M2M vs. IoT - Why You Need to Listen to What Your Machines Are Telling Each Other
IoT represents an evolution from M2M that allows businesses to harness machine data to make better decisions.
Machine-to-Machine and the Internet of Things are systems based on connected devices that can collect, store, exchange data with one another without human input or interaction.
Both technologies have found a place of pride within capital-intensive enterprises that want to improve efficiencies and performance without adding headcount. Yet, even though both M2M and IoT have existed for decades, there's some confusion about what they actually mean and how they can be best applied for enterprise use cases.
In this article, we'll answer some key questions people have regarding these technologies, why thinking in terms of “M2M vs. IoT” isn’t the correct frame to think about them, and how IoT delivers business value in a way M2M doesn’t.
What is M2M?
M2M, also known as machine-to-machine, describes a communication method in which two or more machines interact via wireless or wired connections without human intervention.
M2M technology enables devices to connect and interact with each other without an Internet connection by sending data through cellular networks.
M2M use cases have expanded widely since the technology's inception. M2M technology now includes security, tracking and tracing, automation, manufacturing, facility management, and other critical business processes.
A typical example of M2M technology would be ATMs. If you make a transaction order on an ATM, the internal computer will constantly send the information to a host processor. Then, the processor will work to route the transaction to the appropriate banks and accounts. Finally, they will follow up with the approval codes and go through the host processor again.
What is IoT?
The Internet of Things refers to a broad connection between different physical devices. IoT devices, such as sensors and actuators, are affixed to machines or capital assets, and connected to the Internet via a WiFi connection or through cellular networks. Then, they use cloud platforms to send and receive data that can be used to make informed decisions about the assets, users, or business as a whole.
IoT may be most popularly known for its consumer applications. Smart home devices such as smart thermostats and doorbells come to mind. But according to a recent report from McKinsey, the most significant opportunity for IoT to create economic value comes from its applications in the enterprise.
Specifically, IoT applications have the potential to drive significant value in areas such as
- Operations optimization
- Human productivity
- Condition-based preventative maintenance
- Energy management
- Safety and security
How M2M and IoT Are Different
Conceptually, IoT is both an evolution from and a subset of M2M, according to Raniz Bordoloi, Product Marketing Manager at Particle.
While some overlap exists between the two technologies, some essential distinctions around networks, scalability, interoperability, and human input make them different.
Networks
Based on M2M's concept of connectivity, IoT technology expands from simple machine-to-machine communication to a broader cloud-based network with an adaptation of various devices. From a user experience perspective, IoT technology can provide more flexible and fast networks.
"IoT is much more of an evolved concept, and the idea is that you are connecting assets to the Internet and centralizing all of the information to one system, which is the cloud. That data is much more secure," Raniz said. "M2M is generally more vulnerable to security hacks because you're putting the data inside the data warehouse."
Scalability
Another core difference is scalability for IoT. Since most enterprise IoT platforms are integrated, they have the flexibility of adding new devices to the existing network with minimal hassle.
However, though M2M platforms are deployed on machines that interact without human intervention, setting up or maintaining a machine can be labor-intensive because you can only manually set up the point-to-point connection.
Interoperability
Compared to M2M, IoT offers a greater degree of interoperability because it allows connections between different kinds of devices. This interoperability makes IoT more practical for a wide array of use cases.
For example, if you need to combine trash compactors and pumps with generators and other machines, an IoT solution can provide the variety of devices required to track different data types and send them to decision-makers.
"Every machine has its own programming language. With M2M systems, you need to find devices that are programmed to talk to those machines," Raniz said. "With IoT, you can easily connect different sensors and assets via an IoT gateway. You can easily connect a generator pump and a trash compactor in one factory floor."
Labor
IoT generally offers a far more data-rich experience that improves human productivity than M2M does. IoT deployments usually have applications that make it easy for users to see relevant data and take action.
For example, M2M-enabled machines in a factory setting wouldn't allow users to check fuel levels remotely. They would still have to physically travel to the machine and manually check the fuel level.
Advancements in industrial IoT, however, make it possible for users to get alerts via SMS, email, or through a central dashboard.
Transitioning from M2M to IoT - How IoT Drives Business Value Beyond The Machines
Today, adopting M2M is usually not a conscious decision organizations make. Unless you’re relying on legacy equipment and machinery, most modern machines have some M2M features and functionality built into them by the manufacturer.
Thus, if you’re researching or comparing M2M vs IoT, the real decision to be made isn’t in selecting one or the other. It’s deciding if you need to go from having your machines communicate with each other without readily providing you business intelligence to being able to extract insights from your machines to drive better outcomes.
“The question you should ask yourself is, ‘Do my machines generate data that could be used to solve a key business or customer problem?