Multi objective optimization of Indoor UHF RFID Network Based on Gradient - Cuckoo search

Abstract

Network design, in general is a critical concept due to its effect on efficiency, cost and other significant factors. In recent years, RFID is widely applied for RNP Network design. The large -area network design process requires a significant number of interrogating antennas based on the reader-tag range communication. RFID technology uses a huge number of tags communicate with a small number of the reader, from thus point of view, the challenges of large scale RNP problems include high computational cost due to the time consumption of RFID readers placement error, as a result, these challenges reduce the effectiveness of the RFID system In this study, a model of multi-objective function for RFID reader placement was conducted on various large -area condition to evaluate the impact of network design expansion. A comparative analysis was performed with (GBCS algorithm) Gradient-Based Cuckoo Search. The dataset was performed for the area of 80m x 80m and 150m x 150m. Simulation results exhibited that the performance of (GBCS) Gradient-Based Cuckoo presented a minimum number of deployed readers with maximum RFID tags coverage and was superior in solving large scale RFID-NP problems. Simulation results not only explained that the present algorithm is strong and workable but also showed excellent approximation abilities even in the high scale-area. Consequently, the authors recommend if the area bigger than (80m2), there is a need to divide the area into separate regions and deal with each region separately to be sure that the objective function can work efficiently.