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Implementation of Wireless Vision Sensor Node With a Lightweight Bi-Level Video Coding
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0003-1923-3843
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0002-6484-9260
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
Show others and affiliations
2013 (English)In: IEEE Journal on Emerging and Selected Topics in Circuits and Systems, ISSN 2156-3357, Vol. 3, no 2, p. 198-209, article id 6508941Article in journal (Refereed) Published
Abstract [en]

Wireless vision sensor networks (WVSNs) consist ofa number of wireless vision sensor nodes (VSNs) which have limitedresources i.e., energy, memory, processing, and wireless bandwidth.The processing and communication energy requirements ofindividual VSN have been a challenge because of limited energyavailability. To meet this challenge, we have proposed and implementeda programmable and energy efficient VSN architecturewhich has lower energy requirements and has a reduced designcomplexity. In the proposed system, vision tasks are partitionedbetween the hardware implemented VSN and a server. The initialdata dominated tasks are implemented on the VSN while thecontrol dominated complex tasks are processed on a server. Thisstrategy will reduce both the processing energy consumption andthe design complexity. The communication energy consumption isreduced by implementing a lightweight bi-level video coding on theVSN. The energy consumption is measured on real hardware fordifferent applications and proposed VSN is compared against publishedsystems. The results show that, depending on the application,the energy consumption can be reduced by a factor of approximately1.5 up to 376 as compared to VSN without the bi-level videocoding. The proposed VSN offers energy efficient, generic architecturewith smaller design complexity on hardware reconfigurableplatform and offers easy adaptation for a number of applicationsas compared to published systems.

Place, publisher, year, edition, pages
IEEE Press, 2013. Vol. 3, no 2, p. 198-209, article id 6508941
Keywords [en]
Architecture, smart camera, video coding, wireless vision sensor networks (WVSNs), wireless vision sensor node (VSN)
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:miun:diva-19193DOI: 10.1109/JETCAS.2013.2256816ISI: 000337789200009Scopus ID: 2-s2.0-84879076204Local ID: STCOAI: oai:DiVA.org:miun-19193DiVA, id: diva2:627733
Available from: 2013-06-12 Created: 2013-06-12 Last updated: 2016-10-20Bibliographically approved
In thesis
1. Energy Efficient and Programmable Architecture for Wireless Vision Sensor Node
Open this publication in new window or tab >>Energy Efficient and Programmable Architecture for Wireless Vision Sensor Node
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Wireless Vision Sensor Networks (WVSNs) is an emerging field which has attracted a number of potential applications because of smaller per node cost, ease of deployment, scalability and low power stand alone solutions. WVSNs consist of a number of wireless Vision Sensor Nodes (VSNs). VSN has limited resources such as embedded processing platform, power supply, wireless radio and memory.  In the presence of these limited resources, a VSN is expected to perform complex vision tasks for a long duration of time without battery replacement/recharging. Currently, reduction of processing and communication energy consumptions have been major challenges for battery operated VSNs. Another challenge is to propose generic solutions for a VSN so as to make these solutions suitable for a number of applications.

To meet these challenges, this thesis focuses on energy efficient and programmable VSN architecture for machine vision systems which can classify objects based on binary data. In order to facilitate generic solutions, a taxonomy has been developed together with a complexity model which can be used for systems’ classification and comparison without the need for actual implementation. The proposed VSN architecture is based on tasks partitioning between a VSN and a server as well as tasks partitioning locally on the node between software and hardware platforms. In relation to tasks partitioning, the effect on processing, communication energy consumptions, design complexity and lifetime has been investigated.

The investigation shows that the strategy, in which front end tasks up to segmentation, accompanied by a bi-level coding, are implemented on Field Programmable Platform (FPGA) with small sleep power, offers a generalized low complexity and energy efficient VSN architecture. The implementation of data intensive front end tasks on hardware reconfigurable platform reduces processing energy. However, there is a scope for reducing communication energy, related to output data. This thesis also explores data reduction techniques including image coding, region of interest coding and change coding which reduces output data significantly.

For proof of concept, VSN architecture together with tasks partitioning, bi-level video coding, duty cycling and low complexity background subtraction technique has been implemented on real hardware and functionality has been verified for four applications including particle detection system, remote meter reading, bird detection and people counting. The results based on measured energy values shows that, depending on the application, the energy consumption can be reduced by a factor of approximately 1.5 up to 376 as compared to currently published VSNs. The lifetime based on measured energy values showed that for a sample period of 5 minutes, VSN can achieve 3.2 years lifetime with a battery of 37.44 kJ energy. In addition to this, proposed VSN offers generic architecture with smaller design complexity on hardware reconfigurable platform and offers easy adaptation for a number of applications as compared to published systems.

Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2013. p. 115
Series
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 167
Keywords
Wireless Vision Sensor Node, Smart camera, Wireless Vision Sensor Networks, Architecture, Video coding.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-20179 (URN)STC (Local ID)978-91-87557-12-5 (ISBN)STC (Archive number)STC (OAI)
Public defence
2013-10-22, M108, holmgatan 10,SE 85170, sundsvall, 10:03 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2013-11-11 Created: 2013-11-11 Last updated: 2016-10-20Bibliographically approved
2. Investigation of intelligence partitioning and data reduction in wireless visual sensor network
Open this publication in new window or tab >>Investigation of intelligence partitioning and data reduction in wireless visual sensor network
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2013. p. 208
Series
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 150
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-20976 (URN)STC (Local ID)978-91-87103-75-9 (ISBN)STC (Archive number)STC (OAI)
Supervisors
Available from: 2014-01-08 Created: 2014-01-08 Last updated: 2016-10-20Bibliographically approved
3. Modelling, optimization and design of visual sensor networks for sky surveillance
Open this publication in new window or tab >>Modelling, optimization and design of visual sensor networks for sky surveillance
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2013. p. 210
Series
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 166
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-21022 (URN)978-91-87557-11-8 (ISBN)
Supervisors
Available from: 2014-01-13 Created: 2014-01-13 Last updated: 2014-04-24Bibliographically approved

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