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It is widely expected that future networks of 6G and beyond will deliver on the unachieved goals set by 5G. Technologies such as Internet of Skills and Industry 4.0 will become stable and viable, as a direct consequence of networks that offer sustained and reliable mobile performance levels. The primary challenges for future technologies are not just low-latency and high-bandwidth. The more critical problem Mobile Service Providers (MSPs) will face will be in balancing the inflated demands of network connections and customers' trust in the network service, that is, being able to interconnect billions of unique devices while adhering to the agreed terms of Service Level Agreements (SLAs). To meet these targets, it is self-evident that MSPs cannot operate in a solitary environment. They must enable cooperation among themselves in a manner that ensures trust, both between themselves as well as with customers. In this study, we present the BEAT (Blockchain-Enabled Accountable and Transparent) Infrastructure Sharing architecture. BEAT exploits the inherent properties of permissioned type of distributed ledger technology (i.e., permissioned distributed ledgers) to deliver on accountability and transparency metrics whenever infrastructure needs to be shared between providers. We also propose a lightweight method that enables device-level accountability. BEAT has been designed to be deployable directly as only minor software upgrades to network devices such as routers. Our simulations on a resource-limited device show that BEAT adds only a few seconds of overhead processing time -- with the latest state-of-the-art network devices, we can reasonably anticipate much lower overheads.

Attackers demonstrated the use of remote access to the in-vehicle network of connected vehicles to launch cyber-attacks and remotely take control of these vehicles. Machine-learning-based Intrusion Detection Systems (IDSs) techniques have been proposed for the detection of such attacks. The evaluation of some of these IDS demonstrated their efficacy in terms of accuracy in detecting message injections but was performed offline, which limits the confidence in their use for real-time protection scenarios. This paper evaluates four architecture designs for real-time IDS for connected vehicles using Controller Area Network (CAN) datasets collected from a moving vehicle under malicious speed reading message injections. The evaluation shows that a real-time IDS for a connected vehicle designed as two processes, a process for CAN Bus monitoring and another one for anomaly detection engine is reliable (no loss of messages) and could be used for real-time resilience mechanisms as a response to cyber-attacks.

One of the most critical aspects of enabling next-generation wireless technologies is developing an accurate and consistent channel model to be validated effectively with the help of real-world measurements. From this point of view, remarkable research has recently been conducted to model propagation channels involving the modification of the wireless propagation environment through the inclusion of reconfigurable intelligent surfaces (RISs). This study mainly aims to present a vision on channel modeling strategies for the RIS-empowered communications systems considering the state-of-the-art channel and propagation modeling efforts in the literature. Moreover, it is also desired to draw attention to open-source and standard-compliant physical channel modeling efforts to provide comprehensive insights regarding the practical use-cases of RISs in future wireless networks.

Unmanned Aerial Vehicles (UAVs), also known as drones, have exploded in every segment present in todays business industry. They have scope in reinventing old businesses, and they are even developing new opportunities for various brands and franchisors. UAVs are used in the supply chain, maintaining surveillance and serving as mobile hotspots. Although UAVs have potential applications, they bring several societal concerns and challenges that need addressing in public safety, privacy, and cyber security. UAVs are prone to various cyber-attacks and vulnerabilities; they can also be hacked and misused by malicious entities resulting in cyber-crime. The adversaries can exploit these vulnerabilities, leading to data loss, property, and destruction of life. One can partially detect the attacks like false information dissemination, jamming, gray hole, blackhole, and GPS spoofing by monitoring the UAV behavior, but it may not resolve privacy issues. This paper presents secure communication between UAVs using blockchain technology. Our approach involves building smart contracts and making a secure and reliable UAV adhoc network. This network will be resilient to various network attacks and is secure against malicious intrusions.

CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments. This paper creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. UAVs should be deployed to cover a large area while minimize overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This work develops a multi-agent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this paper employs is autonomously replenishing the UAV's deficient network requirements with communications. Via intensive simulations, our proposed algorithm outperforms the state-of-the-art algorithms in terms of surveillance coverage, user support capability, and computational costs.

Intelligent reflecting surface (IRS) has emerged as a key enabling technology to realize smart and reconfigurable radio environment for wireless communications, by digitally controlling the signal reflection via a large number of passive reflecting elements in real-time. Different from conventional wireless communication techniques that only adapt to but have no or limited control over dynamic wireless channels, IRS provides a new and cost-effective means to combat the wireless channel impairments in a proactive manner. However, despite its great potential, IRS faces new and unique challenges in its efficient integration into wireless communication systems, especially its channel estimation and passive beamforming design under various practical hardware constraints. In this paper, we provide a comprehensive survey on the up-to-date research in IRS-aided wireless communications, with an emphasis on the promising solutions to tackle practical design issues. Furthermore, we discuss new and emerging IRS architectures and applications as well as their practical design problems to motivate future research.

The past few years have witnessed a remarkable rise in interest in driver-less cars; and naturally, in parallel, the demand for an accurate and reliable object localization and mapping system is higher than ever. Such a system would have to provide its subscribers with precise information within close range. There have been many previous research works that have explored the different possible approaches to implement such a highly dynamic mapping system in an intelligent transportation system setting, but few have discussed its applicability toward enabling other 5G verticals and services. In this article we start by describing the concept of dynamic maps. We then introduce the approach we took when creating a spatio-temporal dynamic maps system by presenting its architecture and different components. After that, we propose different scenarios where this fairly new and modern technology can be adapted to serve other 5G services, in particular, that of UAV geofencing, and finally, we test the object detection module and discuss the results.

This paper provides an overview of enhanced network services, while emphasizing on the role of Unmanned Aerial Vehicles (UAVs) as core network equipment with radio and backhaul capabilities. Initially, we elaborate the various deployment options, focusing on UAVs as airborne radio, backhaul and core network equipment, pointing out the benefits and limitations. We then analyze the required enhancements in the Service-Based Architecture (SBA) to support UAV services including UAV navigation and air traffic management, weather forecasting and UAV connectivity management. The use of airborne UAVs network services is assessed via qualitative means, considering the impact on vehicular applications. Finally, an evaluation has been conducted via a testbed implementation, to explore the performance of UAVs as edge cloud nodes, hosting an Aerial Control System (ACS) function responsible for the control and orchestration of a UAV fleet.

Autonomous driving has achieved a significant milestone in research and development over the last decade. There is increasing interest in the field as the deployment of self-operating vehicles on roads promises safer and more ecologically friendly transportation systems. With the rise of computationally powerful artificial intelligence (AI) techniques, autonomous vehicles can sense their environment with high precision, make safe real-time decisions, and operate more reliably without human interventions. However, intelligent decision-making in autonomous cars is not generally understandable by humans in the current state of the art, and such deficiency hinders this technology from being socially acceptable. Hence, aside from making safe real-time decisions, the AI systems of autonomous vehicles also need to explain how these decisions are constructed in order to be regulatory compliant across many jurisdictions. Our study sheds a comprehensive light on developing explainable artificial intelligence (XAI) approaches for autonomous vehicles. In particular, we make the following contributions. First, we provide a thorough overview of the present gaps with respect to explanations in the state-of-the-art autonomous vehicle industry. We then show the taxonomy of explanations and explanation receivers in this field. Thirdly, we propose a framework for an architecture of end-to-end autonomous driving systems and justify the role of XAI in both debugging and regulating such systems. Finally, as future research directions, we provide a field guide on XAI approaches for autonomous driving that can improve operational safety and transparency towards achieving public approval by regulators, manufacturers, and all engaged stakeholders.

TraQuad is an autonomous tracking quadcopter capable of tracking any moving (or static) object like cars, humans, other drones or any other object on-the-go. This article describes the applications and advantages of TraQuad and the reduction in cost (to about 250$) that has been achieved so far using the hardware and software capabilities and our custom algorithms wherever needed. This description is backed by strong data and the research analyses which have been drawn out of extant information or conducted on own when necessary. This also describes the development of completely autonomous (even GPS is optional) low-cost drone which can act as a major platform for further developments in automation, transportation, reconnaissance and more. We describe our ROS Gazebo simulator and our STATUS algorithms which form the core of our development of our object tracking drone for generic purposes.