A MEMS-based wireless sensor network (WSN) is developed for nondestructive monitoring of pipeline systems. It incorporates MEMS accelerometers for measuring vibration on the surface of a pipe to determine the change in water pressure caused by rupture and the damage location. This system enables various sensor boards and camera modules to be daisychained underground and to transmit data with a shared radio board for data uplink. Challenges include reliable long-range communication, precise time synchronization, effective bandwidth usage, and power management. The low-cost MEMS technology, saved wiring cost, and simple installation without destructive modification enable large-scale deployment at an affordable cost.

This paper discusses issues of using wireless sensor systems to monitor structures and pipelines in the case of disastrous events. The platforms are deployed and monitored remotely on lifetime systems, such as underground water pipelines. Although similar systems have been proposed for monitoring seismic events and the structure health of bridges and buildings, several fundamental differences necessitate adaptation or redesign of the module. Specifically, rupture detection in water delivery networks must respond to higher frequency and wider bandwidth than those used in the monitoring of seismic events, structures, or bridges. The monitoring and detection algorithms can also impose a wide range of requirements on the fidelity of the acquired data and the flexibility of wireless communication technologies. We employ a non-invasive methodology based on MEMS accelerometers to identify the damage location and to estimate the extent of the damage.The key issues are low-noise power supply, noise floor of sensors, higher sampling rate, and the relationship among displacement, frequency, and acceleration.Based on the mentioned methodology, PipeTECT, a smart wireless sensor platform was developed. The platform was validated on a bench-scale uniaxial shake table, a small-scale water pipe network, and portions of several regional water supply networks. The laboratory evaluation and the results obtained from a preliminary field deployment show that such key factors in the implementation are crucial to ensure high fidelity of the acquired data. This is expected to be helpful in the understanding of lifeline infrastructure behavior under disastrous events.