Edge-End Collaborative Communication and Computing Resource Scheduling for IoT Empowered PV Monitoring

The swift advancement of Internet of Things (IoT) and consumer electronics along with edge computing offers a feasible solution to meet the stringent data processing requirements imposed by real-time monitoring of distributed solar photovoltaic (PV). For resource scheduling problems in terms of data processing, ant colony algorithm offers an effective solution. However, conventional ant colony suffers from poor convergence and path searching performances due to the lack of adaptability. Hence, we propose an edge-end collaborative multi-timescale resource scheduling algorithm to address these challenges. The large-timescale container selection is optimized by the proposed ant colony algorithm, which dynamically adjusts pheromone and heuristic factors to strike a balance between exploitation and exploration. The small-timescale channel allocation and power control are jointly optimized by integrating bilateral matching with British auction, which solves matching competition with low complexity and achieves queuing delay guarantee based on option combination and elimination. Results from simulations indicate that the algorithm put forward can diminish queuing delay and decrease energy consumption.