ECAR–QIAVO-Based Energy-Efficient Routing for Intelligent Pollution Monitoring of High-Voltage Transmission Lines

Abstract

High voltage transmission lines are exposed to a wide range of environments that could lower system reliability due to dust, industrial emissions, humidity, salt deposits, temperature range, and electromagnetic interference. This research presents an ECAR–QIAVO-based energy-efficient wireless sensor network for smart pollution monitoring system. The selection of suitable cluster heads is based on residual energy, communication distance, and link quality in the clustering mechanism provided by ECAR, while the adaptive multi-hop routing mechanism provided by QIAVO is based on the energy level, transmission delay, hop distance, traffic load and link reliability. The deep learning is embedded to interpret the pollution data, to understand the environmental anomalies and to modify the priorities of pollution sensing and transmission. The framework is assessed by the metrics of packet delivery ratio, energy consumption, network lifetime, delay and control overhead. The results reported show that the proposed system improves the packet delivery rate to nearly 94%, decrease the energy consumption up to 38% and achieve the extension of network lifetime up to 40-45%. The framework is thus a reliable and scalable tool for continuous monitoring and proactive maintenance of high-voltage transmission power systems.