Not every automated machine fits within a Machine-to-Machine (M2M) communication system. While the presence of a PLC is essential for operation, it is not sufficient on its own. To be considered part of such a system, a machine must be connected to a network that enables autonomous data exchange with other nodes in the production process.
This communication must occur with a clear objective, such as optimizing production stages, performing diagnostics, or monitoring variables. For a system to be classified as M2M, it must go beyond simple automation and meet specific criteria. There are three elements that can define such a system:
- Smart devices: capable of generating, collecting, and transmitting data, serving as either the source or recipient of information;
- Communication network: functioning as the medium through which data is transmitted, encompassing industrial networks, wireless networks, or communication protocols;
- Autonomous communication: operating without the need for manual intervention to initiate actions.
For example, in a pulp and paper industry, control over the cooking stage is critical for the quality of the final product. Without Machine-to-Machine communication, the digester (the vessel where wood fibers are processed) relies on manual operator intervention. Every 30 minutes, the operator must physically approach the equipment, manually record readings from analog gauges, and, if necessary, adjust a valve by hand.
In contrast, with M2M technology, the same digester is equipped with digital sensors connected to a PLC that communicates in real time with the plant network. If the pressure exceeds the safe threshold, the PLC automatically issues a command to the valve actuator, which makes the adjustment without human intervention. The data also feeds into the enterprise management system, triggering a maintenance work order, thereby making operations faster and safer.
The evolution of machine-to-machine communication
The way industrial devices communicate directly impacts the efficiency, scope, and complexity of operations. Beyond the concept of M2M, there is another approach to machine communication called Cloud to Machine (C2M), in which the cloud plays a central role in device management and coordination.
While M2M focuses on the direct exchange of information between equipment within a local network, the C2M model uses cloud servers as intermediaries. This architecture enables data and commands to circulate between machines remotely and in a centralized manner, expanding the reach and possibilities for control and analysis.
Comparative table of characteristics between M2M and C2M
Characteristics | Machine to Machine | Cloud to Machine |
Types of communication | Direct (peer-to-peer) | Indirect (cloud-based) |
Range | Local (short-range) | Global (long-range) |
Infrastructure | Local and dedicated networks | Internet connectivity |
Purpose | Local automation and control | Remote analytics |
Considering the pulp and paper manufacturing sector, the M2M model allows the digester to be controlled entirely at the local level. Thus, when internal pressure reaches a certain threshold, the sensor sends a direct signal to the PLC, which actuates the actuator responsible for opening the relief valve. All of this communication takes place within the plant’s own network infrastructure, with no need for an internet connection.
In the C2M model, the same digester continues to operate locally via PLC, but now the pressure, temperature, and flow data are also transmitted to a cloud platform. This enables a production manager to visualize real-time information remotely. In addition to remote monitoring, the cloud can send commands back to the PLC for operational adjustments or automatically trigger maintenance actions.
These two architectures offer distinct pathways for industrial asset operation and management, each with its own strengths and limitations. The choice between them depends on the specific requirements of the process, the desired level of control, and the available infrastructure. Understanding these differences is essential for designing systems that are more efficient, secure, and aligned with both the short- and long-term goals of the operation.
Communication in industry and network logic: M2M vs. C2M
The differences between M2M and C2M models go beyond how data travels between devices. They also have a direct impact on how systems are programmed. The type of code, its complexity, the execution environment, and even the programming languages used all vary according to the role each technology plays in operations.
In the M2M model, the code is designed for real-time control and local automation, typically running on PLCs or robust microcontrollers. In the case of the digester, it is implemented directly on the PLC, focused on physical control and operational safety. It employs low-level languages and deterministic logic to ensure that internal pressure never exceeds safe limits, automatically triggering the relief valve whenever necessary:
// Example of logic in Structured Text (similar to C or Pascal)
VAR
current_pressure : REAL; safety_limit : REAL := 500.0; // PSI or other unit
END_VAR
// Continuous execution loop
LOOP
current_pressure := ReadSensor('Digester_Pressure_Sensor');
IF current_pressure > safety_limit THEN
// Send signal to the relief valve actuator
ActivateActuator('Relief_Valve_Actuator', OPEN);
// Turn on a local warning indicator
ActivateAlarm('Digester_Warning_Light', ON);
ELSE
// Keep the valve closed and the alarm deactivated
ActivateActuator('Relief_Valve_Actuator', CLOSE);
ActivateAlarm('Digester_Warning_Light', OFF);
END_IF
END_LOOP
Example of code for M2M
In the C2M model, the code is geared towards connectivity, data analytics, and remote control, running both on IoT gateways on the factory floor and on cloud servers. It operates in a distributed environment and is divided into two parts. On the gateway, the code does not perform direct control but acts as an intermediary between the PLC and the cloud. In this way, it collects data from the digester—such as pressure, temperature, and flow—and securely transmits them to the cloud platform:
import mqtt_client
import time
def send_data_to_cloud():
# Connect to the MQTT broker in the cloud
client = mqtt_client.Client()
client.connect("broker.cloud.com", 1883, 60)
while True:
# Read data from the PLC (pressure, temperature)
digestor_data = read_plc_data()
# Format the data as JSON
payload = {
"machine_id": "digestor_01",
"pressure": digestor_data['pressure'],
"temperature": digestor_data['temperature'],
"timestamp": time.time()
}
# Publish the JSON payload to the digestor topic in the cloud
client.publish("factory/digestor/data", str(payload))
time.sleep(5) # Transmit data every 5
Example of code for C2M
In this case, the Cloud to Machine code features more complex logic than in the Machine to Machine model. It covers steps such as device authentication, secure data transmission using protocols like MQTT, cloud storage, and the application of predictive algorithms.
Comparative table of code for M2M and C2M communication models
Characteristics | Code M2M | Code C2M |
Local | PLC or microcontroller | Gateway (on the shop floor) and cloud servers |
Language | Ladder logic, structured text | Python, JavaScript, C# |
Objective | Real-time physical control | Data analysis, management, and remote command |
Logic | Deterministic, focused on safety and action | Abstract, focused on data and connectivity |
Main challenge | Equipment reliability and safety | Connectivity, cybersecurity, and scalability |
More than a simple choice between communication models, adopting M2M or C2M requires a clear understanding of the strategic role of information within the production process. The way data is generated, transmitted, and utilized not only defines machine behavior but also determines the operation’s ability to anticipate failures, respond quickly, and evolve consistently.
Discover the Data Circle Space, a place for those exploring innovative solutions at the intersection of data, programming, and industrial processes.
Learn more about ST-One.