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LAPM: The Location Aware Prediction Model in Human Sensing Systems

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  • Ruiyun Yu
  • Pengfei Wang
  • Shiyang Liao

Abstract

The mobile human social network actually might be the hugest and best “sensor network†because of the explosive growth in social network content. Nowadays, more and more mobile social applications offer a much easier way for people to share their feeling including vision, haptic, hearing, and smell with the location information by words, images, or even videos. These new sharing methods appearing in the mobile social network actually give us a precious chance to sense the world. Extra systems, which are specialized in particular sensing, do not need to be created any more. The specific sensing data can be acquired from the social network by handling the heterogeneous data. The contribution of this paper lies in developing a model that collects samples considering the relevancy from the perspective of location from different mobile social networks and estimating the occurrence likelihood of the perceived event with collected samples. The simulations and real-world case studies are also developed to verify the reliability of the model and the effectiveness of the Location Aware EM algorithm.

Suggested Citation

  • Ruiyun Yu & Pengfei Wang & Shiyang Liao, 2015. "LAPM: The Location Aware Prediction Model in Human Sensing Systems," International Journal of Distributed Sensor Networks, , vol. 11(10), pages 814174-8141, October.
    • Handle: RePEc:sae:intdis:v:11:y:2015:i:10:p:814174
    DOI: 10.1155/2015/814174
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