Using clustering technique M-PAM in mobile network planning
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
RFID networks planning using a multi-swarm optimizer
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
An RFID network design methodology for asset tracking in healthcare
Decision Support Systems
The first search right algorithm for redundant reader elimination in RFID network
SEPADS'10 Proceedings of the 9th WSEAS international conference on Software engineering, parallel and distributed systems
TDMA grouping based RFID network planning using hybrid differential evolution algorithm
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
Research on Locating and Tracking Automotive Products in Workshop Based on Active RFID Technology
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
RFID network planning using a multi-swarm optimizer
Journal of Network and Computer Applications
Optimal planning of sensor networks for asset tracking in hospital environments
Decision Support Systems
A fuzzy k-coverage approach for RFID network planning using plant growth simulation algorithm
Journal of Network and Computer Applications
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The problem of choosing the optimum locations for readers (antennas) in a RFID communications system is considered. All these choices must satisfy a set of imperative constraints and optimize a set of objectives. The factors affecting optimum selection are the complex propagation environments, the undesired mutual coverage and the unavoidable interference of multiple readers. Unlike the antenna positioning in traditional cellular networks, uplink signals, i.e. signals from tag towards reader, must be taken into account when dealing with the planning in the RFID networks. This paper presents a genetic approach for tackling this complex optimization problem. To validate this approach, computational results are presented for a typical test scenario.