## 1 Introduction  
The high costs and liabilities associated with potential failures have made structural health monitoring (SHM) an integral and necessary security measure to ensure safe and reliable operation of large-scale structures, e.g., railway, pipelines, dams, bridges, and aircrafts. While these structures are designed to ensure that they operate safely under anticipated loading scenarios, deterioration and damage can occur over their operational lifespan [1]. In particular, repeated exposure to operational and environmental loads over decades of service will inevitably introduce deterioration such as corrosion and fatigue. For example, the last several decades have witnessed unprecedented prosperity in the railway industry globally. The surface of the rail web (cross section connecting the rail head with the foot) and foot (base support of the rail) can be damaged by corrosion, leading to fractures and derailments [2], which will jeopardize the safety. Regardless of the incidence of such failureshas been progressively reducing through greater recognition of the potential failure mechanisms, improvements in materials selection, informed system management, etc. [3], these structures require constant inspections to detect and prevent potential structural problems. Periodic manual inspections, which are primarily visual, are difficult, unreliable, and nearly impossible in situations where the structures are hard to access, for example, harsh environments impede manual monitoring of critical environment data, or defects incur underneath the surface. Many non-destructive testing and evaluation (NDT & E) techniques, such as ultrasonic [4], pulsed eddy current (PEC) [5], and eddy current pulsed thermography (ECPT) [6], were developed for monitoring defects in structures with good resolution, sensitivity, and reliability. However, these techniques are expensive to implement for a large-scale application because of the labor and wiring costs as well as range-limited because of their power and resolution requirements [7]. In addition to significant costs both in time and resources due to the periodic inspections, these techniques might be too cumbersome to continuously monitor the nucleation and growth of potential defects for in-service larger-scale structures. Distributed sensor-based SHM is an attractive option for monitoring the structural health of these structures, which can transform time-based maintenance into cost-effective condition-based maintenance. Previous methods for deploying large-scale sensor networks involved running long lengths of cabling which would source power and collect data from each individual sensor; while these methods were necessary for some situations where real-time data was required, the cost, installation difficulty, and maintenance rarely justified their use over manual data collection [8]. By eliminating electric wiring from conventional sensors, wireless sensor networks (WSNs) are inexpensive and easier to install, giving us the ability to compile massive amounts of data which can greatly improve our knowledge of the environment surrounding us. This technique makes distributing sensors over a large area and with high density a reality. However, to enable large-scale pervasive sensor networks which collect big data [9], the sensing platform has to be reliable, energy efficient, and extremely low cost to become a viable long-term solution [10]. For potential forthcoming applications, spatial granularity is a key issue. Current wireless sensing applications make use of battery-powered sensors, but these sensors are at least two orders of magnitude more expensive than their simpler passive counterparts, which limits the granularity of their deployment [11]. Furthermore, battery-powered sensors have limited battery life and in turn, pose a long-term environmental risks with the disposal of billions of batteries [12].