Wildfires pose a significant threat to ecosystems, human lives, and infrastructure worldwide. Traditional wildfire detection and risk assessment methods often suffer from limitations such as delayed detection and low confidence in certain regions. In this paper, we propose a novel computational system based on machine learning for wildfire risk assessment using data collected by drones. The system can integrate various sensors to capture spatiotemporal data on environmental factors such as temperature, humidity, and vegetation. By leveraging high-resolution data collected through autonomous drone missions, our system enhances wildfire risk estimation and enables proactive mission planning. Although the system is mainly designed to address wildfire monitoring using drone-collected data, it can be easily adapted to other environmental monitoring applications and other sources of data. We demonstrate the effectiveness of our approach through a comprehensive evaluation and validation process in both simulated and real-world environments. Our work contributes to advancing wildfire monitoring capabilities, improving early detection, and mitigating the impact of wildfires on communities and the environment.

Drones can play a game-changing role in reducing both cost and time in the context of last-mile deliveries. This paper addresses the last-mile delivery problem from a complex system viewpoint, where the collective performance of the drones is investigated. We consider a last-mile delivery system with a tradable permit model (TPM) for airspace use. Typically, in other research works regarding lastmile delivery drones, a fully cooperative centralized scenario is contemplated. In our approach, due to the TPM, the agents (i.e. drones) need to compete for airspace permits in a distributed manner. We simulate the system and evaluate how different parameters, such as the arrival rate and airspace dimensions, impact the system behavior in terms of the cost and time needed by the drones to acquire flight permits, and the airspace utilization. We use a simplified simulation model, where the agents’ strategies are naïve, and the drones’ flight dynamics are not accounted for. Nevertheless, the simulation’s level of detail is adequate for capturing interesting properties from the agents’ collective behavior, as our results support. The obtained results show that the system’s performance is satisfactory, even with naïve agents and under high traffic conditions. Moreover, a real-world implementation of our competitive decentralized approach would lead to advantages, such as fast permit transactions, simple computational infrastructures, and error resilience.

According to recent studies, unmanned aerial vehicles can play a game-changing part in cost reduction and speed increase to address the last-mile delivery problem and attend to emergencies. Last-mile delivery services are getting more and more relevant, especially when in times when social distance is required. However, a systemic view of how to provide a feasible solution to enable this application as a vertical market in the urban context is still inexistent. Given this scenario, our paper contributes by proposing a cyber-physical system roadmap applicable to last-mile delivery drones. The proposed CPS guidelines are based on the system of systems to enable an enhanced operation towards smart cities' governance. This paper also discusses topics from air space control and reservation to communication infrastructure and decentralized control supported on a blockchain.