Offline and Online Scheduling Algorithms for Energy Harvesting RSUs in VANETs

• Published in: IEEE transactions on vehicular technology 2018-07, Vol.67 (7), p.6370-6382
• Main Authors: Atoui, Wassim Sellil, Ajib, Wessam, Boukadoum, Mounir
• Format: Article
• Language: English
• Subjects: Algorithms; Communication; Computer simulation; Delays; Energy; Energy consumption; Energy harvesting; Environmental impact; Heuristic methods; Integer programming; Linear programming; Mobile ad hoc networks; Optimal scheduling; Particle swarm optimization; Production scheduling; Renewable energy; Renewable energy sources; Renewable resources; road side units scheduling; Roads; Scheduling; scheduling algorithms; VANETs; Vehicles
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2018.2797002

Using renewable energy to power roadside units (RSUs) in vehicular ad hoc networks is a desirable green alternative to the conventional electric grid, since it lowers both the carbon footprint and the cost of deployment. This paper investigates the problem of scheduling the downlink communication from renewable energy-powered RSUs toward vehicles, with the objective of maximizing the number of served vehicles. First, an offline setting is considered where the RSUs are assumed to have advance knowledge of the incoming communication requests from vehicles and of the amount of energy to be harvested. The problem is formulated as an integer linear programming model that is shown to be NP-hard and two near-optimal solutions are proposed. The first one is a greedy heuristic that prioritizes communications based on their energy cost and the second is the particle swarm optimization metaheuristic. Then, the problem is considered in an online setting and two different solution approaches are investigated. The first one assumes distributed scheduling control between RSUs and two algorithms are proposed, one based on a stochastic model and the other on simple threshold-based selection of communication requests. The second approach assumes centralized scheduling and two algorithms are also investigated. The first one uses a greedy approach and the second one uses threshold-based selection again. Simulations compare the proposed schedulers and show their efficiency in terms of the number of vehicles served and the service delay. It is concluded that employing energy harvesting RSUs is a viable green alternative to grid-powered ones.