Towards context classification and reasoning in IoT

OTEBOLAKU, Abayomi and LEE, G.M. (2017). Towards context classification and reasoning in IoT. In: 2017 14th International Conference on Telecommunications (ConTEL). IEEE, 147-154.

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Official URL: https://ieeexplore.ieee.org/document/8000051
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    Abstract

    Internet of Things (IoT) is the future of ubiquitous and personalized intelligent service delivery. It consists of interconnected, addressable and communicating everyday objects. To realize the full potentials of this new generation of ubiquitous systems, IoT's 'smart' objects should be supported with intelligent platforms for data acquisition, pre-processing, classification, modeling, reasoning and inference including distribution. However, some current IoT systems lack these capabilities: they provide mainly the functionality for raw sensor data acquisition. In this paper, we propose a framework towards deriving high-level context information from streams of raw IoT sensor data, using artificial neural network (ANN) as context recognition model. Before building the model, raw sensor data were pre-processed using weighted average low-pass filtering and a sliding window algorithm. From the resulting windows, statistical features were extracted to train ANN models. Analysis and evaluation of the proposed system show that it achieved between 87.3% and 98.1% accuracies.

    Item Type: Book Section
    Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Uncontrolled Keywords: IoT; context awareness; context sensing; context recognition
    Identification Number: https://doi.org/10.23919/ConTEL.2017.8000051
    Page Range: 147-154
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 04 Jun 2020 16:26
    Last Modified: 04 Jun 2020 16:26
    URI: http://shura.shu.ac.uk/id/eprint/24433

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