Adaptive mobility-aware hierarchical task offloading for delay-sensitive applications in fog computing

Delay-sensitive IoT applications require an immediate response, while computing tasks in a fog-enabled network must be executed efficiently. Fog computing nodes are resource-limited and cannot handle more requests within the deadline. The process of task offloading continues to be difficult because of the user's mobility and the processing capabilities of the fog node. A new multi-level fog layer is proposed in the system architecture of the fog computing. In addition to this, this model utilizes the IoT device as a fog computing node based on certain criteria like computational capacity, mobility, etc. Random mobility prediction model is used to predict the future location of the user and the IoT device. A task offloading scheme that optimize the location aware module before engaging the device for task computation. Comprehensive MobFogSim simulations demonstrate that the proposed model and algorithm converge effectively, reducing latency and improving the user experience of handling sensitive applications by 20% compared to existing methods.