Supporting UAVs with Edge Computing: A Review of Opportunities and Challenges

Over the last years, Unmanned Aerial Vehicles (UAVs) have seen significant advancements in sensor capabilities and computational abilities, allowing for efficient autonomous navigation and visual tracking applications. However, the demand for computationally complex tasks has increased faster than advances in battery technology. This opens up possibilities for improvements using edge computing. In edge computing, edge servers can achieve lower latency responses compared to traditional cloud servers through strategic geographic deployments. Furthermore, these servers can maintain superior computational performance compared to UAVs, as they are not limited by battery constraints. Combining these technologies by aiding UAVs with edge servers, research finds measurable improvements in task completion speed, energy efficiency, and reliability across multiple applications and industries. This systematic literature review aims to analyze the current state of research and collect, select, and extract the key areas where UAV activities can be supported and improved through edge computing

Autonomous flying WiFi access point

Unmanned aerial vehicles (UAVs), aka drones, are widely used civil and commercial applications. A promising one is to use the drones as relying nodes to extend the wireless coverage. However, existing solutions only focus on deploying them to predefined locations. After that, they either remain stationary or only move in predefined trajectories throughout the whole deployment. In the open outdoor scenarios such as search and rescue or large music events, etc., users can move and cluster dynamically. As a result, network demand will change constantly over time and hence will require the drones to adapt dynamically. In this paper, we present a proof of concept implementation of an UAV access point (AP) which can dynamically reposition itself depends on the users movement on the ground. Our solution is to continuously keeping track of the received signal strength from the user devices for estimating the distance between users devices and the drone, followed by trilateration to localise them. This process is challenging because our on-site measurements show that the heterogeneity of user devices means that change of their signal strengths reacts very differently to the change of distance to the drone AP. Our initial results demonstrate that our drone is able to effectively localise users and autonomously moving to a position closer to them

DroneSig: Lightweight Digital Signature Protocol for Micro Aerial Vehicles

Micro aerial vehicles a.k.a. drones, have become an integral part of a variety of civilian and military application domains, including but not limited to aerial surveying and mapping, aerial surveillance and security, aerial inspection of infrastructure, and aerial delivery. Meanwhile, the cybersecurity of drones is gaining significant attention due to both financial and strategic information and value involved in aerial applications. As a result of the lack of security features in the communication protocol, an adversary can easily interfere with on-going communications or even seize control of the drone. In this thesis, we propose a lightweight digital signature protocol, also referred to as DroneSig, to protect drones from a man-in-the-middle attack, where an adversary eavesdrops the communication between Ground Control Station (GCS) and drone, and impersonates the GCS and sends fake commands to terminate the on-going mission or even take control over the drone. The basic idea of the DroneSig is that the drone will only execute the new command after validating the received digital signature from the GCS, proving that the new command message is coming from the authenticated GCS. If the validation of the digital signature fails, the new command is rejected immediately, and the Return-to-Launch (RTL) mode is initiated and forces the drone to return to the take-off position. We conduct extensive simulation experiments for performance evaluation and comparison using OMNeT++, and simulation results show that the proposed lightweight digital signature protocol achieves better performance in terms of energy consumption and computation time compared to the standard Advanced Encryption Standard (AES) cryptographic technique

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.The work of Vinay Chamola and Fei Richard Yu was supported in part by the SICI SICRG Grant through the Project Artificial Intelligence Enabled Security Provisioning and Vehicular Vision Innovations for Autonomous Vehicles, and in part by the Government of Canada's National Crime Prevention Strategy and Natural Sciences and Engineering Research Council of Canada (NSERC) CREATE Program for Building Trust in Connected and Autonomous Vehicles (TrustCAV)

Unmanned Aerial Vehicle Forensic Investigation Process: Dji Phantom 3 Drone As A Case Study

Drones (also known as Unmanned Aerial Vehicles, UAVs) is a potential source of evidence in a digital investigation, partly due to their increasing popularity in our society. However, existing UAV/drone forensics generally rely on conventional digital forensic investigation guidelines such as those of ACPO and NIST, which may not be entirely fit_for_purpose. In this paper, we identify the challenges associated with UAV/drone forensics. We then explore and evaluate existing forensic guidelines, in terms of their effectiveness for UAV/drone forensic investigations. Next, we present our set of guidelines for UAV/drone investigations. Finally, we demonstrate how the proposed guidelines can be used to guide a drone forensic investigation using the DJI Phantom 3 drone as a case study

A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain