Approximation algorithms for deployment of sensors for line segment coverage in wireless sensor networks

Dash, Dinesh; Bishnu, Arijit; Gupta, Arobinda; Nandy, Subhas C.

doi:10.1109/comsnets.2012.6151346

Cited by 9 publications

(23 citation statements)

References 29 publications

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“…Recently, path constraint sensor deployment scheme [25, 26] and data gathering techniques [11, 12] have drawn attention of the research community in sensor network. Gao et al [12] have proposed a path‐constrained data gathering protocol.…”

Section: Related Workmentioning

confidence: 99%

Mobile data sink‐based time‐constrained data collection from mobile sensors: a heuristic approach

Kumar

Dash

2018

IET wirel. sens. syst.

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In wireless sensor network, sensors are deployed to sense useful data from environment. Sensors are energyconstrained devices. To prolong the sensor network lifetime for large-scale network, nowadays mobile data sinks (MDSs) (collectors/mules) are used for collecting the sensed data from the sensors. In this environment, sensor nodes can directly transfer their sensed data to the MDS. Sensors have limited memory. Therefore, to avoid buffer overflow, the data must be collected by the MDSs within a predefined time interval. The authors assume that a set of mobile sensors are moving arbitrarily on a set of paths. The authors objective is to collect data periodically from all mobile sensors using minimum number of MDSs within a fixed time interval. Here, a data-gathering algorithm is proposed. The authors analyse the complexity of the problem, and evaluate time complexity and performances of the proposed solution.

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Section: Related Workmentioning

confidence: 99%

Mobile data sink‐based time‐constrained data collection from mobile sensors: a heuristic approach

Kumar

Dash

2018

IET wirel. sens. syst.

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“…There has been a lot of node deployment research [8,[10][11][12][13], the fundamental goal of all the strategies is to reduce the number of nodes and achieve one or more specific goals at the same time. In accordance with the dimension of network, these studies can be divided into linear network [13], planar network [8,12] and 3D network deployment strategy [14]. Grouped by scenarios, the application scenarios of network deployment include: (1) underwater [14] and (2) the land [15].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Heuristic Subtraction Deployment Strategy to Guarantee Quality of Event Detection for WSNs

Liu

2014

The Computer Journal

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Cooperative sensing and monitoring event is one of the important applications of sensor networks. Node deployment and duty cycle configuration of event detection in a large wireless sensor networks is an enormous challenge. In this paper, through in-depth analysis we find that there is a tradeoff between node duty cycle and event quality monitoring. That is, adopting larger duty cycle enables network to deploy fewer nodes. Or deploying more nodes can reduce their duty cycle. Both of them can reach the event detecting quality requirement of application. Based on the above findings, a novel subtraction deployment strategy (SDS) combined with the unequal node duty cycle in the network is presented. This strategy improves the duty cycle of nodes in non-hotspots area when reduces the number, thereby minimizing the deployment cost on the premise of meeting the detecting quality of application. We introduce a formal model to indicate the tradeoff between deployment cost and the quality of event detection. Subsequently, we present a heuristic algorithm to find nearoptimal deployment solutions for practical scenarios. Both theoretical and experimental simulation results show that the proposed scheme SDS for event detection can reduce the deployed number of nodes by 11.49% and do no harm to detecting quality. In the deployment of the same node number, the weighted delay of detection can be reduced as much as 47%, and the weighted detection percentage can be increased by 1.56 times without reducing the network lifetime compared with those in uniform deployment scheme.

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“…Euclidean distance between x and its projection on e; for a zero-length segment x ∈ R 2 N r (x) denotes a radius r disk centered at x. Each object from N r (E) is a Euclidean r-neighborhood of some segment of E also called r-hippodrome or r-offset in the literature [4].…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, only 100-approximation O(n 4 log n)-time algorithm is known (Kobylkin, 2018) for this problem. Moreover, when segments of E are axis-parallel, 8-approximation is proposed (Dash et al, 2012), working in O(n log n) time. In this work another special setting is considered of the problem where G belongs to classes of special plane graphs, which are of interest in network applications.…”

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confidence: 99%

Fast Approximation Algorithms for Stabbing Special Families of Line Segments with Equal Disks

Kobylkin

2021

Lecture Notes in Computer Science

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An NP-hard problem is considered to stab a given set of n straight line segments on the plane with the smallest size subset of disks of fixed radii r > 0, where the set of segments forms a straight line drawing G = (V, E) of a planar graph without proper edge crossings. To the best of our knowledge, only 100-approximation O(n 4 log n)-time algorithm is known (Kobylkin, 2018) for this problem. Moreover, when segments of E are axis-parallel, 8-approximation is proposed (Dash et al., 2012), working in O(n log n) time. In this work another special setting is considered of the problem where G belongs to classes of special plane graphs, which are of interest in network applications. Namely, three fast O(n 3/2 log 2 n)-expected time algorithms are proposed: a 10approximate algorithm for the problem, considered on edge sets of minimum Euclidean spanning trees, a 12-approximate algorithm for edge sets of relative neighborhood graphs and 14-approximate algorithm for edge sets of Gabriel graphs. The paper extends recent work (Kobylkin et al. 2019) where O(n 2 )-time approximation algorithms are proposed with the same constant approximation factors for the problem on those three classes of sets of segments.

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Approximation algorithms for deployment of sensors for line segment coverage in wireless sensor networks

Cited by 9 publications

References 29 publications

Mobile data sink‐based time‐constrained data collection from mobile sensors: a heuristic approach

Mobile data sink‐based time‐constrained data collection from mobile sensors: a heuristic approach

An Efficient Heuristic Subtraction Deployment Strategy to Guarantee Quality of Event Detection for WSNs

Fast Approximation Algorithms for Stabbing Special Families of Line Segments with Equal Disks

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