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Efficient opportunistic sensing using mobile collaborative platform MOSDEN

Jayaraman, Prem Prakash, Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346, Georgakopoulos, Dimitrios and Zaslavsky, Arkady 2013. Efficient opportunistic sensing using mobile collaborative platform MOSDEN. Presented at: 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, Austin, TX, USA, 20-23 October 2013. 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing. IEEE, pp. 77-86. 10.4108/icst.collaboratecom.2013.254090

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Abstract

Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA76 Computer software
Publisher: IEEE
Date of First Compliant Deposit: 10 August 2020
Date of Acceptance: 11 September 2013
Last Modified: 07 Nov 2022 10:57
URI: https://orca.cardiff.ac.uk/id/eprint/134054

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