Energy-Efficient Computation Offloading in Vehicular Edge Cloud Computing - Xin Li (51274) (2020)
With the development of electrification, automation, and interconnection of the automobile industry, the demand for vehicular computing has entered an explosive growth era. Massive low time-constrained and computation-intensive vehicular computing operations bring new challenges to vehicles, such as excessive computing power and energy consumption. Computation offloading technology provides a sustainable and low-cost solution to these problems. In this article, we study an adaptive wireless resource allocation strategy of computation offloading service under a three-layered vehicular edge cloud computing framework. We model the computation offloading process at the minimum assignable wireless resource block level, which can better adapt to vehicular computation offloading scenarios and can also rapidly evolve to the 5G network. Subsequently, we propose a method to measure the cost-effectiveness of allocated resources and energy savings, named value density function. Interestingly, with respect to the amount of allocation resource, it can obtain the maximum value density when offloading energy consumption equals to half of local energy consumption. Finally, we propose a low-complexity heuristic resource allocation algorithm based on this novel theoretical discovery. Numerical results corroborate that our designed algorithm can gain above 80% execution time conservation and 62% conservation on energy consumption, and it exhibits fast convergence and superior performance compared to benchmark solutions. Other Information Published in: IEEE Access License: https://creativecommons.org/licenses/by/4.0/ See article on publisher's website: https://dx.doi.org/10.1109/access.2020.2975310