Liqing Liu

TL;DR: In this article, the authors utilized queuing theory to bring a thorough study on the energy consumption, execution delay, and payment cost of offloading processes in a fog computing system, where three queuing models were applied, respectively, to the MD, fog, and cloud centers, and the data rate and power consumption of the wireless link were explicitly considered.

TL;DR: This paper takes the social relationships of the EH mobile devices (MDs) into the design of computational off loading scheme in fog computing and proposes a dynamic computation offloading scheme designing the offloading process in fog Computing system with EH MDs to minimize the social group execution cost.

TL;DR: A dynamic optimization scheme for the IoT fog computing system with multiple mobile devices (MDs), where the radio and computational resources, and offloading decisions, can be dynamically coordinated and allocated with the variation of radio resources and computation demands is proposed.

TL;DR: A data offloading and task allocation scheme for a cloudlet-assisted ad hoc mobile cloud in which the master device who has computational tasks can access resources from nearby slave devices or the cloudlet, instead of the centralized cloud, to share the workload, in order to reduce the energy consumption and computational cost.

TL;DR: In this article, a multi-objective optimization problem is formulated with the joint objectives to minimize the energy consumption, execution delay and price cost by finding the optimal offloading probability and optimal transmission power for each mobile device.