Abstract
Roadside Unit (RSU) planning and management is not a straightforward task. Usually, the problem is modeled as an NP-hard mixed-integer combinatorial optimization problem especially when the planner ought to incorporate various problem components. For large-scale problems, heuristic approaches that achieve a trade-off between execution time and planning performance, can be good enough for making planning decisions. In this paper, we design an iterative reduction heuristic algorithm to maximize the coverage efficiency of a network of RSUs in an urban setting, given a daily amortized budget and other planning constraints. The framework also incorporates capture-and-use solar panels to offset operational electricity costs. We perform a sensitivity analysis, to study the model response to variations. The heuristic shows that variations in both financial as well as communication-related parameters have expected model solution response. We find that in 10% to 50% of the convergence time of the optimal solution, the heuristic found solutions that had a coverage efficiency around 10% of the optimal solution.