Yuming Mao

TL;DR: An optimization problem is formulated to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration, and an EECO scheme is designed, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints.

TL;DR: A cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks is proposed, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions, which greatly reduces the cost of computation and improves task transmission efficiency.

TL;DR: This work proposes a hierarchical cloud-based Vehicular Edge Computing (VEC) offloading framework, where a backup computing server in the neighborhood is introduced to make up for the deficit computing resources of MEC servers.

TL;DR: A cloud-based Mobile Edge Computing (MEC) offloading framework in vehicular networks is proposed and a contract-based computation resource allocation scheme is proposed that greatly enhances the utility of the MEC service provider.

TL;DR: This paper presents a comprehensive review of several typical economic incentive approaches adopted in the energy-trading control mechanisms and proposes an optimal contract-based electricity trading scheme, which efficiently increases the generated profit.