Open Access

ARTICLE

Optimized Cognitive Learning Model for Energy Efficient Fog-BAN-IoT Networks

by S. Kalpana^1,*, C. Annadurai²

1 SRM Institute of Science and Technology, Chennai, 600089, India
2 Sri Sivasubramaniya Nadar College of Engineering, Chennai, 603110, India

* Corresponding Author: S. Kalpana. Email:

Computer Systems Science and Engineering 2022, 43(3), 1027-1040. https://doi.org/10.32604/csse.2022.024685

Received 27 October 2021; Accepted 07 December 2021; Issue published 09 May 2022

Abstract

In Internet of Things (IoT), large amount of data are processed and communicated through different network technologies. Wireless Body Area Networks (WBAN) plays pivotal role in the health care domain with an integration of IoT and Artificial Intelligence (AI). The amalgamation of above mentioned tools has taken the new peak in terms of diagnosis and treatment process especially in the pandemic period. But the real challenges such as low latency, energy consumption high throughput still remains in the dark side of the research. This paper proposes a novel optimized cognitive learning based BAN model based on Fog-IoT technology as a real-time health monitoring systems with the increased network-life time. Energy and latency aware features of BAN have been extracted and used to train the proposed fog based learning algorithm to achieve low energy consumption and low-latency scheduling algorithm. To test the proposed network, Fog-IoT-BAN test bed has been developed with the battery driven MICOTT boards interfaced with the health care sensors using Micro Python programming. The extensive experimentation is carried out using the above test beds and various parameters such as accuracy, precision, recall, F1score and specificity has been calculated along with QoS (quality of service) parameters such as latency, energy and throughput. To prove the superiority of the proposed framework, the performance of the proposed learning based framework has been compared with the other state-of-art classical learning frameworks and other existing Fog-BAN networks such as WORN, DARE, L-No-DEAF networks. Results proves the proposed framework has outperformed the other classical learning models in terms of accuracy and high False Alarm Rate (FAR), energy efficiency and latency.

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