Have you ever wondered how to collect streaming data from your phone, pass it to SAS's streaming analytics engine and use the power of unsupervised machine learning models to construct an anomaly detector? If so, you are in luck. This article will show how to collect live accelerometer and gyroscope data from a mobile device and pass it to Event Stream Processing (ESP) using an MQTT protocol. Once in ESP, the JSON data is decoded and passed to the unsupervised machine learning models k-means and subspace tracking (SST). These machine learning models find patterns in data and are trained dynamically from streaming data.
This article will go into quite a bit of detail on how this is all accomplished. However, if you are like me and can't be bothered with the details, you can just watch the following video:
OK, you have watched the video and are still reading, great! That means you are a detail oriented person. Here is what we are going to cover:
More information is also available on this github page.
In this use case, we will consider the mobile device a sensor that can be placed on any stationary piece of equipment on a factory floor. For demo purposes our anomaly detection time frame will be 30 seconds. In a real world scenario, this time frame will be much longer. That is to say as long as the equipment continues to generate 30 seconds of consistent readings, this will be considered the baseline. Once that 30 second baseline is established, new observations will be compared against it and any deviation will be considered an anomaly. KPIs will determine the severity of the anomaly. This type of self-training is awesome because the baseline is dynamically set and does not have to be calculated separately for each piece of equipment. If the sensor is placed on a rotating object, the accelerometer data being produced becomes the norm as long as it is consistent. Imagine a factory floor with 1,000 machines each oscillating at different amounts. Now imagine how much time would be saved if the sensors placed on these machines were able to self-train their own unique anomaly detection models. Very powerful.
Because MQTT is one of the more popular IoT protocols we are going to use it along with the message format JSON to communicate both bound and outbound between the mobile device and ESP. Consider the following diagram:
You may use your own MQTT broker or a freely available one. As long as ESP and the mobile device can reach the broker communication is established. This architecture allows for the data source to be abstracted to where the mobile device data can come in a variety of ways without needing to modify the ESP model.
The mobile application collects data from various phone sensors such as accelerometer, gyroscope and location.
The sensor data is by default collected at 10 events per second and formatted into a JSON string and then sent to the MQTT broker defined and active. The Android application files may be downloaded from this github location.
Configuration is a two-step process. First, configure your broker settings to match your ESP project. Second, select Anomaly Detection from the navigation panel. Here is an example of the broker list. You can see from the green bar that the application is actively connected to the sample MQTT broker. You may define as many brokers as you need. However, remember that the broker name must match the ESP project MQTT connector definition.
Here is an example of the sample broker definition. The broker test.mosquitto.org should always be active.The mobile application will publish to the topic PhoneSensors and subscribe to the topic Outcomes. These topic names may be changed. However, the corresponding ESP project will also need to be updated to match the new names.
Once you have confirmed that the broker is correctly configured and active select Anomaly Detection from the navigation panel to view the results. If the ESP project is running, your "Anomaly Detection" panel will start updating in 30 seconds.
Now let's take a look at the ESP project that will consume the phone sensor data.
Data flows from the top of the diagram starting with the blue source window. The small icon in the lower left of the source window indicates that this source window contains an input connector. If we take a closer look at the connector configuration we can see the host, and inbound topic name. These items must match the settings in the mobile application broker settings. The mqttmsgtype indicates all the JSON data will be placed in one opaqestring. The next step will parse the JSON data in the string and place it into various ESP variables.
The next window is called a function window and will parse the input message into ESP variables. The first step happens in the functions properties section. This is where the opaqestring called $message is loaded into a field called jsonData. The JSON data is then parsed in the functions section into variables. These variables are part of the output schema for the next step in the process. Here is an example of the parsing syntax:
In this example, the Name column contains a value that matches the output schema for this function window. This is the parsed JSON value that will be passed to the next downstream window. The Code column contains any function you would like to run. A complete list of available functions can be found in the Event Stream Manager documentation. Here we are using the "json" function to parse the variable "#jsonData" for various strings. You may note that the JSON string may contain more data than our flow requires. This parsing method provides a method for ignoring superfluous data. You may also notice that variable names may be remapped to more meaningful names. Here I am remapping the JSON variable y_acc to an ESP variable called yGravity.
In order to do in-memory calculations on data we must create retention. Retention is loading a set number of records either by time or count into memory so that they can be used for aggregation and dynamic modeling. We do this in a copy window. Here we are specifying 300 records be saved in memory. Since the mobile application is sending in 10 records per second. 300 records equals 30 seconds worth of data. Note that the lightning bolt in the lower right of the window indicates that this window maintains state or retention.
The next window is an Aggregate window. Since we now have 300 records in memory we can start to use that data to create summary statistics. Using the aggregation window we will create 6 new variables. We will create a standard deviation and average for the X,Y and Z Gravity data sent in by the phone's accelerometer. These values are updated each time a new message is received on our MQTT topic. Only the newest 300 records are used in our calculations.
The train window receives all the phone sensor data and uses it to dynamically create a model based on the analytical model specified, in this case, K-means. Other types of models are available such as:
K-means is a clustering method which aims to partition observations into groups. For example, imagine shooting 100 arrows into a target. Your arrows will form clusters or groups on the target. Each group will have a geographical center point. If you shoot one more arrow, represented here as the green dot, you can then measure the distance from the center point to the location of the new arrow. This distance is a measure of the deviation from the baseline or norm. In the diagram the red circle represents the mathematically defined cluster based on the dispersion of data in the scatter plot. Here the red arrow represents the distance from the center of the circle to the new data. This of course is a simplified view. Keep in mind that our accelerometer data is 3 dimensional and contains data points for the X, Y and Z values.
The training window will use this algorithm to create a model and then pass that model to the scoring window to calculate the distances from the norm.
The dynamically trained model is passed to the scoring window from the training window and used to score each new event. In our phone sensor example, we might place the phone on a flat surface and leave it there for 30 seconds. Since the phone is not moving all the accelerometer readings will be nearly zero. Zero becomes the center point of our k-means cluster. As long as the phone does not move the distance from the norm is also zero. If I start shaking the phone the new events will deviate from the baseline greatly and the distance value will increase. K-means only works as an anomaly detector because our use case produces very consistent values. Next let's look at the more robust algorithm subspace tracking.
Calculate windows are designed for data normalization and transformation methods, as well as for learning models that bundle training and scoring together. Here we are going to use a subspace tracking algorithm also known as moving window PCA (principal component analysis) to determine deviations from the norm. The k-means algorithm attempts to group data points into clusters and measure the distance from the center of the cluster. Principal component analysis reduces the dimensionality of the data from n variables to m principal components. Since we are bringing down the dimension of the original data we are creating a subspace from the higher space and this is how the name is derived.
In SAS ESP, there are 5 metrics available to track the subspace: PCAngleChange, PCAbsoluteAngle, projection angle, rank and residual. For this example we are going to concentrate on the PCAngleChange metric, which is based on the first principal component. When the data creates principal components which fall into the same line they are mapped along a vector. Vectors have a direction and are plotted along an axis. When a new observation arrives, the new direction is recalculated and the angle between the previous direction and the new direction determines the deviation. Therefore, we determine the deviation by measuring the amount of angle between the expected vector and the current one. The larger this angle, the more the possibility of the new observation to be anomalous. Subspace tracking will continually calculate the expected PC vector using our moving window of 300 events and provide a basis for comparison.
If we look at the scatter plot data using SST, instead of a cluster we derive a vector shown here in red from the principal components of the accelerometer data. When a new data point, shown here as a green dot is received, a new PC vector is created. Shown here as the blue vector. The angle between the red and blue vectors is the amount of deviation from the baseline. This information will be passed to our mobile application where thresholds are checked and will identify if the new observation is an anomaly. Our vectors are calculated dynamically from a moving window of 300 samples. As with k-means, this is a simplified view of what is actually happening. Vectors are by nature 3 dimensional and eigenvectors must be used to tune the model. Here are values used to train this model. Here we can see our window length, input fields and eigen settings.
The output map references principal components, angles and our 3 vectors.
Now that all the data science is done we need to join all the information into one event and then send it back to the MQTT broker. The broker will then deliver it back to our mobile device to be displayed. From the following picture you can see our newly combined data includes fields calculated by SST, k-means and summary statistics.
On bottom right of the join window you will see a connector icon. This indicates that this window contains a connector. In this case an MQTT connector. This connector will JSON encode the data from our output schema and send it to the MQTT broker.
Anomaly Detection Example
Now let's take a look at the use case where the mobile device is a sensor and has been placed on a stationary piece of equipment on a factory floor. For demo purposes our anomaly detection time frame will be 30 seconds. At this point we have our ESP project running and our mobile device is connected to an MQTT broker and is receiving accelerometer data. The ESP project analyzes and improves the data and then sends results back to the MQTT broker topic called "Outcomes." The mobile application includes thresholds which determine warning and error levels. We start by selecting anomaly detection from the navigation panel and setting the phone down for 30 seconds to establish a baseline for both k-means and SST. Once this baseline is set the anomaly detection will report green. Later we start moving the phone. The anomaly detection panel will turn yellow and then red based on how much the phone is moved.
This article has shown some key features found in SAS Event Stream Processing. Using ESP you can easily collect data from remote sensors using industry standard protocols. After parsing, we then retained events in memory so that advanced analytical methods can be used to create an anomaly detector. Dynamic model training allows models to be continuously trained based on real time data and is shown here using both the calculate and train windows. Data can be a easy combined using the join window and then published to an outbound topic. The mobile platform provides a level of personal interaction with the data that cannot be easily replicated using canned data sources. It helped me better understand how SST and k-means work in an interactive environment and I hope it will help you as well.
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