An Optimal Model for Allocation Readers with Grid Cell Size and Arbitrary Workspace Shapes in RFID Network Planning

Abstract

RFID Network Planning (RNP) is the problem of deploying RFID readers within a workspace so that each tag can be covered by at least one reader. The objective of RNP is to determine the optimal positions of readers while satisfying certain constraints, such as maximum coverage, minimal interference, load balance among readers, etc. However, most previous studies considered the workspace rectangular or square and assumed a fixed number of readers. They then employed some heuristic methods to find the optimal reader positions. This approach is not practical because the workspace can have any shape, and an approach adaptable to the actual shape of the workspace is needed. This paper proposed an improved adaptive model considering the workspace shape, called RNP-3P. The objectives of RNP-3P are to minimize the number of readers, maximize coverage area, minimize interference, and achieve load balance. RNP-3P optimizes the problem in three phases: Phase 1 involves modeling the workspace with grid cell size, Phase 2 determines the objective function, and Phase 3 proposes the iGAPO algorithm to optimize the number and positions of readers within the workspace. Simulation results demonstrate that the proposed model is more effective compared to other heuristic methods.