Deployment and optimization of wireless network node deployment and optimization in smart cities

摘要

Followed by digital cities and smart cities, another advanced form of information city has emerged, namely smart cities. Such kind of city is integrated with informationization, industrialization and urbanization. Smart cities belong to the fusion of multiple information technologies such as the Internet of Things technology and cloud computing technology. Smart city is the use of various sensors and wireless networks, communication technologies to achieve information interaction. The use of cloud computing and big data effectively integrate information is to make comprehensive decisions on various data to achieve comprehensive coordination of city operation management and industrial development. In the wireless city infrastructure of smart cities, the deployment of network nodes directly affects the quality of network services. The problem can be attributed to the deployment of appropriate ordinary AP nodes as access nodes of wireless terminals on a given geometric plane. The deployment of special nodes as gateways will aggregate the traffic of ordinary nodes into the wired network Taking the wireless mesh network as an example, it is proposed to determine the deployment location and number of AP nodes based on the statistics of regional human traffic, and the gateway node deployment problem is seen as a geometric K-center problem. Taking the minimum path length between the node and the gateway as the optimization goal, an adaptive particle swarm optimization (APSO) algorithm is proposed to solve the gateway node deployment position. In the APSO algorithm, improved strategies such as random adjustment of inertia weights, adaptive change of learning factors, and neighborhood search are introduced. A new calculation method of the fitness function is designed to make the algorithm easier to obtain the optimal solution. Simulation results show that, compared with GA algorithm and K-means algorithm, the improved particle swarm algorithm has a stable solution effect, strong robustness, and can obtain a smaller coverage radius, thereby improving the network service quality.