Self-Aware Fog Layer toward Scalable Resource Allocation and Dynamic Queuing

Abstract

The Internet is overwhelmed with innovative IoT -assisted devices. It is predicted that the number of online-connected devices will be more than 50 billion in 2030. Such IoT devices would need support from enabling technologies to consume less memory and lower the computation cost. The cloud-based services might further increase point-to-point latency. The unprecedentedly high volumes of real-time data generated by IoT devices may suffer from this delay issue. This work proposes a novel cognitive Fog computing-based data processing approach that manages the data influx caused by the sensor devices at the edge router. The proposed cognitive Fog based architecture has empowered edge devices, with the features such as Location awareness, low latency, portability, proximity to end users, diversity, and real-time response. A scalable resource allocation with a dynamic queuing technique was proposed. The simulation results have shown that the proposed architecture boosts the performance of the IoT Fog-based applications more than the existing approaches.