arXiv AI
Aug 20

Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments

The paper introduces a framework for detecting performance drift in Machine Learning as a Service (MLaaS) tailored to Internet of Things (IoT) settings. It first builds an extraction model that learns the service’s behavior from input‑output pairs, then uses this to jointly monitor changes in data and service behavior. An adaptive temporal mechanism adjusts monitoring frequency, and experiments on real datasets show significant accuracy gains and reduced miss‑detection rates compared to baseline methods.

By Deepak Kanneganti, Sajib Mistry, Sheik Mohammad Mostakim Fattah, Erik Elmroth, Aneesh Krishna, Monowar Bhuyan
arXiv Machine Learning
Oct 5

AIGS: Adaptive Incremental Gating System for Online Representation Learning in Non-Stationary Data Streams

The paper introduces AIGS, a lightweight Adaptive Incremental Gating System designed for online representation learning in non‑stationary data streams. AIGS uses a Shock Ratio feedback signal to drive a Continuous Plasticity Controller, enabling smooth adaptation between learning plasticity and memory retention while keeping per‑step complexity linear in the feature dimension. Experiments on smart‑city traffic, meteorological, and industrial datasets show that AIGS improves early‑warning lead times, accelerates recovery after abrupt changes, and enhances anomaly recall in noisy environments.

By SiRui He, Kai Liang Lew, Chui Zi Ong, Chean Khim Toa
arXiv Machine Learning
Jun 16

Continual Backdoor Training in IoT/CPS

arXiv:2606. 14987v1 Announce Type: cross Abstract: Internet of Things (IoT) and Cyber-physical systems (CPS) increasingly rely on continual learning (CL) to adapt to evolving environments, device heterogeneity, and concept drift, thereby improving overall utility.

By Oxana Salish, Kuniyilh S
arXiv AI
Oct 5

CoMemNet: A Continual Memory Network with Drift-Aware Sampling for Traffic Prediction

CoMemNet is a Continual Memory Network designed for traffic prediction on evolving sensor networks. It combines an online branch that adapts to current data with an exponential‑moving‑average target branch for stable reference, and uses a Wasserstein‑based drift sampler to selectively update drift‑sensitive nodes. A lightweight temporal memory replay buffer stores compact states, allowing efficient adaptation without traversing all historical data. Experiments on PeMS datasets show that CoMemNet maintains stable accuracy and outperforms retraining baselines in both prediction performance and training time.

By Mei Wu, Wenchao Weng, Wenxin Su, Wenjie Tang, Wei Zhou
arXiv Machine Learning
Jul 30

Gated Adaptation for Continual Learning in Human Activity Recognition

arXiv:2603. 10046v2 Announce Type: replace Abstract: Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart home automation, all of which rely on robust human activity recognition (HAR).

By Reza Rahimi Azghan, Gautham Krishna Gudur, Mohit Malu, Edison Thomaz, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh