On the Optimization of Multi-UAV Task Offloading in Vehicular Networks

Vehicular networks are essential for enabling smart transportation systems and improving road safety, traffic management, and overall connectivity. As these networks evolve to support increasingly complex applications, the demand for high computational capacity continues to rise. Tasks such as real-Time data processing and surveillance require efficient and scalable solutions. To do so, uncrewed aerial vehicles (UAVs) offer a flexible approach to meet these computational needs by enabling on-The-fly task offloading. Their integration with connected and autonomous vehicles (CAVs) further expands their potential in intelligent transportation systems. In this context, we investigate computation offloading to UAVs in vehicular networks. Specifically, we aim to maximize the successful offloading rate of CAV tasks to a multi-UAV and energy-constrained aerial platform through the optimization of UAVs' launch locations, flight directions, and CAV-UAV associations. Given the complexity of the formulated problem, we propose two low-complex metaheuristic-based approaches, namely the bat algorithm (BA) and particle swarm optimization (PSO)-based methods, to solve it. Moreover, we adapt the iterative exhaustive-linear programming (IE-LP) solution, developed in [1], to the multi-UAV scenario. Through extensive simulations, we show that IE-LP provides the best performance with low complexity for small systems (number of UAVs below 3), while BA and PSO-based approaches are preferred for their low complexity and high scalability.