| Title |
Research on Key Technologies of End to side Computing Network based on Field Level AI Reasoning for Terminal Equipment |
| Authors |
(Mao Ni) ; (Ting Zhou) ; (Hengjiang Wang) ; (Fang Cu) |
| DOI |
https://doi.org/10.5573/IEIESPC.2023.12.6.472 |
| Keywords |
Terminal equipment; Site level AI; End side computing network; Calculate energy consumption |
| Abstract |
This study analyzes the field-level AI reasoning technology of its terminal equipment and applies it to optimizing computing resources under the cooperation of multiple UAVs. The experimental results indicates that the performance of the ICEM method is superior to the other three methods, and the maximum value is 15 × 108 bits/Joule. An increase of 10 thresholds can reduce the number of iterations 50-fold when the amount of CDF iterations is 0.9. This can reach more than 20 times if the number of UAVs and mobile terminal devices doubles. The UAV will run at the maximum speed in most time slots, and the maximum computation of the terminal equipment can reach 15 Mbits when the entire working cycle is 30 seconds. Therefore, the algorithm proposed in this study achieves higher computing energy efficiency based on less convergence time and is effective in end-to-end computing power network computing resource scheduling. |