A discrete multi-swarm optimizer for radio frequency identification network scheduling

Due to the effectiveness,simple deployment and low cost,radio frequency identification(RFID) systems are used in a variety of applications to uniquely identify physical objects.The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another's operation.Such reader collision must be minimized to avoid the faulty or miss reads.Specifically,scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment.Therefore,the aim of this work is to use a successful multi-swarm cooperative optimizer called PS2O to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks.The main idea of PS2O is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations.As the RFID network scheduling model formulated in this work is a discrete problem,a binary version of PS2O algorithm is proposed.With seven discrete benchmark functions,PS2O is proved to have significantly better performance than the original PSO and a binary genetic algorithm.PS2O is then used for solving the real-world RFID network scheduling problem.Numerical results for four test cases with different scales,ranging from 30 to 200 readers,demonstrate the performance of the proposed methodology.

Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another＇s operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. Therefore, the aim of this work is to use a successful multi-swarm cooperative optimizer called pseo to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of pS20 is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS20 algorithm is proposed. With seven discrete benchmark functions, PS20 is proved to have significantly better performance than the original PSO and a binary genetic algorithm, pS20 is then used for solving the real-world RFID network scheduling problem. Numerical results for four test cases with different scales, ranging from 30 to 200 readers, demonstrate the performance of the proposed methodology.