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O'Nils, Mattias
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Publications (10 of 152) Show all publications
Shallari, I., Krug, S. & O'Nils, M. (2020). Communication and Computation Inter-Effects in People Counting Using Intelligence Partitioning. Journal of Real-Time Image Processing
Open this publication in new window or tab >>Communication and Computation Inter-Effects in People Counting Using Intelligence Partitioning
2020 (English)In: Journal of Real-Time Image Processing, ISSN 1861-8200, E-ISSN 1861-8219Article in journal (Other academic) Epub ahead of print
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.

Keywords
Intelligence partitioning, Smart camera, WVSN, Energy-efficiency, IoT, In-sensor processing
National Category
Embedded Systems
Identifiers
urn:nbn:se:miun:diva-37177 (URN)10.1007/s11554-020-00943-6 (DOI)2-s2.0-85078090728 (Scopus ID)
Note

An initial manuscript version of this article was included in the licentiate thesis.

Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2020-02-21Bibliographically approved
Krug, S., Shallari, I. & O'Nils, M. (2019). A Case Study on Energy Overhead of Different IoT Network Stacks. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT): . Paper presented at 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 15-18 April, 2019 (pp. 528-529). IEEE
Open this publication in new window or tab >>A Case Study on Energy Overhead of Different IoT Network Stacks
2019 (English)In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), IEEE, 2019, p. 528-529Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Internet of Things, telecommunication power management, wireless sensor networks, energy overhead, energy budget, sensor nodes, energy efficient communications, energy-efficient protocols, energy status, single protocol, wireless sensor node, nodes energy consumption, Energy consumption, Routing, Synchronization, Routing protocols, Protocol Overhead Comparison, Experimental Observation, Analytical Evaluation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-37175 (URN)10.1109/WF-IoT.2019.8767284 (DOI)000492865800098 ()2-s2.0-85073895557 (Scopus ID)
Conference
2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 15-18 April, 2019
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2020-01-15Bibliographically approved
Nie, Y., Sommella, P., O'Nils, M., Liguori, C. & Lundgren, J. (2019). Automatic Detection of Melanoma with Yolo Deep Convolutional Neural Networks. In: 2019 E-Health and Bioengineering Conference (EHB): . Paper presented at IEEE International Conference on e-Health and Bioengineering 2019. IEEE
Open this publication in new window or tab >>Automatic Detection of Melanoma with Yolo Deep Convolutional Neural Networks
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2019 (English)In: 2019 E-Health and Bioengineering Conference (EHB), IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

In the past three years, deep convolutional neural networks (DCNNs) have achieved promising performance in detecting skin cancer. However, improving the accuracy and efficiency of the automatic detection of melanoma is still urgent due to the visual similarity of benign and malignant dermoscopy. There is also a need for fast and computationally effective systems for mobile applications targeting caregivers and homes. This paper presents the You Only Look Once (Yolo) algorithms, which are based on DCNNs applied to the detection of melanoma. The Yolo algorithms comprise YoloV1, YoloV2, and YoloV3, whose methodology first resets the input image size and then divides the image into several cells. According to the position of the detected object in the cell, the network will try to predict the bounding box of the object and the class confidence score. Our test results indicate that the mean average precision (mAP) of Yolo can exceed 0.82 with a training set of only 200 images, proving that this method has great advantages for detecting melanoma in lightweight system applications.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Image processing, Melanoma, Yolo, Object Detection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-38154 (URN)10.1109/EHB47216.2019.8970033 (DOI)978-1-7281-2603-6 (ISBN)
Conference
IEEE International Conference on e-Health and Bioengineering 2019
Funder
European Regional Development Fund (ERDF)
Available from: 2019-12-19 Created: 2019-12-19 Last updated: 2020-02-07Bibliographically approved
Carratu, M., Liguori, C., Pietrosanto, A., O'Nils, M. & Lundgren, J. (2019). Data Fusion for Timber Bundle Volume Measurement. In: : . Paper presented at 2019 IEEE International Instrumentation & Measurement Technology Conference, Auckland, New Zealand, May 20-23, 2019.
Open this publication in new window or tab >>Data Fusion for Timber Bundle Volume Measurement
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2019 (English)Conference paper (Other academic)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-37176 (URN)2-s2.0-85072824626 (Scopus ID)
Conference
2019 IEEE International Instrumentation & Measurement Technology Conference, Auckland, New Zealand, May 20-23, 2019
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-10-20Bibliographically approved
Alqaysi, H., Lawal, N., Fedorov, I., Thörnberg, B. & O'Nils, M. (2019). Design Exploration of Multi-Camera Dome. In: ICDSC 2019 Proceedings of the 13th International Conference on Distributed Smart Cameras: . Paper presented at 13th InternationalConference on Distributed Smart Cameras (ICDSC 2019), Trento, Italy, 9-11 September, 2019. New York, NY: ACM Digital Library, Article ID Article No. 7a.
Open this publication in new window or tab >>Design Exploration of Multi-Camera Dome
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2019 (English)In: ICDSC 2019 Proceedings of the 13th International Conference on Distributed Smart Cameras, New York, NY: ACM Digital Library, 2019, article id Article No. 7aConference paper, Published paper (Refereed)
Abstract [en]

Visual monitoring systems employ distributed smart cameras toeffectively cover a given area satisfying specific objectives. Thechoice of camera sensors and lenses and their deployment affectsdesign cost, accuracy of the monitoring system and the ability toposition objects within the monitored area. Design cost can bereduced by investigating deployment topology such as groupingcameras together to form a dome at a node and optimize it formonitoring constraints. The constraints may include coverage area,number of cameras that can be integrated in a node and pixelresolution at a given distance. This paper presents a method foroptimizing the design cost of multi-camera dome by analyzing tradeoffsbetween monitoring constraints. The proposed method can beused to reduce monitoring cost while fulfilling design objectives.Results show how to increase coverage area for a given cost byrelaxing requirements on design constraints. Multi-camera domescan be used in sky monitoring applications such as monitoring windparks and remote air-traffic control of airports where all-round fieldof view about a point is required to monitor.

Place, publisher, year, edition, pages
New York, NY: ACM Digital Library, 2019
Keywords
Distributed smart cameras, sky monitoring, volumetric surveillance.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-36762 (URN)10.1145/3349801.3349808 (DOI)2-s2.0-85073333209 (Scopus ID)978-1-4503-7189-6 (ISBN)
Conference
13th InternationalConference on Distributed Smart Cameras (ICDSC 2019), Trento, Italy, 9-11 September, 2019
Projects
SMART
Available from: 2019-07-29 Created: 2019-07-29 Last updated: 2019-11-14Bibliographically approved
Taami, T., Krug, S. & O'Nils, M. (2019). Experimental Characterization of Latency in Distributed IoT Systems with Cloud Fog Offloading. In: IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS: . Paper presented at 15th IEEE International Workshop on Factory Communication Systems, WFCS 2019, Sundsvall, 27 May-29 May 2019. Institute of Electrical and Electronics Engineers (IEEE), Article ID 8757960.
Open this publication in new window or tab >>Experimental Characterization of Latency in Distributed IoT Systems with Cloud Fog Offloading
2019 (English)In: IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS, Institute of Electrical and Electronics Engineers (IEEE), 2019, article id 8757960Conference paper, Published paper (Refereed)
Abstract [en]

The Internet of Things (IoT) enables users to gather and analyze data from a large number of devices. Knowledge obtained by these systems is valuable in order to understand, control, and enhance the monitored process. The mass of information to process leads however to new challenges related to required resources for both data processing and data transportation. Two critical metrics are latency and consumed energy to complete a given task. Both metrics might be exceed if all processing is done locally at the sensor device level. Cloud and Fog computing concepts can help to mitigate this effect. However, using such offloading concepts add complexity and overhead to the system. In this paper, we study the latency for processing and communication tasks in a distributed IoT systems with respect to cloud or fog offloading and derive characteristic cost functions for the studied tasks. Our results give valuable insights into the tradeoffs and constraint within our example scenario. The developed characterization methodology can however be applied to any kind of IoT system and thus allowing more general analysis. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Communication Latency, Distributed IoT Systems, Performance Characterization, Processing Latency
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-36902 (URN)10.1109/WFCS.2019.8757960 (DOI)000490866300011 ()2-s2.0-85070104037 (Scopus ID)9781728112688 (ISBN)
Conference
15th IEEE International Workshop on Factory Communication Systems, WFCS 2019, Sundsvall, 27 May-29 May 2019
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-11-13Bibliographically approved
Shallari, I. & O'Nils, M. (2019). From the Sensor to the Cloud: Intelligence Partitioning for Smart Camera Applications. Sensors, 19(23), Article ID 5162.
Open this publication in new window or tab >>From the Sensor to the Cloud: Intelligence Partitioning for Smart Camera Applications
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 23, article id 5162Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Switzerland: , 2019
Keywords
intelligence partitioning, smart camera, WVSN, IoT, in-sensor processing, fog, cloud, energy-efficiency
National Category
Embedded Systems
Identifiers
urn:nbn:se:miun:diva-34612 (URN)10.3390/s19235162 (DOI)2-s2.0-85075687795 (Scopus ID)
Available from: 2019-11-28 Created: 2019-11-28 Last updated: 2020-01-10Bibliographically approved
Krug, S. & O'Nils, M. (2019). Modeling and Comparison of Delay and Energy Cost of IoT Data Transfers. IEEE Access, 7, 58654-58675
Open this publication in new window or tab >>Modeling and Comparison of Delay and Energy Cost of IoT Data Transfers
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 58654-58675Article in journal (Refereed) Published
Abstract [en]

Communication is often considered as the most costly component of a wireless sensor node. As a result, a variety of technologies and protocols aim to reduce the energy consumption for the communication especially in the Internet of Things context. In order to select the best suitable technology for a given use case, a tool that allows the comparison of these options is needed. The goal of this paper is to introduce a new modular modeling framework that enables a comparison of various technologies based on analytical calculations. We chose to model the cost for a single data transfer of arbitrary application data amounts in order to provide flexibility regarding the data amount and traffic patterns. The modeling approach covers the stack traversal of application data and thus in comparison to other approaches includes the required protocol overhead directly. By applying our models to different data amounts, we are able to show tradeoffs between various technologies and enable comparisons for different scenarios. In addition, our results reveal the impact of design decisions that can help to identify future development challenges.

Keywords
Analytical models, communication networks, data transfer, Internet of Things, performance evaluation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-36650 (URN)10.1109/ACCESS.2019.2913703 (DOI)000468544300001 ()2-s2.0-85065892488 (Scopus ID)
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2019-07-08 Created: 2019-07-08 Last updated: 2019-09-09Bibliographically approved
Krug, S., Bader, S., Oelmann, B. & O'Nils, M. (2019). Suitability of Communication Technologies for Harvester-Powered IoT-Nodes. In: IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS: . Paper presented at 15th IEEE International Workshop on Factory Communication Systems, WFCS 2019, Sundsvall, 27 May-29 May 2019. Institute of Electrical and Electronics Engineers (IEEE), Article ID 8758042.
Open this publication in new window or tab >>Suitability of Communication Technologies for Harvester-Powered IoT-Nodes
2019 (English)In: IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS, Institute of Electrical and Electronics Engineers (IEEE), 2019, article id 8758042Conference paper, Published paper (Refereed)
Abstract [en]

The Internet of Things introduces Internet connectivity to things and objects in the physical world and thus enables them to communicate with other nodes via the Internet directly. This enables new applications that for example allow seamless process monitoring and control in industrial environments. One core requirement is that the nodes involved in the network have a long system lifetime, despite limited access to the power grid and potentially difficult propagation conditions. Energy harvesting can provide the required energy for this long lifetime if the node is able to send the data based on the available energy budget. In this paper, we therefore analyze and evaluate which common IoT communication technologies are suitable for nodes powered by energy harvesters. The comparison includes three different constraints from different energy sources and harvesting technologies besides several communication technologies. Besides identifying possible technologies in general, we evaluate the impact of duty-cycling and different data sizes. The results in this paper give a road map for combining energy harvesting technology with IoT communication technology to design industrial sensor nodes. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Energy Harvesting, Industrial Applications, Internet of Things, Network Access Technologies
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-36903 (URN)10.1109/WFCS.2019.8758042 (DOI)000490866300029 ()2-s2.0-85070056989 (Scopus ID)9781728112688 (ISBN)
Conference
15th IEEE International Workshop on Factory Communication Systems, WFCS 2019, Sundsvall, 27 May-29 May 2019
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2020-01-29Bibliographically approved
Aurangzeb, K., Alhussein, M. & O'Nils, M. (2018). Analysis of Binary Image Coding Methods for Outdoor Applications of Wireless Vision sensor Networks. IEEE Access, 6, 16932-16941
Open this publication in new window or tab >>Analysis of Binary Image Coding Methods for Outdoor Applications of Wireless Vision sensor Networks
2018 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 16932-16941Article in journal (Refereed) Published
Abstract [en]

The processing of images at the vision sensor nodes (VSN) requires a high computation power and their transmission requires a large communication bandwidth. The energy budget is limited in outdoor applications of wireless vision sensor networks (WVSN). This means that both the processing of images at the VSN and the communication to server must be energy efficient. The wireless communication of uncompressed data consumes huge amounts of energy. Data compression methods are efficient in reducing data in images and can be used for the reduction in transmission energy. We have evaluated seven binary image coding techniques. Our evaluation is based on the processing complexity and energy consumption of the compression methods on the embedded platforms. The focus is to come up with a binary image coding method, which has good compression efficiency and short processing time. An image coding method with such attributes will result in reduced total energy requirement of the node. We have used both statistically generated images and real captured images, in our experiments. Based on our results, we conclude that International Telegraph and Telephone Consultative Committee Group 4, gzip_pack and JPEG-LS are suitable coding methods for the outdoor applications of WVSNs.

Keywords
Embedded systems, energy consumption, image compression, wireless vision sensor network
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-33623 (URN)10.1109/ACCESS.2018.2816162 (DOI)000430436500001 ()2-s2.0-85043782275 (Scopus ID)
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2019-09-09Bibliographically approved
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