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:2606. 30701v1 Announce Type: cross Abstract: As the Internet of Things (IoT) continues its rapid expansion, the attack surface grows accordingly, with emerging threats targeting smart objects and their interactions.
By Marco Arazzi, Mert Cihangiroglu, Serena Nicolazzo, Antonino Nocera, Vinod P
arXiv:2412.06210v3 Announce Type: replace
Abstract: With the rapid development of the Internet of Things (IoT), federated learning (FL) has gained increasing attention for its privacy-preserving use...
By Jiechao Gao, Yuangang Li, Jie Wang, Yue Zhao, Michael Lepech, Brad Campbell
arXiv:2512. 20627v2 Announce Type: replace-cross Abstract: Intent-Based Networking (IBN) offers a promising paradigm for intelligent and automated network control in Industrial Internet of Things (IIoT) environments by translating high-level user intents into executable network strategies.
By Shaowen Qin, Jianfeng Zeng, Haodong Guo, Xiaohuan Li, Jiawen Kang, Qian Chen
arXiv:2608. 15465v1 Announce Type: cross Abstract: Identification of IoT device types from passive traffic is increasingly used for security management in enterprise and ISP networks.
By Shayan Azizi, Norihiro Okui, Masataka Nakahara, Ayumu Kubota, Gustavo Batista, Hassan Habibi Gharakaheili
arXiv:2606. 02606v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed as continuously evolving services, where frequent base-model updates may invalidate previously deployed task-specific Low-Rank Adaptation (LoRA) adapters.
By Yang Xu, Zihuai Xu, Hongli Xu, Yunming Liao, Zhiwei Yao, Xitong Fu
The paper introduces a constrained Bayesian Optimization framework to efficiently configure Hierarchical Federated Learning (HFL) for plant disease classification in IoT networks. It jointly optimizes the deep learning backbone, aggregation strategy, and communication rounds while respecting energy, execution time, and accuracy constraints. Experiments on an IoT-based plant disease task show the method explores only 11.11% of the search space yet finds solutions within 1% of exhaustive search, achieving a mean optimality gap of 0.056%.
By Athanasios Papanikolaou, Athanasios Tziouvaras, Apostolos Xenakis, Periklis Chatzimisios, Shameem A. Puthiya Parambath, George Floros, Enrica Zereik, Ivan Petrovic, Fabio Bonsignorio
arXiv:2609.23097v1 Announce Type: cross
Abstract: Internet of Things (IoT) systems are increasingly deployed in smart homes, transportation, energy systems, and critical infrastructure. This broad co...
By Li Yang
arXiv:2510. 13817v2 Announce Type: replace Abstract: The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountability.
By Rameen Mahmood, Tousif Ahmed, Sai Teja Peddinti, Danny Yuxing Huang
BIPPO (Budget-aware Independent Proximal Policy Optimization) is a multi‑agent reinforcement learning framework designed for energy‑efficient client selection in federated learning (FL) over IoT systems. It addresses infrastructure constraints such as limited resources and device churn, which traditional FL and RL approaches overlook. Evaluated on two image‑classification tasks with non‑IID data, BIPPO improves mean accuracy over non‑RL methods, standard PPO, and IPPO while consuming only a negligible portion of the budget, even as client numbers grow.
By Anna Lackinger, Andrea Morichetta, Pantelis A. Frangoudis, Schahram Dustdar
arXiv:2606. 15653v1 Announce Type: cross Abstract: The validation of networking and security solutions for the Internet of Things (IoT) requires realistic and reproducible experimental data.
By Vagner E. Quincozes, Diego Kreutz, Silvio E. Quincozes
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