Name
Abstract | ||
|---|---|---|
The Internet of Things (IoT) opens up tremendous opportunities to location-based industrial applications that leverage both Internet-resident resources and phones' processing power and sensors to provide location information. Location-based service is one of the vital applications in commercial, economic, and public domains. In this paper, we propose a novel localization scheme called NextMe, which is based on cellular phone traces. We find that the mobile call patterns are strongly correlated with the co-locate patterns. We 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, call pattern recognition, and a hybrid predictor. To design the call pattern recognition module, we introduce the notions of critical calls and corresponding patterns. In addition, NextMe does not require that the cell tower addresses should be bounded with concrete coordinates, e.g., global positioning system (GPS) coordinates. We validate NextMe across MIT Reality Mining Dataset, involving 500 000 h of continuous behavior information and 112 508 cellular calls. Experimental results show that NextMe achieves fine-grained prediction accuracy at cell tower level in the forthcoming 1-6 h with 12% accuracy enhancement averagely from cellular calls. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TII.2015.2389656 | IEEE Trans. Industrial Informatics |
Keywords | Field | DocType |
localization,data preprocessing,mobile calls,cellular radio,localization scheme,temporal perspectives,iot,mobility management (mobile radio),cellular calls,location prediction,location-based industrial applications,cell towers,internet of things (iot),mobile call patterns,nextme,social interplay,cellular phone traces,internet-resident resources,cellular traces localization,co-locate patterns,hybrid predictor,spatial perspectives,location-based service,call pattern recognition module,internet-phones,mobile computing,mit reality mining dataset,internet of things,fine-grained prediction accuracy,mobile communication,location based service,pattern recognition,internet of things iot,computer architecture | Computer science,Internet of Things,Data pre-processing,Real-time computing,Phone,Global Positioning System,Location prediction,Reality mining,Mobile telephony,Bounded function | Journal |
Volume | Issue | ISSN |
11 | 2 | 1551-3203 |
Citations | PageRank | References |
16 | 0.67 | 23 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daqiang Zhang | 1 | 803 | 42.58 |
| Shengjie Zhao | 2 | 72 | 16.24 |
| Laurence T. Yang | 3 | 6870 | 682.61 |
| M. Chen | 4 | 205 | 22.23 |
| Yunsheng Wang | 5 | 204 | 12.54 |
| Huazhong Liu | 6 | 51 | 4.54 |