The Artificial Intelligence (AI) has become a disruptive enabler in the distributed systems workload balancing addressing the long-term issue related to the distribution, the heterogeneity, the uncertainty, and the availability of the resource dynamically. The traditional methods of workload balancing, including fixed partitioning, round-robin, and load-sharing based on heuristics, are not usually applicable to the modern distributed systems based on the cloud computing architectures, edge/fog systems, Internet of Things (IoT) systems, and other systems with the intensive usage of data sizes. In this paper, workload balancing methods in distributed systems based on AI are undertaken systematically and in depth as experienced in their theoretical background, architectures, algorithms and performance implications. The paper expounds on machine learning, deep learning, reinforcement learning, and hybrid AI techniques adopted in the intelligent workload allocation, task migration, and adaptive resource management. An elaborate literature review is performed, including the traditional load balancing approaches and their development to the AI-based ones. The suggested approach describes an AI-based workload balancing model and model that includes the workload prediction, model of system state, smart decision-making, and continuous learning. Thousands of experiments and discussions prove that AI-based workload balancing is effective regarding a shorter response time, better resource consumption, a higher scale, and fault tolerance. Lastly, the paper identifies open research opportunities and future directions, including the topics of explainable AI, federated learning, and energy-conscious scheduling in the next-generation distributed systems.
Liam Walker, Grace Young· International Journal of Mod...· 0 citations
Structural Health Monitoring (SHM) has become an important science to help keep civil infrastructure like bridges, buildings, dams, and industrial plants safe, reliable and lasting. Traditional methods of SHM have depended primarily on wired sensor networks and periodic manual inspections that are regularly expensive, labor-intensive and lack real-time responsiveness. The present-day breakthroughs in Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have made it possible to create intelligent, scalable, and energy-efficient SHM systems that would allow continuous monitoring and remote diagnostics. This paper provides an extensive exploration of Structural Health Monitoring with wireless IoT sensors in terms of system architecture, sensing modalities, communication protocols, data processing methods, and damage detection methods. The intricate literature survey reveals the development of the SHM technologies and outlines the gaps in the research. The suggested methodology will combine low-power wireless sensors, edge computing, and cloud-based analytics to track such structural parameters as strain, vibration, displacement, and temperature. Damage detection and feature extraction mathematical models are discussed and the experimental validation strategies. The findings support the idea that IoT-based SHM systems can greatly improve the accuracy of fault detection, decrease the cost of maintenance, and predictive maintenance. The paper has reached the conclusion that wireless IoT-based SHM is an innovative solution to smart infrastructure management and offers the research perspectives on its future development in terms of scalability and artificial intelligence incorporation.
Chloe King, Liam Walker, Grace Young· International Journal of Dat...· 0 citations
Accurate production has been facilitated as a pillar in the contemporary industrialization, prompted by the growing desire of quality products, decreasing production expenses, minimal wastes, and accelerated time-to-market. The technology behind Internet of Things (IoT) has converged with Big Data analytics which has drastically changed conventional manufacturing paradigms that have permitted real time monitoring, intelligent decision-making and predictive control of manufacturing processes. IoT makes it easy to pervasively sense and interconnect machines, tools, products and human operators, creating large volumes of heterogeneous data from the manufacturing lifecycle. The computation and analytical resources needed to store, process and extract actionable information out of this data are available through the use of big data technologies. The following paper demonstrates an in-depth study of the methods of integrating IoT and Big Data to achieve precision manufacturing. It examines system architecture, data acquisition, architecture, analytics, and decision-support models that promote accuracy and efficiency in the processes and quality in the products. An extensive literature review identifies areas of recent innovations and research lag in the field of smart manufacturing, Industrial IoT (IIoT) and data-driven manufacturing. The suggested methodology gives a description of an end-to-end system that includes the deployment of sensors, data ingestion, data preprocessing, analytics, and feedback control. The experimental findings and discussion illustrate how the Big Data analytics based on IoTs enhance predictive maintenance, quality assurance and optimization processes. The paper also ends with a note on the major challenges, research directions and the use of new technologies like artificial intelligence and digital twins in enhancing precision manufacturing.
Liam Walker, Grace Young· International Journal of Mod...· 0 citations
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