Architecture and sustainability assessment of cable multi-state monitoring system driven by green computing

• Published in: Sustainable computing informatics and systems Vol. 47; p. 101153
• Main Authors: Yin, Zhimin, Jiang, Yuntu, Lai, Jun, Yue, Lingping, Wu, Guoqiang, Liu, Pingping, Li, Pengbo
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2025
• Subjects: Cable; Energy consumption; Green computing; Multi-state monitoring; Sustainability; Cable; Green computing; Multi-state monitoring; Energy consumption; Sustainability
• DOI: 10.1016/j.suscom.2025.101153
• ISSN: 2210-5379

The rapid development of urban infrastructure requires the development of advanced monitoring systems for the continuous assessment of cable health to guarantee operational reliability, safety, and long-term sustainability. The research proposed a novel Cable Multi-State Monitoring System (CMSMS) leveraging the computational capabilities of Edge compute allied with Green Computing principles to recover energy consumption and monitor. The suggested system employs heterogeneous sensors for real-time data acquisition, capturing dangerous cable parameters such as temperature, vibration, and strain. Pre-processing includes handling missing values and feature extraction using Discrete Wavelet Transform (DWT) to enhance the quality and relevance of the sensor data. Edge devices, clearly low-power platform such as Raspberry Pi and NVIDIA Jetson, serve as dispersed nodes for local data processing. These strategies permit the categorization of cable conditions into three discrete states: normal, degradation, and fault prediction, thereby support early detection of potential cable failure. For fault detection, the system includes an Extreme Gradient Boosting (XGBoost) model to adeptly handle complex, non-linear interdependencies with sensor data. Its parallel processing capabilities significantly improve computational competence, making it well-suitable for edge-based application. To further reduce energy consumption, the Shuffled Frog Leaping Algorithm (SFLA) is employed for the optimization of system parameters; ensure a balance between computational performance and energy efficacy. A comprehensive sustainability valuation is conduct to evaluate system performance, converging on energy consumption, processing speed, and fault detection accuracy. The simulation result implement using python, SFLA-XGBoost method outperformed the existing method in CMSMS fault identification, as established by it’s almost "higher accuracy" of 99.50 %. The outcomes establish a considerable decrease in effective costs and energy usage while preserve high precision in fault classification and detection. The recommended CMSMS design suggest a scalable, reliable, and energy-efficient key that is suitable across several industry, including telecommunications, power distribution, and smart cities. •A novel Cable Multi - State Monitoring System (CMSMS) uses edge computing and green principles.•The system's XGBoost model for fault detection handles complex data well with parallel processing.•TheXGBoost-SFLA technology performs well in CMSMS fault recognition and is environmentally friendly.