An Integrated Model of Placement Optimization and Redundancy Elimination in RFID Network Planning

Abstract

Radio frequency identification network planning (RNP) is the issue of placing readers in a work area so that the readers cover most of the tags, while satisfying some constraints such as the minimum number of readers used, the minimum interference, the minimum energy consumption, etc. RNP is assessed as an NP-hard problem and natural evolution-based approaches are often used to solve them. The paper proposes an integrated model of placement optimization and redundancy elimination, in which the optimal position of readers is found by a genetic-based method and the elimination of redundant readers is accomplished by a filtering policy. Gridding the work area is also analyzed, in which the finer the cell size is, the more efficient the reader placement is. The simulation results show that the cell whose size equal to the radius of the reader's interrogation area gives the best efficiency in terms of coverage, amount of used readers and fast convergence, but it also suffers a little extra interference.