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Megerian, Seapahn; Koushanfar, Farinaz; Qu, Gang; Veltri, Giacomino; Potkonjak, Miodrag

doi:10.1023/a:1016586011473

Cited by 154 publications

(19 citation statements)

References 38 publications

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“…In a related paper, Megerian et al [22] introduced the notion of "exposure." This measure takes into account, not only how close the path goes to the sensor points, but also the amount of time that is spent close to sensor points.…”

Section: Background On Barrier Coveragementioning

confidence: 99%

“…Informally, minimum shrinkage measures the strength of the barrier by estimating the possibility that the "best" path through the sensors is detected, taking into account that a path that goes very close to a sensor has a greater chance of detection than a path that only skirts the edge of the unit disk. We use the sum of shrinkages to obtain a cumulative measure, as in previous work [20,22]; more complicated measures that take exposure time into account are discussed in Section 3.1. We model sensor deterioration linearly.…”

Section: Introductionmentioning

confidence: 99%

“…We model sensor deterioration linearly. More realistically, one should use an inverse power with an exponent between 1 and 4 [22], but we leave this for future work.…”

Section: Introductionmentioning

confidence: 99%

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Minimum shared‐power edge cut

et al. 2020

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We introduce a problem called minimum shared‐power edge cut (MSPEC). The input to the problem is an undirected edge‐weighted graph with distinguished vertices s and t, and the goal is to find an s‐t cut by assigning “powers” at the vertices and removing an edge if the sum of the powers at its endpoints is at least its weight. The objective is to minimize the sum of the assigned powers. MSPEC is a graph generalization of a barrier coverage problem in a wireless sensor network: given a set of unit disks with centers in a rectangle, what is the minimum total amount by which we must shrink the disks to permit an intruder to cross the rectangle undetected, that is, without entering any disk. This is a more sophisticated measure of barrier coverage than the minimum number of disks whose removal breaks the barrier. We develop a fully polynomial time approximation scheme for MSPEC. We give polynomial time algorithms for the special cases where the edge weights are uniform, or the power values are restricted to a bounded set. Although MSPEC is related to network flow and matching problems, its computational complexity (in P or NP‐hard) remains open.

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Section: Background On Barrier Coveragementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Minimum shared‐power edge cut

et al. 2020

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“…Another aspect to be considered is the spatial one. Moreover, [9] defines coverage as a measure of the ability to detect objects within a sensor field. The lower the variation of the physical variable being measured across the area, the shorter has to be the radius of coverage for each sensor while measuring the phenomenon.…”

Section: The Wireless Sensor Networkmentioning

confidence: 99%

Handing Optimization Energy Consumption in Heterogeneous Wireless Sensor Networks

Pinheiro

Neto

Aguiar

2013

International Journal of Distributed Sensor Networks

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This paper presents an integer linear programming model devoted to optimize the energy consumption efficiency in heterogeneous wireless sensor networks. This model is based upon a schedule of sensor allocation plans in multiple time intervals subject to coverage and connectivity constraints. By turning off specifics sets of redundant sensors in each time interval, it is possible to reduce the total energy consumption in the network and, at the same time, avoid partitioning the whole network by losing some strategic sensors too prematurely. Since the network is heterogeneous, sensors can sense different phenomena from different demand points, with different sample rates. By resorting to this model, it is possible to provide extra lifetime to heterogeneous wireless sensor networks, reducing their setup and maintenance costs. This is an important issue to be considered when deploying sensor devices in hostile and inaccessible environments.

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“…Thus, an analysis framework has been established for sensor net-works in Clifford geometric space. To evaluate the quality of network coverage, Megerian [4] used Voronoi diagram and Delaunay triangulation respectively to define the worst and best-case coverage in sensor networks. There are also a lot of improved methods to solve these problems [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Target Detection in Three-Dimension Sensor Networks Based on Clifford Algebra

He¹,

Xie²,

Cao³

2009

WSN

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The three-dimensional sensor networks are supposed to be deployed for many applications. So it is signifi-cant to do research on the problems of coverage and target detection in three-dimensional sensor networks. In this paper, we introduced Clifford algebra in 3D Euclidean space, developed the coverage model of 3D sensor networks based on Clifford algebra, and proposed a method for detecting target moving. With Clif-ford Spinor, calculating the target moving formulation is easier than traditional methods in sensor node’s coverage area

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Cited by 154 publications

References 38 publications

Minimum shared‐power edge cut

Minimum shared‐power edge cut

Handing Optimization Energy Consumption in Heterogeneous Wireless Sensor Networks

Target Detection in Three-Dimension Sensor Networks Based on Clifford Algebra

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