Unmanned farms employ a variety of sensors, automated systems, and data analysis techniques to enable fully automated and intelligent management. This not only heightens agricultural production efficiency but also reduces the costs associated with human resources. As integral components of unmanned farms’ automation systems, agricultural UAVs have been widely adopted across various operational stages due to their precision, high efficiency, environmental sustainability, and simplicity of operation. However, present-day technological advancement levels and relevant policy regulations pose significant restrictions on UAVs in terms of payload and endurance, leading to diminished task efficiency when a single UAV is deployed over large areas. Accordingly, this paper aggregates and analyzes research pertaining to UAV swarms from databases such as Google Scholar, ScienceDirect, Scopus, IEEE Xplorer, and Wiley over the past decade. An initial overview presents the current control methods for UAV swarms, incorporating a summary and analysis of the features, merits, and drawbacks of diverse control techniques. Subsequently, drawing from the four main stages of agricultural production (cultivation, planting, management, and harvesting), we evaluate the application of UAV swarms in each stage and provide an overview of the most advanced UAV swarm technologies utilized therein. Finally, we scrutinize and analyze the challenges and concerns associated with UAV swarm applications on unmanned farms and provide forward-looking insights into the future developmental trajectory of UAV swarm technology in unmanned farming, with the objective of bolstering swarm performance, scalability, and adoption rates in such settings.

As a promising technique for offloading computation tasks from mobile devices, Unmanned Aerial Vehicle (UAV)- assisted Mobile Edge Computing (MEC) utilizes UAVs as computational resources. A popular method for enhancing the quality of service (QoS) of UAV-assisted MEC systems is to jointly optimize UAV deployment and computation task offloading. This imposes the challenge of dynamically adjusting UAV deployment and computation offloading to accommodate the changing positions and computational requirements of mobile devices. Due to the real-time requirements of MEC computation tasks, finding an efficient joint optimization approach is imperative. This paper proposes an algorithm aimed at minimizing the average response delay in a UAV-assisted MEC system. The approach revolves around the joint optimization of UAV deployment and computation offloading through convex optimization. We break down the problem into three sub-problems: UAV deployment, Ground Device (GD) access, and computation tasks offloading, which we address using the block coordinate descent algorithm. Observing the NP-hardness nature of the original problem, we present near-optimal solutions to the decomposed sub-problems. Simulation results demonstrate that our approach can generate a joint optimization solution within seconds and diminish the average response delay compared to state-of-the-art algorithms and other advanced algorithms, with improvements ranging from 4.70% to 42.94%.

The rise of autonomous driving technology and the prosperity of mobile vehicular applications have brought tremendous pressure and put forward high bandwidth and low latency requirements for vehicular networks. Controller Area Network with Flexible Data-rate (CAN-FD) is a feasible choice to meet the high bandwidth and low latency requirements of in-vehicle communication of mobile vehicles. However, CAN-FD is vulnerable to masquerade attacks because it lacks necessary security authentication mechanisms and protection measures. This study presents a security enhancement technique called forward-backward exploration for independent in-vehicle CAN-FD messages while still guaranteeing each message is real-time. In the forward-backward exploration solution, a novel dual-pointer (including the forward pointer and the backward pointer) solution is proposed; the Message Authentication Code (MAC) size of each message is then dynamically adjusted by presenting the dual-pointer movement rules until the total payload no longer increases. Experimental results with real-life CAN-FD message set provided by an automaker demonstrate the effectiveness of our solution.

Cyber-physical systems (CPSs) will play an important role in future real-world applications through the deep integration of computing, communication, and control technologies. CPSs are increasingly deployed in critical infrastructure, industry, and homes to achieve a smart grid, smart transportation, and smart healthcare and to bring many benefits to citizens, businesses, and governments. However, the openness and complexity brought by network and wireless communication technology, as well as the intelligence and dynamic of network intrusions make CPS more vulnerable to network intrusions and bring more serious threats to human life, enterprise productivity, and national security. Therefore, intrusion detection and defense in CPS have attracted considerable attention and have become a fundamental aspect of CPS security. However, a new challenging problem arises: how to improve the efficiency and accuracy of intrusion detection while protecting user privacy during the intrusion detection process. To address this challenge, we propose a deep reinforcement learning-based privacy-enhanced intrusion detection and defense mechanism (PIDD) for CPS. The PIDD is composed of three modules: privacy-enhanced topology graphs generation module, graph convolutional networks-based user evaluation module, and the deep reinforcement learning-based intruder identification and handling module. The experimental results show that the proposed PIDD achieves excellent performance in intrusion detection accuracy, intrusion defense percentage, and privacy protection.