Latency Reduction in Medical IoT Using Fuzzy Systems by Enabling Optimized Fog Computing

International Journal of Electrical and Electronics Engineering

© 2022 by SSRG - IJEEE Journal

Volume 9 Issue 12

Year of Publication : 2022

Authors : S. Aiswarya, Angelina Geetha, K. Ramesh, S. Sasikumar, D. Sheema

10.14445/23488379/IJEEE-V9I12P114

How to Cite?

S. Aiswarya, Angelina Geetha, K. Ramesh, S. Sasikumar, D. Sheema, "Latency Reduction in Medical IoT Using Fuzzy Systems by Enabling Optimized Fog Computing," SSRG International Journal of Electrical and Electronics Engineering, vol. 9,  no. 12, pp. 156-166, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I12P114

Abstract:

Fog computing technology is an emerging computing method that functions in a distributed decentralized environment. Cloud computing features are being brought closer to edge devices with fog computing. Healthcare IoT devices should benefit from this approach by achieving minimum latency requirements. A variety of devices in healthcare produce a huge amount of data. The massive volume of data generates network congestion and high latency due to high traffic. With IoTs and cloud servers transmitting data at high rates and having big hop counts, round-trip delays can make data useless and insufficient for users. It is essential to have real-time data for medical applications that are time-critical. Traditional computing servers cannot serve IoT devices at the other end of the medical IoT chain because of their latency requirements. So, the data transmission of different latencies like computation, communication, and network must be reduced. IoT must operate at low latency since data transmission must occur in real-time. This requirement can't be achieved through cloud computing. Due to data volume and factors related to Internet connectivity, analyzing and acting on data can result in high network latency. Fog computing makes it possible to store, process, and examine data from cloud computing at the network edge. As mentioned earlier, the current work presents an innovative solution to the problem. With the help of fog computing, the fuzzy-based reinforcement learning algorithm is integrated with an analytical model. The objective is latency reduction for IoTs in healthcare, including cloud servers. The projected smart fog computing model and algorithms combine fuzzy inference with reinforcement learning and other neural network development methods to allocate and select data packets. A simulation approach is tested using iFogSim and Spyder. Simulated results show the proposed Optimized Latency Fog Computing (OLFC) model has 52% and 30% minimal latency compared with the existing iFogStor and FC models.

Keywords:

Fog computing, Internet of things, Big data, Healthcare, Fuzzy Systems, Cloud computing.

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