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With Open Source Solutions, Edge Solutions for Rail Transportation provide Efficiency, Security, and Flexibility

Posted by Marbenz Antonio on September 27, 2022

Rail is becoming more and more popular among passengers, and railway systems have been in existence for decades. They are a highly cost-effective technique for delivering freight. Rail systems that have a sizable legacy operational technology (OT) footprint are only now starting their journey toward digital transformation.

By introducing cutting-edge technology and operational intelligence to rail transportation, greater efficiencies can be generated while safety is increased. Sensors supply the information needed to create a complete picture of operations that are represented by a digital duplicate of the train network. The movement of rolling stock can be automatically and most effectively coordinated using the data’s intelligence. This blog post examines how Alstom transformed railroad operations using Red Hat edge technology.

Alstom is a market leader that creates and distributes mobility solutions, including turnkey systems, services, infrastructure, signaling, and digital mobility. These solutions include high-speed trains, metros, monorails, and trams. The business has activities in 70 different countries, with its headquarters in France.

Climate changes make it difficult to supply dependable solutions in places like Kazakhstan, necessitating extensive testing of the components. With the help of edge processing and data collected from sensors, Alstom’s signaling solutions improve capacity, reliability, and safety.

Over the next ten years, rail firms’ need for cutting-edge solutions is projected to rise because of the rising demand for quicker, more environmentally friendly transportation options and the unpredictable cost of gasoline. The following difficulties were overcome by Alstom’s rail network solutions using Red Hat technologies:

1. Hardened Cybersecurity: Security is a must since transportation is a vital resource, and a hacker taking over OT systems might have disastrous effects. Vendors of railway systems need thorough testing and total control over hardware and software.
2. High-availability devices: An end-to-end Internet of Things (IoT) solution necessitates durable built-for-purpose devices because rolling stock and railway lines are situated in regions with various climatic extremes, from desert to sub-zero temperatures.
3. Limited connectivity: Devices must be able to function independently in remote locations with minimal to no connectivity. In these settings, edge solutions are perfectly suited to provide essential functionality.
4. Integration: A trustworthy message service with guaranteed delivery and integrated security is required for the integration of current legacy OT systems with the current container technology of IT systems.

Here is an illustration of the Alstom solution’s components:

On the Red Hat website, you can find out more about each component, such as Red Hat Enterprise Linux (RHEL) and Red Hat Ansible Automation Platform. Here, we’ll outline the advantages of using open source components in an implementation of an edge solution:

1. Agility: There could be thousands of computers running software, depending on the size of the rail network. In an edge architecture, the patching of these apps and the underlying operating system software must be automated.
2. Security: Hackers routinely target vulnerable targets, such as the IT infrastructures of small enterprises. Hacking a country’s infrastructure is very alluring because it requires total command over all hardware and software throughout a railway network. The risk of cybercriminals taking control of the hardware and software stack is decreased by internal management and ownership of the software supply chain pipeline.
3. Response time: In a rail network with hundreds of roadside devices, processing constant communications from rolling stock requires a quick response time to events. A low-latency environment meeting the needs of an edge solution is made possible by cloud-native technology using containers executing applications on bare metal.

The following are some examples of how an edge solution used in rail networks adds to the bottom line:

Energy efficiency: Information about the fixed rail network is used by railroad operators to arrange freight and passenger traffic. The energy utilization of rolling stock can be optimized using sensor data by continuously adjusting schedules in real-time.

Inscreased usage infrastructure: Trains may travel with closer spacing between them thanks to automation using discrete data from the rail network. By reducing wait times without requiring additional capital investment in infrastructure, the increased capacity accelerates freight transportation and improves the passenger experience. Increased use of railroads for transportation due to dependability and regularity reduces carbon footprint.

Security and safety: Combining IT and OT may make it possible for malicious actors to affect enhance efficiency, which could have negative effects. Stronger IT security requires using operating systems that prevent the inclusion of new software outside of the image-creating process and getting upgrades to the edge to close known vulnerabilities. The infrastructure is protected from cybersecurity threats by using fewer vendors and a stack of open-source software that is regularly updated.

Customer safety and experience: Real-time scheduling modifications enhance overall punctuality. A train network can proactively alert users of delays by gathering detailed data.

Predictive maintenance: The ability to predict impending problems using data from sensors that monitor variations in vibration and sound helps to increase uptime while lowering the cost of maintaining rolling stock. Unexpected delays can be decreased by improving equipment uptime and dependability.

Other industries that combine IT and OT at the edge

Alstom’s use of an edge solution is a creative application of open source container technology that integrates IT and OT to produce commercial value. Such solutions can be used in the following sectors as well:

Local governments: Utilize in-vehicle data and sensor data to reroute traffic in real-time throughout a municipality to improve transportation systems. The efficiency of transportation can be increased by integrating data with nearby cities. To improve customer satisfaction and reduce vehicle breakdowns, metro bus networks can integrate sensors for predictive maintenance. Information from surveillance cameras and gate sensors could be used for crowd control at stadiums.

Shipping services: To reduce its carbon footprint, trucking can use vehicle data to plan and improve crew scheduling as well as trip routing.

Manufacturing plants: Predictive maintenance can lower the cost of equipment operation while improving production efficiencies. Using data from sensors displayed in a digital twin of a manufacturing plant, they may also optimize operations.

Public utilities: Data from sensors can be used by 5G technology to proactively route repair personnel to the most suitable sites and identify water leaks.

Commercial buildings: By integrating natural air with artificial heating or cooling systems, air conditioning systems can be improved to provide a constant climate experience throughout a facility. Boost energy efficiency by using intelligence derived from information gathered by sensors both inside and outside the building.

Modernizing edge environments should result in much higher efficiency. Enterprises can transition to being completely digital companies by fusing OT and IT. An example of a group of open source programs that contribute to the innovation from the core to the cloud to the edge is Alstom’s implementation of an edge solution.

The Alstom solution enhances the passenger experience and increases freight efficiency without requiring the addition of expensive physical infrastructure. The solution satisfies the critical infrastructure’s requirements for security and high availability. Businesses should employ tried-and-true architectural frameworks, like those used by Alstom, to hasten the digital transformation process for cutting-edge innovation in the future.

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