State-machine driven collaborative mobile sensing serving multiple Internet-of-Things applications

Loomba, Radhika and Shi, Lei and Jennings, Brendan (2017) State-machine driven collaborative mobile sensing serving multiple Internet-of-Things applications. In: Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management :. Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management . Institute of Electrical and Electronics Engineers Inc., PRT, pp. 1229-1237. ISBN 9783901882890

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Abstract

The myriad of sensor information that can be collected using smartphones, wearables and other IoT devices greatly benefits context-aware applications. These applications rely heavily on mobile devices, present in locations of interest, to offload raw or processed sensor data in order to accurately capture, recognize and classify the surrounding real-time context. However, continuous sensing and offloading of large volumes of mainly redundant sensor data significantly impacts energy-constrained mobile devices. This results in a trade-off between sensing accuracy and the energy consumed by these devices. We propose the use of application-specific state machines that encode the context of interest to determine when sensed data should be offloaded to the cloud. Our control algorithm, 'Assisted-Aggregation' applies frequent pattern mining to reduce the number of active devices by sharing sensed data between multiple applications. Our evaluation shows an improvement in terms of the residual energy of the mobile devices, the number of devices actively offloading and the volume of the offloaded data.

Item Type:	Book Section
Additional Information:	Funding Information: ACKNOWLEDGEMENTS This work was funded by: 1) the Irish Research Council Enterprise Partnership Scheme Postgraduate Research Scholarship, co-funded by Intel Labs Europe (grant no. EP-SPG/2012/407); 2) by the Irish Research Council via the ELEVATE Fellowship 2013 (grant no. ELEVATEPD/2013/26); and 3) by Science Foundation Ireland (SFI) via the CONNECT Research Centre (grant no. 13/RC/2077). Publisher Copyright: © 2017 IFIP.
Uncontrolled Keywords:	/dk/atira/pure/subjectarea/asjc/1700/1705
Departments or Groups:
Depositing User:	Admin SSL
Date Deposited:	19 Oct 2022 23:14
Last Modified:	07 Jun 2023 18:37
URI:	http://repository-testing.wit.ie/id/eprint/4822

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