Blockchain has emerged as a promising technology to ensure trust between parties. By using this technology, we can establish a secure communication paradigm, where data integrity and immutability can be guaranteed. These inherited features underline blockchain as a suitable technology to optimise the adopted processing model in several domains, such as health, trade supply chain and food safety. In this paper, we present a detailed overview of the use of blockchain technology in (international) trade supply chains. Furthermore, the discussed proposals have been classified based on the target application scenarios. Our goal is to clarify the benefits of applying this technology to the trading domain and highlight the challenges that are associated with applying this technology to optimise the trading domain. Accordingly, we underline several issues that occur during the designing of the blockchain solution to optimise the (international) trade supply chain.

Flying Ad-Hoc Networks (FANETs) are groups of UAVs connected in an Ad-Hoc manner to accomplish a common mission. The widespread acceptance of UAVs due to their low cost and high efficiency has attracted malicious security attacks against them. These attacks cannot be easily prevented due to UAVs’ limited computational power, short battery life, and inability to execute complex algorithms. FANETs rely on the Global Positioning System (GPS) for localization. GPS Spoofing, an easy-to-launch attack, is one of the main challenges in the FANET environment. In which, the legitimate and not-encrypted civilian GPS signals are overridden by counterfeit signals to deceive the UAVs to collide or to be hijacked. Researchers proposed various countermeasures to address GPS Spoofing attacks. To further assist future research, this paper provides a systematic literature review on GPS Spoofing attacks in FANETs and various defense mechanisms. After formulating specific research questions and applying well-defined inclusion/exclusion criteria and quality assessment tools, 70 research articles were extracted. The existing defense mechanisms were classified based on their objectives, namely, detection, mitigation, and/or prevention. This paper further classifies the prior research works based on the basis of defense mechanisms into those that rely on readings at various devices/sensors on drones, process the signals received by various sensors, defend against attacks employing various machine learning algorithms, rely on game theory, and leverage cryptographic techniques to authenticate and protect the confidentiality of GPS signals received by the drones. The extracted articles that proposed various defense mechanisms were analyzed to identify the attacker models, impacts of the attack, and detection performance. This study found that most of the proposed methods are detection approaches, rather than mitigation or prevention. It is noticed that almost all papers used simulation experiments rather than a proof-of-concept implementation, which does not demonstrate a convincing performance under more realistic mobility and propagation models. Furthermore, most solutions addressed GPS Spoofing for a single UAV. Only eight articles addressed multiple UAV scenarios and none of them provided a proof-of-concept evaluation.

The use of reinforcement learning (RL) for dynamic obstacle avoidance (DOA) algorithms and path planning (PP) has become increasingly popular in recent years. Despite the importance of RL in this growing technological era, few studies have systematically reviewed this research concept. Therefore, this study provides a comprehensive review of the literature on dynamic reinforcement learning-based path planning and obstacle avoidance. Furthermore, this research reviews publications from the last 5 years (2018–2022) to include 34 studies to evaluate the latest trends in autonomous mobile robot development with RL. In the end, this review shed light on dynamic obstacle avoidance in reinforcement learning. Likewise, the propagation model and performance evaluation metrics and approaches that have been employed in previous research were synthesized by this study. Ultimately, this article’s major objective is to aid scholars in their understanding of the present and future applications of deep reinforcement learning for dynamic obstacle avoidance.