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  • 1.
    Krug, Silvia
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Shallari, Irida
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    O'Nils, Mattias
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    A Case Study on Energy Overhead of Different IoT Network Stacks2019In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), IEEE, 2019, p. 528-529Conference paper (Refereed)
    Abstract [en]

    Due to the limited energy budget for sensor nodes in the Internet of Things (IoT), it is crucial to develop energy efficient communications amongst others. This need leads to the development of various energy-efficient protocols that consider different aspects of the energy status of a node. However, a single protocol covers only one part of the whole stack and savings on one level might not be as efficient for the overall system, if other levels are considered as well. In this paper, we analyze the energy required for an end device to maintain connectivity to the network as well as perform application specific tasks. By integrating the complete stack perspective, we build a more holistic view on the energy consumption and overhead for a wireless sensor node. For better understanding, we compare three different stack variants in a base scenario and add an extended study to evaluate the impact of retransmissions as a robustness mechanism. Our results show, that the overhead introduced by the complete stack has an significant impact on the nodes energy consumption especially if retransmissions are required.

  • 2.
    Shallari, Irida
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Intelligence Partitioning for IoT: Communication and Processing Inter-Effects for Smart Camera Implementation2019Licentiate 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.

  • 3.
    Shallari, Irida
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Anwar, Qaiser
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Imran, Muhammad
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    O'Nils, Mattias
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Background Modelling, Analysis and Implementation for Thermographic Images2017In: PROCEEDINGS OF THE 2017 SEVENTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA 2017), IEEE, 2017Conference 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.

  • 4.
    Shallari, Irida
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Imran, Muhammad
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design. HIAB AB.
    Lawal, Najeem
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    O'Nils, Mattias
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Evaluating Pre-Processing Pipelines for Thermal-Visual Smart Camera2017In: Proceedings of the 11th International Conference on Distributed Smart Cameras, ACM Digital Library, 2017, Vol. F132201, p. 95-100Conference paper (Refereed)
    Abstract [en]

    Smart camera systems integrating multi-model image sensors provide better spectral sensitivity and hence better pass-fail decisions. In a given vision system, pre-processing tasks have a ripple effect on output data and pass-fail decision of high level tasks such as feature extraction, classification and recognition. In this work, we investigated four pre-processing pipelines and evaluated the effect on classification accuracy and output transmission data. The pre-processing pipelines processed four types of images, thermal grayscale, thermal binary, visual and visual binary. The results show that the pre-processing pipeline, which transmits visual compressed Region of Interest (ROI) images, offers 13 to 64 percent better classification accuracy as compared to thermal grayscale, thermal binary and visual binary. The results show that visual raw and visual compressed ROI with suitable quantization matrix offers similar classification accuracy but visual compressed ROI offers up to 99 percent reduced communication data as compared to visual ROI.

  • 5.
    Shallari, Irida
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Krug, Silvia
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    O'Nils, Mattias
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Architectural evaluation of node: server partitioning for people counting2018In: ACM International Conference Proceeding Series, New York: ACM Digital Library, 2018, article id Article No. 1Conference 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.

  • 6.
    Shallari, Irida
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Krug, Silvia
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    O'Nils, Mattias
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    Communication and Computation Inter-Effects in People Counting Using Intelligence Partitioning2020In: Journal of Real-Time Image Processing, ISSN 1861-8200, E-ISSN 1861-8219Article in journal (Other academic)
    Abstract [en]

    The rapid development of the Internet of Things is affecting the requirements towards wireless vision sensor networks (WVSN). Future smart camera architectures require battery-operated devices to facilitate deployment for scenarios such as industrial monitoring, environmental monitoring and smart city, consequently imposing constraints on the node energy consumption. This paper provides an analysis of the inter-effects between computation and communication energy for a smart camera node. Based on a people counting scenario, we evaluate the trade-off for the node energy consumption with different processing configurations of the image processing tasks, and several communication technologies. The results indicate that the optimal partition between the smart camera node and remote processing is with background modelling, segmentation, morphology and binary compression implemented in the smart camera, supported by Bluetooth Low Energy (BLE) version 5 technologies. The comparative assessment of these results with other implementation scenarios underlines the energy efficiency of this approach. This work changes pre-conceptions regarding design space exploration in WVSN, motivating further investigation regarding the inclusion of intermediate processing layers between the node and the cloud to interlace low-power configurations of communication and processing architectures.

  • 7.
    Shallari, Irida
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    O'Nils, Mattias
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    From the Sensor to the Cloud: Intelligence Partitioning for Smart Camera Applications2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 23, article id 5162Article in journal (Refereed)
    Abstract [en]

    The Internet of Things has grown quickly in the last few years, with a variety of sensing, processing and storage devices interconnected, resulting in high data traffic. While some sensors such as temperature, or humidity sensors produce a few bits of data periodically, imaging sensors output data in the range of megabytes every second. This raises a complexity for battery operated smart cameras, as they would be required to perform intensive image processing operations on large volumes of data, within energy consumption constraints. By using intelligence partitioning we analyse the effects of different partitioning scenarios for the processing tasks between the smart camera node, the fog computing layer and cloud computing, in the node energy consumption as well as the real time performance of the WVSN (Wireless Vision Sensor Node). The results obtained show that traditional design space exploration approaches are inefficient for WVSN, while intelligence partitioning enhances the energy consumption performance of the smart camera node and meets the timing constraints.

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