications the environment can be hostile where human Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions

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Coverage problems in wireless ad-hoc sensor networks

Cited by 1,260 publications

References 13 publications

A minimum cost heterogeneous sensor network with a lifetime constraint

A minimum cost heterogeneous sensor network with a lifetime constraint

Improving network lifetime using sensors with adjustable sensing ranges

Coverage and Connectivity Issues in Wireless Sensor Networks

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