TY - GEN
T1 - Using humans as sensors
T2 - 13th IEEE/ACM International Conference on Information Processing in Sensor Networks, IPSN 2014
AU - Wang, Dong
AU - Amin, Md Tanvir
AU - Li, Shen
AU - Abdelzaher, Tarek
AU - Kaplan, Lance
AU - Gu, Siyu
AU - Pan, Chenji
AU - Liu, Hengchang
AU - Aggarwal, Charu C.
AU - Ganti, Raghu
AU - Wang, Xinlei
AU - Mohapatra, Prasant
AU - Szymanski, Boleslaw
AU - Le, Hieu
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - data reliability
KW - expectation maximization
KW - humans as sensors
KW - maximum likelihood estimation
KW - social sensing
KW - uncertain data provenance
UR - http://www.scopus.com/inward/record.url?scp=84904631675&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904631675&partnerID=8YFLogxK
U2 - 10.1109/IPSN.2014.6846739
DO - 10.1109/IPSN.2014.6846739
M3 - Conference contribution
AN - SCOPUS:84904631675
SN - 9781479931460
T3 - IPSN 2014 - Proceedings of the 13th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)
SP - 35
EP - 46
BT - IPSN 2014 - Proceedings of the 13th International Symposium on Information Processing in Sensor Networks (Part of CPS Week)
PB - IEEE Computer Society
Y2 - 15 April 2014 through 17 April 2014
ER -