L3Fog: Fog Node Selection and Task Offloading Framework for Mobile IoT

Abstract

The mobility of end users in fog-computing environment necessitates the need for task offloading and migration. But, the resource-constrained nature of the fog devices makes them challenging. While offloading, fog node selection based on the location of the mobile devices remains an intricate issue. This paper proposes L3Fog, a learning-based location-aware low-latency fog selection and offloading scheme for Internet of Things. The proposed solution predicts the location of the mobile nodes using machine learning-based approaches. The found location is mapped with the service area of fog nodes. A task offloading decision is taken by using the mapping function to offload to the nearest and suitable fog node. The proposed approach considers the available computing and storage resources and quality of service criterion for choosing a fog node for seamless computation. The location of the mobile device is calculated using a real data set. The proposed scheme’s performance analysis shows significant improvement compared to baseline algorithms. Index Terms–Task offloading, Machine Learning, Location prediction, Networking, Mobile Fog computing, Task migration, Quality-of-service, Internet of Things.