TY - GEN
T1 - Parameter optimization for deriving bluetooth-based social network graphs
AU - Simoski, Bojan
AU - Klein, Michel C.A.
AU - Fernandes De Mello Araújo, Eric
AU - Van Halteren, Aart T.
AU - Van Woudenberg, Thabo
AU - Bevelander, Kirsten E.
AU - Buijzen, Moniek
AU - Bal, Henri
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Pervasive technologies such as Bluetooth (BT) are capable of detecting close proximity. As a result, they are increasingly used for deriving social networks. However, the validity and reliability of the inferred networks is questionable as evaluation procedures are often omitted. In this paper, we consider the process of deriving and evaluating a Bluetooth derived network as a parameter optimization problem. Using the BNEA algorithm, we investigate the effect of the number of detected connections, time window in which these are detected, and the direction of the resulting connection. Our results confirm the importance of conducting a throughout evaluation procedure when deriving social networks based on BT data. Going through the parameter optimization process, we are able to increase the accuracy of the derived BT networks by a maximum of 10%, compared to deriving the networks without it. Our outcomes indicate that reducing the false positives can be achieved by setting a particular connection weight. Furthermore, with the window size parameter we show that more BT observations does not necessarily mean more accurate networks. With respect to the connection type, we observe the accuracy of deriving undirected networks is higher than the accuracy of deriving directed networks. Finally, based on the outcomes we are able to come up with a set of recommendations for the future developers of similar BT data collection systems.
AB - Pervasive technologies such as Bluetooth (BT) are capable of detecting close proximity. As a result, they are increasingly used for deriving social networks. However, the validity and reliability of the inferred networks is questionable as evaluation procedures are often omitted. In this paper, we consider the process of deriving and evaluating a Bluetooth derived network as a parameter optimization problem. Using the BNEA algorithm, we investigate the effect of the number of detected connections, time window in which these are detected, and the direction of the resulting connection. Our results confirm the importance of conducting a throughout evaluation procedure when deriving social networks based on BT data. Going through the parameter optimization process, we are able to increase the accuracy of the derived BT networks by a maximum of 10%, compared to deriving the networks without it. Our outcomes indicate that reducing the false positives can be achieved by setting a particular connection weight. Furthermore, with the window size parameter we show that more BT observations does not necessarily mean more accurate networks. With respect to the connection type, we observe the accuracy of deriving undirected networks is higher than the accuracy of deriving directed networks. Finally, based on the outcomes we are able to come up with a set of recommendations for the future developers of similar BT data collection systems.
UR - http://www.scopus.com/inward/record.url?scp=85083561925&partnerID=8YFLogxK
U2 - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00318
DO - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00318
M3 - Conference proceeding
AN - SCOPUS:85083561925
T3 - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
SP - 1795
EP - 1803
BT - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
Y2 - 19 August 2019 through 23 August 2019
ER -