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, 4. pp. 9027-9042. ISSN 2169-3536

[img]
Preview
Text
QoI-Aware Unified Framework for Node.pdf - Published Version

Download (11MB)

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 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
Additional Information: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
Uncontrolled Keywords: 3D field-of-view modelling, dynamic reconfiguration, energy optimization, node classification, 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 > REF2021 UoA11: Computer Science and Informatics
Depositing User: Ian Mcdonald
Date Deposited: 28 Mar 2017 08:50
Last Modified: 30 Nov 2017 12:24
URI: http://www.open-access.bcu.ac.uk/id/eprint/4174

Actions (login required)

View Item View Item

Research

In this section...