Connected and Autonomous Vehicles (CAVs) are an emerging solution to the issues of safe and sustainable transportation systems in the future. One major transport technology for CAVs is Cooperative Adaptive Cruise Control (CACC), for which unsignalized autonomous intersection crossing is a growing use case. CACC relies heavily on inter-vehicular communication and is thus vulnerable to message forgery and jamming attacks. Most solutions for CACC focus exclusively on enhancing efficiency or security but do not offer an integrated framework for achieving both on a large scale. In this paper, we propose a Blockchain-integrated Multi-Agent Deep Reinforcement Learning (Block-MADRL) architecture for enhancing the efficiency of CACC while cooperatively detecting attacks, reducing the fuel efficiency of identified attackers and securely notifying the overall network. Our approach uses multi-agent deep reinforcement learning to find fuel and throughput optimizing solutions for CACC and a cooperative verification mechanism based on Extended Isolation Forest (EIF) for attack detection. Attacker data is securely stored in a Road Side Unit (RSU) level blockchain, and we design a low-latency, high throughput consensus protocol for speedy and secure data dissemination. Simulation results indicate over 29.5% better lane throughput with our approach during acceleration forgery attack, up to 23% induced reduction in fuel efficiency of malicious vehicles, 17.6% higher blockchain throughput through our consensus protocol and over 8% improvement in attack detection rate compared to the state-of-the-art.

Unmanned Aerial Vehicles (UAVs) are widely used for commercial transportation and data collection in many applications. Recently, UAVs have been used as flying relays to support terrestrial cellular networks for enhancing coverage and connectivity. Multiple UAVs serve as data collectors at a common altitude in a UAV-aided Internet of Things (IoT) network. Data collected is processed by Federated Learning (FL) before being sent to central servers to protect users’ privacy and reduce communication costs. Batteries in UAVs are tiny and efficient only for a short duration, making FL challenging to execute for many iterations. Therefore, a Multi-UAV Energy Efficient Coverage Deployment algorithm based on a Spatial Adaptive Play (MUECD-SAP) is proposed in this article. MUECD uses a modified Particle Swarm Optimization (PSO) to optimize the accurate location of deployed UAVs based on the Signal-toInterference Ratio (SINR) to increase the data collection rate. Also, to make the FL run for multiple iterations, SAP dynamically allocates resources using Deep Deterministic Policy Gradient (DDPG) to optimize the energy consumed and the link latency between the user and the UAV system. The proposed MUECD algorithm outperforms all the state-of-the-art algorithms by achieving an improved data rate and balanced SINR value. The proposed SAP resource allocation strategy has improved the FL execution by 66.67% and attains a very low latency and energy consumed compared to other resource allocation techniques.