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Abstrak :
This book provides a comprehensive treatment of the theoretical foundation and algorithmic tools necessary in the design of efficient tag counting and monitoring algorithms in emerging RFID systems. The book delivers an in-depth analysis on the following problems ranging from theoretical modeling and analysis, to practical algorithm design and optimization: Stability analysis of the frame slotted Aloha protocol, the de facto standard in RFID tag counting and identification; Tag population estimation in dynamic RFID systems; Missing tag event detection in the presence of unexpected tags; Missing tag event detection in multi-group multi-region RFID systems. The target readers are researchers and advanced-level engineering students interested in acquiring in-depth knowledge on the topic and on RFID systems and their applications.

Abstrak :
This book is to presents and evaluates a way of modelling and optimizing nonlinear RFID Network Planning (RNP) problems using artificial intelligence techniques. It uses Artificial Neural Network models (ANN) to bind together the computational artificial intelligence algorithm with knowledge representation an efficient artificial intelligence paradigm to model and optimize RFID networks. This effort leads to proposing a novel artificial intelligence algorithm which has been named hybrid artificial intelligence optimization technique to perform optimization of RNP as a hard learning problem. This hybrid optimization technique consists of two different optimization phases. First phase is optimizing RNP by Redundant Antenna Elimination (RAE) algorithm and the second phase which completes RNP optimization process is Ring Probabilistic Logic Neural Networks (RPLNN). The hybrid paradigm is explored using a flexible manufacturing system (FMS) and the results are compared with well-known evolutionary optimization technique namely Genetic Algorithm (GA) to demonstrate the feasibility of the proposed architecture successfully.