IoT and M2M Data Analytics

The Data Complexity Problem

With billions of sensors collecting trillions of data points, the Internet of Things will amass information on a scale never before seen. However, collecting, managing, and analyzing data on this scale presents a big challenge.

AerVoyance provides IoT analytics to effectively manage your IoT/M2M deployment using an intuitive, visual presentation. It is designed to help address the challenges of gaining visibility and insight into devices, connectivity, and billing information.

Proactively Manage IoT Connectivity

Not only can you identify devices with connectivity issues, but you can focus on a specific device, time, or usage activity including data, SMS, voice, sessions, registrations, etc. This lets you find specific devices that are outliers or are behaving abnormally. Also, you can see a summary view of the device portfolio including billing, usage, and status. Now you can understand aggregate and average connectivity usage over time to better plan your deployment.

Figure 1: AerVoyance dashboard provides an overall view of your account.

Manage Device Usage and Deployment

AerVoyance enables you to proactively monitor and manage your deployment in real-time. You will be quickly alerted to any failures, thus helping to avoid expensive diagnostics.

Understand Billing Behavior

AerVoyance allows you to manage your costs by revealing coverage, data, SMS, and roaming charges. You can even view monthly costs and their components in aggregate and over time. Plus, you can view the total and average billing over time to understand trends in device behavior.

Set Smart Alerts

To effectively monitor the IoT deployment, you can set up alerts when specific events occur instead of continuously reviewing all the relevant data. AerVoyance’s alerts can further enable you to build pre-defined actions to help to fix or mitigate a problem.

Figure 2: Set up alerts to proactively manage your IoT / M2M deployment.

Detect Anomalies and Take Recommended Action

AerVoyance has a built-in anomaly detection and machine learning engine that identifies anomalies such as certain devices using higher-than-normal amounts of data and presents this in a simplified format. It can also provide predefined actions to take based on that behavior and enable you to narrow down specific devices causing the anomalies.

Key Problems and Solutions

Problem	Solution
Can’t monitor devices in real-time for more efficient deployment.	View devices deployed and track their usage and billing well before you receive your monthly bill.
Planning ineffectively without understanding of deployment progression over time.	Able to see number of provisioned, billed, suspended, and canceled devices over time.
Surprised by unexpected bills and significant usage changes that leads to high bills.	Gain visibility into the billing history, current usage, and projected based on latest usage data.
Have little visibility or ability to manage billing costs.	Set billing or usage alerts that will enable the user to take appropriate action to manage costs.
Troubleshooting erratic devices - e.g., frequent registration, multiple sessions, too high data usage by some devices, etc.	Automated anomalies and specific alerts that enable user to be notified of device behavior changes and also ability to automate actions.
Don’t know where the devices are deployed and if they are connected and behaving normally.	Map view of devices and summary status showing all devices working properly.
Difficult to use raw data reports to extract the key insights.	Simple interface shows the relevant data in a graphical format.