International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 71 | Issue 11 | Year 2023 | Article Id. IJCTT-V71I11P101 | DOI : https://doi.org/10.14445/22312803/IJCTT-V71I11P101

Mitigating Latency in IoT Devices: A Machine Learning Approach to Identifying and Addressing Key Contributing Factors

Karan Gupta, Manvendra Sharma

Received Revised Accepted Published
06 Sep 2023 12 Oct 2023 01 Nov 2023 15 Nov 2023

Citation :

Karan Gupta, Manvendra Sharma, "Mitigating Latency in IoT Devices: A Machine Learning Approach to Identifying and Addressing Key Contributing Factors," International Journal of Computer Trends and Technology (IJCTT), vol. 71, no. 11, pp. 1-7, 2023. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V71I11P101

Abstract

In the realm of Internet of Things (IoT) systems, latency emerges as a pivotal challenge, jeopardizing both performance and usability, especially in time-sensitive applications. Recognizing the urgency of addressing this challenge, this paper embarks on a comprehensive analysis of synthetic IoT sensor data [1] to discern the predominant factors inducing highlatency events. By employing a lasso regression model [2], the research unveils network availability, communication failures, elevated memory utilization, and high CPU usage as the chief culprits behind latency issues. Augmenting our approach, a random forest classification [3] was employed, which impressively yielded a precision and recall rate between 93-95% in prognosticating high-latency events. Drawing on these insights, the paper advocates for strategies encompassing enhanced connectivity, protocol optimization, additional memory/CPU headroom provision, and a holistic approach to performance management as potent solutions to curtail IoT latency.

Keywords

Internet of Things (IoT), Latency, Classification models, Network availability, Memory utilization, Communication failures.

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