LAPM: The Location Aware Prediction Model in Human Sensing Systems

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...

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Main Authors: Ruiyun Yu, Pengfei Wang, Shiyang Liao
Format: Article
Language:English
Published: Hindawi - SAGE Publishing 2015-10-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2015/814174
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author Ruiyun Yu
Pengfei Wang
Shiyang Liao
author_facet Ruiyun Yu
Pengfei Wang
Shiyang Liao
author_sort Ruiyun Yu
collection DOAJ
description 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.
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spelling doaj.art-f0166a77744a442d96b863792ec146672023-09-02T07:59:08ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772015-10-011110.1155/2015/814174814174LAPM: The Location Aware Prediction Model in Human Sensing SystemsRuiyun YuPengfei WangShiyang LiaoThe 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.https://doi.org/10.1155/2015/814174
spellingShingle Ruiyun Yu
Pengfei Wang
Shiyang Liao
LAPM: The Location Aware Prediction Model in Human Sensing Systems
International Journal of Distributed Sensor Networks
title LAPM: The Location Aware Prediction Model in Human Sensing Systems
title_full LAPM: The Location Aware Prediction Model in Human Sensing Systems
title_fullStr LAPM: The Location Aware Prediction Model in Human Sensing Systems
title_full_unstemmed LAPM: The Location Aware Prediction Model in Human Sensing Systems
title_short LAPM: The Location Aware Prediction Model in Human Sensing Systems
title_sort lapm the location aware prediction model in human sensing systems
url https://doi.org/10.1155/2015/814174
work_keys_str_mv AT ruiyunyu lapmthelocationawarepredictionmodelinhumansensingsystems
AT pengfeiwang lapmthelocationawarepredictionmodelinhumansensingsystems
AT shiyangliao lapmthelocationawarepredictionmodelinhumansensingsystems