Abstract
Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT–FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.
Highlights
The latest report by the International Data Corporation stated that the number of Internetrelated sensors will increase to 30 million by 2020, and the number of Internet-of-thing (IoT) devices will be in the range of 50 billion to 1 trillion [1]
Predictive analysis using a support vector machine (SVM) was performed on patient health data (PHD) to examine the robustness of the performance measures
The performance of the Fog computing (FC) model that incorporates the proposed algorithm is analyzed through simulation and experiments
Summary
The latest report by the International Data Corporation stated that the number of Internetrelated sensors will increase to 30 million by 2020, and the number of Internet-of-thing (IoT) devices will be in the range of 50 billion to 1 trillion [1]. An analytical fog computing model to minimize the latency in healthcare internet-of-things contain 500 million sensors, where 212 billion sensors will be available in the market [2]. Approximately 110 million cars will be connected to 5.5 billion sensors, while 1.2 million houses connected with 200 million sensors; 237.1 million wearable body devices are estimated to be available in the market by 2020 [3]. The healthcare market for IoTs is estimated to reach $117 billion by 2020 [2] with 507.5 zettabytes of data to be generated by 50 billion connected devices [4]
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