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QoI-Aware Unified Framework for Node Classification and Self-Reconfiguration Within Heterogeneous Visual Sensor Networks

AMJAD, Anas, GRIFFITHS, Alison and Patwary, Mohammad (2016) QoI-Aware Unified Framework for Node Classification and Self-Reconfiguration Within Heterogeneous Visual Sensor Networks. IEEE Access, 4. pp. 9027-9042. ISSN 2169-3536

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Abstract or description

Due to energy and throughput constraints of visual sensing nodes, in-node energy conservation is one of the prime concerns in visual sensor networks (VSNs) with wireless transceiving capability. To cope with these constraints, the energy efficiency of a VSN for a given level of reliability can be enhanced by reconfiguring its nodes dynamically to achieve optimal configurations. In this paper, a unified framework for node classification and dynamic self-reconfiguration in VSNs is proposed. The proposed framework incorporates quality-of-information (QoI) awareness using peak signal-to-noise ratio-based representative metric to support a diverse range of applications. First, for a given application, the proposed framework provides a feasible solution for the classification of visual sensing nodes based on their field-of-view by exploiting the heterogeneity of the targeted QoI within the sensing region. Second, with the dynamic realization of QoI, a strategy is devised for selecting suitable configurations of visual sensing nodes to reduce redundant visual content prior to transmission without sacrificing the expected information retrieval reliability. The robustness of the proposed framework is evaluated under various scenarios by considering: 1) target QoI thresholds; 2) degree of heterogeneity; and 3) compression schemes. From the simulation results, it is observed that for the second degree of heterogeneity in targeted QoI, the unified framework outperforms its existing counterparts and results in up to 72% energy savings with as low as 94% reliability.

Item Type: Article
Faculty: School of Creative Arts and Engineering > Engineering
Depositing User: Alison GRIFFITHS
Date Deposited: 21 Jan 2019 16:14
Last Modified: 21 Jan 2019 16:14
URI: http://eprints.staffs.ac.uk/id/eprint/5102

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