@conference{9798011,

title = {A Hybrid RF-FSO Offloading Scheme for Autonomous Industrial Internet of Things},

author = { Dimitrios Pliatsios and Thomas Lagkas and Vasileios Argyriou and Antonios Sarigiannidis and Dimitrios Margounakis and Theocharis Saoulidis and Panagiotis Sarigiannidis},

doi = {10.1109/INFOCOMWKSHPS54753.2022.9798011},

isbn = {978-1-6654-0926-1},

year  = {2022},

date = {2022-01-01},

booktitle = {IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)},

pages = {1-6},

abstract = {The ever increasing demand for bandwidth triggered by data-intensive applications is imposing a considerable burden on the radio-frequency (RF) spectrum. A promising solution to address the spectrum congestion problem is the adoption of free-space optical (FSO) communications. In this work, we consider a hybrid RF-FSO system that enables the task offloading process from Industrial Internet-of-Things devices to a multi-access edge computing (MEC)-enabled base station (BS). We propose a solution that minimizes the total energy consumption of the system by deciding whether the RF or FSO link will be used for the task offloading and optimally allocating the device transmission power while taking into account the task requirements in terms of delay. The proposed solution is based on a decomposition-driven algorithm that employs integer linear programming (ILP) and Lagrange dual decomposition. Finally, we carry out system-level Monte Carlo simulations to evaluate the performance of the solution. The simulation results show that the proposed solution can minimize the total energy consumption within a few iterations, while also considering the respective latency requirements.},

keywords = {Computation offloading, energy efficiency, Free-space Optical Communications, Industrial Internet of Things, Multi-access Edge Computing},

pubstate = {published},

tppubtype = {conference}

}