TY - JOUR
T1 - Predictive data delivery to mobile users through mobility learning in wireless sensor networks
AU - Lee, Hyung June
AU - Wicke, Martin
AU - Kusy, Branislav
AU - Gnawali, Omprakash
AU - Guibas, Leonidas
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea funded by the Korean Ministry of Science, ICT, and Future Planning through the Basic Science Research Program under Grant NRF-2013R1A1A1009854.
Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/1
Y1 - 2015/12/1
N2 - We consider applications, such as indoor navigation, evacuation, or targeted advertising, where mobile users equipped with a smartphone-class device require access to sensor network datameasured in their proximity. Specifically, we focus on efficient communication protocols between static sensors and users with changing location. Our main contribution is to predict a set of possible future paths for each user and store data at sensor nodes with which the user is likely to associate. We use historical data of radio connectivity between users and static sensor nodes to predict the future user-node associations and propose a network optimization process, i.e., data stashing, which uses the predictions to minimize network and energy overheads of packet transmissions. We show that data stashing significantly decreases routing cost for delivering data from stationary sensor nodes to multiple mobile users compared with routing protocols where sensor nodes immediately deliver data to the last known association nodes of mobile users. We also show that the scheme provides better load balancing, avoiding collisions and consuming energy resources evenly throughout the network, leading to longer overall network lifetime. Finally, we demonstrate that even limited knowledge of the location of future users can lead to significant improvements in routing performance.
AB - We consider applications, such as indoor navigation, evacuation, or targeted advertising, where mobile users equipped with a smartphone-class device require access to sensor network datameasured in their proximity. Specifically, we focus on efficient communication protocols between static sensors and users with changing location. Our main contribution is to predict a set of possible future paths for each user and store data at sensor nodes with which the user is likely to associate. We use historical data of radio connectivity between users and static sensor nodes to predict the future user-node associations and propose a network optimization process, i.e., data stashing, which uses the predictions to minimize network and energy overheads of packet transmissions. We show that data stashing significantly decreases routing cost for delivering data from stationary sensor nodes to multiple mobile users compared with routing protocols where sensor nodes immediately deliver data to the last known association nodes of mobile users. We also show that the scheme provides better load balancing, avoiding collisions and consuming energy resources evenly throughout the network, leading to longer overall network lifetime. Finally, we demonstrate that even limited knowledge of the location of future users can lead to significant improvements in routing performance.
KW - Data delivery to mobile users
KW - Mobility pattern
KW - Network optimization
KW - Sensor networks
KW - Trajectory prediction
UR - http://www.scopus.com/inward/record.url?scp=84959201751&partnerID=8YFLogxK
U2 - 10.1109/TVT.2014.2388237
DO - 10.1109/TVT.2014.2388237
M3 - Article
AN - SCOPUS:84959201751
SN - 0018-9545
VL - 64
SP - 5831
EP - 5849
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 12
M1 - 7001098
ER -