RBAC-LBRM: An RBAC-Based Load Balancing Assisted Efficient Resource Management Framework for IoT-Edge-Fog Network

Abstract

The inefficient handling of Internet of Things (IoT) network traffic may result in load-imbalance and unauthorized access, causing network performance degradation. The performance can be improved by off-loading some of the tasks to the network edge and fog layers. The existing works have discussed the issues of load balancing and access control in IoT using edge and fog environments; however, the design of an efficient resource management framework for an IoT-edge-fog environment is still under development. This work presents a role-based access control (RBAC)-based load balancing-assisted efficient resource management framework for IoT-edge-fog network named RBAC-LBRM. The overall average improvements of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">29.85, 16.82, 13.63, and 16.55%</i> have been achieved by RBAC-LBRM over other approaches w.r.t. CPU usage, memory usage, delay, and jitter metrics, respectively.