Priority-based target coverage in directional sensor networks using a genetic algorithm

Wang, Jian; Niu, Changyong; Shen, Ruimin

doi:10.1016/j.camwa.2008.10.019

Cited by 88 publications

(62 citation statements)

References 11 publications

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“…Among those existing literature, Yang et al 15 and Wang et al 19 are particularly relevant to our work. Yang et al 15 study the maximal network lifetime scheduling (MNLS) problem where the coverage quality requirements of targets are different.…”

Section: Related Workmentioning

confidence: 98%

Sensor scheduling for target coverage in directional sensor networks

Jia

Dong

et al. 2017

International Journal of Distributed Sensor Networks

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Directional sensor networks have a lot of practical applications, and target coverage is one of the most important issues. In this article, we study the target coverage problem in directional sensor network, where directional sensors can rotate freely around their centers and targets are attached with differentiated coverage quality requirements. Our goal is to maximize the lifetime of sensor network, satisfying the coverage quality requirements of all targets. We model it as the maximum cover sets problem and address it by organizing the directions of sensors into a group of non-disjoint cover sets, which can cover all targets, satisfying their coverage quality requirements, and then schedule them alternately to extend the network lifetime. It consists of two parts. First, since directional sensor has infinite directions by rotating continuously, we propose sensing direction partition algorithm to find all non-redundancy directions for each sensor according to the targets deployed within the sensing region of the sensor. Then, based on the result of the sensing direction partition algorithm, we propose an efficient heuristic algorithm for the maximum cover sets problem to organize the directions of sensors into a number of non-disjoint cover sets and allocate work time for each cover set. Besides, we get an upper bound of the optimal solution for the problem. Finally, simulation results are presented to demonstrate the performance of our algorithm.

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Section: Related Workmentioning

confidence: 98%

Sensor scheduling for target coverage in directional sensor networks

Jia

Dong

et al. 2017

International Journal of Distributed Sensor Networks

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“…Target may have incorporated priority; it might depend on the distance because due to the increasing distance the quality degrades. Hence for such sort of problems Jan Wan [6] proposed a genetic algorithm for approximating the minimum subset of the directional sensors required to monitor the target by considering the priorities. The algorithm defined in [6]; considers the incorporated priorities of the individual nodes and the distance between the nodes and the targets.…”

Section: Incorporating Connectivity In Target Coverage Problemmentioning

confidence: 99%

The Issues of Coverage in Directional Sensor Network

Mishra¹,

Sharma²,

Saxena³

2015

IJCA

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The wireless sensor networks (WSNs) come into prominence because they have the capability of changing our economy and life. A very vast range of applications supported by WSNs has served the human society very effectively; and, monitoring has been proven the most attention drawing application among these. As a tributary of sensor networks, Directional sensor networks (DSNs) consist of several camera sensors where nodes cover only a fraction of surrounding defined by a specific angle called vertex angle at a time. As sensors are randomly deployed, their sensing direction may overlap with one another and hence coverage optimization has become the most popular area for research over the years. In this paper , the problem of coverage is surveyed on the basis of the solutions proposed, and hence, classified into three categories as (1) Target coverage (2) Maximal coverage (subset of area coverage ) and (3) Maximum coverage with minimum sensors (subset of target coverage). The impact of various parameters of directional sensor networks as: sensor's location, its working direction, number of orientations, FoVs on the performance of the deployed sensor network is analyzed. Maximum coverage with minimum sensors (subset of target coverage)

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“…Select the orientation , with the maximum profit value ( , ) among the orientations covering the critical target (line 15). Various profit functions can be defined and we consider two kinds of criteria; one is a reliability profit International Journal of Distributed Sensor Networks 5 Input parameters: , , 0 ( ) , , , (1) = , = 0, = 0 (2) for each ∈ do (3) ( ) = 0 ( ) (4) for each orientation , operated by do (5) = ∪ { , } (6) end for (7) end for (8) while ̸ = 0 and ⋃ , ∈ ( , ) = do (9) = + 1, = , = , Υ ( ) = 0 (10) while ̸ = 0 do (11) Find a critical target ∈ and calculate . (12) = 0 (13) Find all orientations ∈ that cover and insert them into (14) while < do (15) Select * , ∈ with the maximum profit ( * , )…”

Section: The Proposed Greedy Algorithm To Solve the Dccr Problemmentioning

confidence: 99%

Maximizing Network Lifetime of Directional Sensor Networks Considering Coverage Reliability

Gil

Park

Jeong

2013

International Journal of Distributed Sensor Networks

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Directional sensor networks composed of a large number of directional sensors equipped with a limited battery and with a limited angle of sensing have recently attracted attention. Maximizing network lifetime is a challenge in developing energy-efficient directional sensor networks, while covering all of the targets in a given area. However, the existing schemes have considered target coverage problem as only the maximization of network lifetime. In real sensor networks, the quality of target coverage will be varied according to the detection probability of the sensors covering targets. In this paper, we address the directional cover-sets with coverage reliability (DCCR) problem of organizing directional sensors into a group of nondisjoint subsets to extend network lifetime maximally while maintaining networks' satisfied coverage reliability. For the DCCR problem, we first present a coverage reliability model that mainly takes into account the detection probability of each sensor in cover-sets and eventually supports coverage reliability for target coverage. We also develop a heuristic algorithm called directional coverage and reliability (DCR) greedy algorithm to solve the DCCR problem. To verify and evaluate the algorithm, we conduct simulations and show that it extends network lifetime to a reasonable degree while guaranteeing the minimum coverage reliability.

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Priority-based target coverage in directional sensor networks using a genetic algorithm

Cited by 88 publications

References 11 publications

Sensor scheduling for target coverage in directional sensor networks

Sensor scheduling for target coverage in directional sensor networks

The Issues of Coverage in Directional Sensor Network

Maximizing Network Lifetime of Directional Sensor Networks Considering Coverage Reliability

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