Boundary Detection Method for Large-Scale Coverage Holes in Wireless Sensor Network Based on Minimum Critical Threshold Constraint

Jing, Rong; Kong, Lingfu; Kong, Liang

doi:10.1155/2014/985854

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…Authors in [22] propose a large-scale coverage hole algorithm for hole boundary detection based on the notion of minimum critical threshold constraint (BLW-MCT). The BLW-MCT is a geometric topological hybrid method, which allows topological global rough boundary detection and geometric local large-scale rough boundary detection.…”

Section: Related Workmentioning

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: Related Workmentioning

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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“…However, end-to-end delay is increased due to active and sleep mode switching mechanism. Jing et al 24 propose a boundary detection method (BDM) for large-scale coverage holes in terrestrial WSNs which is based on minimum critical threshold constraint. BDM has low computational complexity when detecting large-scale coverage holes in terrestrial WSNs.…”

Section: Related Workmentioning

confidence: 99%

Monitoring square and circular fields with sensors using energy-efficient cluster-based routing for underwater wireless sensor networks

Sher

Javaid

Azam

et al. 2017

International Journal of Distributed Sensor Networks

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In this article, to monitor the fields with square and circular geometries, three energy-efficient routing protocols are proposed for underwater wireless sensor networks. First one is sparsity-aware energy-efficient clustering, second one is circular sparsity-aware energy-efficient clustering, and the third one is circular depth-based sparsity-aware energy-efficient clustering routing protocol. All three protocols are proposed to minimize the energy consumption of sparse regions, whereas sparsity search algorithm is proposed to find sparse regions and density search algorithm is used to find dense regions of the network field. Moreover, clustering is performed in dense regions to minimize redundant transmissions of a data packet, while sink mobility is exploited to collect data from sensor nodes with an objective of minimum energy consumption. A depth threshold (d th) value is also used to minimize number of hops between source and destination for less energy consumption. Simulation results show that our schemes perform better than their counterpart schemes (depth-based routing and energy-efficient depth-based routing) in terms of energy efficiency.

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“…Many techniques are proposed for detection of coverage hole problems in terrestrial WSNs. In [12], Jing et al, proposed boundary detection method (BDM) for large-scale coverage holes in terrestrial WSNs which is based on minimum critical threshold constraint. BDM has low computational complexity when detecting large-scale coverage holes in terrestrial WSNs.…”

Section: Related Workmentioning

confidence: 99%

SEEC: Sparsity-Aware Energy Efficient Clustering Protocol for Underwater Wireless Sensor Networks

Azam

Majid

Ahmad

et al. 2016

2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)

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Many routing protocols are proposed regarding energy efficiency in underwater wireless sensor networks (UWSNs). We propose sparsity-aware energy efficient clustering (SEEC) protocol for UWSNs. SEEC specially search sparse regions of the network. We divide the network region into subregions of equal size and search sparse and dense regions of the network field with the help of sparsity search algorithm (SSA) and density search algorithm (DSA). SEEC improves network lifetime through sink mobility in sparse regions and clustering in dense regions of the network. SEEC also achieves network stability with optimal number of clusters formation in dense regions of the network where each dense region logically represents a static cluster. The division of the network region into subregions control routing hole problem in the UWSNs. SEEC minimizes network energy consumption with balanced scheme operations. Effectiveness of our proposed protocol is verified by simulation results.

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Boundary Detection Method for Large-Scale Coverage Holes in Wireless Sensor Network Based on Minimum Critical Threshold Constraint

Cited by 5 publications

References 18 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

Monitoring square and circular fields with sensors using energy-efficient cluster-based routing for underwater wireless sensor networks

SEEC: Sparsity-Aware Energy Efficient Clustering Protocol for Underwater Wireless Sensor Networks

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