Intelligent contextual information collection in internet of things

Anagnostopoulos, C. (2016) Intelligent contextual information collection in internet of things. International Journal of Wireless Information Networks, 23(1), pp. 28-39. (doi:10.1007/s10776-015-0293-9)

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Abstract

As we are moving towards the internet of things (IoT), a significant growth of stationary and mobile sensing and computing IoT devices continuously generate enormous amounts of contextual information, e.g., environmental data. Contextual information collection, reasoning, and inference plays critical role in IoT. In this paper, we consider the contextual information collection and harvesting problem in which stationary sensing and computing devices (sources), which are incapable to communicate with each other either due to their long distance, or for energy efficiency, or spatially dispersed network, rely on mobile IoT devices (collectors) to ‘drain’ their acquired contextual information. (e.g., generating from IoT applications: smart cities, smart metering, and smart agriculture). At the contact instances with the collectors, sources have to decide whether to deliver the contextual information obtained so far or postpone their delivery for later hitting epochs in an effort to sense fresher (or more critical) contextual information. We rest on the principles of Optimal Stopping Theory and propose an intelligent context collection scheme in IoT environments. We show through simulations with synthetic and real mobility data the effectiveness of our scheme compared to other approaches.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Anagnostopoulos, Dr Christos
Authors: Anagnostopoulos, C.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:International Journal of Wireless Information Networks
Publisher:Springer
ISSN:1068-9605
ISSN (Online):1572-8129
Published Online:01 February 2016

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