Supporting the Internet of Things and Smart City applications is one of the most important goals in the ongoing design process of 5G cellular systems. Another trend is an increasing focus on data-driven optimization and Self-Organized Networking, in order to automate network deployments and increase performance and efficiency. This approach, however, does not fully take advantage of the data generated by the Smart City. In this work, we propose to process and use the information flowing through the network from the city sensors to increase the awareness of the network itself, improving the communication performance. We exploit vehicular traffic data from the Traffic for London (TfL) sensor network to infer mobility patterns and improve the efficiency of LTE handovers
So far, research on Smart Cities and self-organizing networking techniques for 5G cellular systems has been one-sided: a Smart City relies on 5G to support massive M2M communications, but the actual network is unaware of the information flowing through it. However, a greater synergy between the two would make the relationship mutual, since the insights provided by the massive amount of data gathered by sensors can be exploited to improve the communication performance. In this work, we concentrate on self-organization techniques to improve handover efficiency using vehicular traffic data gathered in London. Our algorithms exploit mobility patterns between cell coverage areas and road traffic congestion levels to optimize the handover bias in HetNets and dynamically manage mme loads to reduce handover completion tim
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