Autonomous Wireless Sensor System Design for Structural Health Monitoring Application

Infrastructures such as roads, railway tracks, buildings, bridges and tunnels etc. suffer from degradation over a period of time. Phenomena such as ageing, hazards and natural disasters significantly affect the structure's sustainability. Structures require timely monitoring and maintenance to assure long term structural integrity, health and safety. Structural Health Monitoring (SHM) helps in early damage detection and improvement in the reliability of structures by enabling retrofitting of wireless sensors to capture data to help detecting potential failures. In this work, an innovative design concept for an autonomous system based on Self-repairing Spiking Astrocyte Neural Networks (SANNs) with Self-powered Sensor Nodes has been described. SANNs offer an energy efficient computing approach with the capability to mimic the self-repairing principles of the human brain for highly-reliable information processing. It is adept to tolerating failures by adapting the network topology and re-learning the input-output mapping, whereby it can bypass a significant number of non-operational nodes whilst still maintaining an adequate SHM sensory infrastructure. The design also provides an opportunity to reduce the power consumption of the wireless sensor by implementing the model as a Big-little architecture. This enables extremely low energy always-on ‘little’ sensor nodes (SN) to operate in combination with ‘big’ on-demand, neuromorphic anomaly detectors (NAD). Energy harvesting through vibrational or solar ambient energies can help supply sufficient power for the sensor node to maintain an ‘always-on’ state, while periodic analysis of sensor output data exceeding a threshold-level could help in anomalies detection for self-repairing algorithms. A pulsed mode with on-time and event-based duty cycle management helps in minimizing the average power consumption of sensor nodes. The NAD, powered typically by solar energy harvesting, is activated on threshold attainment and analyses the incoming data for further anomalies.