The Internet of Things (IoT) is rapidly spreading, reaching a multitude of different domains, including personal health care, environmental monitoring, home automation, smart mobility, and Industry 4.0. As a consequence, more and more IoT devices are being deployed in a variety of public and private environments, progressively becoming common objects of everyday life. It is hence apparent that, in such a scenario, cybersecurity becomes critical to avoid threats like leakage of sensible information, denial of service attacks, unauthorized network access, and so on. Unfortunately, many low-end IoT commercial products do not usually support strong security mechanisms, and can hence be target of -or even means for -a number of security attacks. The aim of this paper is to provide a broad overview of the security risks in the IoT sector and to discuss some possible counteractions. To this end, after a general introduction to security in the IoT domain, we discuss the specific security mechanisms adopted by the most popular IoT communication protocols. Then, we report and analyze some of the attacks against real IoT devices reported in the literature, in order to point out the current security weaknesses of commercial IoT solutions and remark the importance of considering security as an integral part in the design of IoT systems. We conclude the paper with a reasoned comparison of the considered IoT technologies with respect to a set of qualifying security attributes, namely integrity, anonymity, confidentiality, privacy, access control, authentication, authorization, resilience, self organization.

The term “ranging” is often used to indicate the operations that make it possible to estimate the distance between two nodes by processing some signals generated and/or received by the nodes. In wireless systems, a very popular ranging method makes use of the radio signal strength (RSS), which is a measure of the received radio signal power. However, RSS-based ranging is considered very inaccurate, particularly in indoor environments, mainly because of the randomness of the received signal power. In this tutorial paper, we provide an in-depth analysis of the main factors that affect the variability of the received signal power and the accuracy of the RSS measurements. Starting from a survey of the most common and widely accepted models for the radio signal propagation and the RSS-based ranging, we then focus our attention on some technological and procedural pitfalls that are often overlooked, but may significantly affect the accuracy of the RSS-based ranging, and we suggest possible techniques to alleviate such problems. The theoretical argumentation is backed up by a set of empirical results in different scenarios. We conclude this paper by providing some best-practice recommendations for proper RSS-based ranging estimation in wireless networks and discussing new approaches and open research challenges.

The ever-increasing demand for seamless high-definition video streaming, along with the widespread adoption of the Dynamic Adaptive Streaming over HTTP (DASH) standard, has been a major driver of the large amount of research on bitrate adaptation algorithms. The complexity and variability of the video content and of the mobile wireless channel make this an ideal application for learning approaches. Here, we present D-DASH, a framework that combines Deep Learning and Reinforcement Learning techniques to optimize the Quality of Experience (QoE) of DASH. Different learning architectures are proposed and assessed, combining feed-forward and recurrent deep neural networks with advanced strategies. D-DASH designs are thoroughly evaluated against prominent algorithms from the state-of-the-art, both heuristic and learning-based, evaluating performance indicators such as image quality across video segments and freezing/rebuffering events. Our numerical results are obtained on real and simulated channel traces and show the superiority of D-DASH in nearly all the considered quality metrics. Besides yielding a considerably higher QoE, the D-DASH framework exhibits faster convergence to the rate-selection strategy than the other learning algorithms considered in the study. This makes it possible to shorten the training phase, making D-DASH a good candidate for client-side runtime learning.

Over the years, the Internet has been enriched with new available communication technologies, for both fixed and mobile networks and devices, exhibiting an impressive growth in terms of performance, with steadily increasing available data rates. The Internet research community has kept trying to evolve the transport layer protocols to match the capabilities of modern networks, in order to fully reap the benefits of the new communication technologies. This paper surveys the main novelties related to transport protocols that have been recently proposed, identifying three main research trends: (i) the evolution of congestion control algorithms, to target optimal performance in challenging scenarios, possibly with the application of machine learning techniques; (ii) the proposal of brand new transport protocols, alternative to the tcp and implemented in the user-space; and (iii) the introduction of multipath capabilities at the transport layer.