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Architecture Exploration Based on Tasks Partitioning Between Hardware, Software and Locality for a Wireless Vision Sensor Node
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationsteknologi och medier.ORCID-id: 0000-0003-1923-3843
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationsteknologi och medier.ORCID-id: 0000-0002-6484-9260
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationsteknologi och medier.
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationsteknologi och medier.
Vise andre og tillknytning
2012 (engelsk)Inngår i: International Journal of Distributed Systems and Technologies, ISSN 1947-3532, E-ISSN 1947-3540, Vol. 3, nr 2, s. 58-71Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Wireless Vision Sensor Networks (WVSNs) is an emerging field which consists of a number of Visual Sensor Nodes (VSNs). Compared to traditional sensor networks, WVSNs operates on two dimensional data, which requires high bandwidth and high energy consumption. In order to minimize the energy consumption, the focus is on finding energy efficient and programmable architectures for the VSN by partitioning the vision tasks among hardware (FPGA), software (Micro-controller) and locality (sensor node or server). The energy consumption, cost and design time of different processing strategies is analyzed for the implementation of VSN. Moreover, the processing energy and communication energy consumption of VSN is investigated in order to maximize the lifetime. Results show that by introducing a reconfigurable platform such as FPGA with small static power consumption and by transmitting the compressed images after pixel based tasks from the VSN results in longer battery lifetime for the VSN.

sted, utgiver, år, opplag, sider
IGI Global, USA., 2012. Vol. 3, nr 2, s. 58-71
Emneord [en]
Wireless Vision Sensor Networks; Vision Sensor Node; Hardware/Software Partitioning; Reconfigurable Architecture; Image Processing.
HSV kategori
Identifikatorer
URN: urn:nbn:se:miun:diva-14940DOI: 10.4018/jdst.2012040104Scopus ID: 2-s2.0-84880522514OAI: oai:DiVA.org:miun-14940DiVA, id: diva2:472670
Prosjekter
Onparticle detectionTilgjengelig fra: 2012-01-04 Laget: 2011-11-27 Sist oppdatert: 2017-12-08bibliografisk kontrollert
Inngår i avhandling
1. Energy Efficient and Programmable Architecture for Wireless Vision Sensor Node
Åpne denne publikasjonen i ny fane eller vindu >>Energy Efficient and Programmable Architecture for Wireless Vision Sensor Node
2013 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Sundsvall: Mid Sweden University, 2013. s. 115
Serie
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 167
Emneord
Wireless Vision Sensor Node, Smart camera, Wireless Vision Sensor Networks, Architecture, Video coding.
HSV kategori
Identifikatorer
urn:nbn:se:miun:diva-20179 (URN)STC (Lokal ID)978-91-87557-12-5 (ISBN)STC (Arkivnummer)STC (OAI)
Disputas
2013-10-22, M108, holmgatan 10,SE 85170, sundsvall, 10:03 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Knowledge Foundation
Tilgjengelig fra: 2013-11-11 Laget: 2013-11-11 Sist oppdatert: 2016-10-20bibliografisk kontrollert

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Imran, MuhammadKhursheed, KhursheedMalik, Abdul WaheedAhmad, NaeemO'Nils, MattiasLawal, NajeemThörnberg, Benny
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