Distributed Real-time Anomaly Detection in Networked Industrial Sensing Systems

Reliable real-time sensing plays a vital role in ensuring the reliability and safety of industrial cyber-physical systems (CPSs) such as wireless sensor and actuator networks. For many reasons, such as harsh industrial environments, fault-prone sensors, or malicious attacks, sensor readings may be abnormal or faulty. This could lead to serious system performance degradation or even catastrophic failure. Current anomaly detection approaches are either centralized and complicated or restricted due to strict assumptions, which are not suitable for practical large-scale networked industrial sensing systems (NISSs), where sensing devices are connected via digital communications, such as wireless sensor networks or smart grid systems. In this paper, we introduce a fully distributed general anomaly detection (GAD) scheme, which uses graph theory and exploits spatiotemporal correlations of physical processes to carry out real-time anomaly detection for general large-scale NISSs. We formally prove the scalability of our GAD approach and evaluate the performance of GAD for two industrial applications: building structure monitoring and smart grids. Extensive trace-driven simulations validate our theoretical analysis and demonstrate that our approach can significantly outperform state-of-the-art approaches in terms of detection accuracy and efficiency

Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT

Wireless sensor networks (WSN) are fundamental to the Internet of Things (IoT) by bridging the gap between the physical and the cyber worlds. Anomaly detection is a critical task in this context as it is responsible for identifying various events of interests such as equipment faults and undiscovered phenomena. However, this task is challenging because of the elusive nature of anomalies and the volatility of the ambient environments. In a resource-scarce setting like WSN, this challenge is further elevated and weakens the suitability of many existing solutions. In this paper, for the first time, we introduce autoencoder neural networks into WSN to solve the anomaly detection problem. We design a two-part algorithm that resides on sensors and the IoT cloud respectively, such that (i) anomalies can be detected at sensors in a fully distributed manner without the need for communicating with any other sensors or the cloud, and (ii) the relatively more computation-intensive learning task can be handled by the cloud with a much lower (and configurable) frequency. In addition to the minimal communication overhead, the computational load on sensors is also very low (of polynomial complexity) and readily affordable by most COTS sensors. Using a real WSN indoor testbed and sensor data collected over 4 consecutive months, we demonstrate via experiments that our proposed autoencoder-based anomaly detection mechanism achieves high detection accuracy and low false alarm rate. It is also able to adapt to unforeseeable and new changes in a non-stationary environment, thanks to the unsupervised learning feature of our chosen autoencoder neural networks.Comment: 6 pages, 7 figures, IEEE ICC 201

A high performance LIA-based interface for battery powered sensing devices

This paper proposes a battery-compatible electronic interface based on a general purpose lock-in amplifier (LIA) capable of recovering input signals up to the MHz range. The core is a novel ASIC fabricated in 1.8 V 0.18 µm CMOS technology, which contains a dual-phase analog lock-in amplifier consisting of carefully designed building blocks to allow configurability over a wide frequency range while maintaining low power consumption. It operates using square input signals. Hence, for battery-operated microcontrolled systems, where square reference and exciting signals can be generated by the embedded microcontroller, the system benefits from intrinsic advantages such as simplicity, versatility and reduction in power and size. Experimental results confirm the signal recovery capability with signal-to-noise power ratios down to -39 dB with relative errors below 0.07% up to 1 MHz. Furthermore, the system has been successfully tested measuring the response of a microcantilever-based resonant sensor, achieving similar results with better power-bandwidth trade-off compared to other LIAs based on commercial off-the-shelf (COTS) components and commercial LIA equipment

Cluster-based Vibration Analysis of Structures with GSP

This article describes a divide-and-conquer strategy suited for vibration monitoring applications. Based on a low-cost embedded network of microelectromechanical accelerometers, the proposed architecture strives to reduce both power consumption and computational resources. Moreover, it eases the sensor deployment on large structures by exploiting a novel clustering scheme, which consists of unconventional and nonoverlapped sensing configurations. Signal processing techniques for inter- and intracluster data assembly are introduced to allow for a fullscale assessment of the structural integrity. More specifically, the capability of graph signal processing is adopted for the first time in vibration-based monitoring scenarios to capture the spatial relationship between acceleration data. The experimental validation, conducted on a steel beam perturbed with additive mass, reveals high accuracy in damage detection tasks. Deviations in spectral content and mode shape envelopes are correctly revealed regardless of environmental factors and operational uncertainties. Furthermore, an additional key advantage of the implemented architecture relies on its compliance with blind modal investigations, an approach that favors the implementation of autonomous smart monitoring systems