Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 412-417· 0 citations· 12 references
Abstract
The ubiquitous use of Internet of Things (IoT) system in dynamic and real-life contexts presents a high level of challenges because of the constant changes in the data distributions, otherwise known as concept drift. The conventional machine learning models that are implemented in IoT systems are usually trained in static mode and cannot respond to changing trends in data, which causes deterioration in performance as time goes by. The drawback restricts the reliability and independence of intelligent IoT applications in the long run. In order to overcome these challenges, this paper will present a omprehensive lifelong learning model that can be used to enable drift-robust and autonomous IoT intelligence. The framework proposed combines the real-time drift detection, incremental model adaptation with memory-based knowledge retention into a single framework. It allows the IoT systems to continuously learn stream data and retain the past knowledge, thus eliminating catastrophic forgetting. The architecture is edge deportable, which means that it is low-latency inference and less reliant on centralized retraining. This is proven by on-the-job evidence of the proposed approach; it has been shown to be able to sustain stable performance even when data distribution changes, it converts to concept drift faster and it has a higher level of robustness than the traditional batch and online approaches to learning. The framework offers a scalable and effective way of facilitating self-adaptive, resilient, and autonomous IoT systems in the new generation smart environments.
The analysis of large amounts of data generated in real time by applications and services of the Internet of Things (IoT) has become a real challenge, since the data streams are continuous and non-stationary, therefore they require a high quality classification and prediction. Therefore, the use of static machine learning models becomes inefficient, since the behavior of the devices and the distribution of the sensors can change over time, and they require an adaptive model that can be updated in real time. In this work, an adaptive machine learning framework for real-time device activity recognition and future-state prediction is proposed. It is tested on a simulated IoT stream with 30,000 observations and 5 different device states (sleep, sensing, processing, transmitting, idle) under different types of concept drift (abrupt, gradual, incremental, and recurring). The framework consists of an online stochastic gradient descent (SGD) classifier, a recent-window random forest, dynamic model weighting, and Page–Hinkley drift detection. A prequential test-then-train approach is used after an initial set of 2,000 observations. For the task of activity recognition, the framework achieves an accuracy of 93.92% and a corresponding macro-F1 score of 93.81%, outperforming a static random forest as well as a non-adaptive online SGD model by 9.39% and 1.83% percentage points, respectively. For the task of one-step-ahead state prediction, it achieves an accuracy of 68.32% and corresponding macro-F1 score of 68.27%. Average processing latency per observation is below 0.5 ms. The results of this work show that adaptive online learning can efficiently support reliable IoT classification and prediction in changing operational scenarios, while keeping low computational latency.
A. S. Naik, R. E. Franklin Jino, Poonam Singh et al.· International journal of com...· 0 citations
This work proposes a novel MLaaS Performance Drift Detection framework for IoT environments that employs an MLaaS extraction model that learns service behavior from input-output pairs and identifies prediction-influenced features, and designs an Adaptive-Temporal Performance Drift Detection Mechanism that dynamically adjusts monitoring frequency based on behavioral and data variations.
Deepak Kanneganti, Sajib Mistry, S. Fattah et al.· IEEE Transactions on Network...· 0 citations
This paper proposes a transfer learning-based anomaly detection framework coupled with adaptive learning for
predictive maintenance in Industrial Internet of Things (IIoT) systems. The proposed framework is designed and validated using
a small-scale prototype to demonstrate its feasibility and effectiveness. The proposed architecture addresses the computational
limitations of edge devices by employing a cloud-assisted/remote processing framework, where sensor data are streamed through
Apache Kafka for real-time analysis. A lightweight Conditional Autoencoder is used to detect anomalies from streaming sensor
data, making the framework suitable for resource-constrained industrial environments. To overcome the scarcity of machinespecific training data, a global model is trained using data collected from machines belonging to the same machine category.
The trained global model is subsequently adapted to individual machines using transfer learning whenever machine-specific
data are available. The framework continuously monitors incoming sensor data for concept drift and performs adaptive
retraining when persistent drift is detected, enabling the model to learn changes in machine behavior caused by aging and wear.
The system processes incoming data through parallel anomaly detection and drift detection pipelines while storing labeled
operational data for future model adaptation and retraining. The proposed framework provides an adaptive and scalable
approach for real-time anomaly detection in Industrial IoT predictive maintenance systems. The experimental implementation is
carried out using a remote processing framework. The proposed architecture is cloud-ready and is intended to support cloudassisted deployment for large-scale Industrial IoT environments in future work.
Abinya S, S. Sasirekha· International Journal for Re...· 0 citations
This paper presents CRAFTER, an automated framework for designing and deploying self-adaptive IoT systems using Causal Reinforcement Learning (CRL). As IoT devices increasingly populate pervasive computing spaces, smart environments are enabled with advanced monitoring and interactive services. The dynamic nature of these environments, such as fluctuating workloads and evolving application demands, poses significant challenges in maintaining consistent Quality of Service (QoS) levels of IoT applications. While existing self-adaptation techniques offer adaptive capabilities, they are often designed to deal with specific application domains, hindering the design of self-adaptive solutions that can be re-used across multiple IoT verticals. In addition, there is a lack of automated pipelines that act on identifying key performance drivers to take effective adaptation decisions. CRAFTER addresses these issues by using Causality as a formal framework for performance analysis of IoT systems. CRAFTER generates causal graphs to uncover dependencies among system components and guide adaptation decisions based on cause–effect relationships. Then, adaptation agents can leverage this knowledge to take more effective adaptation decisions in dynamic situations. Our experimental evaluation demonstrates how CRAFTER enables deriving causal graphs spanning diverse IoT use cases. Furthermore, we showcase how CRAFTER improves self-adaptation performance by 25% compared to state-of-the-art Reinforcement Learning-based approaches.
Houssam Hajj Hassan, A. Kattepur, Denis Conan et al.· SEAMS@ICSE· 0 citations
An offensive-defensive system based on Deep Reinforcement Learning (DRL) algorithms is proposed, which outperform several state-of-the-art machine learning approaches in the literature, revealing that the systematic incorporation of accurate data engineering and reinforcement learning frameworks generates a field-tested security barrier that offers an expedient reaction to counteract multifaceted threats to IoT networks.
Hawraa A. Habeeb, M. Manaa· Journal of Intelligent Infor...· 0 citations
IoT sensor networks are increasingly deployed in smart environments, generating continuous streams of data that require intelligent and efficient analysis. This paper proposes an adaptive deep learning framework for anomaly detection in IoT sensor data to identify faults, intrusions, and abnormal patterns in real time. The system utilizes an Autoencoder-based deep neural network that dynamically learns normal behavioral patterns and adapts to evolving data distributions, effectively addressing concept drift.
A key contribution of this work is the integration of an energy-aware communication strategy that reduces redundant data transmission, thereby extending the lifetime of resource-constrained IoT devices. Furthermore, a lightweight edge–cloud collaborative architecture is introduced, where preliminary anomaly filtering is performed at the edge to minimize latency and bandwidth usage.
The model is evaluated using the UNSW-NB15 and IoT-23 datasets, comprising over 2.5 million network flow records with both normal and anomalous classes. Data preprocessing includes normalization, feature selection, and noise filtering, followed by a 70:15:15 train–validation–test split. Unlike traditional threshold-based methods, the proposed framework achieves higher detection accuracy with reduced false positives and demonstrates strong generalization across multiple IoT domains. Experimental results validate improved detection performance, reduced latency, and enhanced energy efficiency.
Gigi Joseph, Susheel George Joseph· CogNexus· 0 citations
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