Self-Adaptive Sensor Networks for Infrastructure Health Monitoring

Lucas Martin

Self-Adaptive Sensor Networks for Infrastructure Health Monitoring

Author(s)	Dr. Lucas Martin
Country	United States
Abstract	Continuous monitoring of bridges, buildings, tunnels, transportation structures, and other critical infrastructure increasingly depends on distributed sensing systems capable of collecting structural response data under changing operational and environmental conditions. Conventional wireless sensor networks reduce installation complexity compared with wired monitoring but frequently operate through fixed sampling, communication, routing, and decision rules. Such static configurations may consume unnecessary energy during stable periods, respond inefficiently to unexpected structural events, and remain vulnerable to communication disruption, sensor degradation, environmental variability, and changing monitoring priorities. This study develops a self-adaptive sensor-network framework for infrastructure health monitoring in which sensing frequency, edge processing, network communication, anomaly thresholds, routing behavior, and fault-response functions can change according to structural and network conditions. Because no primary field dataset was provided, the investigation is explicitly structured as a simulation-based methodological study. A reproducible synthetic dataset representing 180 hypothetical infrastructure-monitoring units was generated across three architectures: static wireless sensor networks, adaptive sensing networks, and self-adaptive edge-enabled wireless sensor networks. Six operational indicators were assessed: damage-detection accuracy, alert latency, packet-delivery rate, energy efficiency, sensor-fault recovery, and false-alarm rate. These indicators were combined into an Infrastructure Health Monitoring Index (IHMI). Mean IHMI performance increased from 57.6 under the static architecture to 75.1 under adaptive sensing and 87.9 under the self-adaptive edge architecture. The self-adaptive condition also produced the highest modeled damage-detection accuracy, packet delivery, energy efficiency, and sensor-fault recovery while producing the lowest alert latency and false-alarm rate. Inferential analysis demonstrated strong internal separation among the deliberately parameterized scenarios. The numerical findings are synthetic and therefore should not be interpreted as measured improvements in actual infrastructure. The study contributes a transparent architecture for evaluating self-adaptation as a coordinated monitoring capability rather than as isolated dynamic sampling. It argues that future infrastructure-monitoring systems should integrate context-aware sensing, edge intelligence, adaptive thresholds, energy-aware communication, sensor-fault diagnosis, and human-governed maintenance decision support while preserving engineering interpretability, cybersecurity, calibration, and fail-safe operation.
Keywords	structural health monitoring; self-adaptive sensor networks; wireless sensor networks; infrastructure monitoring; edge intelligence; smart sensors; adaptive sensing; anomaly detection; IoT; predictive maintenance
Field	Engineering
Published In	Volume 2, Issue 1, January-February 2021
Published On	2021-02-28

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American Journal of Advanced Multidisciplinary Innovation and Research

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Self-Adaptive Sensor Networks for Infrastructure Health Monitoring

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