casa 18(13): e1

Research Article

AndroCon: An Android-Based Context-Aware Middleware Framework

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  • @ARTICLE{10.4108/eai.14-3-2018.154338,
        author={Jian Yu and Quan Z. Sheng and Olayinka Adeleye and Chris Wang},
        title={AndroCon: An Android-Based Context-Aware Middleware Framework},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
        volume={4},
        number={13},
        publisher={EAI},
        journal_a={CASA},
        year={2018},
        month={3},
        keywords={Data Provisioning, Context-Awareness, Integration, Middleware, Android},
        doi={10.4108/eai.14-3-2018.154338}
    }
    
  • Jian Yu
    Quan Z. Sheng
    Olayinka Adeleye
    Chris Wang
    Year: 2018
    AndroCon: An Android-Based Context-Aware Middleware Framework
    CASA
    EAI
    DOI: 10.4108/eai.14-3-2018.154338
Jian Yu1,*, Quan Z. Sheng2, Olayinka Adeleye1, Chris Wang1
  • 1: Department of Computer Science,Auckland University of Technology, Auckland, 1010, New Zealand
  • 2: Department of Computing, Faculty of Science and Engineering Macquarie University Sydney, NSW 2109, Australia
*Contact email: jian.yu@aut.ac.nz

Abstract

Mobile devices have become major sources of context-aware data due to their ubiquity and sensing capabilities. However, deploying mobile devices as dynamic, unabridged context data provider either locally or remotely is challenging, due to their limited computing capabilities. Moreover, mobile sensors are limited to physical context data acquisition and there is a need to integrate physical data provided by these sensors with social context data provided by various mobile applications. Such data integration is necessary in order to have a robust data sources for various context-aware applications. In this paper, we present AndroCon, an Android-based, context-aware middleware framework that enables mobile devices to acquire, integrate, manage context data and to provision the data to applications both locally and remotely. AndroCon enables integration of both raw physical and social related context data. Instances of AndroCon have been achieved by interpreting and storing high-level context knowledge locally and utilizing web service technologies for data provisioning. We perform extensive experiments using AndroCon to collect, provision and manage both social and physical context data from di erent sources. We have also analyzed AndroCon’s performances based on its power consumption and CPU utilization.