A Generalized Dynamic Planning Framework for Green UAV-Assisted Intelligent Transportation System Infrastructure

Abstract

Roadside unit (RSU) planning is vital for the operation of an intelligent transportation system (ITS). RSUs provide ground coverage limited by obstacles. Unmanned aerial vehicles (UAVs) can complement RSU coverage by providing flexible connectivity capable of adapting coverage for traffic fluctuations, energy consumption, and budgetary constraints that all have effects on ITS operations. This article proposes a general RSU/UAV joint planning solution, where complex dynamic parameters are investigated. The objective is to maximize the effective coverage of placed RSUs and UAV docks given: a budget comprised of periodic operating expenses and capital expenditures, limitations of the ground transceivers and UAVs, and use of renewable energy to offset the on-grid electricity cost. We formulate a mixed-integer quadratically constrained problem that can determine the optimal placement of RSUs and UAV stations, RSU activation schedules, if solar panels are attached, and their coverage during each time period. Due to NP-hard complexity of such a planning problem, we design a heuristic algorithm that produces suboptimal solutions in less time. Afterward, we perform a sensitivity analysis and show that changes to the parameters lead to logical shifts in infrastructure coverage. Additionally, we visualize the algorithm's performance on a large setting—Manhattan Island.