Reconfigurable Intelligent Surface-assisted Edge Computing to Minimize Delay in Task Offloading

TY - GEN

T1 - Reconfigurable Intelligent Surface-assisted Edge Computing to Minimize Delay in Task Offloading

AU - Mukherjee, Mithun

AU - Kumar, Vikas

AU - Kumar, Suman

AU - Lloret, Jaime

AU - Zhang, Qi

AU - Guo, Mian

N1 - Publisher Copyright: © 2021 IEEE.

PY - 2021

Y1 - 2021

N2 - The advantage of computational resources in edge computing near the data source has kindled growing interest in delay-sensitive Internet of Things (IoT) applications. However, the benefit of the edge server is limited by the uploading and downloading links between end-users and edge servers when these end-users seek computational resources from edge servers. The scenario becomes more severe when the user-end's devices are in the shaded region resulting in low uplink/downlink quality. In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted edge computing system, where the benefits of RIS are exploited to improve the uploading transmission rate. We further aim to minimize the delay of worst-case in the network when the end-users either compute task data in their local CPU or offload task data to the edge server. Next, we optimize the uploading bandwidth allocation for every end-user's task data to minimize the maximum delay in the network. The above optimization problem is formulated as quadratically constrained quadratic programming. Afterward, we solve this problem by semidefinite relaxation. Finally, the simulation results demonstrate that the proposed strategy is scalable under various network settings.

AB - The advantage of computational resources in edge computing near the data source has kindled growing interest in delay-sensitive Internet of Things (IoT) applications. However, the benefit of the edge server is limited by the uploading and downloading links between end-users and edge servers when these end-users seek computational resources from edge servers. The scenario becomes more severe when the user-end's devices are in the shaded region resulting in low uplink/downlink quality. In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted edge computing system, where the benefits of RIS are exploited to improve the uploading transmission rate. We further aim to minimize the delay of worst-case in the network when the end-users either compute task data in their local CPU or offload task data to the edge server. Next, we optimize the uploading bandwidth allocation for every end-user's task data to minimize the maximum delay in the network. The above optimization problem is formulated as quadratically constrained quadratic programming. Afterward, we solve this problem by semidefinite relaxation. Finally, the simulation results demonstrate that the proposed strategy is scalable under various network settings.

UR - http://www.scopus.com/inward/record.url?scp=85132261746&partnerID=8YFLogxK

U2 - 10.1109/GLOBECOM46510.2021.9685374

DO - 10.1109/GLOBECOM46510.2021.9685374

M3 - Article in proceedings

AN - SCOPUS:85132261746

BT - 2021 IEEE Global Communications Conference (GLOBECOM)

PB - IEEE

T2 - 2021 IEEE Global Communications Conference, GLOBECOM 2021

Y2 - 7 December 2021 through 11 December 2021

ER -