Telemetry degradation and sequential stability in LLM-integrated IoT decision systems
File(s)
Accepted version
Author(s)
Lopez, Julio C Amador Diaz
Abou-Najm, Christopher
Dawhan, Saksham
Serban, Alina-Irina
Barnaghi, Payam
Type
Journal Article
Abstract
AI systems for human performance/interface must transform raw IoT telemetry into consistent actionable output. Large language models (LLMs) function as such interpreters. However behavioral stability through real-world data degradation remains unexplored; applications demanding consistent output (e.g. medicine, athletics) remain unrealized. We introduce a framework to evaluate behavioral stability of LLM decision systems transforming IoT telemetry to semantic output. The framework is validated in a case study of AI-tennis coaching using stroke-level IoT smartwatch streams. Local perturbations are mapped to global output through graded dropout, resolution downsampling and reliability signaling. We establish baseline temporal stability under idealized conditions, then quantify impact of increasing degradation severity on sequential decision behavior. Temporal volatility (FlipRate), directional escalation and cumulative safety outcomes are assessed. We show telemetry degradation significantly increases decision instability in already unstable systems. In a balanced regime, FlipRate rises from 0.144 under clean conditions to 0.207 under downsampling (+44%), while in a serve-dominant regime it reaches 0.300. Regimes characterized by strong feature dominance exhibit amplified sensitivity to information loss. A workload accumulation model shows observed volatility constitutes a structural pathway to safety-bound violations. Reliability signaling alone produces smaller effects than structural information loss, indicating informational cues cannot compensate for lost telemetry. Structured prompting partially mitigates instability but does not eliminate degradation-induced effects. We argue behavioral stability in LLM-integrated IoT systems must be evaluated jointly with sensing reliability and regime structure. Our robustness analysis provides a basis for LLM-telemetry interpretation, opening new AI-implementation avenues in human performance and digital health.
Date Issued
2026-07-31
Date Acceptance
2026-07-01
Citation
IEEE Internet of Things Journal, 2026
ISSN
2327-4662
Publisher
Institute of Electrical and Electronics Engineers
Journal / Book Title
IEEE Internet of Things Journal
Copyright Statement
Copyright © 2026 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published online
Date Publish Online
2026-07-31