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This material is published in the open archive of Mid Sweden University DIVA http://miun.diva-portal.org to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders.All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract-A light field is commonly described by a twoplane representation with four dimensions. Refocused threedimensional contents can be rendered from light field images. A method for capturing these images is by using cameras with microlens arrays. A dense sampling of the light field results in large amounts of redundant data. Therefore, an efficient compression is vital for a practical use of these data. In this paper, we propose a displacement intra prediction scheme with a maximum of two hypotheses for the compression of plenoptic contents from focused plenoptic cameras. The proposed scheme is further implemented into HEVC. The work is aiming at coding plenoptic captured contents efficiently without knowing underlying camera geometries. In addition, the theoretical analysis of the displacement intra prediction for plenoptic images is explained; the relationship between the compressed captured images and their rendered quality is also analyzed. Evaluation results show that plenoptic contents can be efficiently compressed by the proposed scheme. Bit rate reduction up to 60 percent over HEVC is obtained for plenoptic images, and more than 30 percent is achieved for the tested video sequences.

Interactive photo-realistic graphics can be rendered by using light field datasets. One way of capturing the dataset is by using light field cameras with microlens arrays. The captured images contain repetitive patterns resulted from adjacent microlenses. These images don't resemble the appearance of a natural scene. This dissimilarity leads to problems in light field image compression by using traditional image and video encoders, which are optimized for natural images and video sequences. In this paper, we introduce the full inter-prediction scheme in HEVC into intra-prediction for the compression of light field images. The proposed scheme is capable of performing both uni-directional and bi-directional prediction within an image. The evaluation results show that above 3 dB quality improvements or above 50 percent bit-rate saving can be achieved in terms of BD-PSNR for the proposed scheme compared to the original HEVC intra-prediction for light field images.

This material is published in the open archive of Mid Sweden University DIVA http://miun.diva-portal.org to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders.All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. Abstract-One of the light field capturing techniques is the focused plenoptic capturing. By placing a microlens array in front of the photosensor, the focused plenoptic cameras capture both spatial and angular information of a scene in each microlens image and across microlens images. The capturing results in significant amount of redundant information, and the captured image is usually of a large resolution. A coding scheme that removes the redundancy before coding can be of advantage for efficient compression, transmission and rendering. In this paper, we propose a lossy coding scheme to efficiently represent plenoptic images. The format contains a sparse image set and its associated disparities. The reconstruction is performed by disparity-based interpolation and inpainting, and the reconstructed image is later employed as a prediction reference for the coding of the full plenoptic image. As an outcome of the representation, the proposed scheme inherits a scalable structure with three layers. The results show that plenoptic images are compressed efficiently with over 60 percent bit rate reduction compared to HEVC intra, and with over 20 percent compared to HEVC block copying mode.article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

Digitalization is a global trend becoming ever more important to our connected and sustainable society. This trend also affects industry where the Industrial Internet of Things is an important part, and there is a need to conserve spectrum as well as energy when communicating data to a fog or cloud back-end system. In this paper we investigate the benefits of fog computing by proposing a novel distributed learning model on the sensor device and simulating the data stream in the fog, instead of transmitting all raw sensor values to the cloud back-end. To save energy and to communicate as few packets as possible, the updated parameters of the learned model at the sensor device are communicated in longer time intervals to a fog computing system. The proposed framework is implemented and tested in a real world testbed in order to make quantitative measurements and evaluate the system. Our results show that the proposed model can achieve a 98% decrease in the number of packets sent over the wireless link, and the fog node can still simulate the data stream with an acceptable accuracy of 97%. We also observe an end-to-end delay of 180 ms in our proposed three-layer framework. Hence, the framework shows that a combination of fog and cloud computing with a distributed data modeling at the sensor device for wireless sensor networks can be beneficial for Industrial Internet of Things applications.

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Abstract:The Internet of Things is predicted to consist of over 50 billion devices aiming to solve problems in most areas of our digital society. A large part of the data communicated is expected to consist of various multimedia contents, such as live audio and video. This article presents a solution for the communication of high definition video in low-delay scenarios (<200 ms) under the constraints of devices with limited hardware resources, such as the Raspberry Pi. We verify that it is possible to enable low delay video streaming between Raspberry Pi devices using a distributed Internet of Things system called the SensibleThings platform. Specifically, our implementation transfers a 6 Mbps H.264 video stream of 1280 × 720 pixels at 25 frames per second between devices with a total delay of 181 ms on the public Internet, of which the overhead of the distributed Internet of Things communication platform only accounts for 18 ms of this delay. We have found that the most significant bottleneck of video transfer on limited Internet of Things devices is the video coding and not the distributed communication platform, since the video coding accounts for 90% of the total delay.

We see a shift from todays Internet-of-Things (IoT) 1 to include more industrial equipment and metrology systems, 2 forming the Industrial Internet of Things (IIoT). However, this 3 leads to many concerns related to confidentiality, integrity, 4 availability, privacy and non-repudiation. Hence, there is a need 5 to secure the IIoT in order to cater for a future with smart grids, 6 smart metering, smart factories, smart cities, and smart manu-7 facturing. It is therefore important to research IIoT technologies 8 and to create order in this chaos, especially when it comes to 9 securing communication, resilient wireless networks, protecting 10 industrial data, and safely storing industrial intellectual property 11 in cloud systems. This research therefore presents the challenges, 12 needs, and requirements of industrial applications when it comes 13 to securing IIoT systems.14