TY - GEN
T1 - A Comparison of Axiomatic Distance-Based Collective Intelligence Methods for Wireless Sensor Network State Estimation in the Presence of Information Injection
AU - Kyle Skolfield, J.
AU - Yasmin, Romena
AU - Escobedo, Adolfo R.
AU - Huie, Lauren M.
N1 - Funding Information:
The authors gratefully acknowledge funding support from the National Science Foundation (Award 1850355) and the Army Research Office (Award 74113NSII). This research was supported in part by the Air Force Research Laboratory Information Directorate (AFRL/RI) through the Information Directorates Information Institute©R under contract number FA8750-16-3-6003.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Wireless sensor networks are a cost-effective means of data collection, especially in areas which may not have significant infrastructure. There are significant challenges associated with the reliability of measurements, in particular due to their distributed nature. As such, it is important to develop methods that can extract reliable state estimation results in the presence of errors. This work proposes and compares methods based on collective intelligence ideas, namely consensus ranking and rating models, which are founded on axiomatic distances and intuitive social choice properties. The efficacy of these methods to assess a transmitted signal's strength with varying quantity and quality of incompleteness in the network's readings is tested.
AB - Wireless sensor networks are a cost-effective means of data collection, especially in areas which may not have significant infrastructure. There are significant challenges associated with the reliability of measurements, in particular due to their distributed nature. As such, it is important to develop methods that can extract reliable state estimation results in the presence of errors. This work proposes and compares methods based on collective intelligence ideas, namely consensus ranking and rating models, which are founded on axiomatic distances and intuitive social choice properties. The efficacy of these methods to assess a transmitted signal's strength with varying quantity and quality of incompleteness in the network's readings is tested.
UR - http://www.scopus.com/inward/record.url?scp=85095606822&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85095606822&partnerID=8YFLogxK
U2 - 10.1109/WF-IoT48130.2020.9221131
DO - 10.1109/WF-IoT48130.2020.9221131
M3 - Conference contribution
AN - SCOPUS:85095606822
T3 - IEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings
BT - IEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE World Forum on Internet of Things, WF-IoT 2020
Y2 - 2 June 2020 through 16 June 2020
ER -