O(log n)-Localized Algorithms on the Coverage Problem in Heterogeneous Sensor Networks

Thai, My T.; Yingshu, Li; Wang, Feng

doi:10.1109/pccc.2007.358882

Cited by 14 publications

(10 citation statements)

References 22 publications

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“…Recently, the authors of [23] propose the first constant factor approximation algorithm to the domatic partition problem in Unit Disk Graphs. The theoretical result of [23] comes in accordance with our experimental results published around the same time in [16], where we empirically show by using several graph coloring algorithms in Random Geometric Graphs, better results on the domatic number than the log n approximation factor of [6][22] [27] in arbitrary graphs.…”

Section: Introductionsupporting

confidence: 85%

“…The majority of the recent work [6] [10,11] [20,21] [22,23,24][27] focuses on designing centralized and distributed logarithmic or constant factor approximation solutions to the strict domatic partition problem which means the output of the algorithm should be the maximum number of disjoint fully dominating sets, where that maximum is never larger than the minimum degree δ plus one. Some of these works tie in the problem solution to maximizing the clustering or target coverage lifetime in sensor networks [22] [27]. For further discussion, we refer the reader to our work in [16].…”

Section: Introductionmentioning

confidence: 99%

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Employing (1 − ∊) Dominating Set Partitions as Backbones in Wireless Sensor Networks

Mahjoub

Matula

2010

2010 Proceedings of the Twelfth Workshop on Algorithm Engineering and Experiments (ALENEX)

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For a random geometric graph G(n, r) of minimum degree δ, we introduce an efficient algorithm for selecting (δ + 1) backbones with disjoint node sets that are each independent (1 − ε) dominating sets of G. The backbone node sets are determined by a graph coloring algorithm employing only the topology (not the geometry) of G(n, r), and the backbone links are selected with link lengths in a narrow window between r and 2r and further to form a planar graph backbone. For large vertex sets (n = 1600, 3200) the resulting backbones are shown to each cover typically over 99% of the vertices of G (i.e. ε < 0.01), with about 30% being fully dominating, which is consistent with the 1 4 constant approximation factor algorithm proposed recently in [23] for the domatic partition problem in Unit Disk Graphs. We establish experimentally by measures of node degrees, link lengths, and interior triangular face counts that each individual backbone has most of the coverage behavior and routing convenience of the triangular "perfect packing" lattice. We further show for each sample G(n, r) that the relatively few vertices not covered by all (δ + 1) backbones are covered by most of the backbones. Hence backbone rotation in a wireless sensor network would reach all sensors (vertices) sufficiently frequently. Our novel backbone generation algorithm confirms experimentally the existence of these (δ + 1) backbones in a random geometric graph, and provides an efficient topologically based centralized algorithm for determining the backbones. We also point out that our novel backbone construction method is flexible such that any efficient coloring algorithm can be plugged into it. In this paper, we experiment with several coloring algorithms: Smallest Last, Largest First, Lexicographic, Radial Sweep and Random and we compare their respective performance. Our emphasis is, however, on SL since it offers robust properties and interesting expected behavior. We also experiment with several random node distributions: uniform, skewed and normal in both unit square and disk of which we also discuss the results. * Boby B. Lyle School of Engineering. Southern Methodist University. Dallas, TX 75275-0122, USA.

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Section: Introductionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Employing (1 − ∊) Dominating Set Partitions as Backbones in Wireless Sensor Networks

Mahjoub

Matula

2010

2010 Proceedings of the Twelfth Workshop on Algorithm Engineering and Experiments (ALENEX)

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“…Thai et al [35] have proposed a distributed algorithm to maximize the network lifetime up to an O(log n) factor, while ensuring coverage of a given set of targets. However, the paper does not provide a coordinatefree algorithm for the area coverage problem, which we focus on.…”

Section: A Related Literaturementioning

confidence: 99%

“…However, the paper does not provide a coordinatefree algorithm for the area coverage problem, which we focus on. Also, the coverage and lifetime guarantees in [35] are probabilistic, whereas we provide deterministic guarantees on both coverage and lifetime.…”

Section: A Related Literaturementioning

confidence: 99%

Lifetime and coverage guarantees through distributed coordinate-free sensor activation

Kasbekar

Bejerano²,

Sarkar

2009

Proceedings of the 15th Annual International Conference on Mobile Computing and Networking

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Abstract-In wireless sensor networks, a large number of sensors perform distributed sensing of a target field. A sensor cover is a subset of the set of all sensors that covers the target field. The lifetime of the network is the time from the point the network starts operation until the set of all sensors with non-zero remaining energy does not constitute a sensor cover any more. An important goal in sensor networks is to design a schedule, that is, a sequence of sensor covers to activate in every time slot, so as to maximize the lifetime of the network. In this paper, we design a polynomial-time, distributed algorithm for maximizing the lifetime of the network and prove that its lifetime is at most a factor O(log n * log nB) lower than the maximum possible lifetime, where n is the number of sensors and B is an upper bound on the initial energy of each sensor. Our algorithm does not require knowledge of the locations of nodes or directional information, which is difficult to obtain in sensor networks. Each sensor only needs to know the distances between adjacent nodes in its transmission range and their sensing radii. In every slot, the algorithm first assigns a weight to each node that is exponential in the fraction of its initial energy that has been used up so far. Then, in a distributed manner, it finds an O(log n) approximate minimum weight sensor cover, which it activates in the slot.

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“…Focusing on distributed and localized solutions, Thai et al proposed two O(log n)-approximation distributed and localized algorithms for the target coverage problem [14]. The proposals organize sensors into non-disjoint set covers such that each set completely covers all the targets.…”

Section: Distributed and Localized Algorithmsmentioning

confidence: 99%

Coverage problems in wireless sensor networks: designs and analysis

Thai

Wang

et al. 2008

IJSNET

Self Cite

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Recently, a concept of wireless sensor networks has attracted much attention due to its widerange of potential applications. Wireless sensor networks also pose a number of challenging optimization problems. One of the fundamental problems in sensor networks is the coverage problem, which reflects the quality of service that can be provided by a particular sensor network. The coverage concept is defined from several points of view due to a variety of sensors and a wide-range of their applications. Several different designs and formulations of coverage problems have been proposed. They are subject to various topics such as types of interest regions (areas vs. targets) and different objectives (maximum network lifetime, minimum coverage breach) with other constraints. In this paper, we survey the state-of-the-art coverage formulations, present an overview and analysis of the solutions proposed in recent research literature.

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O(log n)-Localized Algorithms on the Coverage Problem in Heterogeneous Sensor Networks

Cited by 14 publications

References 22 publications

Employing (1 − ∊) Dominating Set Partitions as Backbones in Wireless Sensor Networks

Employing (1 − ∊) Dominating Set Partitions as Backbones in Wireless Sensor Networks

Lifetime and coverage guarantees through distributed coordinate-free sensor activation

Coverage problems in wireless sensor networks: designs and analysis

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