Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Grid Coverage for Surveillance and Target Location in Distributed Sensor Networks
IEEE Transactions on Computers
Sensor deployment and target localization in distributed sensor networks
ACM Transactions on Embedded Computing Systems (TECS)
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
Keyframe-based video summarization using Delaunay clustering
International Journal on Digital Libraries
Mobility Limited Flip-Based Sensor Networks Deployment
IEEE Transactions on Parallel and Distributed Systems
Fuzzy-Based Movement-Assisted Sensor Deployment Method in Wireless Sensor Networks
CICSYN '09 Proceedings of the 2009 First International Conference on Computational Intelligence, Communication Systems and Networks
FSPNS: Fuzzy Sensor Placement Based on Neighbors State
UKSIM '10 Proceedings of the 2010 12th International Conference on Computer Modelling and Simulation
Scan-Based Movement-Assisted Sensor Deployment Methods in Wireless Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
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Adequate coverage is one of the main problems for Sensor Networks. The effectiveness of distributed wireless sensor networks highly depends on the sensor deployment scheme. Optimizing the sensor deployment provides sufficient sensor coverage and saves cost of sensors for locating in grid points. This article applies the modified binary particle swarm optimization algorithm for solving the sensor placement in distributed sensor networks. PSO is an inherent continuous algorithm, and the discrete PSO is proposed to be adapted to discrete binary space. In the distributed sensor networks, the sensor placement is an NP-complete problem for arbitrary sensor fields. One of the most important issues in the research fields, the proposed algorithms will solve this problem by considering two factors: the complete coverage and the minimum costs. The proposed method on sensors surrounding is examined in different area. The results not only confirm the successes of using the new method in sensor placement, also they show that the new method is more efficiently compared to other methods like Simulated AnnealingSA, PBIL and LAEDA.