Perimeter discovery in wireless sensor networks

Khedr, Ahmed M.; Osamy, Walid; Agrawal, Dharma P.

doi:10.1016/j.jpdc.2009.08.002

Cited by 32 publications

(14 citation statements)

References 13 publications

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“…The work of [28] is focused on the perimeter detection using a decentralized algorithm in wireless sensor networks based on the location neighborhood information combined with the Barycentric technique (used to determine whether a point is inside or outside of a triangle ABC). The algorithm uses the location neighborhood information of three neighbor nodes of each node to form a triangle and determines if it is enclosed by them and therefore not a perimeter node.…”

Section: The Work Of [25] Presents a Theoretical Analysis Methods For mentioning

confidence: 99%

D-LPCN: A distributed least polar-angle connected node algorithm for finding the boundary of a wireless sensor network

et al. 2017

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A boundary of wireless sensor networks (WSNs) can be used in many fields, for example, to monitor a frontier or a secure place of strategic sensitive sites like oil fields or frontiers of a country. This situation is modeled as the problem of finding a polygon hull in a connected Euclidean graph, which represents a minimal set of connected boundary nodes. In this paper we propose a new algorithm called D-LPCN (Distributed Least Polar-angle Connected Node) which represents the distributed version of the LPCN algorithm introduced in [1]. In each iteration, any boundary node, except the first one, chooses its nearest polar angle node among its neighbors with respect to the node found in the previous iteration. The first starting node can be automatically determined using the Minimum Finding algorithm, which has two main advantages. The first one is that the algorithm works with any type of a connected network, given as planar or not. Furthermore, it takes into account any blocking situation and contains the necessary elements to avoid them. The second advantage is that the algorithm can determine all the boundaries of the different connected parts of the network. The proposed algorithm is validated using the CupCarbon, Tossim and Contiki simulators. It has also been implemented using real sensor nodes based on the TelosB and Arduino/XBee platforms. We have estimated the energy consumption of each node and we have found that the consumption of the network depends on the number of the boundary nodes and their neighbors. The simulation results show that the proposed algorithm is less energy consuming than the existing algorithms and its distributed version is less energy consuming than the centralized version.

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Section: The Work Of [25] Presents a Theoretical Analysis Methods For mentioning

confidence: 99%

D-LPCN: A distributed least polar-angle connected node algorithm for finding the boundary of a wireless sensor network

et al. 2017

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“…The algorithm works well in a dense WSN satisfying a minimal degree of connectivity. In [24], the authors presented a decentralized localized algorithm to determine if SNs are located along the perimeter of a WSN. The proposed algorithm utilizes location neighborhood information in conjunction with the Barycentric technique to determine whether the SN is enclosed by a neighboring node, and whether it is located inside the WSN.…”

Section: Related Researchmentioning

confidence: 99%

Minimum perimeter coverage of query regions in a heterogeneous wireless sensor network

Khedr

Osamy

2011

Information Sciences

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“…Perimeter discovery [16] has been studied in wireless sensor networks. It focuses on how sensor nodes can determine if they are located along the perimeter of a sensor network.…”

Section: Related Workmentioning

confidence: 99%

Distinguishing locations across perimeters using wireless link measurements

Zhang

Kasera

Patwari

et al. 2011

2011 Proceedings IEEE INFOCOM

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Abstract-Perimeter distinction in a wireless network is the ability to distinguish locations belonging to different perimeters. It is complementary to existing localization techniques. A drawback of the localization method is that when a transmitter is at the edge of an area, an algorithm with isotropic error will estimate its location in the wrong area at least half of the time. In contrast, perimeter distinction classifies the location as being in one area or the adjacent regardless of the transmitter position within the area. In this paper, we use the naturally different wireless fading conditions to accurately distinguish locations across perimeters. We examine the use of two types of wireless measurements: received signal strength (RSS) and wireless link signature (WLS), and propose multiple methods to retain good distinction rates even when the receiver faces power manipulation by malicious transmitters. Using extensive measurements of indoor and outdoor perimeters, we find that WLS outperforms RSS in various fading conditions. Even without using signal power WLS can achieve accurate perimeter distinction up to 80%. When we train our perimeter distinction method with multiple measurements within the same perimeter, we show that we are able to improve the accuracy of perimeter distinction, up to 98%.

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Perimeter discovery in wireless sensor networks

Cited by 32 publications

References 13 publications

D-LPCN: A distributed least polar-angle connected node algorithm for finding the boundary of a wireless sensor network

D-LPCN: A distributed least polar-angle connected node algorithm for finding the boundary of a wireless sensor network

Minimum perimeter coverage of query regions in a heterogeneous wireless sensor network

Distinguishing locations across perimeters using wireless link measurements

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