Decoding IoT Data: The Case for Network Intelligence

With a steady increase in connected devices, sensor data is also rising. When you have billions of devices in operation, it’s simply impossible to use human efforts alone to analyze that data. Syed Zaeem Hosain, Founder and CTO at Aeris Communications, explains why network intelligence is the key to unlocking IoT data potential. 

The expected data surge is a key reason people have begun to think of artificial intelligence (AI) as necessary. This is not just at the device level or at the edge (where you may be applying some AI techniques), but all the way through your networks to the servers storing the data and the actions you want to take in response to inputs from IoT devices.

The rapid adoption of applications and the growing maturity of networks have rightly focused a lot of attention on using AI and machine learning (ML) to assist in processing IoT data. Their computational power and pattern recognition capabilities enable users to extract actionable intelligence from the vast amounts of data generated.

The Intelligent IoT Network

To support this data explosion, certain cellular network capabilities are needed to optimize AI/ML. Early IoT networks involved manual data processing, but when millions or billions of devices are connected (not to mention the growing sophistication of applications), it’s no longer possible for people to manage all that data. Instead, the network must have built-in intelligence. 

An intelligent network incorporates data analytics, AI, and ML to process data autonomously. It should optimize resource usage and minimize manual processes by continually seeking device awareness, providing information and recommendations, and taking automatic action based on what it discovers.

There are six levels of network intelligence:

1. 1. Observability: At this level, IoT data drives learning and automation. For example, a network should observe the data transmission patterns of each device so it can create a baseline and be able to detect anomalies that might be security breaches.
   2. Reporting: The network aggregates data into reports that show, for example, the number of devices, groups of devices, network changes, traffic changes, and behavioral anomalies.
   3. Description: This involves equipping data with business context so managers can take appropriate actions that impact the business, such as adding or replacing devices.
   4. Prediction: The network’s AI function begins to predict trends in device behavior, and the ML function learns these trends and applies them to future data inputs.
   5. Prescription: This is where the network’s AI function recommends courses of action based on data inputs so that human network managers can take immediate action.
   6. Autonomy: The network, having described what is occurring, predicts the outcome of action and inaction, prescribes the best course to take, executes the recommended action, and notifies human managers about it. For example, if the intelligent network detects an anomalous traffic pattern from an IoT device, it can automatically block its network access.

By incorporating all these levels, you can improve oversight, act on possible security threats, and save valuable IT staff time using automation and the intelligent network’s AI and ML processes.

Optimizing Intelligent Networks

An intelligent network will deliver tighter security, better performance and higher reliability. Since networks and applications are becoming more complex, the network must be optimized to manage devices, data, costs, and processes throughout the program lifecycle. One overall recommendation is to engage your staff data scientists to help you make the right decisions and find a suitable digital partner who can help you. 

To optimize IoT programs and costs, you need to manage devices and gather intelligence as to whether you have an asset that is stationary, not performing, sending too much data of the wrong kind or sending data to the wrong place. You must ensure access to the right data and optimize the cellular data rate plans.

You must also consider where to do the data processing – should it be done at the network edges or in the central cloud? The volume of data and your expectations in timely processing and analysis of the data – at speed – are key considerations.

See More: 4 Tips To Implement Observability to Ship High-quality, Secure IoT Products

Future Applications

As an example of a future application that produces a large amount of data, consider autonomous vehicles or AVs. These AVs must process gigabits of data per hour, whether location data, situational data or image data. For example, the network and the AV application need to understand whether the data sent by a vehicle or a fleet is sent to an unknown destination with a malicious intent or a configuration bug causing an unanticipated behavior. This requires significant amounts of data processing to determine the behavioral intent (at the vehicle and fleet level) to recommend or take preventable actions proactively.

Other applications include everything from logistics and transportation to banking and government services and remote healthcare monitoring. But as applications grow and evolve, the IoT network will only become more data-intensive. That’s why it’s important now to build solutions and programs on a secure, intelligent network that can leverage AI/ML technologies to analyze network conditions and data traffic continuously. This will allow customers to capture actionable insights to make decisions faster, run IoT programs more effectively and deliver superior experiences to the end-user. 

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