BioSmartSense+: A Bio-Inspired Probabilistic Data Collection Framework for Priority-Based Event Reporting in IoT Environments
Recent years have seen a widespread use of information and communication technology (ICT) in the implementation of smart city applications. A key enabler in the smart city paradigm is the Internet-of-Things (IoT), which facilitates automated real-time sensing, communication, and actuation, assisting in unmanned monitoring of physical phenomenon and supports intelligent decision making. Nevertheless, designing a smart and energy-efficient IoT network for sustainability and near-perfect device actuation is a major challenge. To address this, our preliminary work (Roy et al., 2019) proposed a gene regulatory network (GRN)-based distributed event sensing and data collection framework called bioSmartSense. It attempted to make sensing and reporting tasks energy-efficient through bio-inspired self-modulation of IoT device energy levels. In this paper we extend it, under the name bioSmartSense+, to conceive realistic sensing and reporting mechanisms by incorporating device heterogeneity, probabilistic sensing, and priority-based event reporting. For experimental study, we used both simulated and real data to evaluate energy and coverage-related performances. Experimental results establish the efficacy of our framework in terms of energy-efficiency and event reporting rate compared to a state-of-the-art data collection approach.
S. Roy et al., "BioSmartSense+: A Bio-Inspired Probabilistic Data Collection Framework for Priority-Based Event Reporting in IoT Environments," Pervasive and Mobile Computing, vol. 67, Elsevier, Sep 2020.
The definitive version is available at https://doi.org/10.1016/j.pmcj.2020.101179
Center for High Performance Computing Research
Keywords and Phrases
Gene regulatory networks; Internet-of-Things; Inverse Gompertz; Probabilistic sensing
International Standard Serial Number (ISSN)
Article - Journal
© 2020 Elsevier, All rights reserved.
01 Sep 2020