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Mobile Information Systems
Volume 2017 (2017), Article ID 7892545, 16 pages
https://doi.org/10.1155/2017/7892545
Research Article

Two-Tier VoI Prioritization System on Requirement-Based Data Streaming toward IoT

Graduate School of Information Science and Technology, Osaka University, Osaka, Japan

Correspondence should be addressed to Sunyanan Choochotkaew; pj.ca.u-akaso.tsi@hc-aynus

Received 5 March 2017; Revised 27 June 2017; Accepted 2 July 2017; Published 20 August 2017

Academic Editor: Laurence T. Yang

Copyright © 2017 Sunyanan Choochotkaew et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Toward the world of Internet of Things, people utilize knowledge from sensor streams in various kinds of smart applications. The number of sensing devices is rapidly increasing along with the amount of sensing data. Consequently, a bottleneck problem at the local gateway has attracted high concern. An example scenario is smart elderly houses in rural areas where each house installs thousands of sensors and all connect to resource-limited and unstable 2G/3G networks. The bottleneck state can incur unacceptable latency and loss of significant data due to the limited waiting-queue. Orthogonally to the existing solutions, we propose a two-tier prioritization system to enhance information quality, indicated by VoI, at the local gateway. The proposed system has been designed to support several requirements with several conflicting criteria over shared sensing streams. Our approach adopts Multicriteria Decision Analysis technique to merge requirements and to assess the VoI. We introduce the framework that can reduce the computational cost by precalculation. Through a case study of building management systems, we have shown that our merge algorithm can provide 0.995 cosine-similarity for representing all user requirements and the evaluation approach can obtain satisfaction values around 3 times higher than the naïve strategies for the top-list data.