Autonomic Computing for High-Performance IoT Systems: Self-Managing Intelligence across Cloud, AI, Blockchain, and Quantum Technologies

The rapid expansion of the Internet of Things (IoT) has fundamentally transformed our digital landscape, creating ecosystems characterized by massive scale, extreme heterogeneity, and high volatility. As IoT devices permeate sectors like smart cities, healthcare, and industrial automation, they generate unprecedented volumes of data that demand real-time processing and sophisticated management. Traditional centralized computing models are increasingly insufficient for these demands, leading to the necessary convergence of IoT with High-Performance Computing (HPC) and edge-cloud infrastructures. This integration, however, introduces significant architectural complexity. Managing these large-scale systems manually is no longer feasible due to the dynamic nature of workloads and the potential for frequent hardware or network failures.Autonomic computing emerges as a vital paradigm to address these challenges, drawing inspiration from the human autonomic nervous system. By embedding self-management capabilities, specifically self-configuration, self-optimization, self-healing, and self-protection - systems can independently adapt to changing conditions and mitigate threats without human intervention. The integration of Artificial Intelligence (AI) and Machine Learning (ML) has further accelerated this field, providing the predictive capabilities necessary for proactive resource allocation and anomaly detection. Furthermore, emerging technologies like blockchain for decentralized security and quantum computing for complex optimization tasks are redefining the boundaries of what autonomous systems can achieve. Despite these advancements, significant research gaps remain in ensuring the seamless, secure, and energy-efficient orchestration of autonomic behaviors across the distributed continuum from the edge to the HPC core.The primary goal of this Research Topic is to curate a collection of high-quality research that advances the state of the art in autonomic computing tailored for IoT-driven high-performance environments. We aim to foster a cross-disciplinary dialogue between researchers in IoT, HPC, and AI to develop innovative frameworks that ensure system resilience, scalability, and efficiency. Ultimately, this Topic seeks to bridge the gap between theoretical autonomic models and their practical implementation in mission-critical, large-scale computing infrastructures.This Research Topic invites original research articles, comprehensive surveys, and detailed case studies that contribute to the theoretical and practical advancement of autonomic IoT systems. We are particularly interested in submissions that provide rigorous validation through simulations, testbeds, or real-world deployments. Submissions should offer novel insights into how autonomic principles can be scaled to meet the rigors of modern high-performance demands.The scope of this topic includes, but is not limited to, the following sub-themes:- Self-adaptive resource management and scheduling in IoT–HPC continuums.- AI/ML-driven predictive maintenance and self-healing for distributed sensors.- Decentralized trust management and self-protecting IoT architectures using blockchain.- Energy-efficient autonomic control for green edge-computing environments.- Explainable AI (XAI) for transparent autonomous decision-making in IoT.- Performance modeling and simulation of large-scale autonomic IoT ecosystems.- Quantum-inspired optimization for autonomic network configurations and security.We welcome contributions from both academia and industry to help shape the future of autonomous, high-performing computing ecosystems. Topic editors: Manoj Kumar, Bhupesh Kumar Dewangan Source: https://www.frontiersin.org/research-topics/78601/autonomic-computing-for-high-performance-iot-systems-self-managing-intelligence-across-cloud-ai-blockchain-and-quantum-technologies