  The increasing use of Internet of Things (IoT) devices generates a greater
demand for data transfers and puts increased pressure on networks.
Additionally, connectivity to cloud services can be costly and inefficient. Fog
computing provides resources in proximity to user devices to overcome these
drawbacks. However, optimisation of quality of service (QoS) in IoT
applications and the management of fog resources are becoming challenging
problems. This paper describes a dynamic online offloading scheme in vehicular
traffic applications that require execution of delay-sensitive tasks. This
paper proposes a combination of two algorithms: dynamic task scheduling (DTS)
and dynamic energy control (DEC) that aim to minimise overall delay, enhance
throughput of user tasks and minimise energy consumption at the fog layer while
maximising the use of resource-constrained fog nodes. Compared to other
schemes, our experimental results show that these algorithms can reduce the
delay by up to 80.79% and reduce energy consumption by up to 66.39% in fog
nodes. Additionally, this approach enhances task execution throughput by
40.88%.
