## 1 Introduction  
The application of distributed sensors to pervasive monitoring of physical processes is one of the most critical and relevant domains. In particular, Structural Health Monitoring (SHM) is a key deployment scenario, where ensuring the safety of infrastructures such as buildings and bridges can be, in principle, achieved by deploying low-cost sensors to detect structural variations due to damages. The convergence of Artificial Intelligence (AI) to Internet-of-Things (IoT) and edge computing in this domain can help approach these challenges. In this context, executing AI detection algorithms directly on the IoT sensor nodes potentially reduces data transmission overheads and improves response time. A review of recent embedded AI approaches to detection in SHM can be found in [1]. Due to the low-cost, low-power and increasing accuracy of MEMS accelerometers, their application to distributed SHM is becoming popular. Signal compression techniques on-edge have also been proposed to compress MEMS data gathered from several nodes and send them to the cloud storage and analytic facility [2]. Still, on MEMS data, on-sensor modal estimation was proposed by implementing procedures to detect relevant peaks in the acquired signal spectrum [3]. Optimized machine learning algorithms on-edge have been recently proposed to increase the intelligence of distributed detection for SHM. Considering the availability of code and libraries to implement machine learning algorithms on low-power microcontrollers [4; 5], it is a viable solution to execute detection algorithms on-edge and on-sensor. In this context, hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing is proposed, exploiting a Convolutional Neural Network (CNN) implemented on a low-power microcontroller, and can be found in [6]. Applied to anomaly detection on a highway bridge, in [7] a compression technique to identify anomalies in the structure using a semi-supervised approach is proposed using either a fully connected or a convolutional autoencoder implemented on the sensor node. In the present work we provide an alternative solution applied to the same case study using a supervised algorithm for near-sensor anomaly detection based on Spiking Neural Networks (SNNs). SNNs gained interest in the research community in various application domains, including SHM, because of their brain-inspired, event-based nature, which potentially allows reduced energy requirement compared to traditional ANN [8; 9; 10; 11; 12]. While Artificial Neural Networks (ANNs) have been successfully applied to SHM [1; 13; 14; 15; 16], SNNs are of increasing interest in this field because of their theoretical greater information processing efficiency achieved by exploiting a sparse computation approach. In [17] a feed forward SNN has been applied to low-cost, MEMS-based inspection of damaged buildings. However, the state-of-art in SNN applications to SHM misses a real implementation of a data processing pipeline with the execution of SNN directly on the sensor node. Moreover, embedded machine learning libraries currently lack efficient SNN implementations. In the context of SNN, when time-series data from sensors are concerned, recurrent neural networks have shown to be effective [18]. For this reason, instead of more simple feed-forward architectures, we investigate recurrent SNNs for SHM data processing. In particular we consider a state-of-art recurrent implementation of SNN called LSNN (long short-term SNN) introduced in [19] because of its interesting signal processing features and learning effectiveness. Moreover, a relevant aspect to be explored is the encoding of the input signal, which impacts subsequent computation steps and associated energy consumption. In particular, SNNs have been used with event-based input such as pixel variations from Dynamic Vision Sensor (DVS) cameras [20], but they can also effectively process \"continuous\" data streams in speech recognition applications [19].