Research on Optimal Coverage Problem of Wireless Sensor Networks

Wang, Xueqing; Sun, Fayi; Kong, Xiangping

doi:10.1109/cmc.2009.231

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…Optimal deployment of sensors is achieved when all sensors have the same equal sensing radius. Moreover, the boundary of sensing field should have a regular shape and there are no obstacles in the sensing field [20]. The optimal deployment is achieved by deploying sensors regularly with same threshold distance ( d th ) between them.…”

Section: Analytical Optimal Deploymentmentioning

confidence: 99%

Utilisation of multi‐objective immune deployment algorithm for coverage area maximisation with limit mobility in wireless sensors networks

Abo‐Zahhad

Ahmed

Sabor

et al. 2015

IET wirel. sens. syst.

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Coverage is one of the most important performance metrics for wireless sensor network (WSN) since it reflects how well a sensor field is monitored. The coverage issue in WSNs depends on many factors, such as the network topology, sensor sensing model and the most important one is the deployment strategy. Random deployment of the sensor nodes can cause coverage holes formulation. This problem is non-deterministic polynomial-time hard problem. So in this study, a new centralised deployment algorithm based on the immune optimisation algorithm is proposed to relocate the mobile nodes after the initial configuration to maximise the coverage area. Moreover, the proposed algorithm limits the moving distance of the mobile nodes to reduce the dissipation energy in mobility and to ensure the connectivity among the sensor nodes. The performance of the proposed algorithm is compared with the previous algorithms using Matlab simulation. Simulation results clear that the proposed algorithm based on binary and probabilistic sensing models improves the network coverage and the redundant covered area with minimum moving consumption energy. Furthermore, the simulation results show that the proposed algorithm also works when obstacles appear in the sensing field.

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Section: Analytical Optimal Deploymentmentioning

confidence: 99%

Utilisation of multi‐objective immune deployment algorithm for coverage area maximisation with limit mobility in wireless sensors networks

Abo‐Zahhad

Ahmed

Sabor

et al. 2015

IET wirel. sens. syst.

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“…Sensor Deployments Based on Square 1) Four sensors at four vertexes of the square(its edge length is equal to r) [2] have proved that the sensor deployment illustrated in Fig. 9 [ 2 ] of the large gray field in Fig. 10 is S = S x 4 = 2;r -3Jj rZ Z 1 6 (9) (10) Fig.…”

Section: Sensor Deploymentsmentioning

confidence: 99%

“…The coverage problem is also one of basic problem in wireless sensor networks. References [1], [2] give the analysis formula of the minimum number of nodes, the maximum efficient coverage area and its ratio, the maximum of net efficient coverage area of nodes and the maximum of net efficient coverage area ratio of nodes under the condition of seamless coverage. By analyzing both the deployments and considering different coverage redundancy requirement for different applications, the documents [3], [4], [5] proposes Virtual Rhomb Grid-based Movement-assisted Sensor Deployment algorithm (VRGMSD), k-variable Movement-assisted Sensor Deployment based on Virtual Rhomb Grid (kMSDVRG) and the s-Redundant Movement-assisted Sensor Deployment based on Virtual Rhomb Grid (sMSDVRG).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of several sensor deployments in wireless sensor networks

Wang

Zhang

2010

2010 International Conference on E-Health Networking Digital Ecosystems and Technologies (EDT)

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In wireless sensor networks , the number of sensor nodes has direct relation to the cost of total wireless sensor networks, and at the same time, the problem is closely connected to wireless sensor networks'performance, such as robust, fauIt-tolerance, and furthermore, it is considered at first as wireless sensor networks are designed. Therefore, the research on the number of sensor nodes has significant meanings of theory and practice to design of wireless sensor networks. By computation and analysis, the sensor deployments in the form of equilateral triangle, as a rule, are better than those in the form of square, and the efficient coverage area ratios decrease with increasing number of sensor nodes.

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“…Typical determin schemes model the sensing/target area as grid and place sensors on the vertices of regular triangular [1], hexagon, and square patterns [2] In order to achieve desired coverage and co same time, some evolving patterns (e.g., Di improved placement schemes are proposed ratios ⁄ of sensor communication radius over sensing radius (denoted by ) [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

GRLD: A Seamless Growth Rings like Deployment of Sensors Avoiding Boundary Effects in WSNs

Liao

Wang

Xie

et al. 2010

2010 IEEE Wireless Communication and Networking Conference

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Common deployment schemes in networks (WSNs) are built upon the assu boundary effects can be ignored in the sensing f in additional repair work to detect bounda coverage holes along them. This repair not uniformity of the deployed network, but also cau and material costs in practice. According characteristics of a target area, we propos deployment scheme GRLD (Growth Rings L which can achieve coverage as well as con boundary effects. We also prove the full coverage of this scheme and compare its efficiency wi regular deployment patterns, such as the squ hexagon grid. Our work is the first to apply bio to the study and design of deployment schemes avoids boundary effects efficiently and adaptability.

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Research on Optimal Coverage Problem of Wireless Sensor Networks

Cited by 11 publications

References 14 publications

Utilisation of multi‐objective immune deployment algorithm for coverage area maximisation with limit mobility in wireless sensors networks

Utilisation of multi‐objective immune deployment algorithm for coverage area maximisation with limit mobility in wireless sensors networks

Comparison of several sensor deployments in wireless sensor networks

GRLD: A Seamless Growth Rings like Deployment of Sensors Avoiding Boundary Effects in WSNs

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