ArticleScientific reports2025
Optimized IoT protocol stack for seamless smart home communication using Random Forest-based interoperability analysis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- HeteroEdge: Latency-Aware Adaptive Protocol Parsing with Digital Twin Intelligence for Heterogeneous 5G IoT Edge Networks.Entropy (Basel, Switzerland) · 2026Article
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Authors and funding
4 authors.
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Abstract
This paper introduces an optimised and harmonised Internet of Things (IoT) protocol stack aimed at enabling seamless device communication in smart home settings, utilising a Random Forest-based interworking analysis framework. Smart homes usually have a lot of different devices that use different communication protocols, which makes it very hard for them to work together without problems. To solve these problems, we suggest a self-adaptive, machine learning-based networking protocol stack that can quickly check the state of the network and improve how protocols work together. The Random Forest algorithm is used to find hidden connectivity patterns, predict the best ways for devices to talk to each other, and sort device interactions based on both past and present data. The system architecture includes a context-aware protocol manager, a learning-driven communication controller, and adaptive interface mapper layers that all work together to make sure that data is exchanged smoothly and coherently. Tests show that the system consistently has a communication success rate of over 85% and a compatibility score of over 0.7 for 70% of the time it is in use. In 70% of cases, latency is kept under 150 milliseconds. The solution greatly improves the interconnectivity, energy efficiency, and overall user experience of smart home devices by adding multiple intelligent layers. The built-in AI framework also saves up to 30% on energy costs while making the home safer and more comfortable. This improves the capabilities of smart home automation and user interaction.
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