Environmental Condition Monitoring of Infrastructure Projects Based on Wireless Sensor Networks

Abstract

Infrastructure monitoring represents a critical challenge for ensuring public safety and operational efficiency in modern urban environments, where traditional wired monitoring systems face significant limitations in terms of cost, scalability, and deployment complexity. To fulfill this demand, this paper introduces a complete wireless sensor network (WSN) framework used for infrastructure project environmental condition monitoring, including multi-parameter sensing, a suite of big data applications, and a high-speed data communication protocol with ultra-low power. The designed system is based on a hierarchical network architecture using environment sensors such as temperature, humidity, air quality in addition to structural sensors such as vibration, strain and displacement sensors. Field deployment on four different infrastructure sites (bridges, buildings, rail and road corridors) exhibited excellent performance with respect to network connectivity success of 98.7% and system uptime of 99.2% over the duration of 1 year while being operated continuously. High fidelity environmental monitoring with temperature measurement accuracy of ±0.3°C and humidity monitoring accuracy of ±2%RH was achieved to capture seasonal and pollution oriented infrastructure impacts. Performance evaluation on the network results showed satisfying average data delivery ratio > 97.5%, average packet end-to-end delay of 1.05 s, and energy efficiency of 8.0 nJ/bit, which confirmed the efficiency of the adaptive communication protocols. In Structural Health Monitoring, excellent temperature-strain relationship was found (R² = 0.85) and vibration monitoring could contribute to the early identification of possible structural problems. A machine learning-based tool with Random Forest regression resulted in strong and reliable prediction accuracy (R² = 0.952, RMSE = 5.63 μstrain) of strain based on environmental factors, for early maintenance planning. Energy management techniques like solar harvesting accounted for 65% of power demands and adaptive algorithms led to a reduction of 34% and surpassed projected battery lifetimes of greater than 3 years. Economic evaluation indicates significant benefits compared to conventional systems: 65% cost reduction, 1 year payback, and 115% 10-year ROI. System fault tolerance analysis verified successful operation with a 20% failure rate of nodes or higher, hence proving that INDEPT is resilient enough for deployment in critical infrastructure environments. The research concludes wireless sensor networks can revolutionize infrastructure monitoring, by providing an efficient, low-cost, and reliable means -ability that greatly enhances condition assessment of the environment for present infrastructure management.