LANDMARC: indoor location sensing using active RFID
Wireless Networks - Special issue: Pervasive computing and communications
Genetic Approach for Network Planning in the RFID Systems
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Load balancing in large-scale RFID systems
Computer Networks: The International Journal of Computer and Telecommunications Networking
Distributed Deployment Schemes for Mobile Wireless Sensor Networks to Ensure Multilevel Coverage
IEEE Transactions on Parallel and Distributed Systems
Efficient tag detection in RFID systems
Journal of Parallel and Distributed Computing
On the energy consumption of Pure and Slotted Aloha based RFID anti-collision protocols
Computer Communications
Multiobjective Estimation of Distribution Algorithm Combined with PSO for RFID Network Optimization
ICMTMA '10 Proceedings of the 2010 International Conference on Measuring Technology and Mechatronics Automation - Volume 02
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
Optimal layout and deployment for RFID systems
Advanced Engineering Informatics
Flaws on RFID grouping-proofs. Guidelines for future sound protocols
Journal of Network and Computer Applications
RFID network planning using a multi-swarm optimizer
Journal of Network and Computer Applications
On the problem of k-coverage in mission-oriented mobile wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Optimization of network planning is important for the performance improvement of the radio frequency identification (RFID) networks before deployment. This paper proposes an efficient approach for RFID network planning based on k-coverage. A k-coverage model, which is formulated as a multi-dimensional optimization problem with constraint conditions, is developed to evaluate the network performance. The plant growth simulation algorithm (PGSA) is used to optimize the RFID networks by determining the optimal adjustable parameters in the model. In the proposed approach, the objective function and the constraints are separately processed and no external control parameter is required. The simulation results demonstrate the effectiveness of the proposed approach using PGSA in comparison to other algorithms.