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Recently, using drones for forest fire management has gained a lot of attention from the research community due to their advantages such as low operation and deployment cost, flexible mobility, and high-quality imaging. It also minimizes human intervention, especially in hard-to-reach areas where the use of ground-based infrastructure is troublesome. Drones can provide virtual reality to firefighters by collecting ondemand high-resolution images with adjustable zoom, focus, and perspective to improve fire control and eliminate human hazards. In this paper, we propose a novel model for fire expansion as well as a distributed algorithm for drones to relocate themselves towards the front-line of an expanding fire field. The proposed algorithm comprises a light-weight image processing for fire edge detection that is highly desirable over computational expensive deep learning methods for resource-constrained drones. The positioning algorithm includes motions tangential and normal to fire frontline to follow the fire expansion while keeping minimum pairwise distances for collision avoidance and non-overlapping imaging. We proposed an action-reward mechanism to adjust the drones’ speed and processing rate based on the fire expansion rate and the available onboard processing power. Simulations results are provided to support the efficacy of the proposed algorithm. 

Emerging Internet of Things (IoT) provides connectivity to a wide range of mobile nodes including indoor wireless users, pedestrian, ground robotics, vehicles, and flying objects. Such decentralized network require rethinking user-centric communication protocols which accommodate extremely dynamic environments of autonomous nodes. The authors recently proposed a predictive routing algorithm, which enables a delay-optimal communication through incorporating network topology prediction into the Dijkstra's shortest path algorithm. In this work, we extend the proposed solution to jointly optimize the end-to-end latency and total transmission power. Further, we develop a ground robotics platform in order to study the utility of the proposed algorithm in real-world applications. The simulation results which verified by the test platform, confirm the superiority of the proposed algorithm compared to the conventional shortest path algorithms by improving the delay and power consumption by a factor of 10% to 15%.