Coverage Optimization Algorithm of Wireless Sensor Network

Xue-zheng, Han; Ли, Шуай; Pang, Xiaowu

doi:10.1007/978-3-642-29387-0_6

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…In this paper, the coverage of wireless sensor networks is defined as the ratio of the area of pixels covered by all nodes to the total monitored area [21], that is:…”

Section: Coverage Modelmentioning

confidence: 99%

Wireless sensor network coverage optimization based on whale group algorithm

Wang

et al. 2018

ComSIS

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For all of types of applications in wireless sensor networks (WSNs), coverage is a fundamental and hot topic research issue. To monitor the interest field and obtain the valid data, the paper proposes a wireless sensor network coverage optimization model based on improved whale algorithm. The mathematic model of node coverage in wireless sensor networks is developed to achieve full coverage for the interest area. For the model, the idea of reverse learning is introduced into the original whale swarm optimization algorithm to optimize the initial distribution of the population. This method enhances the node search capability and speeds up the global search. The experiment shows that this algorithm can effectively improve the coverage of nodes in wireless sensor networks and optimize the network performance.

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“…In this paper, the coverage of wireless sensor networks is defined as the ratio of the area of pixels covered by all nodes to the total monitored area [21], that is:…”

Section: Coverage Modelmentioning

confidence: 99%

Wireless sensor network coverage optimization based on whale group algorithm

Wang

et al. 2018

ComSIS

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“…Hybrid WSNs consisting of static and mobile sensors are more robust and fault tolerant to the changing environment conditions than conventional WSNs which only contain static resource constrained sensors [2,3]. By incorporating mobile sensors to WSNs, the network capability can be further improved in many aspects, such as automatic sensor deployment [2][3][4][5][6][7][8][9][10][11][12], dynamic event coverage [13,14], mobiles target tracking [15][16][17], flexible topology adjustment [18,19], and efficient data collection and processing [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…Observing that mobile sensors can physically react to the events of the environment, support self-configuration mechanisms, and guarantee adaptability and optimal performance, they are utilized to improve the coverage performance if coverage holes are generated at the initial deployment or in the running course due to the failed sensors. Considering the previously mentioned, how to schedule spare mobile sensors to obtain an optimal coverage performance has gained considerable attention, see: for example, [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] and references therein.…”

Section: Introductionmentioning

confidence: 99%

“…One is to scatter all sensors in the area of interest. For example, [5,6,11,12] discuss how scattered sensors interact to achieve the optimal coverage through rules of the virtual force algorithm (VFA). In [7], Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) algorithms are adopted to solve Minimum Weight Sensor Cover Problem (MWSCP).…”

Section: Introductionmentioning

confidence: 99%

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Optimal Mobile Sensor Scheduling for a Guaranteed Coverage Ratio in Hybrid Wireless Sensor Networks

You

2013

International Journal of Distributed Sensor Networks

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This paper proposes an optimal mobile sensor-scheduling algorithm for recovering the failure sensors in hybrid wireless sensor networks (WSNs). To maintain a guaranteed coverage over the area of interest, spare mobile sensors in WSNs will be activated to replace the failure sensors. The optimal scheduling problem is formulated into two optimization problems, one of which precisely determines the minimum value of the largest distance required to travel for mobile sensors, while the other one gives the optimal dispatch for mobile sensors to minimize the total travel distance. Furthermore, a distributed suboptimal scheduling, which only requires the local matching information of mobile sensors, is developed as well. Both regular and random network topologies are provided to illustrate the proposed algorithms in the simulation.

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“…Coverage is an important metric of the quality of service (QoS) in WSNs [4,5] and is a fundamental issue since each sensor is energy constrained. The reason why some areas are not covered is due to random deployment of sensors, node energy depletion, or environmental damages.…”

Section: Introductionmentioning

confidence: 99%

A Vector Algebraic Algorithm for Coverage Compensation in Hybrid Wireless Sensor Networks

Qin

Guo

Ding

et al. 2013

International Journal of Distributed Sensor Networks

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In wireless sensor networks, coverage holes are caused by energy depletion at some nodes, and the aim of this paper is to study how to utilize the redundant nodes with remaining energy. Particularly, this paper proposes a vector algebra based algorithm by exploring redundant nodes as an extra dimension for coverage compensation. This algorithm consists of two parts. One is to find the locations of potential redundant nodes for coverage compensation; and the other is to opportunistically select the best redundant nodes by jointly considering the hole boundaries and the remaining energy of nodes. Simulation results are provided to demonstrate that the proposed algorithm minimizes the energy consumption when repairing the holes for full coverage. Furthermore, compared with other algorithms, the proposed one exhibits better performance in terms of moving distance and energy consumption.

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Coverage Optimization Algorithm of Wireless Sensor Network

Cited by 6 publications

References 5 publications

Wireless sensor network coverage optimization based on whale group algorithm

Wireless sensor network coverage optimization based on whale group algorithm

Optimal Mobile Sensor Scheduling for a Guaranteed Coverage Ratio in Hybrid Wireless Sensor Networks

A Vector Algebraic Algorithm for Coverage Compensation in Hybrid Wireless Sensor Networks

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