Name
Abstract | ||
|---|---|---|
As the computing nodes of a fog computing system are located at the network edge, it can provide low-latency and reliable computing services to Internet of Things (IoT) mobile devices (MDs). By wirelessly offloading all/part of the computational tasks from MDs to the infrastructure fog nodes, it addresses the contradiction between the limited battery capacity of MDs and their long-lasting operation requirement. Different from previous works, the uncertainty caused by the channel measurements is taken into account in this paper, which yields a robust offloading strategy against realistic channel estimation errors. For this system, we design an energy-efficient computation offloading strategy, while satisfying the delay constraint. By using the Conditional Value-at-Risk (CVaR) framework, the original offloading problem is transformed into a Mixed Integer Nonlinear Programming (MINLP) problem, which is complicated and very challenging to solve. To overcome this issue, we apply Benders decomposition to find the optimal offloading solution. Numerical results show that proposed offloading strategy efficiently achieves obtain the optimal solution of the MINLP problem, and is robust to channel estimation errors. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TVT.2020.2975056 | IEEE Transactions on Vehicular Technology |
Keywords | DocType | Volume |
Task analysis,Delays,Channel estimation,Robustness,Edge computing,Wireless communication,Computational modeling | Journal | 69 |
Issue | ISSN | Citations |
4 | 0018-9545 | 1 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhikun Wu | 1 | 5 | 2.78 |
| Bin Li | 2 | 1 | 0.68 |
| Zesong Fei | 3 | 699 | 86.33 |
| Zhong Zheng | 4 | 102 | 15.69 |
| Bin Li | 5 | 924 | 94.55 |
| Zhu Han | 6 | 11215 | 760.71 |