A Delaunay-Based Coordinate-Free Mechanism for Full Coverage in Wireless Sensor Networks

Qiu, Chenxi; Shen, Haiying

doi:10.1109/tpds.2013.134

Cited by 42 publications

(8 citation statements)

References 30 publications

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“…Particle swarm optimization (PI-BPSO) algorithm [31] Camera coverage Improved homogeneous camera network coverage Delaunay-based coordinate-free mechanism (DECM) [32] Area coverage Coverage hole detection and healing…”

Section: Coverage In Wsnsmentioning

confidence: 99%

Energy Efficient Fault Tolerant Coverage in Wireless Sensor Networks

Henna

2017

Journal of Sensors

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Energy efficiency and fault tolerance are two of the major concerns in wireless sensor networks (WSNs) for the target coverage. Design of target coverage algorithms for a large scale WSNs should incorporate both the energy efficiency and fault tolerance. In this paper, we study the coverage problem where the main objective is to construct two disjoint cover sets in randomly deployed WSNs based on relay energy (Erelay). Further, we present an approximation algorithm called Energy Efficient Maximum Disjoint Coverage (EMDC) with provable approximation ratios. We analyze the performance of EMDC theoretically and also perform extensive simulations to demonstrate the effectiveness of EMDC in terms of fault tolerance and energy efficiency.

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Section: Coverage In Wsnsmentioning

confidence: 99%

Energy Efficient Fault Tolerant Coverage in Wireless Sensor Networks

Henna

2017

Journal of Sensors

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“…So it is important to compute the node density as clusters are formed dynamically. The work by Bulusu in [21] proposed a method to compute the network density as where N is the number of nodes in unit area A and r is the radius of the transmission range. Along with distance of a node with neighboring nodes, we need to incorporate one-hop connectivity of the node with other neighbors should be considered.…”

Section: Node Densitymentioning

confidence: 99%

An Improved Energy Balanced Dissimilar Clustered Routing Architecture for Wireless Sensor Networks

Anandh¹,

Baburaj²

2016

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In wireless sensor networks, clustering of nodes effectively conserves considerable amount of energy resulting in increased network life-time. Clustering protocols do not consider density of nodes in cluster formation, which increases the possibility of hotspots in areas where the density of nodes is very less. If the node density is very high, cluster-heads may expend high energy leading to their early death. Existing cluster protocols that concentrate on energy conservation have not exhibited their impact on packet delivery and delay. In this proposed protocol, clusters are constructed based on the range of nodes, distance between neighbouring nodes and density of nodes over a region resulting in the formation of dissimilar clusters. With this method, the entire sensing region is considered to be a large circular region with base station positioned at the centre. Initially, the nodes that can be able to reach base station in a single hop are considered for constructing inner smaller circular regions over the entire region. This method is iterated for n-hop nodes until n-concentric circular regions are formed. These circular boundaries are reconstructed based on a distance metric, density of nodes and a divergence factor. Using this architecture, network analysis is done by routing data to the base station from different sized clusters. Based on simulation results, this new protocol Dynamic Unequal Clustered Routing (D-UCR), despite being energy efficient, showed better data delivery ratio and minimized delay when compared with other traditional clustering algorithms such as Leach and Equal Clustered Routing.

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“…The coverage problem concerns how well a sensor network is monitored or tracked by sensors, and it is one of the fundamental issues in sensor networks. Three categories of the coverage problem exist in the literature based on the coverage subject [ 5 , 6 ]: target coverage (e.g., [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]), area coverage (e.g., [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]) and barrier coverage (e.g., [ 29 , 30 , 31 , 32 , 33 , 34 ]).…”

Section: Introductionmentioning

confidence: 99%

Target Coverage in Wireless Sensor Networks with Probabilistic Sensors

Shan

Cheng

2016

Sensors

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Sensing coverage is a fundamental problem in wireless sensor networks (WSNs), which has attracted considerable attention. Conventional research on this topic focuses on the 0/1 coverage model, which is only a coarse approximation to the practical sensing model. In this paper, we study the target coverage problem, where the objective is to find the least number of sensor nodes in randomly-deployed WSNs based on the probabilistic sensing model. We analyze the joint detection probability of target with multiple sensors. Based on the theoretical analysis of the detection probability, we formulate the minimum ϵ-detection coverage problem. We prove that the minimum ϵ-detection coverage problem is NP-hard and present an approximation algorithm called the Probabilistic Sensor Coverage Algorithm (PSCA) with provable approximation ratios. To evaluate our design, we analyze the performance of PSCA theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm.

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A Delaunay-Based Coordinate-Free Mechanism for Full Coverage in Wireless Sensor Networks

Cited by 42 publications

References 30 publications

Energy Efficient Fault Tolerant Coverage in Wireless Sensor Networks

Energy Efficient Fault Tolerant Coverage in Wireless Sensor Networks

An Improved Energy Balanced Dissimilar Clustered Routing Architecture for Wireless Sensor Networks

Target Coverage in Wireless Sensor Networks with Probabilistic Sensors

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