Modeling and Optimizing RFID Network Planning by using Genetic Algorithms as a Computational Intelligent Technique

The Radio Frequency Identification (RFID) is a kind of technology, which utilizes radio frequency waves to examine and read transporters or tags. RFID wireless network planning is an emerging automatic device, which has gained increasing popularity in last decades. It is one of the advanced devices, which have many applications in various branches like fraud and counterfeit prevention, military, supply chain and asset management. In a range of applications, the use of RFID systems has led to RFID network planning (RNP) problem. This problem must be resolved if RFID systems are to be used optimally in a large scale. It is worth mentioning that RNP problem is an arguing issue to resolve. Generally speaking, RNP attempts to optimize certain applications such as load balance, economic efficiency and interference between readers by regulating the control variables of the system such as reader coordinates, reader numbers, aerial parameters and coverage of system, all at the same time. The positions of these readers and tags cannot be designed or preplanned because the position of readers and tags can be changed in various areas due to the tags, which are randomly deployed in the area. This investigation, studies the modeling and optimizing RFID network planning by using Genetic Algorithms (GA) as a computational intelligent technique, in order to achieve optimal solution for the best number of readers based on maximum coverage of tags by deploying minimum number of readers in the network. The findings showed that the result of GA’s is more beneficial than the other methods that mentioned in the references. Keywords: Radio Frequency Identification (RFID), RFID Network Planning (RNP), Optimization, Genetic Algorithms, Wireless Network Planning.