A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy

Feedback Control Goes Wireless: Guaranteed Stability over Low-power Multi-hop Networks

Closing feedback loops fast and over long distances is key to emerging applications; for example, robot motion control and swarm coordination require update intervals of tens of milliseconds. Low-power wireless technology is preferred for its low cost, small form factor, and flexibility, especially if the devices support multi-hop communication. So far, however, feedback control over wireless multi-hop networks has only been shown for update intervals on the order of seconds. This paper presents a wireless embedded system that tames imperfections impairing control performance (e.g., jitter and message loss), and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for physical processes with linear time-invariant dynamics. Using experiments on a cyber-physical testbed with 20 wireless nodes and multiple cart-pole systems, we are the first to demonstrate and evaluate feedback control and coordination over wireless multi-hop networks for update intervals of 20 to 50 milliseconds.Comment: Accepted final version to appear in: 10th ACM/IEEE International Conference on Cyber-Physical Systems (with CPS-IoT Week 2019) (ICCPS '19), April 16--18, 2019, Montreal, QC, Canad

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Cyber Security and Critical Infrastructures 2nd Volume

The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems

Flexible Congestion Management for Error Reduction in Wireless Sensor Networks

The dissertation is concerned with the efficient resolution of data congestion on wireless sensor networks (WSNs). WSNs are of increasing relevance due to their applications in automation, industrial processes, natural-disaster detection, weather prediction, and climate monitoring. In large WSNs where measurements are periodically made at each node in the network and sent in a multi-hop fashion via the network tree to a single base-station node, the volume of data at a node may exceed the transmission capabilities of the node. This type of congestion can negatively impact data accuracy when packets are lost in transmission. We propose flexible congestion management for sensor networks (FCM) as a data-collection scheme to reduce network traffic and minimize the error resulting from data-volume reduction. FCM alleviates all congestion by lossy data fusion, encourages opportunistic fusion with an application-specific distortion tolerance, and balances network traffic. We consider several data-fusion methods including the k-means algorithm and two forms of adaptive summarization. Additional fusion is allowed when like data may be fused with low error up to some limit set by the user of the data-collection application on the network. Increasing the error limit tends to reduce the overall traffic on the network at the cost of data accuracy. When a node fuses more data than is required to alleviate congestion, its siblings are notified that they may increase the sizes of their transmissions accordingly. FCM is further improved to re-balance the network traffic of subtrees such that subtrees whose measurements have lower variance may decrease their output rates while subtrees whose measurements have higher variance may increase their output rates, while still addressing all congestion in the network. We verify the effectiveness of FCM with extensive simulations

Guest Editorial Special Issue on: Big Data Analytics in Intelligent Systems

The amount of information that is being created, every day, is quickly growing. As such, it is now more common than ever to deal with extremely large datasets. As systems develop and become more intelligent and adaptive, analysing their behaviour is a challenge. The heterogeneity, volume and speed of data generation are increasing rapidly. This is further exacerbated by the use of wireless networks, sensors, smartphones and the Internet. Such systems are capable of generating a phenomenal amount of information and the need to analyse their behaviour, to detect security anomalies or predict future demands for example, is becoming harder. Furthermore, securing such systems is a challenge. As threats evolve, so should security measures develop and adopt increasingly intelligent security techniques. Adaptive systems must be employed and existing methods built upon to provide well-structured defence in depth. Despite the clear need to develop effective protection methods, the task is a difficult one, as there are significant weaknesses in the existing security currently in place. Consequently, this special issue of the Journal of Computer Sciences and Applications discusses big data analytics in intelligent systems. The specific topics of discussion include the Internet of Things, Web Services, Cloud Computing, Security and Interconnected Systems