TY - GEN
T1 - State-machine driven opportunistic sensing by mobile devices
AU - Loomba, Radhika
AU - Shi, Lei
AU - Jennings, Brendan
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/2/9
Y1 - 2014/2/9
N2 - As mobile devices increasingly incorporate a range of sensors, there is significant potential to apply opportunistic sensing techniques to allow collections of these devices to provide context information to applications. Focussing on a use case involving the use of mobile devices to sense and localize increasing levels of gases in a work environment, we show that the use of application-specific state machines that control the rate at which sensed data is reported, can lead to a significant reduction in battery consumption by the devices in comparison to continuous sensing approaches wherein the reporting rate remains constant.
AB - As mobile devices increasingly incorporate a range of sensors, there is significant potential to apply opportunistic sensing techniques to allow collections of these devices to provide context information to applications. Focussing on a use case involving the use of mobile devices to sense and localize increasing levels of gases in a work environment, we show that the use of application-specific state machines that control the rate at which sensed data is reported, can lead to a significant reduction in battery consumption by the devices in comparison to continuous sensing approaches wherein the reporting rate remains constant.
KW - Context-aware Applications
KW - Opportunistic Sensing
KW - People-centric Sensing
UR - http://www.scopus.com/inward/record.url?scp=84949923559&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2014.7037222
DO - 10.1109/GLOCOM.2014.7037222
M3 - Conference contribution
AN - SCOPUS:84949923559
T3 - 2014 IEEE Global Communications Conference, GLOBECOM 2014
SP - 2739
EP - 2744
BT - 2014 IEEE Global Communications Conference, GLOBECOM 2014
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 December 2014 through 12 December 2014
ER -