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Energy Efficient and Programmable Architecture for Wireless Vision Sensor Node

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Energy Efficient and Programmable Architecture for Wireless Vision Sensor Node Thesis work for the degree of Doctor of Technology Sundsvall 2013 Energy Efficient and Programmable Architecture for Wireless Vision Sensor Node Muhammad Imran Supervisors: Prof. Mattias O’Nils Dr. Najeem Lawal Prof. Bengt Oelmann Faculty of Science, Technology and Media, Mid Sweden University, SE-851 70 Sundsvall, Sweden ISSN 1652-893X Mid Sweden University Doctoral Thesis 167 ISBN 978-91-87557-12-5 Akademisk avhandling som med tillstånd av Mittuniversitetet i Sundsvall framläggs till offentlig granskning för avläggande av teknologie doktors examen i elektronik tisdagden 22 October 2013, klockan 13:15 i sal M108, Mittuniversitetet Sundsvall. Seminariet kommer att hållas på engelska. Energy Efficient and Programmable Architecture for Wireless Vision Sensor Node Muhammad Imran © Muhammad Imran, 2013 Faculty of Science, Technology and Media Mid Sweden University, SE-851 70 Sundsvall Sweden Telephone: +46 (0)60 148561 Printed by Kopieringen Mittuniversitetet, Sundsvall, Sweden, 2013 ABSTRACT 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 i 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. ii ACKNOWLEDGEMENTS First of all, I would express my deepest regards and gratitude to my supervisor Prof. Mattias O’Nils for his guidance, support and encouragement throughout this work. I can honestly say that his guidance has played an important role in transforming my personal and professional life for which I owe him a lot. Najeem Lawal is gratefully acknowledged for his support, valuable feedback and motivation throughout this work. Bengt Oelmann, Fanny Burman, Carolina Blomberg, Christine Grafström and Lotta Frisk are thankfully acknowledged for their support. Prof. Bernhard Rinner from Alpen-Adria-Universität Klagenfurt, Austria is gratefully acknowledged for being an opponent to this thesis. Dr. Khaled Benkrid, program manager ARM Worldwide University, UK is gratefully acknowledged for giving feedback on this work. Further, I am thankful to my colleagues for developing and maintaining such a wonderful working environment. Specifically, I am indebted to Naeem Ahmad, Abdul Waheed, Khurram Shahzad, Jawad Saleem, Abdul Majid, Khursheed, Anzar Alam, Benny Thörnberg, Cheng Peng, Claes Mattsson, Kent Bertilsson, Anatholy Manuilsky, Magnus Engholm, Jan Thim, Börje Norlin, Göran Thungström, Krister Aldén, Nazar ul Islam, Amir Yousaf, Mazhar Hussain, Xiaozhou Meng, Mikael Bylund, Shakeel Ahmed, Art Mulder, Omeime Esebamen, Reza Salim, David Krapohl, Sebastian Bader and everyone else (whose name is missing ☺) for the discussions and cooperation. I am thankful to Nisar Khan, Azhar Iqbal, Khalid Khattak, Qaiser Anwar and Asif Sherani for all off-campus activities and fun. In addition to this, many people especially Dr. Zahid Saleem, Dr. Saleem Shaukat and Prof. Oscar Gustafsson are gratefully acknowledged, all of whom have been instrumental at different times in my academic career. STC Research program in Mid Sweden University and Higher Education Commission (HEC), Pakistan are acknowledged for their financial and administrative support. I humbly but proudly thank my parents, Talimat Khan and Nargis Khatoon for choosing some of the finest institutions of the country for their children, sometime at the cost of their own comfort. Moreover, I am grateful to my siblings especially Akram for the support and inspiration. Finally, a big gratitude and a credit go to my dear wife Farri and my lovely daughter Aleena Imran whose presence made Sundsvall a wonderful place for me. This thesis truly deserves dedication to you and to my parents. Sundsvall, Sept. 2013. Muhammad Imran iii TABLE OF CONTENTS ABSTRACT ............................................................................................................... I ACKNOWLEDGEMENTS ..................................................................................... III TABLE OF CONTENTS ...........................................................................................V ABBREVIATIONS AND ACRONYMS .................................................................IX LIST OF FIGURES ..................................................................................................XI LIST OF TABLES .................................................................................................. XV 1 INTRODUCTION ............................................................................................ 1 1.1 APPLICATIONS OF WIRELESS VISION SENSOR NETWORKS ................................ 4 1.2 PROCESSING PLATFORMS FOR VSN ............................................................... 4 1.2.1 Software platforms ............................................................................... 4 1.2.2 Hardware platforms ............................................................................. 5 1.3 RELATED WORK -ARCHITECTURES FOR WIRELESS VISION SENSOR NODES ........ 7 1.4 THE AUTHOR ’S RESEARCH FOCUS ................................................................. 8 1.5 MAIN CONTRIBUTIONS ................................................................................ 10 1.6 THESIS OUTLINE ......................................................................................... 12 2 SYSTEM TAXONOMY AND COMPLEXITY MODEL FOR WIRELESS VISION SENSOR NODE ............................................................................... 13 2.1 METHODOLOGY FOR TAXONOMY ................................................................. 13 2.2 PROPOSED SYSTEM TAXONOMY .................................................................. 14 2.2.1 Evaluation of Taxonomy .................................................................... 15 2.3 COMPLEXITY MODEL ................................................................................... 19 2.3.1 Case study for taxonomy and complexity model ................................... 24 3 EVALUATED HARDWARE AND APPLICATIONS ................................... 31 3.1 IMAGE SENSOR .......................................................................................... 31 3.2 PROCESSING PLATFORMS ........................................................................... 31 3.3 COMMUNICATION ....................................................................................... 32 3.4 MEMORY MODULE ...................................................................................... 33 3.5 APPLICATIONS ........................................................................................... 33 3.5.1 Remote meter reading ......................................................................... 34 3.5.2 People counting
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