When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective

Chen, Pin-Yu, Cheng, Shin-Ming, Ting, Pai-Shun, Lien, Chia-Wei, Chu, Fu-Jen

arXiv.org Machine Learning 

Wireless sensor network (WSN) explores the avenues to collect and use information from the physcial world by deploying low-cost tiny sensor nodes on the ground, in the air, under water, on bodies, in vehicles, and inside buildings. With sensing, processing, and communication capabilities, networked sensor nodes cooperatively collect information on entities of interest and WSNs have emerged as a promising technology with numerous and various applications. As shown in Figure 1, sensor nodes locally collect information and then forward the sensed result over a wireless medium to a remote static sink, where it is fused and analyzed in order to determine the global status of the sensed area. In order to successfully gather sufficient information, a static sink could send a mobile agent to collect data from individual sensor nodes by following a trajectory spanning all the nodes (see Figure 1). To accomplish large-scale sensing, WSN evolves not only at the sink side (such as mobile agents), but also at the sensor node side. Mature mobile networks consisted of mobile devices with advanced processing and communication capabilities become a possible sensing infrastructure of WSN.

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