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Intelligence Partitioning for IoT: Communication and Processing Inter-Effects for Smart Camera Implementation
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The Internet of Things (IoT) is becoming a tangible reality, with a variety of sensors, devices and data centres interconnected to support scenarios such as Smart City with information about traffic, city administration, health-care services and entertainment. Decomposing these systems into smaller components, results in a variety of requirements for processing and communication resources for each subsystem. Wireless Vision Sensor Network (WVSN) is one of the subsystems, relying on visual sensors that produce several megabytes of data every second, unlike temperature or pressure sensors producing several bytes of data every hour. In addition, to facilitate the deployment of the nodes for different environments, we consider themas battery-operated devices. The high data rates from the imaging sensor have extensive computational and communication requirements, which in the meantime should meet the constraints regarding the energy efficiency of the device, to ensure a satisfactory battery lifetime.

In this thesis we analyse the energy efficiency of the smart camera, including the smart camera architecture, the distribution of the image processing tasks between several processing elements, and the inter-effects of processing and communication. Sensor selection and algorithmic implementation of the image processing tasks affects the processing energy consumption of the node, alongside to the hardware and software implementation of the tasks.

Furthermore, considerations of different intelligence partitioning configurations are included in the analysis of communication related elements, such as communication delays and channel utilisation. The inter-effects resulting from the variety of configurations in image processing allocation and communication technologies with different characteristics provide an insight into the overall variations of the smart camera node energy consumption. The aim of thesis is to facilitate the design of energy efficient smart cameras, while providing an understanding of energy consumption variations related to processing and communication configurations.

Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University , 2019. , p. 54
Series
Mid Sweden University licentiate thesis, ISSN 1652-8948 ; 152
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:miun:diva-37178ISBN: 978-91-88527-85-1 (print)OAI: oai:DiVA.org:miun-37178DiVA, id: diva2:1349832
Presentation
2019-01-17, O102, Sundsvall, 10:00 (English)
Supervisors
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Note

Vid tidpunkten för framläggningen av avhandlingen var följande delarbete opublicerat: delarbete 3 (manuskript).

At the time of the defence the following paper was unpublished: paper 3 (manuscript).

Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-09-10Bibliographically approved
List of papers
1. Background Modelling, Analysis and Implementation for Thermographic Images
Open this publication in new window or tab >>Background Modelling, Analysis and Implementation for Thermographic Images
2017 (English)In: PROCEEDINGS OF THE 2017 SEVENTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA 2017), IEEE, 2017Conference paper, Published paper (Refereed)
Abstract [en]

Background subtraction is one of the fundamental steps in the image-processing pipeline for distinguishing foreground from background. Most of the methods have been investigated with respect to visual images, in which case challenges are different compared to thermal images. Thermal sensors are invariant to light changes and have reduced privacy concerns. We propose the use of a low-pass IIR filter for background modelling in thermographic imagery due to its better performance compared to algorithms such as Mixture of Gaussians and K-nearest neighbour, while reducing memory requirements for implementation in embedded architectures. Based on the analysis of four different image datasets both indoor and outdoor, with and without people presence, the learning rate for the filter is set to 3×10-3 Hz and the proposed model is implemented on an Artix-7 FPGA.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Infrared; visual; pedestrian detection; smart camera; architecture; surveillance.
National Category
Embedded Systems
Identifiers
urn:nbn:se:miun:diva-32445 (URN)10.1109/IPTA.2017.8310078 (DOI)000428743900002 ()2-s2.0-85050756650 (Scopus ID)978-1-5386-1842-4 (ISBN)
Conference
Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA 2017), Montreal, Canada; November 28 - December 1, 2017
Projects
City MovementsSMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2019-09-10Bibliographically approved
2. Architectural evaluation of node: server partitioning for people counting
Open this publication in new window or tab >>Architectural evaluation of node: server partitioning for people counting
2018 (English)In: ACM International Conference Proceeding Series, New York: ACM Digital Library, 2018, article id Article No. 1Conference paper, Published paper (Refereed)
Abstract [en]

The Internet of Things has changed the range of applications for cameras requiring them to be easily deployed for a variety of scenarios indoor and outdoor, while achieving high performance in processing. As a result, future projections emphasise the need for battery operated smart cameras, capable of complex image processing tasks that also communicate within one another, and the server. Based on these considerations, we evaluate in-node and node – server configurations of image processing tasks to provide an insight of how tasks partitioning affects the overall energy consumption. The two main energy components taken in consideration for their influence in the total energy consumption are processing and communication energy. The results from the people counting scenario proved that processing background modelling, subtraction and segmentation in-node while transferring the remaining tasks to the server results in the most energy efficient configuration, optimising both processing and communication energy. In addition, the inclusion of data reduction techniques such as data aggregation and compression not always resulted in lower energy consumption as generally assumed, and the final optimal partition did not include data reduction.

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2018
Keywords
Image processing, people counting, smart camera, WVSN, thermography
National Category
Embedded Systems Signal Processing
Identifiers
urn:nbn:se:miun:diva-34613 (URN)10.1145/3243394.3243688 (DOI)000455840700001 ()2-s2.0-85056618892 (Scopus ID)978-1-4503-6511-6 (ISBN)
Conference
12th International Conference on Distributed Smart Cameras, ICDSC 2018; Eindhoven; Netherlands; 3 September 2018 through 4 September 2018
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2018-10-03 Created: 2018-10-03 Last updated: 2019-09-10Bibliographically approved
3. Communication and Computation Inter-Effects in People Counting Using Intelligence Partitioning
Open this publication in new window or tab >>Communication and Computation Inter-Effects in People Counting Using Intelligence Partitioning
(English)Manuscript (preprint) (Other academic)
National Category
Embedded Systems
Identifiers
urn:nbn:se:miun:diva-37177 (URN)
Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-09-10Bibliographically approved

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