Decentralized boundary detection without location information in wireless sensor networks

Chu, Wei-Cheng; Ssu, Kuo‐Feng

doi:10.1109/wcnc.2012.6214061

Cited by 16 publications

(28 citation statements)

References 14 publications

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“…In future work, we would like to discover more topology information (e.g., skeleton) of WSNs based on the proposed [20]; (b) the algorithm in [30]; (c) the algorithm in [23].…”

Section: Discussionmentioning

confidence: 99%

“…Khan et al [26] proposed a variant of [20] by examining the 2-hop isocontour of each node and show that if there exists a closed cycle in the 2-hop isocontour of one node, this node is an interior node, and vice versa. Another algorithm proposed by Chu and Ssu [30], similar to [26], relaxes the condition of differentiating boundary nodes from interior nodes and thus identifies more nodes as boundary nodes. Although, these algorithms are as simple as the one in [20], they have the same weakness as well, namely, requiring International Journal of Distributed Sensor Networks 3 a high node density, identifying more nodes than actual ones, and not showing how the identified boundary nodes are connected in a meaningful way.…”

Section: Related Workmentioning

confidence: 99%

“…Last but not least, in contrast to the outer boundary of a WSN, inner boundaries denote boundaries of inner holes of the WSN and are important indicators of the general health of a WSN, such as insufficient coverage and connectivity [13,14]; for instance, identifying inner boundaries reveals groups of destroyed sensors due to physical destruction or power depletion, where additional sensor deployment is needed [15]. Therefore, boundary detection is of great importance for various WSN applications, such that considerable efforts have been invested in the development of boundary detection algorithms [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recognizing Boundaries in Wireless Sensor Networks Based on Local Connectivity Information

Huang

Gao

et al. 2014

International Journal of Distributed Sensor Networks

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This paper develops an efficient and distributed boundary detection algorithm to precisely recognize wireless sensor network (WSN) boundaries using only local connectivity information. Specifically, given any node in a WSN, the proposed algorithm constructs its 2-hop isocontour and locally makes a rough decision on whether this node is suspected to be on boundaries of the WSN by examining the associated 2-hop isocontour. Then, a heuristic operation is performed to refine this decision, with the result that the suspected boundary node set is significantly shrunk. Lastly, tight boundary cycles corresponding to both inner and outer WSN boundaries are derived by searching the suspected boundary node set. Furthermore, regarding WSNs with relatively low node densities, the proposed algorithm is adapted to improve the quality of boundary detection. Even though the proposed algorithm is initially presented under the assumption of the idealized unit disk graph (UDG) model, we further consider the more realistic quasi-UDG (QUDG) model. In addition, a message complexity analysis confirms the energy efficiency of the proposed algorithm. Finally, we carry out a thorough evaluation showing that our algorithm is applicable to both dense and sparse deployments of WSNs and is able to produce accurate results.

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“…In future work, we would like to discover more topology information (e.g., skeleton) of WSNs based on the proposed [20]; (b) the algorithm in [30]; (c) the algorithm in [23].…”

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recognizing Boundaries in Wireless Sensor Networks Based on Local Connectivity Information

Huang

Gao

et al. 2014

International Journal of Distributed Sensor Networks

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show abstract

“…In [3][4][5][6][7][8][9][10], the boundary detection algorithms were designed for sensor networks with static nodes so they do not require node locations and relative distance between nodes rather than using connectivity information in the network. In [3], the algorithm was developed for high density of static sensor networks by using a key feature of the distribution of nodes in which the nodes on the boundary have a smaller degree than the interior ones.…”

Section: Introductionmentioning

confidence: 99%

“…In [5], a distributed boundary detection algorithm for wireless sensor networks (WSNs) was developed by exploiting special structures of the shortest paths to detect the existence of hole boundaries in the network. Another algorithm using three-hop neighbouring node information for detecting boundary around an obstacle can be found in [7]. In [8], the Localized Voronoi Polygons (LVP) was used to identify boundary nodes.…”

Section: Introductionmentioning

confidence: 99%

An Online Distributed Boundary Detection and Classiﬁcation Algorithm for Mobile Sensor Networks

Hung¹,

Vinh²,

Ngo³

2017

REV J. Electron. Commun.

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Abstract-We present a novel online distributed boundary detection and classification algorithm in order to improve accuracy of boundary detection and classification for mobile sensor networks. This algorithm is developed by incorporating a boundary detection algorithm and our newly proposed boundary error correction algorithm. It is a fully distributed algorithm based on the geometric approach allowing to remove boundary errors without recursive process and global synchronization. Moreover, the algorithm allows mobile nodes to identify their states corresponding to their positions in network topologies, leading to self-classification of interior and exterior boundaries of network topologies. We have demonstrated effectiveness of this algorithm in both simulation and real-world experiments and proved that the accuracy of the ratio of correctly identified nodes over the total number of nodes is 100%.

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Finding the polygon hull of a network without conditions on the starting vertex

Bounceur

Bezoui

Hammoudeh

et al. 2019

Trans Emerging Tel Tech

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Many real-life problems arising within the fields of wireless communication, image processing, combinatorial optimisation, etc., can be modeled by means of Euclidean graphs. In the case of Wireless Sensor Networks (WSN), the overall topology of the graph is not known, because sensor nodes are often randomly deployed. One of the significant problems in this field is the search for boundary nodes. This problem is important in cases such as the surveillance of an area of interest, image contour reconstruction, graph matching problems, routing or clustering data, etc. In the literature, many algorithms are proposed to solve this problem, a recent one of which is the LPCN algorithm and its distributed version D-LPCN which are both based on the concept of a polar angle visit. An inconvenience of these algorithms is the determination of the starting vertex. In effect, the point with the minimum x-coordinate is a possible starting point but it has to be known at the beginning which considerably increases the algorithms' complexity. In this article, we propose a new method called RRLPCN (Reset and Restart with Least Polar-angle Connected Node) which is based on the LPCN algorithm to find the boundary vertices of a Euclidean graph. The main idea is to start the LPCN algorithm from an arbitrary vertex and, whenever it finds a vertex with an x-coordinate smaller than that of the starting one, LPCN is reset and restarted from this new vertex. The algorithm stops as soon as it visits the

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Decentralized boundary detection without location information in wireless sensor networks

Cited by 16 publications

References 14 publications

Recognizing Boundaries in Wireless Sensor Networks Based on Local Connectivity Information

Recognizing Boundaries in Wireless Sensor Networks Based on Local Connectivity Information

An Online Distributed Boundary Detection and Classiﬁcation Algorithm for Mobile Sensor Networks

Finding the polygon hull of a network without conditions on the starting vertex

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