Architecture for Latency Reduction in Healthcare Internet-of-Things Using Reinforcement Learning and Fuzzy Based Fog Computing

Abstract

Internet-of-Things (IoT) generate large data that is processed, analysed and filtered by cloud data centres. IoT is getting tremendously popular: the number of IoT devices worldwide is expected to reach 50.1 billion by 2020 and from this, 30.7% of IoT devices will be made available in Healthcare. Transmission and analysis of this much amount of data will increase the response time of cloud computing. The increase in response time will lead to high service latency to the end-users. The main requirement of IoT is to have low latency to transfer the data in real-time. Cloud cannot fulfill the QoS requirement in a satisfactory manner. Both the volume of data as well as factors related to internet connectivity may lead to high network latency in analyzing and acting upon the data. The propose research work introduces a hybrid approach that combines fuzzy and reinforcement learning to improve service and network latency in healthcare IoT and cloud. This hybrid approach integrates healthcare IoT devices with the cloud and uses fog services with Fuzzy Reinforcement Learning Data Packet Allocation (FRLDPA) algorithm. The propose algorithm performs batch workloads on IoT data to minimize latency and manages the QoS of the latency-critical workloads. It has the potential to automate the reasoning and decision making capability in fog computing nodes.