Information aggregation for collaborative sensing in mobile Cloud computing

Radhika Loomba; Lei Shi; Brendan Jennings; Roy Friedman; John Kennedy; Joe Butler

doi:10.1109/MobileCloud.2014.23

Information aggregation for collaborative sensing in mobile Cloud computing

Radhika Loomba
, Lei Shi
, Brendan Jennings
, Roy Friedman
, John Kennedy
, Joe Butler

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The proliferation of smart mobile devices, having multiple sensing capabilities and significant computing power, enables their inclusion into mobile sensing systems. Sensor-driven mobile applications are drastically altering various sectors like healthcare, social networks and environmental monitoring. However, using a large number of mobile devices has an impact on the viability of techniques involved in sensing systems. Moreover, continuous sensing affects the battery performance of the mobile device. This motivates a need for energy efficient sensor-data collection with a minimum number of mobile devices. This paper presents an algorithm that makes a trade-off between the energy consumption and the number of involved mobile devices in the sensing environment, subject to satisfying the sensing needs of multiple applications. We compare our algorithm with an energy-efficient solution for sensor allocation. Results show that our algorithm suffers from less than 6% battery loss difference while reducing the number of involved mobile devices by more than 30%.

Original language	English
Title of host publication	Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014
Publisher	IEEE Computer Society
Pages	149-158
Number of pages	10
ISBN (Print)	9781479925049
DOIs	https://doi.org/10.1109/MobileCloud.2014.23
Publication status	Published - 2014
Externally published	Yes
Event	2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014 - Oxford, United Kingdom Duration: 7 Apr 2014 → 10 Apr 2014

Publication series

Name	Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014

Conference

Conference	2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014
Country/Territory	United Kingdom
City	Oxford
Period	7/04/14 → 10/04/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Frequent pattern mining
Info aggregation
Mobile application
Mobile cloud
Mobile sensing
Optimization

Access to Document

10.1109/MobileCloud.2014.23Licence: CC BY

Cite this

Loomba, R., Shi, L., Jennings, B., Friedman, R., Kennedy, J., & Butler, J. (2014). Information aggregation for collaborative sensing in mobile Cloud computing. In Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014 (pp. 149-158). Article 6834957 (Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014). IEEE Computer Society. https://doi.org/10.1109/MobileCloud.2014.23

Loomba, Radhika ; Shi, Lei ; Jennings, Brendan et al. / Information aggregation for collaborative sensing in mobile Cloud computing. Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014. IEEE Computer Society, 2014. pp. 149-158 (Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014).

@inproceedings{17e9b986cd6d49e6b54a951dd8c3498f,

title = "Information aggregation for collaborative sensing in mobile Cloud computing",

abstract = "The proliferation of smart mobile devices, having multiple sensing capabilities and significant computing power, enables their inclusion into mobile sensing systems. Sensor-driven mobile applications are drastically altering various sectors like healthcare, social networks and environmental monitoring. However, using a large number of mobile devices has an impact on the viability of techniques involved in sensing systems. Moreover, continuous sensing affects the battery performance of the mobile device. This motivates a need for energy efficient sensor-data collection with a minimum number of mobile devices. This paper presents an algorithm that makes a trade-off between the energy consumption and the number of involved mobile devices in the sensing environment, subject to satisfying the sensing needs of multiple applications. We compare our algorithm with an energy-efficient solution for sensor allocation. Results show that our algorithm suffers from less than 6\% battery loss difference while reducing the number of involved mobile devices by more than 30\%.",

keywords = "Frequent pattern mining, Info aggregation, Mobile application, Mobile cloud, Mobile sensing, Optimization",

author = "Radhika Loomba and Lei Shi and Brendan Jennings and Roy Friedman and John Kennedy and Joe Butler",

year = "2014",

doi = "10.1109/MobileCloud.2014.23",

language = "English",

isbn = "9781479925049",

series = "Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014",

publisher = "IEEE Computer Society",

pages = "149--158",

booktitle = "Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014",

address = "United States",

note = "2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014 ; Conference date: 07-04-2014 Through 10-04-2014",

}

Loomba, R, Shi, L, Jennings, B, Friedman, R, Kennedy, J & Butler, J 2014, Information aggregation for collaborative sensing in mobile Cloud computing. in Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014., 6834957, Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014, IEEE Computer Society, pp. 149-158, 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014, Oxford, United Kingdom, 7/04/14. https://doi.org/10.1109/MobileCloud.2014.23

Information aggregation for collaborative sensing in mobile Cloud computing. / Loomba, Radhika; Shi, Lei; Jennings, Brendan et al.
Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014. IEEE Computer Society, 2014. p. 149-158 6834957 (Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Information aggregation for collaborative sensing in mobile Cloud computing

AU - Loomba, Radhika

AU - Shi, Lei

AU - Jennings, Brendan

AU - Friedman, Roy

AU - Kennedy, John

AU - Butler, Joe

PY - 2014

Y1 - 2014

N2 - The proliferation of smart mobile devices, having multiple sensing capabilities and significant computing power, enables their inclusion into mobile sensing systems. Sensor-driven mobile applications are drastically altering various sectors like healthcare, social networks and environmental monitoring. However, using a large number of mobile devices has an impact on the viability of techniques involved in sensing systems. Moreover, continuous sensing affects the battery performance of the mobile device. This motivates a need for energy efficient sensor-data collection with a minimum number of mobile devices. This paper presents an algorithm that makes a trade-off between the energy consumption and the number of involved mobile devices in the sensing environment, subject to satisfying the sensing needs of multiple applications. We compare our algorithm with an energy-efficient solution for sensor allocation. Results show that our algorithm suffers from less than 6% battery loss difference while reducing the number of involved mobile devices by more than 30%.

AB - The proliferation of smart mobile devices, having multiple sensing capabilities and significant computing power, enables their inclusion into mobile sensing systems. Sensor-driven mobile applications are drastically altering various sectors like healthcare, social networks and environmental monitoring. However, using a large number of mobile devices has an impact on the viability of techniques involved in sensing systems. Moreover, continuous sensing affects the battery performance of the mobile device. This motivates a need for energy efficient sensor-data collection with a minimum number of mobile devices. This paper presents an algorithm that makes a trade-off between the energy consumption and the number of involved mobile devices in the sensing environment, subject to satisfying the sensing needs of multiple applications. We compare our algorithm with an energy-efficient solution for sensor allocation. Results show that our algorithm suffers from less than 6% battery loss difference while reducing the number of involved mobile devices by more than 30%.

KW - Frequent pattern mining

KW - Info aggregation

KW - Mobile application

KW - Mobile cloud

KW - Mobile sensing

KW - Optimization

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BT - Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014

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ER -

Loomba R, Shi L, Jennings B, Friedman R, Kennedy J, Butler J. Information aggregation for collaborative sensing in mobile Cloud computing. In Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014. IEEE Computer Society. 2014. p. 149-158. 6834957. (Proceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014). doi: 10.1109/MobileCloud.2014.23

Information aggregation for collaborative sensing in mobile Cloud computing

Abstract

Publication series

Conference

UN SDGs

Keywords

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Cite this