www.icact.org

Member Login

TACT Journal Homepage

Call for Paper

Committee

Find My Paper

Author Homepage

Statistics & History

Welcome Message

Paper Procedure

FAQ

Paper Archives

Outstanding Papers

Program & Proceedings

Presentation Platform

Hotel & Travel Info

Photo Gallery

Scheduler Login

Seminar

Archives Login

Sponsors

Virual Oral Question and Answer


IEEE/ICACT20260058 Question.2 Questioner: abilaaniloshan@gmail.com 2026-02-09 오후 4:32:54	IEEE/ICACT20260058 Answer.2 Answer by Auhor sudhakar@ccet.ac.in 2026-02-09 오후 4:32:54
Have you evaluated the latency and resource overhead introduced by the LLM explanation layer, and how feasible is real-time deployment of IoT-XPLAIN on constrained edge devices?	We evaluated the LLM layer from a deployment feasibility perspective and designed IoT-XPLAIN to keep latency and resource overhead outside the critical detection path. The Random Forest classifier performs real-time anomaly detection locally at the edge with minimal computational cost, while the LLM explanation module is triggered only after an anomaly is detected and can be executed asynchronously on a nearby edge gateway or cloud service. This decoupled design ensures that fault detection remains real-time, while explanation generation introduces acceptable, non-blocking latency. For constrained edge devices, IoT-XPLAIN supports selective explanation generation, lightweight prompting, and offloading of LLM inference, making real-time deployment practical in operational IoT environments.

Select Voice