@inproceedings {25273,
	title = {Optimal Delay Constrained Offloading for Vehicular Edge Computing Networks},
	journal = {2017 IEEE International Conference on Communications (ICC)},
	year = {2017},
	publisher = {IEEE},
	abstract = {The increasing number of smart vehicles and their resource hungry applications pose new challenges in terms of computation and processing for providing reliable and efficient vehicular services. Mobile Edge Computing (MEC) is a new paradigm with potential to improve vehicular services through computation offloading in close proximity to mobile vehicles. However, in the road with dense traffic flow, the computation limitation of these MEC servers may endanger the quality of offloading service. To address the problem, we propose 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. Based on this framework, we adopt a Stackelberg game theoretic approach to design an optimal multilevel offloading scheme, which maximizes the utilities of both the vehicles and the computing servers. Furthermore, to obtain the optimal offloading strategies, we present an iterative distributed algorithm and prove its convergence. Numerical results indicate that our proposed scheme greatly enhances the utility of the offloading service providers.},
	isbn = {978-1-4673-8999-0},
	issn = {1938-1883},
	doi = {10.1109/ICC.2017.7997360},
	author = {Zhang, Ke and Mao, Yuming and Leng, Supeng and Maharjan, Sabita and Zhang, Yan},
	editor = {IEEE, }
}
@article {24611,
	title = {Energy-efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks},
	journal = {IEEE Access},
	volume = {4},
	year = {2016},
	pages = {5896-5907},
	publisher = {IEEE},
	abstract = {Mobile Edge Computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading mechanisms for MEC in 5G heterogenous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogenous network, we then design an Energy-Efficient Computation Offloading (EECO) scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.},
	doi = {10.1109/ACCESS.2016.2597169},
	author = {Zhang, Ke and Mao, Yuming and Leng, Supeng and Zhao, Quanxin and Li, Longjiang and Peng, Xin and Pan, Li and Zhang, Guoyun and Maharjan, Sabita and Zhang, Yan}
}
