Anomaly Detection for Industrial IoT Devices
Tags: iot, machinelearning, industrial, anomalydetection
Published: 2022-02-06 05:36:29

An anomaly, described as any change in usual behavior, seriously affects industrial products' production in Industrial IoT (IIoT).

Anomalies in an IoT sensor's time-series data can imply a failure in a manufacturing unit; hence accurately and opportunely detecting anomalies is becoming increasingly crucial.

This blog will discuss anomaly detection in IoT, challenges faced because of anomaly detection, and possible anomaly detection techniques.

<u>##What is anomaly detection in IoT?</u>
The industrial internet of things (IoT) consists of various intelligent devices adept at data collection, storage, processing, communication, and widespread deployment of edge devices in this paradigm has spawned a variety of emerging applications with edge computing. 

For instance, intelligent manufacturing, intelligent transportation, and smart logistics provide powerful computation resources to facilitate real-time, flexible, and quick decision-making for Industrial IoT device examples.

However, the IIoT applications are suffering from critical security risks, and there are several threats and vulnerabilities as emerging protocols—for example, engines with sensors that have abnormal behaviors or abnormal traffic and varying reporting frequency.

An anomaly is a sequence of patterns in IoT networks that significantly deviate from standard behavior and typically collect sensing data from IIoT nodes, especially time-series data, to interpret and capture the behaviors and functional needs of IIoT nodes by edge computing.

<u>##Challenges of Anomaly Detection in the Industrial IoT</u>
The detection time of anomaly detection schemes in the Industrial IoT environment is challenging due to multiple factors such as:

<u>###1. Lack of IoT Resources</u>
There are a lot of data collection methods that perform well. Still, industrial anomaly detection can be restrained by the limitations in storage, processing, communication, and power resources. Furthermore, analytics must be accomplished in real-time and adapted to the fast pace. Hence, companies need dedicated resources.

<u>###2. Dimensionality of Data</u>
Industrial internet of things data for time-series anomaly detection can be univariate as key-value or multivariate as temporally correlated univariate. Determining a specific anomaly detection mechanism in IoT applications hinges on data dimensionality. Multivariate data raises the complexity of processing models, whereas univariate data may not designate finding patterns and correlations that improve machine learning models' performance.

<u>###3. Profiling Expected Behaviour</u>
With some firms gathering over 250 petabytes of data per day, anomalous behaviors might be collected within normal behaviors. With a shortage of datasets defining both IoT normal and abnormal data, structured information becomes a real challenge.

<u>##What are the possible anomaly detection techniques?</u>
Conventional anomaly detection techniques cant keep pace with the extreme data volumes and velocity of today's industrial IoT devices; hence industrial anomaly detection in real-time is becoming more and more difficult.

Utilizing Deep Anomaly Detection (DAD) can help detect abnormal behaviors of IIoT devices by investigating sensing time-series data and learning hierarchical discriminative features from historical time-series data.

A more automated approach is to detect anomalies by configuring static threshold-based alerts. Although this is an improved method still has some demerits like proper domain knowledge is required for the admin to set the appropriate threshold, and static signals are challenging to maintain in ever-changing environments.

Another technique is edge computing which provides a more practical method to leverage ML-based anomaly detection. You can reduce workloads on the cloud by redirecting crucial data processing workloads closer to the data source (IoT devices). An anomaly detection example using edge computing is to monitor machine health in real-time, which otherwise might indicate a failure in a system.

<u>##Conclusion</u>
Anomaly detection is excellently positioned to guard the Industrial IoT network, and it is a crucial tool to identify and alert abnormal activities in the system. The motive behind today's blog was to present a standard survey of anomaly detection for Industrial IoT devices and help you understand the challenges and possible methods available for anomaly detection.