An Optimal Deployment of Readers for RFID Network Planning Using NSGA-II

Radio frequency identification (RFID) is an automated data collection technology with the aim to facilitate data acquisition and storage without human intervention. RFID process depends on radio-frequency waves to transfer data between a reader and an electronic tag attached to an item, in order to identify objects or persons, which allows an automated traceability. The deployment of RFID readers is an important component in RFID system, and plays a key role in RFID Network Planning (RNP). Therefore, in order to optimize the deployment of RFID reader problem, we propose a new approach based on multi-level strategy using as main objectives the coverage, the number of deployed readers and the interference. In this way, Non-dominated Sorting Genetic algorithm II (NSGA-II) is adopted in order to minimize the total quantity of readers required to identify all tags in a given area. The proposed multi-level approach based on NSGA-II algorithm has a several attractive features which makes it ideal for our research and the simulation results show its effectiveness and performance.

[1]  Chien-Chang Hsu,et al.  The design and implementation of an intelligent deployment system for RFID readers , 2011, Expert Syst. Appl..

[2]  Ming Liu,et al.  A genetic algorithm for two-stage no-wait hybrid flow shop scheduling problem , 2013, Comput. Oper. Res..

[3]  Jose M. Arroyo,et al.  Application of a genetic algorithm to n-K power system security assessment , 2013 .

[4]  Yunlong Zhu,et al.  Cooperative artificial bee colony algorithm for multi-objective RFID network planning , 2014, J. Netw. Comput. Appl..

[5]  Ben Niu,et al.  Multiobjective RFID Network Optimization Using Multiobjective Evolutionary and Swarm Intelligence Approaches , 2014 .

[6]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[7]  A. P. McCabe.  Constrained optimization of the shape of a wave energy collector by genetic algorithm , 2013 .

[8]  Yunlong Zhu,et al.  RFID networks planning using a multi-swarm optimizer , 2009, 2009 Chinese Control and Decision Conference.

[9]  Meie Shen,et al.  Optimizing RFID Network Planning by Using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination , 2012, IEEE Transactions on Industrial Informatics.

[10]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[11]  Ernö Pretsch,et al.  Application of genetic algorithms in molecular modeling , 1994, J. Comput. Chem..

[12]  Hakima Chaouchi,et al.  RFID network topology design based on Genetic Algorithms , 2011, 2011 IEEE International Conference on RFID-Technologies and Applications.

[13]  Yunlong Zhu,et al.  Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning , 2010, Appl. Soft Comput..

[14]  Shunzheng Yu,et al.  A fuzzy k-coverage approach for RFID network planning using plant growth simulation algorithm , 2014, J. Netw. Comput. Appl..

[15]  Yunlong Zhu,et al.  Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization , 2014, TheScientificWorldJournal.