Joint Optimization of Energy Consumption and Delay in Cloud-to-Thing Continuum

Unmanned aerial vehicles (UAVs) are considered a promising solution for carrying communications and computational facilities to increase the flexibility of cloud-to-thing continuum, where short-range and long-range wireless links are adopted to connect mobile devices to the fog node and the fog node to the remote data center, respectively. Most existing UAV-involved resource allocation algorithms focus mainly on the radio resource allocation problem, and much less attention has been paid to the allocation of computational resources. Moreover, the dynamic arrival of tasks and the queueing delay at each computation entity is usually neglected. In this paper, a joint optimization problem is formulated that takes the weighted sum of energy consumption and delay experienced by tasks as the objective function. Processing frequencies and transmission powers of mobile devices and the fog node are the decision variables in the problem. To solve this problem, three decision-making algorithms are presented. The first one is used to decide the UAV's position. The processing frequency, transmission power, and task assignment results at mobile devices are determined by the second algorithm. The last one is adopted by the fog node to optimize its processing frequency and transmission power. A series of simulation experiments are conducted to evaluate the effectiveness of the proposed algorithms. Compared with the random task assignment scheme with fixed parameters, the combination of our three algorithms always perform much better for a wide range of parameter settings.