Intelligent workload allocation in IoT-Fog-cloud architecture towards mobile edge computing

Because of the tremendous growth in the number of smart vehicular devices and 5G mobile technologies, the Internet of Things (IoT) has experienced rapid expansion. This has led to a considerable increase in the volume of sensory data produced from, but not limited to, monitoring devices, traffic congestion in cities, safety, and pollution control. Cloud computing can deal with the corresponding workload by providing virtually unlimited computational resources. But, given the importance of the quality of service and security in delay-sensitive requests, other solutions like fog computing have also been introduced to speed up processing and management of sensory data in real scenarios like smart grid and IoT. Processing workloads at the network edge reduces the delay in mobile edge computing, but it highly increases the consuming power. Therefore, there is an urgent need for the improvement of the energy model of fog devices at the network edge. This paper is an attempt to modify this model using the green energy concept and reduce both delay and power consumption in multi-sensorial frameworks in secure IoT systems. In the proposed method, a Genetic Algorithm (GA) is used for handling a large number of requests and the corresponding quality and security limitations. Simulation results show that the proposed method can simultaneously reduce the delay and the power consumption of edge devices compared to a baseline strategy.