Abstract
Proposed herein is a technique to address the challenge of excessive multicast DNS (mDNS) traffic and uneven service usage in large wireless local area networks (e.g., Wi-Fi® networks) by using a pre-trained K-means machine learning clustering model. The proposed technique intelligently prioritizes and ranks service providers based on key factors such as proximity, load, protocol reliability, and device capabilities. A novel "Golden Centroid Method" is used to rank clusters and optimize resource usage, improving network determinism and user experience. This approach reduces network traffic bursts and efficiently balances the resources and optimizes the service discovery.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
K Kothamasu, Vijay; P Bhavanasi, Srihari; Divecha, Ravi Ashvin; Mohapatro, Abhisekh; Gusain, Shubhankar; and Birla, Harsh, "ML-DRIVEN METHOD AND MECHANISM FOR SMARTER MDNS SERVICE DISCOVERY AND OPTIMIZATION", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9614