NextMe: Localization Using Cellular Traces in Internet of Things


The Internet of Things (IoT) unveil tremendous opportunities to location-based industrial applications that leverage both Internet-resident resources and phones' processing power and sensors to produce location info. Location-based service is one in all the very important applications in commercial, economic, and public domains. During this paper, we propose a unique localization theme known as NextMe, that is predicated on mobile phone traces. We realize that the mobile call patterns are strongly correlated with the co-locate patterns. We tend to extract such correlation as social interplay from cellular calls, and use it for location prediction from temporal and spatial perspectives. NextMe consists of data preprocessing, decision pattern recognition, and a hybrid predictor. To design the call pattern recognition module, we introduce the notions of crucial calls and corresponding patterns. In addition, NextMe does not need that the cell tower addresses should be bounded with concrete coordinates, e.g., global positioning system (GPS) coordinates. We have a tendency to validate NextMe across MIT Reality Mining Dataset, involving five hundred 000 h of continuous behavior data and 112 508 cellular calls. Experimental results show that NextMe achieves fine-grained prediction accuracy at cell tower level within the forthcoming one-6 h with twelvepercent accuracy enhancement averagely from cellular calls.

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