Research On Urban Signal Station Deployment Method Based On Image Super Resolution

With the boosting of mobile devices,wireless sensor networks,and the internet of things,abundant multi-modal data,such as GPS signal,sensor data,are produced intentionally or unintentionally,which can represent the people's active patterns,vehicle's routes,and city's flows to develop a smart city.These multi-modal data are usually transmitted and received by signal stations deployed in the city.However,reasonably choosing the signal stations' locations is still an open issue for enhancing people's life quality in the smart city.Nowadays,the smartphone has become an indispensable part of people's daily life.The signal strength of a smartphone often greatly affects the user experience.Specifically,due to the limited load capacity of signal stations,the high traffic flow may cause the overload of the signal stations and affects the signal of the smartphone.Whereas,the region with fewer people may face the problem of signal station deployment waste.Hence,how to choose reasonable locations to deploy new signal stations is particularly important.We need a reliable method to deploy new signal stations to share the GPS data load of the existing signal stations.To this end,we propose the Super Resolution Deduction(SRD)model for solving the signal station selection problem.SRD first initializes the city map as a coarsegrained heat map representing the capacity of the signal stations.Then an image-based super-resolution deduction model is proposed to obtain a fine-grained signal station capacity for deploying.To be specific,we employ Dense Block to capture the spatiotemporal correlations,C-Attention to selectively enhance useful feature maps,and SDistribution to impose structural constraints.By sharing the GPS data load with the new deployment of signal stations,we ensure the smart city's efficiency and effectiveness.Extensive experimental results on real-world dataset Changchun City demonstrate that our proposed model achieves the superior performance among the state-of-the-art baselines.