Authors: Pranita Lohani
Abstract: The rapid growth of Internet of Things (IoT) devices and the increasing reliance on cloud computing have driven the need for autonomous wireless Cloud–IoT systems capable of supporting large-scale, real-time applications. These systems integrate heterogeneous devices, sensors, and networks with cloud-based platforms to enable seamless data collection, processing, and decision-making without significant human intervention. The architecture of such systems typically involves layered structures, including edge computing, fog nodes, and centralized cloud services, to optimize performance, reduce latency, and enhance scalability. Despite these benefits, the deployment of autonomous Cloud–IoT systems introduces substantial risks, particularly in terms of cybersecurity, data privacy, and system reliability. Vulnerabilities in communication protocols, improper access controls, and potential failures in autonomous decision-making mechanisms pose significant challenges. To address these concerns, robust risk assessment frameworks, adaptive security mechanisms, and fault-tolerant architectural designs are essential. Moreover, ensuring interoperability among diverse devices and standards while maintaining energy efficiency further complicates system design. This study explores the architectural models and operational strategies of autonomous wireless Cloud–IoT systems, emphasizing both their functional advantages and potential threats. It examines current methodologies for risk identification, mitigation, and continuous monitoring to achieve resilient and secure operation. By providing a comprehensive perspective on architecture and risk, this work aims to guide the development of reliable, scalable, and secure autonomous Cloud–IoT systems capable of supporting emerging applications in smart cities, healthcare, industrial automation, and environmental monitoring.
DOI: http://doi.org/10.5281/zenodo.18162732
