Paper Info
Title
Content-centric data and computation offloading in AI-supported fog networks for next generation IoT
Abstract
Fog Computing (FC) based IoT applications are encountering a bottleneck in the data management and resource optimization due to the dynamic IoT topologies, resource-limited devices, resource diversity, mismatching service quality, and complicated service offering environments. Existing problems and emerging demands of FC based IoT applications are hard to be met by traditional IP-based Internet model. Therefore, in this paper, we focus on the Content-Centric Network (CCN) model to provide more efficient, flexible, and reliable data and resource management for fog-based IoT systems. We first propose a Deep Reinforcement Learning (DRL) algorithm that jointly considers the content type and status of fog servers for content-centric data and computation offloading. Then, we introduce a novel virtual layer called FogOrch that orchestrates the management and performance requirements of fog layer resources in an efficient manner via the proposed DRL agent. To show the feasibility of FogOrch, we develop a content-centric data offloading scheme (DRLOS) based on the DRL algorithm running on FogOrch. Through extensive simulations, we evaluate the performance of DRLOS in terms of total reward, computational workload, computation cost, and delay. The results show that the proposed DRLOS is superior to existing benchmark offloading schemes.
Year
DOI
Venue
2022
10.1016/j.pmcj.2022.101654
Pervasive and Mobile Computing
Keywords
DocType
Volume
Next Generation Internet of Things (NGIoT),Deep reinforcement learning,FogOrch,Fog computing,Data offloading,Computation offloading
Journal
85
ISSN
Citations 
PageRank 
1574-1192
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
İbrahim Kök101.01
Suat Ozdemir235026.30