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

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

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Abstract

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 con�figurations. In this paper, a unifi�ed framework
for node classifi�cation and dynamic self-reconfi�guration 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 classi�fication 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 confi�gurations of visual sensing nodes to
reduce redundant visual content prior to transmission without sacrifi�cing 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 unifi�ed framework
outperforms its existing counterparts and results in up to 72% energy savings with as low as 94% reliability.

Item Type: Article
Uncontrolled Keywords: 3D �field-of-view modelling, dynamic reconfi�guration, energy optimization, node classi�fication, quality-of-information, reliability analysis, visual sensor networks.
Subjects: G400 Computer Science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Cloud Computing
UoA Collections > UoA11: Computer Science and Informatics
Depositing User: $ Ian McDonald
Date Deposited: 28 Mar 2017 08:50
Last Modified: 28 Mar 2017 08:50
URI: http://www.open-access.bcu.ac.uk/id/eprint/4174

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