Accuracy-Based Task Offloading and Resource Allocation for Edge Intelligence in IoT | IEEE Journals & Magazine | IEEE Xplore

Accuracy-Based Task Offloading and Resource Allocation for Edge Intelligence in IoT


Abstract:

Machine learning (ML) tasks in Internet of Things (IoT) are sensitive to task inference accuracy. In this letter, an ML task offloading scheme is proposed to minimize the...Show More

Abstract:

Machine learning (ML) tasks in Internet of Things (IoT) are sensitive to task inference accuracy. In this letter, an ML task offloading scheme is proposed to minimize the total delay of task processing in an edge-intelligence-enabled IoT scenario, while guaranteeing the accuracy requirements of tasks, and taking into account the multiple attributes of tasks, task inference accuracy, and impact of error inference on task processing delay. The problem of wireless channel allocation, and computing resource allocation is modeled along with the task offloading. Considering the high complexity of the optimization problem, we design an algorithm which decomposes the problem into a computing resource allocation sub-problem and a task offloading and channel allocation sub-problem, and then solves them separately. In extensive simulations, the superiority of our scheme is demonstrated in comparisons with 4 other schemes.
Published in: IEEE Wireless Communications Letters ( Volume: 11, Issue: 2, February 2022)
Page(s): 371 - 375
Date of Publication: 18 November 2021

ISSN Information:

Funding Agency:


Contact IEEE to Subscribe

References

References is not available for this document.