Improving power consumption in IoT devices through neural network-based decision making

Abstract

Advances in the Internet of Things (IoT) have led to several challenges across domains, creating opportunities for new solutions. The large volume of data and high processing demands of IoT applications, typically handled by cloud providers, make this approach costly regarding storage, computation, and latency. This fact has led to the emergence of the fog computing paradigm that enables the integrated use of edge and cloud resources. In this new scenario, one of the main challenges is managing containerised IoT applications that may migrate from fog nodes (IoT Devices) to the cloud and vice versa. This management is needed to preserve system performance and user experience while considering cloud resource costs. The container migration becomes especially critical for battery-powered devices, whose energy autonomy is a significant concern. Existing solutions do not consider power consumption and the cost of fog-cloud settings in the migration process. This paper proposes IRAS-IoT (Intelligent Replacement as a Service for IoT), a solution for autonomously migrating containers between fog nodes and the cloud. Based on MAPE-K, IRAS-IoT adopts a Multilayer Perceptron (MLP) neural network model to decide on migrating containers and optimising workload distribution according to devices’ power consumption. Regarding cost optimisation, in scenarios where fog nodes become overloaded, the solution may also allocate containers to a cloud provider, selecting the most cost-effective option. Experimental results show that IRAS-IoT reduces energy consumption by up to 18% and extends battery life by 34%, and lowers cloud operational costs compared to the baseline scenario.