Once registered, note down the device ID by looking up your device on the All Devices page of your tenant’s Device Management application. Info: Set “1 sec” as INTERVAL (secs) for Acceleration and Gyroscope sensors in the Cumulocity IoT Sensor App. Register a smartphone in the platformįollow the steps described in Cumulocity IoT Sensor App in the User guide and register a smartphone in Cumulocity IoT. Info: The phone used for the entire workflow must be of the same type because the data and sensors may vary for different devices. Then follow the sections below for collecting data, training the model, and using the model to detect anomalies via the phone. First of all, you need to register your smartphone. This section deals with the basic data science steps of creating an anomaly detection model with self-collected data. Anomaly detection using a simulated demo device.The documentation provides instructions for the following devices: Subscription of the Apama-ctrl microservice and the Apama-epl application on the tenant.Ī phone or a phone-like device is required for this use case so that the measurement data of that particular device can be captured and used for detecting anomalies.Subscription of the Zementis microservice (10.5.0.x.x or higher),the MLW microservice (10.13.0.x.x or higher),the Machine Learning application and the Machine Learning Workbench application on the tenant.Familiarity with Cumulocity IoT and its in-built apps.Prior experience and understanding of data science processes.Creates an anomaly detection alarm if the model predicts the input data to be anomalous.ĭownload the AnomalyDetectionDemo.zip file which contains the project ZIP with name AnomalyDetectionDemoProject.zip and the EPL rule with name DetectAnomalies.mon.Sends the data via REST request to the Zementis microservice API for processing.Gathers specific measurements coming from the source device and conducts any necessary pre-processing steps.Create and upload an EPL rule to Cumulocity IoT which does the following:.Deploy the model to Cumulocity IoT using the Machine Learning Workbench application.With the collected data, train an anomaly detection model using the Jupyter Notebook and convert the model to PMML.Collect sensor data from a user performing regular everyday tasks.For the purpose of showcasing this use case, we followed these steps: As soon as an irregularity in behavior data is observed (for example, the person falls down) an anomaly can be detected. This data can then be used to train an anomaly detection model. Regular behavior sensor data of a person can be collected over a period of time. In essence, this technique allows the classifier to create the label.Anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a data set.Ĭapturing anomalous events through the sensor data of a mobile device on an IoT platform can for instance serve the purpose of detecting accidents of elderly people living without a caretaker. Lastly, a semi-supervised anomaly detection technique requires a classifier to be trained on a "normal" set of data to establish a preset, and then analyzes the intended data to detect for anomalies. In contrast, supervised anomaly detection requires a data set to be trained with specific "normal" and "abnormal" labels. This technique detects anomalies in an unlabeled data set by comparing data points to each other, establishing a baseline "normal" outline for the data, and looking for differences between the points. The first type of anomaly detection is unsupervised anomaly detection. There are three main forms of anomaly detection. Anomalies, or outliers as they are also called, can represent security errors, structural defects, and even bank fraud or medical problems. Anomaly Detection is the identification of rare occurrences, items, or events of concern due to their differing characteristics from majority of the processed data.
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