With companies such as Netflix and YouTube accounting for more than 50% of the peak download traffic on North American fixed networks in 2015, video streaming represents a significant source of Internet traffic. Multimedia delivery over the Internet has evolved rapidly over the past few years. The last decade has seen video streaming transitioning from User Datagram Protocol to Transmission Control Protocol-based technologies. Dynamic adaptive streaming over HTTP (DASH) has recently emerged as a standard for Internet video streaming. A range of rate adaptation mechanisms are proposed for DASH systems in order to deliver video quality that matches the throughput of dynamic network conditions for a richer user experience. This survey paper looks at emerging research into the application of client-side, server-side, and in-network rate adaptation techniques to support DASH-based content delivery. We provide context and motivation for the application of these techniques and review significant works in the literature from the past decade. These works are categorized according to the feedback signals used and the end-node that performs or assists with the adaptation. We also provide a review of several notable video traffic measurement and characterization studies and outline open research questions in the field.

Recent scientific and technological advancements driven by the Internet of Things (IoT), Machine Learning (ML) and Artificial Intelligence (AI), distributed computing and data communication technologies have opened up a vast range of opportunities in many scientific fields—spanning from fast, reliable and efficient data communication to large-scale cloud/edge computing and intelligent big data analytics. Technological innovations and developments in these areas have also enabled many opportunities in the space industry. The successful Mars landing of NASA’s Perseverance rover on 18 February 2021 represents another giant leap for humankind in space exploration. Emerging research and developments of connectivity and computing technologies in IoT for space/non-terrestrial environments is expected to yield significant benefits in the near future. This survey paper presents a broad overview of the area and provides a look-ahead of the opportunities made possible by IoT and space-based technologies. We first survey the current developments of IoT and space industry, and identify key challenges and opportunities in these areas. We then review the state-of-the-art and discuss future opportunities for IoT developments, deployment and integration to support future endeavors in space exploration.

The Industry 4.0 digital transformation envisages future industrial systems to be fully automated, including the control, upgrade, and configuration processes of a large number of heterogeneous wired/wireless interconnected devices in Industrial Internet of Things environments. Most of the industrial automation systems today are based on the traditional International Society of Automation (ISA)-95 model, with some recently transitioned to Cloud Automation systems. Latest developments in network connectivity technologies, artificial intelligence, and Cloud/Fog computing technologies have motivated us to rethink the ISA-95 model. In this article, we propose a vision that aims to migrate most of the computational and automation tasks closer to the ground, which we term the collaborative “Cloud-Fog Automation” paradigm. We perform a comprehensive survey of the state-of-the-art and formulate the three pillars of this vision: Deterministic connectivity, deterministic connected intelligence, and deterministic networked computing. In each of these pillars, we review their latency and reliability, security, and functional safety requirements and challenges. Finally, we articulate and highlight key future research directions to realize this vision.

Open Platform Communications Unified Architecture (OPC UA) incorporates a wide range of features and covers most of the requirements for a platform-independent interoperability standard which can be used to transmit data and information from the factory production floor to the enterprise and management level. Due to its highly scalable and interoperable architecture, it is well-positioned for future deployment in smart embedded devices towards Industry 4.0, especially in environments where there are heterogeneous communication nodes. In this paper, we aim to evaluate the performance of OPC UA for communication in Industrial Internet of Things (IIoT) environments to better understand the technical implementation of OPC UA and the feasibility of incorporating OPC UA directly to resource-constrained edge devices. We propose an architectural system framework for OPC UA performance evaluation across a wide range of experiments. Our experimental results demonstrated the efficacy of the proposed system and evaluation framework. The OPC UA-based IIoT system architecture and budget-friendly/cost-effective testbed setup can be flexibly adopted for protocol testing, prototyping and educational purposes.

Recent years have seen the rapid development of technologies in Smart Grids (SGs) to enhance electricity networks with digital and data communication technologies. SGs can proactively detect, react, and respond to dynamic changes in the network. SGs can also enhance the efficiency and reliability of electricity supplies and promote the integration of renewable energy sources. Smart Meters (SMs) are often seen as the first step to a successful implementation of SGs. While SMs enable Utility Providers and consumers to obtain near real-time information of energy consumption, they can also be exploited to infer sensitive consumer data. Therefore, privacy preservation in SMs is paramount in ensuring the widespread and successful deployment of SGs. In this paper, we present a comprehensive survey of the state-of-the-art SM privacy-preserving techniques published in the literature over the past decade. We categorize these techniques based on the attack types and their objectives. We aim to offer a unique perspective in this survey article through the lens of privacy preservation, cross-cutting the wide range of techniques presented in the literature. We conclude by identifying the challenges and highlighting key future research directions in the field.

Internet of Things (IoT) applications such as telehealth, smart appliances, and smart energy are becoming more common within the home. However, they must compete for bandwidth with traditional applications such as video streaming, video conferencing, and bulk file transfers. Such competition can be detrimental to the IoT applications when home gateways use traditional first-in-first-out (FIFO) queue management. Simply increasing bandwidth between the home gateway and the Internet Service Provider (ISP), even when possible, provides no guarantee of bandwidth for IoT applications since many traditional applications will consume as much bandwidth as is available. In this paper, we explore whether active queue management (AQM), now being implemented in home gateways, can provide protection for IoT flows. We investigate the effect of different AQM algorithms deployed at the home gateway in scenarios with multiple concurrent application flows. We find that deploying multiqueue FlowQueue Controlled Delay (FQ-CoDel) or the hybrid FlowQueue Proportional Integral Controller Enhanced (FQ-PIE) at the home gateway can provide excellent capacity sharing, flow isolation, and good protection in terms of throughput and queuing delays for IoT flows and other applications, which cannot be achieved with traditional FIFO or other single-queue AQMs such as Proportional Integral Controller Enhanced (PIE).