Edge Computing’s solutions deliver a processing capability of the most critical data, closer to the point of origin, at the edge of the network, significantly reducing operational latencies. With this model, computing and storage technologies are moved to the periphery of the network to create a new generation of unprecedented IIoT applications, coupling the interest of the short loop and the processing on the field to that of the ex post analysis on the cloud.
Low-power Edge Computing goes a step further by meeting the needs of IIoT applications in harsh environments where critical data processing on the field is essential, but constrained by the lack of a power source, by difficult conditions of accessibility and / or mobile contexts.
The solution is based on systems of optimized size, designed for the least intrusive possible retrofit installation. With low power consumption, they offer the levels of autonomy and performance required for an in situ data processing that will supply applications of “smart monitoring” and “smart actuation” in harsh environments.
It is now possible to generate the missing information for the needs of operations and maintenance in difficult environments. These appliances offer the benefits of wireless technologies, combined with the energy autonomy to bring computing and processing power, to the field, closer to the source of the data and the operational need. Low power and wireless edge computing opens the door to a multitude of new applications.
These include the monitoring of the vehicle-infrastructure interface and / or critical systems health and usage in rail transport.
Smart monitoring in rail transport
An example would be pantographs, these articulated devices allowing the train to capture the current by friction on a catenary. If it deteriorates and breaks, the supply is no longer available, resulting in a violent train stop. It is therefore crucial to know the events that can impact the pantograph and their location: repeated events in the same place are potentially an indicator of the infrastructure default and not the pantograph itself.
But here, the pantograph is on the roof, in a sensitive electrical environment, access to the power supply of the train is not possible, the space is extremely reduced and potentially accessible only during the maintenance phases of the train and the monitoring must take place in a situation of mobility, when the train is running. An equation that seems difficult to solve!
That’s where Edge Computing’s wireless appliances fit in, integrating a cost-effective, simple and efficient solution. It becomes easier to acquire, collect and continuously process data (GPS position, speed and shocks) in situ before controlling their periodic transmission to the cloud via a telecom network. No cabling operations, miniaturized and hardened electronic equipment, minimized energy consumption for an optimized system autonomy in relation to the periodicity of the train maintenance phases: all the barriers to the implementation of the monitoring of the pantograph device for detections of extraordinary events and their geolocation are lifted.
Thus, low-power wireless edge computing solutions offer the possibility of relegating to the past the lack of knowledge and information about the health and usage of rail infrastructure and rolling stock components for a greater control of operations and an effective safety.
Edge for responsiveness, cloud for analytics
When designing low-power wireless appliances, it’s about determining the best balance between battery capacity and expected performance. The autonomy of an Edge Computing wireless system is the result of a multivariate equation. It’s about finding the trade-off between battery life, computing power and the communications options that will make it the most suitable Appliance.
With MEMS technologies, it is now possible to design ever smaller devices that require less energy and have more and more computing power. Data processing can be done locally, on the field.
These new generation systems thus make it possible to implement IIoT applications on assets previously impossible to monitor and allows intelligent actuation functions during the exploitation of these assets that will depend on specific behaviors and conditions.
Edge Computing is the cornerstone for optimizing the responsiveness of operations directly in the field and the Cloud takes over, for carrying out analyzes and / or further calculations, but it also finds its utility for the administration and configuration of Edge Computing systems.