Optimal Deployment Of Nodes In The Integration Of RFID And WSN

Along with the progress of The Times, people recognized the power of science and technology, in the 21st century, people's exploration of science has reached unprecedented levels. Sensing network is considered to be the most influential one of 10 technologies which will change the world in the 21st century. According to the different perception, Sensing network can be divided into the wireless sensor network (WSN) technology, radio frequency identification (RFID), Zigbee technology (Zigbee), Bluetooth technology (Bluetooth) and so on. And the integration of RFID and WSN will maximize their effectiveness, give new perspectives to a broad range of useful applications, and bridge the gap between the real and the research world. This is because the resulting integrated technology will have extended capabilities, scalability, and portability as well as reduced unnecessary costs. It is a development tendency of sensing network in future.In the application of integrated network, the first problem is to deploy the nodes. Nodes'deployment is a key technique to reduce the energy consumption. It can not only ensure the quality of integrated network's connectivity and data, but also reduce the energy consumption of the integrated network effectively. So it is significant how to design the reasonable deployment of nodes. In this background, this paper studies the optimal deployment of nodes in the integration of RFID and WSN.The integration of RFID and WSN is a network, which has large-scale, complex environment, limited resources, random deployment, self-organization and so on. According to the propagation law of electromagnetic wave in the free space, we establish the deployment model of integrating RFID readers with wireless sensor nodes.Based on genetic optimization strategy, this paper presents an improved genetic algorithm to find the best position of smart nodes. It must satisfy a set of imperative constraints such as the complex propagation environments, the undesired mutual coverage and the unavoidable interference between smart nodes. By choosing proper parameters, the improved algorithm speeds up the search process. It can not only accelerate the speed of finding optimal deployment of nodes, but can also converge to the optimal solution effectively and rapidly. What is more, it can have a good coverage rate and can minimize the interference.Based on the linearly decrease inertia weight, this paper presents an improved Particle Swarm Optimization approach for tackling this complex optimization problem. To validate this approach, computational results are presented for a typical test scenario. It can not only accelerate the speed of finding optimal nodes, but can also effectively and rapidly converge to the optimal solution. The simulation results show that the optimization control method of nodes deployment is effective in the integrated network system.