The continuous rise of multimedia entertainment has led to an increased demand for delivering outstanding user experience of multimedia content. However, modelling user-perceived Quality of Experience (QoE) is a challenging task, resulting in efforts for better understanding and measurement of user-perceived QoE. To evaluate user QoE, subjective quality assessment, where people watch and grade videos, and objective quality assessment in which videos are graded using one or many objective metrics are conducted. While there is a plethora of video databases available for subjective and objective video quality assessment, these videos are artificially infused with various temporal and spatial impairments. Videos being assessed are artificially distorted with startup delay, bitrate changes, and stalls due to rebuffering events. To conduct a more credible quality assessment, a reproduction of original user experiences while watching different types of streams on different types and quality of networks is needed. To aid current efforts in bridging the gap between the mapping of objective video QoE metrics to user experience, we developed DashReStreamer, an open-source framework for re-creating adaptively streamed video in real networks. The framework takes inputs in the form of video logs captured by the client in a non-regulated setting, along with an.mpd file or a YouTube URL. The ultimate result is a video sequence that encompasses all the data extracted from the video log. DashReStreamer also calculates popular video quality metrics like PSNR, SSIM, MS-SSIM and VMAF. Finally, DashReStreamer allows creating impaired video sequences from the popular streaming platform, YouTube. As a demonstration of framework usage we created a database of 332 realistic video clips, based on video logs collected from real mobile and wireless networks. Every video clip is supplemented with bandwidth trace and video logs used in its creation and also with objective metrics calculation reports. In addition to dataset, we performed subjective evaluation of video content, assessing its effect on overall user QoE. We believe that this dataset and framework will allow the research community to better understand the impacts of video QoE dynamics.

As technology is the driver of the economy, it is necessary to follow emerging technological trends and to create appropriate conditions for its adoption and implementation as a human-centred technology. In this regard, rules and standards for the Internet of Things (IoT) and Artificial Intelligence (AI) should be established to best use the benefits of technology and to prevent or minimize the consequences of technology misuse. The fifth industrial revolution (Industry 5.0) has already begun, although Industry 4.0 is still developing. Consequently, the original attention has shifted from IoT to AI, with the IoT debate now being a prerequisite for the AI debate. As AI is transforming our lives, a growing number of countries have considered or already adopted national AI strategies. However, in many developing countries, national AI strategies and initiatives for establishing AI and IoT regulation and legislation frameworks yet need to be discussed. The subject of this article is the research of existing initiatives related to establishing the IoT and AI regulatory and legislative framework in the EU and its applicability in developing countries.

The cloud has become an essential part of modern computing, and its popularity continues to rise with each passing day. Currently, cloud computing is faced with certain challenges that are, due to the increasing demands, becoming urgent to address. One such challenge is the problem of load balancing, which involves the proper distribution of user requests within the cloud. This paper proposes a genetic algorithm for load balancing of the received requests across cloud resources. The algorithm is based on the processing of individual requests instantly upon arrival. The conducted test simulations showed that the proposed approach has better response and processing time compared to round robin, ESCE and throttled load balancing algorithms. The algorithm outperformed an existing genetic based load balancing algorithm, DTGA, as well.

The development of blockchain has allowed for the development of new concepts and ideas. A completely immutable ledger might not be appropriate for all new applications that are being envisaged for the blockchain. One of them is self-sovereign identity. The aim of this paper is to analyze the possible use cases for blockchain redaction in SSI. Main concepts of redaction and a summary of the current research progress are given. Use cases for redaction in SSI are categorized and described alongside their existing solutions. Detailed proposal for possible use cases is given and comparison is drawn between this solution and existing solution. Future challenges are introduced.

Similar to any spoof detection systems, power grid monitoring systems and devices are subject to various cyberattacks by determined and well-funded adversaries. Many well-publicized real-world cyberattacks on power grid systems have been publicly reported. Phasor Measurement Units (PMUs) networks with Phasor Data Concentrators (PDCs) are the main building blocks of the overall wide area monitoring and situational awareness systems in the power grid. The data between PMUs and PDC(s) are sent through the legacy networks, which are subject to many attack scenarios under with no, or inadequate, countermeasures in protocols, such as IEEE 37.118-2. In this paper, we consider a stealthier data spoofing attack against PMU networks, called a mirroring attack, where an adversary basically injects a copy of a set of packets in reverse order immediately following their original positions, wiping out the correct values. To the best of our knowledge, for the first time in the literature, we consider a more challenging attack both in terms of the strategy and the lower percentage of spoofed attacks. As part of our countermeasure detection scheme, we make use of novel framing approach to make application of a 2D Convolutional Neural Network (CNN)-based approach which avoids the computational overhead of the classical sample-based classification algorithms. Our experimental evaluation results show promising results in terms of both high accuracy and true positive rates even under the aforementioned stealthy adversarial attack scenarios.

Various devices and monitoring systems have been developed and deployed in order to monitor the power grid. Indeed, several real-world cyberattacks on power grid systems have been publicly reported. For the transmission and distribution, Phasor Measurement Units (PMUs) constitute the main sensing equipment of the overall wide area monitoring and situational awareness systems by collecting high-resolution data and sending them to Phasor Data Concentrators (PDCs). In this paper, we consider data spoofing attacks against PMU networks. The data between PMUs and PDC(s) are sent through the legacy networks, which are subject to many attack scenarios under with no, or inadequate, countermeasures in protocols, such as IEEE 37.118-2. We consider one potential attack, where an adversary may simply keep injecting a repeated measurement through a compromised PMU to disrupt the monitoring system. This attack is referred to as a Repeated Last Value (RLV) attack. We develop and evaluate countermeasures against RLV attacks using a 2D Convolutional Neural Network (CNN)-based approach, which operates in frames for each second mimicking images, in order to avoid the computational overhead of the classical sample-based classification algorithms, such as SVM. Further, we take this frame-based approach and use it with Support Vector Machine (SVM) for performance evaluation. Our preliminary results show that frame-based CNN as well as SVM provide promising results for RLV attacks while the efficacy of CNN over SVM frame becomes more pronounced as the attack intensity increases.

Multimedia streaming over the Internet (live and on demand) is the cornerstone of modern Internet carrying more than 60% of all traffic. With such high demand, delivering outstanding user experience is a crucial and challenging task. To evaluate user Quality of Experience (QoE) many researchers deploy subjective quality assessments where participants watch and rate videos artificially infused with various temporal and spatial impairments. To aid current efforts in bridging the gap between the mapping of objective video QoE metrics to user experience, we developed DashReStreamer, an open-source framework for re-creating adaptively streamed video in real networks. DashReStreamer utilises a log created by a HTTP adaptive streaming (HAS) algorithm run in an uncontrolled environment (i.e., wired or wireless networks), encoding visual changes and stall events in one video file. These videos are applicable for subjective QoE evaluation mimicking realistic network conditions. To supplement DashReStreamer, we re-create 234 realistic video clips, based on video logs collected from real mobile and wireless networks. In addition our dataset contains both video logs with all decisions made by the HAS algorithm and network bandwidth profile illustrating throughput distribution. We believe this dataset and framework will permit other researchers in their pursuit for the final frontier in understanding the impact of video QoE dynamics.

Self-Sovereign Identity (SSI) is a novel and emerging, decentralized digital identity approach that enables entities to control and manage their digital identifiers and associated identity data while enhancing trust, privacy, security, and the many other properties identified and analyzed in this paper. The paper provides an overview and classification of the SSI properties, focusing on an in-depth analysis, furthermore, presenting a comprehensive collection of SSI properties that are important for the implementation of the SSI system. In addition, it explores the general SSI process flow, and highlights the steps in which individual properties are important. After the initial purification and classification phase, we then validated properties among experts in the field of Decentralized and Self-Sovereign Identity Management using an online questionnaire, which resulted in a final set of classified and verified SSI properties. The results can be used for further work on definition and standardization of the SSI field.

The Internet of Things (IoT) is a leading trend with numerous opportunities accompanied by advantages as well as disadvantages. Parallel with IoT development, significant privacy and personal data protection challenges are also growing. In this regard, the General Data Protection Regulation (GDPR) is often considered the world’s strongest set of data protection rules and has proven to be a catalyst for many countries around the world. The concepts and interaction of the data controller, the joint controllers, and the data processor play a key role in the implementation of the GDPR. Therefore, clarifying the blurred IoT actors’ relationships to determine corresponding responsibilities is necessary. Given the IoT transformation reflected in shifting computing power from cloud to the edge, in this research we have considered how these computing paradigms are affecting IoT actors. In this regard, we have introduced identification of IoT actors according to a new five-computing layer IoT model based on the cloud, fog, edge, mist, and dew computing. Our conclusion is that identifying IoT actors in the light of the corresponding IoT data manager roles could be useful in determining the responsibilities of IoT actors for their compliance with data protection and privacy rules.