An efficient algorithm for large-scale RFID Network Planning

Radio frequency identification RFID is widely used technology for RNP Network Planning. The large scale network planning design process requires a significant number of interrogating antennas based on the reader-tag range communication. The challenges of large scale RNP problems considered high computational cost due to the time consuming of RFID readers placement error. It results less than optimal tag coverage. In this paper, a model of multi-objective function for RFID reader placement were conducted on different large scale condition to evaluate the effect of network planning expansion. A comparative analysis was performed with three developed algorithms based on the same dataset. The dataset were performed for the area of $80\mathrm{m} \times 80\mathrm{m}$ and $150\mathrm{m} \times 150\mathrm{m}.$ Simulation results exhibited that the performance of Gradient-Based Cuckoo Search (GBCS) presented optimal tags coverage with minimum number of deployed readers and was superior in solving large scale RNP problems. Simulation results not only demonstrated that the present algorithm is robust and workable but also showed superior approximation capabilities even in high dimensional space.