Self-adaptive Systems Research Articles

Leveraging Hybrid Edge-Cloud Predictive Maintenance in Pharmaceutical MES: An Industry 4.0 Approach Using Big Data

The pharmaceutical sector relies on stringent manufacturing environments to safeguard product integrity and uphold regulatory standards. Unexpected equipment failures can lead to costly downtime, regulatory exposure, and compromised quality. To address these challenges, this paper presents an integrated Hybrid Edge-Cloud Predictive Maintenance (HEC-PdM) framework embedded within a Manufacturing Execution System (MES). By combining edge computing for real-time anomaly detection with cloud-based machine learning (ML) analytics, manufacturers can transition from reactive to predictive and prescriptive maintenance strategies. The methodology includes data collection and preprocessing at the edge, federated learning in the cloud, and seamless MES integration to automate maintenance workflows and compliance documentation. Case studies highlight significant benefits, such as a 45% reduction in maintenance costs, minimized downtime, and improved production quality. Finally, the paper discusses future directions, including enhanced security protocols for federated learning, self-adaptive AI systems, and quantum ML to further address the complexities of pharmaceutical manufacturing.

A hybrid search and model-based approach for testing the self-adaptive unmanned aircraft system software

A hybrid search and model-based approach for testing the self-adaptive unmanned aircraft system software

Design and implementation of an automatic and self-adaptive NILM system using unsupervised learning and an IoT platform

Design and implementation of an automatic and self-adaptive NILM system using unsupervised learning and an IoT platform

AI for self-adaptive cloud systems: Towards fully autonomous data centers

The increasing complexity of modern computing systems, coupled with growing demands for energy-efficient and cost-effective data centers, has driven the need for self-adaptive cloud systems. Advancements in artificial intelligence hold the promise of enabling fully autonomous data centers that can adapt to dynamic workloads, optimize resource utilization, and reduce environmental impact. This paper explores the applications of AI techniques in the context of self-adaptive cloud systems, highlighting the potential for AI-powered solutions to address key challenges in the design, operation, and maintenance of modern data centers. The rapid growth of cloud computing and the proliferation of data-intensive applications have placed significant strain on the infrastructure of modern data centers. To meet the demands for increased computing power, storage, and energy efficiency, cloud providers and data center operators must navigate a complex landscape of operational challenges, including workload fluctuations, resource allocation, energy management, and fault tolerance. AI-driven approaches offer a promising avenue to address these challenges, enabling cloud systems to become more self-aware, self-healing, and self-optimizing

Aegis AI - Intelligent Cyber Resilience

As cyber threats continue to evolve in complexity and scale, traditional security measures have become insufficient. Aegis AI (AAI): Intelligent Cyber Resilience presents a cutting-edge approach that integrates artificial intelligence (AI) and machine learning (ML) to strengthen cybersecurity defenses. This study explores the role of AI-driven threat intelligence, automated incident response, and adaptive learning in combating cyberattacks. The proposed AAI framework utilizes deep learning, anomaly detection, and reinforcement learning techniques to predict and mitigate threats in real time. By enhancing cyber resilience, AAI reduces response times, minimizes false positives, and ensures robust security automation. The research also addresses adversarial machine learning risks and ethical concerns surrounding AI in cybersecurity. Our findings demonstrate that AI-powered security systems significantly improve detection accuracy and automate cyber defense strategies. Future research will focus on integrating federated learning, real-time behavioural analytics, and AI-driven compliance frameworks to further enhance AAI’s effectiveness in securing digital ecosystems. Aegis AI is an advanced AI-driven cybersecurity framework designed to enhance cyber resilience by integrating intelligent threat detection, automated incident response, and adaptive learning. It leverages machine learning models to identify, predict, and mitigate cyber threats in real time, reducing response times and minimizing security breaches. By continuously updating its knowledge through federated learning and behavioural analytics, Aegis AI ensures proactive defense against evolving cyber threats, making digital ecosystems more secure and resilient. Aegis AI transforms cybersecurity by providing an intelligent, self-adaptive defense system that not only detects and mitigates threats in real time but also evolves with emerging cyber risks. By automating security responses and continuously learning from new threats, Aegis AI ensures a resilient, proactive, and future-proof cybersecurity framework, safeguarding digital assets with unmatched efficiency. Keywords: AI in Cybersecurity, Threat Intelligence, Machine Learning, Cyber Resilience, Automated Incident

Self-adaptive Federated Learning in Internet of Things Systems: A Review

In recent years, Federated Learning (FL) and the Internet of Things (IoT) have enabled numerous Artificial Intelligence (AI) applications. FL offers advantages over traditional Machine Learning (ML) and Deep Learning (DL) by shifting model training to the edge. However, the dynamic nature of IoT environments often interferes with FL’s ability to converge quickly and deliver consistent performance. Therefore, a self-adaptive approach is necessary to react to context changes and maintain system performance. This paper provides a systematic overview of current efforts to integrate self-adaptation in FL for IoT. We review key computing disciplines, including Self-Adaptive Systems (SAS), Feedback Controls, IoT, and FL. Additionally, we present (i) a multidimensional taxonomy to highlight the core characteristics of self-adaptive FL systems and (ii) a conceptual architecture for self-adaptive FL in IoT, applied to Anomaly Detection (AD) in smart homes. Finally, we discuss the motivations, implementations, applications, and challenges of self-adaptive FL systems in IoT contexts.

An Architectural Viewpoint for Benefit-Cost-Risk-Aware Decision-Making in Self-Adaptive Systems

Self-adaptation equips a software system with a feedback loop that resolves uncertainties during operation and adapts the system to deal with them when necessary. Most self-adaptation approaches today use decision-making mechanisms that select for execution the adaptation option with the best-estimated benefit expressed as a set of adaptation goals. A few approaches also consider the estimated (one-off) cost of executing the candidate adaptation options. We argue that besides benefit and cost, decision-making in self-adaptive systems should also consider the estimated risk the system or its users would be exposed to if an adaptation option were selected for execution. Balancing all three concerns when evaluating the options for adaptation to mitigate uncertainty is essential for satisfying stakeholders’ concerns and ensuring the safety and public acceptance of self-adaptive systems. In this article, we present a reference model for decision-making in self-adaptation that considers the estimated benefit, cost, and risk as core concerns of each adaptation option. Leveraging this model, we then present an ISO/IEC/IEEE 42010 compatible architectural viewpoint that aims at supporting software architects responsible for designing robust decision-making mechanisms for self-adaptive systems. We demonstrate the applicability, usefulness, and understandability of the viewpoint through a case study where participants with experience in the engineering of self-adaptive systems performed a set of design tasks in DeltaIoT, an Internet-of-Things exemplar for research on self-adaptive systems.

Self-Adaptive Traffic Anomaly Detection System for IoT Smart Home Environments

Self-Adaptive Traffic Anomaly Detection System for IoT Smart Home Environments

A Modified Kalman Filter Based on Radial Basis Function Neural Networks for the Improvement of Numerical Weather Prediction Models

This study introduces a novel enhancement to the Kalman filter algorithm by integrating it with Radial Basis Function neural networks to improve numerical weather prediction models. Traditional Kalman filters frequently underperform when used by dynamical systems due to their reliance on fixed covariance matrices, resulting in inaccuracies and forecast uncertainty. The proposed modified Kalman filter utilizes Radial Basis Function neural networks to estimate covariance matrices adaptively during the filtering process. This self-adaptive computational system enables the simultaneous targeting of the systematic and the remaining non-systematic parts of the forecast error, producing an innovative and efficient post-process strategy. The suggested methodology is evaluated on predictions of 10-meter wind speed and 2-meter air temperature obtained from the Weather Research and Forecasting model for observation stations in northern Greece. The derived results demonstrate a significant reduction in systematic error, as the bias decreased by up to 88% for 10-meter wind speed and 58% for 2-meter air temperature. Additionally, the forecast variability was successfully mitigated, with the RMSE reduced by 39% and 40%, respectively. Compared to the traditional Kalman filter, which exhibited increased RMSE in several cases and failed to control forecast uncertainty, the proposed approach consistently outperformed by providing stable and reliable predictions across all examined scenarios. These improvements validate the robustness of the method in comparison to conventional techniques, highlighting its potential to produce reliable and stable predictions for environmental applications.

Coupling NLP for Intelligent Knowledge Management in Organizations: A Framework for AI-Powered Decision Support

Knowledge management (KM) is crucial component for business development in modern enterprises and this type of management is facilitated through technology. Nevertheless, conventional knowledge management systems (KMS) face problems concerning, but not limited to, information silos, difficulty in accessing data, and the complexity in managing unstructured data. As new advancements are made towards Natural Language Processing (NLP), Artificial Intelligence (AI) technologies that allow for contextual knowledge discovery, intelligent search, automated summarization, and real time content classification become readily available. This research analyzes the application of NLP systems concerning their integration with knowledge systems in business, information retrieval, enterprise search, and knowledge recommendation systems. For these integrations to be successful, Name Entity Recognition (NER), semantic search, Retrieval-Augmented Generation (RAG), Optical Character Reader (OCR), and Explainable AI (XAI) technologies need to be utilized. This will assure that decision-making processes are secure and ethical. This paper also presents an NLP-Driven Knowledge Management Framework (NLP-KMF), which is a novel framework that helps manage knowledge. The paper discusses the real-world usage of NLP-powered knowledge management in corporate learning, customer service, and compliance with Google, Accenture, IBM, and JPMorgan Chase serving as the centers of case studies. Strategies to counter issues such as AI bias and misinformation alongside privacy threats are discussed as well. The last section of the paper analyzes the forthcoming research areas that could include topics such as multimodal AI for knowledge management, AI repositories that continuously learn, and decision intelligence driven by AI. This serves as a constructive and precise plan for organizations that wish to evolve from static knowledge databases to dynamic self-adapting AI systems.

A Self-Adaptive Traffic Signal System Integrating Real-Time Vehicle Detection and License Plate Recognition for Enhanced Traffic Management

Traffic management systems play a crucial role in smart cities, especially because increasing urban populations lead to higher traffic volumes on roads. This results in increased congestion at intersections, causing delays and traffic violations. This paper proposes an adaptive traffic control and optimization system that dynamically adjusts signal timings in response to real-time traffic situations and volumes by applying machine learning algorithms to images captured through video surveillance cameras. This system is also able to capture the details of vehicles violating signals, which would be helpful for enforcing traffic rules. Benefiting from advancements in computer vision techniques, we deployed a novel real-time object detection model called YOLOv11 in order to detect vehicles and adjust the duration of green signals. Our system used Tesseract OCR for extracting license plate information, thus ensuring robust traffic monitoring and enforcement. A web-based real-time digital twin complemented the system by visualizing traffic volume and signal timings for the monitoring and optimization of traffic flow. Experimental results demonstrated that YOLOv11 achieved a better overall accuracy, namely 95.1%, and efficiency compared to previous models. The proposed solution reduces congestion and improves traffic flow across intersections while offering a scalable and cost-effective approach for smart traffic and lowering greenhouse gas emissions at the same time.

An Extended Study of the Performance of Flexible Controllers Composed of Micro-controllers

Generic controllers for self-adaptive systems can be configured parametrically according to system needs, even though their reuse is restricted because of the wide range of services that may be provided by each stage of a feedback control loop, like MAPE-K. Rainbow is a typical example of such a generic, monolithic controller. This article revisits and extends prior work that advocates structurally flexible controllers, as ensembles of micro-controllers each providing specific services. We experimented with our approach with three different architectural configurations for the controller: monolithic, decentralised, and decentralised with a meta-controller. Our results indicate that despite the decentralised configuration with a meta-controller demanding more computational resources, it performed comparatively well compared to the other configurations, including when measuring the target system’s response time. Moreover, we found that variations of the control loop timing at the different layers of the controller impact the stability of the target system. We have also evolved the controller by adding a new micro-controller, which caused no impact on the other micro-controllers, and mostly kept the target system’s performance. We conclude that a multi-layered controller design, based on micro-controllers, provides the basis for defining structurally flexible controllers at operational-time, and may promote reuse at development-time.

The Future of AI in Governance: Building Self-Adapting GRC Systems

The potential of artificial intelligence to revolutionize the creation of self-adapting Governance, Risk, and Compliance (GRC) systems is explored in this article. It discusses how organizations are transitioning from traditional, manual GRC processes to sophisticated AI-driven solutions that can autonomously adapt to evolving regulatory landscapes. The article illustrates how reinforcement learning, adaptive algorithms, and neural networks reshape GRC practices by examining current implementation challenges, technological foundations, and future opportunities. It also discusses critical implementation factors across technological, regulatory, and organizational aspects while highlighting real-world applications in intelligent policymaking, predictive risk management, and ongoing compliance monitoring. The article addresses future developments in GRC technology, such as enhanced ecosystem creation, advanced analytics, and integration capabilities. Analyzing prospects and challenges provides valuable insights for companies implementing self-adapting GRC systems while maintaining robust governance frameworks.

Artificial intelligence advances in anomaly detection for telecom networks

Telecommunication networks are becoming increasingly dynamic and complex due to the massive amounts of data they process. As a result, detecting abnormal events within these networks is essential for maintaining security and ensuring seamless operation. Traditional methods of anomaly detection, which rely on rule-based systems, are no longer effective in today’s fast-evolving telecom landscape. Thus, making AI useful in addressing these shortcomings. This review critically examines the role of Artificial Intelligence (AI), particularly deep learning, in modern anomaly detection systems for telecom networks. It explores the evolution from early strategies to current AI-driven approaches, discussing the challenges, the implementation of machine learning algorithms, and practical case studies. Additionally, emerging AI technologies such as Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) are highlighted for their potential to enhance anomaly detection. This review provides AI’s transformative impact on telecom anomaly detection, addressing challenges while leveraging 5G/6G, edge computing, and the Internet of Things (IoT). It recommends hybrid models, advanced data preprocessing, and self-adaptive systems to enhance robustness and reliability, enabling telecom operators to proactively manage anomalies and optimize performance in a data driven environment.

Feature-Oriented Modelling and Analysis of a Self-Adaptive Robotic System

Improved autonomy in robotic systems is needed for innovation in, e.g., the marine sector. Autonomous robots that are let loose in hazardous environments, such as underwater, need to handle uncertainties that stem from both their environment and internal state. While self-adaptation is crucial to cope with these uncertainties, bad decisions may cause the robot to get lost or even to cause severe environmental damage. Autonomous, self-adaptive robots that operate in uncontrolled environments full of uncertainties need to be reliable! Since these uncertainties are hard to replicate in test deployments, we need methods to formally analyse self-adaptive robots operating in uncontrolled environments. In this paper, we show how feature-oriented techniques can be used to formally model and analyse self-adaptive robotic systems in the presence of such uncertainties. Self-adaptive systems can be organised as two-layered systems with a managed subsystem handling the domain concerns and a managing subsystem implementing the adaptation logic. We consider a case study of an autonomous underwater vehicle (AUV) for pipeline inspection, in which the managed subsystem of the AUV is modelled as a family of systems, where each family member corresponds to a valid configuration of the AUV which can be seen as an operating mode of the AUV’s behaviour. The managing subsystem of the AUV is modelled as a control layer that is capable of dynamically switching between such valid configurations, depending on both environmental and internal uncertainties. These uncertainties are captured in a probabilistic and highly configurable model. Our modelling approach allows us to exploit powerful formal methods for feature-oriented systems, which we illustrate by analysing safety properties, energy consumption, and multi-objective properties, as well as performing parameter synthesis to analyse to what extent environmental conditions affect the AUV. The case study is realised in the probabilistic feature-oriented modelling language and verification tool ProFeat, and in particular exploits family-based probabilistic and parametric model checking.

Enhancing power quality in distribution networks with self-adaptive VPI-controlled grid-forming battery energy storage system

Enhancing power quality in distribution networks with self-adaptive VPI-controlled grid-forming battery energy storage system

Context-Aware Proactive Self-Adaptation: A Two-layer Model Predictive Control Approach

In self-adaptive software systems, the role of context is paramount, especially for proactive self-adaptation. Current research, however, does not fully explore context’s impact, for example on priorities of the requirements. To address this gap, we introduce a novel contextual goal model to capture these factors and their influence on the system. Using this, we propose a two-layer control mechanism with a context-aware model predictive control to achieve proactive adaptation for the software system and adaptation for the controller itself. By contextual prediction and a more accurate system model, our approach utilizes model predictive control to facilitate timely and efficient system adaptations, improving both performance and adaptability. Meanwhile, we perform requirement adaptation to update the contextual goal model, which in turn updates the objective function and constraints of the controller. Our experimental evaluations across two scenarios demonstrate the significant benefits of our approach in enhancing system performance.

Modelling, Analysis and Validation of Hydraulic Self-Adaptive Bearings for Elevated Floating Bridges.

Conventional floating bridge systems used during emergency repairs, such as during wartime or after natural disasters, typically rely on passive rubber bearings or semi-active control systems. These methods often limit traffic speed, stability, and safety under dynamic conditions, including varying vehicle loads and fluctuating water levels. To address these challenges, this study proposes a novel Hydraulic Self-Adaptive Bearing System (HABS). The system integrates real-time position closed-loop control and a flexible support compensation method to enhance stability and adaptability to environmental changes. A modified three-variable controller is introduced to optimise load response, while a multi-state observer control strategy effectively reduces vibrations and improves traffic smoothness. A 1:15 scale prototype was constructed, and a co-simulation model combining MATLAB/Simulink and MSC Adams was developed to simulate various operational conditions. Results from both experiments and simulations demonstrate the HABS's ability to adapt to varying loads and environmental disturbances, achieving a 72% reduction in displacement and a 54% reduction in acceleration. These improvements enhance traffic speed, stability, and safety, making the system a promising solution for emergency and floating bridges, providing superior performance under challenging and dynamic conditions.

Analysing self-adaptive systems as software product lines

Analysing self-adaptive systems as software product lines

Editorial

Dear Readers, First of all, I am very happy to announce that the funding round of the KOALA initiative initiated by the TIB was successful and that our journal will be part of the computer science and mathematics cluster for the next three years. The entire J.UCS team is very grateful for the great efforts of the KOALA team and for the support of the J.UCS community. This enables us to continue our work to maintain J.UCS as a diamond open access journal and to further improve the service and scientific quality. Also, I would like to thank all the authors for their sound research and the editorial board for their extremely valuable review effort and suggestions for improvement. These contributions, together with the generous support of the consortium members, contribute to the quality of our journal. It gives me great pleasure to announce the eleventh regular issue of 2024. In this issue, 6 papers by 21 authors from 5 countries - Brazil, India, Algeria, Italy, Sweden - cover various topical aspects of computer science. In an ongoing effort to further strengthen our journal, I am continuously looking for new editorial board members: If you are a tenured associate professor or higher with a good publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. Geová Junio da Silva Tavares and Nelson Souto Rosa from Brazil present the results of a survey on humans in the loop for self-adaptive systems. Aluizio Haendchen Filho, Adson Marques da Silva Esteves, Hércules Antonio do Prado, Edilson Ferneda and André Luis Alice Raabe from Brazil discuss their study on adaptive content recommendations to improve logic and programming teaching and learning by using learning paths to group students and provide personalized recommendations based on peers' progress. Rupesh Kumar Verma, A. J. Khan, Sunil Kashyap and Manoj Kumar Chande from India conduct their study on the certificateless aggregate signature scheme in terms of their computational performance and security, which can be widely used in areas such as IoT or healthcare systems. Besma Hezili and Hichem Talbi from Algeria address the collaborative auto-diversified optimization scheme (CADOS) for solving continuous and combinatorial optimization problems by exploring the synergy of various optimization algorithms and enhance their effectiveness and efficiency, particularly for higher-dimensional problems. Maroua Chemlal, Amina Zedadra, Ouarda Zedadra, Antonio Guerrieri and Med Nadjib Kouahla from Algeria outline their approach and findings on a multi-criteria food and restaurant recommendation system. And last but not least, Ashish Ranjan Mishra, Rakesh Kumar and Rajkumar Saini from India present their study on the improvement of the effectiveness of person authentication by using deep learning techniques on electroencephalogram (EEG) signals by applying a multiscale convolutional neural network (CNN) and a Bidirectional LSTM (BiLSTM) model to extract features and classify raw EEG data. Enjoy Reading! Kind regards, Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria