Coverage problems in sensor networks

Wang, Bang

doi:10.1145/1978802.1978811

Cited by 488 publications

(251 citation statements)

References 123 publications

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“…A target object is covered by a sensor if it is within the sensing area of the sensor. This can be represented by the Boolean disk coverage model [5]. The coverage function of the model is given by (1) In (1), is the Euclidean distance between the location of sensor and the object .It is implied that all objects within the circle are covered by this sensor.…”

Section: Assumptionsmentioning

confidence: 99%

“…Many studies for efficient sensor placement under varieties of situation and environment have been proposed to achieve maximum coverage in WSNs [5]- [7]. Wang [5] presents a deep survey of coverage problems in sensor networks.…”

Section: Related Workmentioning

confidence: 99%

“…Wang [5] presents a deep survey of coverage problems in sensor networks. The strategies for solving the coverage problem are categorized as the force-based, the grid-based, and the computational geometry based [8]; Force-based deployment strategies are based on the sensors' mobility, using virtual repulsive and attractive forces.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Covering the Target Objects with Mobile Sensors by Using Genetic Algorithm in Wireless Sensor Networks

Ta¹,

Huang²,

Bình³

2015

JCP

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Wireless sensor networks have several applications, such as target detection and tracking, and monitoring battlefields. Coverage is one of the most important performance metrics for wireless sensor networks since it reflects how appropriate an event can be detected and monitored in the sensing field. To achieve optimal coverage, an efficient algorithm should be employed to find the best positions of sensor node deployment. In this paper, an efficient genetic algorithm is proposed to solve the coverage problem of the target objects. The optimal number of sensor nodes starts from fewer randomly deployed nodes and increases gradually in subsequence generations. The performance of the proposed genetic algorithm was evaluated, and the simulation results show that this approach can cover all the target objects as well as minimize the number of additional mobile sensor nodes.

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Section: Assumptionsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Covering the Target Objects with Mobile Sensors by Using Genetic Algorithm in Wireless Sensor Networks

Ta¹,

Huang²,

Bình³

2015

JCP

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“…Coverage models determine first if a given target can be covered or not by some sensor, but can also measure a corresponding quality parameter [11]. This is ac-complished by calculating the geometric relation between sensors and targets.…”

Section: Coverage Modelmentioning

confidence: 99%

Optimizing Deployment Cost in Camera-Based Wireless Sensor Networks

Serik

Kaddour

2015

IFIP Advances in Information and Communication Technology

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Abstract. We discuss in this paper a deployment optimization problem in camera-based wireless sensor networks. In particular, we propose a mathematical model to solve the problem of minimizing the number of cameras required to cover a set of targets with a given level of quality. Since solving this kind of problems with exact methods is computationally expensive, we rather rely on an adapted version of Binary Particle Swarm Optimization (BPSO). Our preliminary results are motivating since we obtain near-optimal solutions in few iterations of the algorithm. We discuss also the relevance of hybrid meta-heuristics and parallel algorithms in this context. Keywords:Camera-based wireless sensor networks, minimum cost deployment, coverage quality, binary particle swarm optimization.

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“…When it comes to surveillance of large areas, such as parking lots, public areas, and large stores, the complete coverage of the area at all times is required [2] . Therefore, seamless coverage of a vast area is an important issue in these applications.…”

Section: Introductionmentioning

confidence: 99%

Coverage-enhancing Algorithm Based on Priority in WMSN

Xiong¹,

Nie²,

Zhang³

et al. 2016

Proceedings of the 2016 International Conference on Computer Engineering and Information Systems

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Abstract-Coverage area and network lifetime are major problems in wireless multimedia sensor networks (WMSN). In order to optimize the network coverage and maximize the network's lifetime, we propose a coverage-enhancing algorithm based on sensors' priority. To increase the lifetime of the network, the density of distributed sensors could be such that a subset of sensors can cover the required air space. The sensors are prioritized based on their visual and Communicative properties and the selection is performed according to the prioritizing function. Simulation results show that the proposed algorithm achieves more effective enhancement on network coverage compared to the existing algorithm.

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Coverage problems in sensor networks

Cited by 488 publications

References 123 publications

Covering the Target Objects with Mobile Sensors by Using Genetic Algorithm in Wireless Sensor Networks

Covering the Target Objects with Mobile Sensors by Using Genetic Algorithm in Wireless Sensor Networks

Optimizing Deployment Cost in Camera-Based Wireless Sensor Networks

Coverage-enhancing Algorithm Based on Priority in WMSN

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