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Energy-efficient location and activity-aware on-demand mobile distributed sensing platform for sensing as a service in IoT clouds

Perera, Charith, Talagala, Dumidu S., Liu, Chi Harold and Estrella, Julio C. 2015. Energy-efficient location and activity-aware on-demand mobile distributed sensing platform for sensing as a service in IoT clouds. IEEE Transactions on Computational Social Systems 2 (4) , pp. 171-181. 10.1109/TCSS.2016.2515844

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Abstract

The Internet of Things (IoT) envisions billions of sensors deployed around us and connected to the Internet, where the mobile crowd sensing technologies are widely used to collect data in different contexts of the IoT paradigm. Due to the popularity of Big Data technologies, processing and storing large volumes of data have become easier than ever. However, large-scale data management tasks still require significant amounts of resources that can be expensive regardless of whether they are purchased or rented (e.g., pay-as-you-go infrastructure). Further, not everyone is interested in such large-scale data collection and analysis. More importantly, not everyone has the financial and computational resources to deal with such large volumes of data. Therefore, a timely need exists for a cloud-integrated mobile crowd sensing platform that is capable of capturing sensors data, on-demand, based on conditions enforced by the data consumers. In this paper, we propose a context-aware, specifically, location and activity-aware mobile sensing platform called context-aware mobile sensor data engine (C-MOSDEN) for the IoT domain. We evaluated the proposed platform using three real-world scenarios that highlight the importance of selective sensing. The computational effectiveness and efficiency of the proposed platform are investigated and are used to highlight the advantages of context-aware selective sensing.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA76 Computer software
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 2329-924X
Date of First Compliant Deposit: 11 August 2020
Date of Acceptance: 6 January 2015
Last Modified: 25 Nov 2020 19:31
URI: http://orca.cf.ac.uk/id/eprint/134069

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