Using Humans as Sensors: An Estimation-theoretic Perspective
Author(s)
Wang, Dong
Amin, Md Tanvir Al
Li, Shen
Kaplan, Lance
Gu, Siyu
Pan, Chenji
Liu, Hengchang
Aggarwal, Charu
Ganti, Raghu K.
Wang, Xinlei
Mohapatra, Prasant
Szymanski,Boleslaw
Le, Hieu
Abdelzaher, Tarek F.
Issue Date
2014
Keyword(s)
humans as sensors, social sensing, data reliability, uncertain data provenance, maximum likelihood estimation, expectation maximization
Abstract
"The explosive growth in social network content suggests that the largest ""sensor network"" yet might be human. Extending the participatory sensing model, this paper explores the prospect of utilizing social networks as sensor networks, which gives rise to an interesting reliable sensing problem. In this problem, individuals are represented by sensors (data sources) who occasionally make observations about the physical world. These observations may be true or false, and hence are viewed as binary claims. The reliable sensing problem is to determine the correctness of reported observations. From a networked sensing standpoint, what makes this sensing problem formulation different is that, in the case of human participants, not only is the reliability of sources usually unknown but also the original data provenance may be uncertain. Individuals may report observations made by others as their own. The contribution of this paper lies in developing a model that considers the impact of such information sharing on the analytical foundations of reliable sensing, and embed it into a tool called Apollo that uses Twitter as a ""sensor network"" for observing events in the physical world. Evaluation, using Twitter-based case-studies, shows good correspondence between observations deemed correct by Apollo and ground truth."
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