fbpx
Industrial IoT (IIoT) edge solution for railway operators – a Kapsch ObjectBox Case Study

Industrial IoT (IIoT) edge solution for railway operators – a Kapsch ObjectBox Case Study

Executive Summary of IIoT Edge Case Study

The biggest challenge railway providers face today is digitization to increase operational efficiency. Issues like unscheduled downtimes or track repairs are very costly and have a strong impact on customer satisfaction. On top, railway providers constantly need to work on ensuring and improving passenger security. The problem behind these issues is that railway operators are often still lacking data when it comes to knowing what is happening on the tracks, in the tunnels and trains.

This case study looks at how Kapsch and ObjectBox collaborated on a Kapsch Industrial Internet of Things (IIoT) edge solution for the railway industry. The project enables railway operators to optimize their operational efficiency and asset management via rapid processing of real time mission critical data, ensuring extremely reliable asset operations and timely decision-making.

Kapsch

Founded in 1892, today this family-owned company headquartered in Vienna is a globally-operating technology group with offices and subsidiaries on all continents. The Kapsch Group focuses on peoples’ requirements in the fields of communication and mobility. With innovative products and solutions, Kapsch BusinessCom and Kapsch TrafficCom make a significant contribution to the digital transformation and a sustainable future in public and private transportation.

ObjectBox

ObjectBox makes real time data consistently accessible from sensor to server – including sensors (client), Android and iOS devices, IoT gateways, on-premise and cloud servers. ObjectBox is 10 times faster than any alternative, smaller than 1 MB and uniquely designed for IoT and Mobile settings.

Kapsch and ObjectBox

Kapsch is a longtime partner of railway operators and is thus helping the industry with digitization. By integrating ObjectBox’ database and synchronization solution into the Kapsch railway offering, Kapsch can provide superior speed and data continuity to their railway customers. This means critical data is available when needed and can be interacted with in real-time. This heightens operational efficiency and passenger security, because speed matters in that environment. It also decreases networking costs significantly. Last not least, keeping data locally as much as possible increases data security.

The challenge: Having data up to date – fast, reliably, across devices

As a long-term partner of railway providers across the world, Kapsch addresses their major pain points with its new IIoT solution. Central to optimizing railway providers’ operational efficiency and security is having real-time information about tracks and trains available when needed where needed.

Project requirements at a glance:

    • Performance and operation on all kinds of platforms from sensor to IoT gateway to server to iOS and Android devices
    • Reliable data synchronization between devices
    • Fast (near real-time) and network-connection-independent operation on all devices, meaning on the edge. Therefore, a superfast database across entities is required.
    • Possibility of opening the APIs for external developer projects in the future.

What we developed in the course of our digitalization initiative, was an end to end solution for IIoT, and with this we have an edge computing solution. We were looking for possible partners and one of them was ObjectBox. When we saw their synchronization methodology, we understood it was a perfect fit.

Jochen Nowotny

Vice President of Product Management, R&D, Delivery & Support, Kapsch

The project environment

Developed in a co-creation process with railway operators, the Kapsch IIoT-solution is uniquely tailored to the main needs and challenges of the railway industry: Passenger experience, operational efficiency and safety / security.

The Kapsch railway cross-platform solution applies mission-critical data to avoid costly downtimes and repairs, reducing maintenance times and delays. Thus, timetables can be kept more accurately up-to-date, making travelling a better experience for end users.

At the core of the solution lies the Kapsch mission-critical network. Another essential part of this solution is the storage, processing and delivery of data, so data is accessible where it is needed, when it is needed. Doing this efficiently provides a competitive edge to railway operators. Having the data needed to make decisions in real time – across any part of the railway network – improves operational management and the experience for both employees and customers.

ObjectBox’ solution

This is where ObjectBox comes in. ObjectBox offers a fast data storage and synchronization solution that works seamlessly across devices, from sensors to mobile (iOS and Android) to server to cloud. Because of its out-of-the-box synchronization solution, it also saves time to market and costs. On top, ObjectBox’ easy APIs could easily be extended to external developers. Therefore, Kapsch decided to implement and benchmark the startup’s solution.

 

How did Kapsch find ObjectBox’ solution?

Sourcing new innovative approaches is like finding a needle in the haystack. That is why Kapsch runs the later stage accelerator program Factory1. Factory1 focuses on piloting startup solutions at Kapsch. In a rigorous process, Kapsch’ top management and experts from around the world assess startups and define pilot projects together with them. In the final evaluation step, the startups pitch these pilots to the Kapsch board. After going through this thorough process, ObjectBox convinced the board of their solution’s great potential and usefulness for Kapsch.

The solution: A unique hardware/software stack with out-of-the-box synchronization

While the rigorous sourcing process already ensured a good paper and personal fit, in software projects it always comes down to the nitty-gritty details of the technology stack.

Working together – from technology fit to project-setup

The Kapsch project was developed mainly in Java for the operating systems centOS, Android and iOS. ObjectBox supports all platforms and languages used.

So the first step was to integrate the ObjectBox database for storing data, which meant replacing the persistence layer with ObjectBox. When exchanging a database (the data persistence layer) in an existing project, four cases can be distinguished:

SQL database with abstraction layerNoSQL database with abstraction layer
SQL database without abstraction layerNoSQL database without abstraction layer

In this project, a NoSQL database with an abstraction layer needed to be exchanged.

The second step was to synchronize data between IoT edge gateways and central servers.

Each IoT edge gateway (located along the tracks and in the trains) collects local sensor data and makes it accessible to local devices like smartphones, as well as centralized locations (e.g. on the headquarter’s servers). This setup allows high speed operations independent of the availability and quality of network connections.

This is not a simple case of “sending data one way” or “full replication”, rather a more sophisticated technology referred to as “data synchronization”. It is transactionally safe (aka ACID compliant), meaning no data is lost in transit. The network and gateway can go down any time – once everything is up again, data is safely transmitted. This data synchronization is a built-in feature of ObjectBox and therefore does not require any additional, costly software development efforts from Kapsch.

The third step was to compare ObjectBox to alternatives. Therefore, KPIs were defined and a benchmarking application was set up. The KPIs used in this evaluation were: internal project goals, ease of use, the performance of the database, the speed and efficiency of synchronization, and data consistency across devices.

Pilot project results

Together, ObjectBox and Kapsch achieved their goals and created a solution that adds a competitive edge.

“ObjectBox integration in our IIoT solution has enabled significant performances improvement … far beyond other databases. More, we’ve learn a lot on how to operate efficiently a database from the collaboration with ObjectBox team.”

Farid Bazizi

Head of R&D Development - Mission Critical Communication & Industrial IoT Technologies, Kapsch

Overall goals

More specifically, the following goals were met in the 3 month project:

  • There was a successful integration of the ObjectBox database, demonstrating how fast and easy it can be implemented. Because ObjectBox is flexible and easy to use, it can help ensure that will be usable and maintainable for 10+ years.
  • The performance benchmarks done in the project clearly showed that ObjectBox outperformed the alternative solution, which was based on Couchbase, with regards to speed (CRUD operations), as well as CPU and memory (RAM) usage.
  • Last but not least, ObjectBox Synchronization proved to be easy to implement, transferring data seamlessly and reliably between devices. Benchmarked against the existing solution, measurements showed that ObjectBox synchronized the data 61 to 94 times faster.

Benchmarks

Please note, the benchmarks done here are project-specific. The benchmarks simply compare the internal existing Kapsch implementation, which is based on Couchbase, to the ObjectBox implementation. Thus, they should be read as project-specific results only.

Database Performance (CRUD), higher is better

ObjectBox is faster on all basic database operations within this project. This means you can process more data faster, and run more complex applications, like artificial intelligence or machine learning applications on the device – and respond to incidents faster, before it gets costly or dangerous.

 

CPU & Memory Usage, lower is better

All hardware comes with limited CPU and memory resources. These resources are shared between all the apps running on the device. The less CPU and memory that the data storage solution and sync uses, the more is left for other uses.

This means it is possible to:

  • install smaller (and often cheaper) hardware
  • run more applications and do more complex computing (e.g. edge AI) on the existing hardware, allowing you to capitalize on existing infrastructure.

Both are huge cost factors in the project settings.

Synchronization speed

ObjectBox’ synchronization implementation was much faster than the alternative, processing 37,629 objects in one second, as compared to the 400 synchronized by the alternative. In practice this means that in the given setup, ObjectBox supports 10 times more clients (nodes) for every server. This translates to huge cost savings on hardware as servers are expensive and only one server is now required where before 10 were needed.

Startup-Corporate Collaboration: A win-win situation

As this case study shows, it pays off for projects to evaluate solutions independent from their provider’s company maturity level.

On the journey, both companies learned and benefited from each other’s expertise. Apart from solving the concrete technological challenges, the project yielded further positive side effects. Sebastian Opitz, Head of Controlling and ObjectBox’ project mentor, gives an example: “ObjectBox helped to quickly identify a faster way forward with a new technical solution. It would have taken us much longer doing it on our own and probably only would have been discovered much later.”

From the startup perspective, the collaboration gave ObjectBox access to new departments and new markets. “Kapsch has a deep market knowledge and a lot of experience with digitization projects. This experience helped ObjectBox to reach the next level”, concludes Vivien Dollinger, ObjectBox CEO.

How to set up ObjectBox Go on Raspberry Pi

How to set up ObjectBox Go on Raspberry Pi

ObjectBox is a fast object-oriented database for IoT and Mobile edge computing. With its Go support, ObjectBox is a perfect match for Raspberry Pi and will add instant speed to your next Raspberry project.

To get started right away, jump straight to the Installation section.

Local data persistence

In general, databases are great to add data persistence to your project. If you need to turn off the device or if there is a power outage, your sensor statistics or other interesting information are safe. The next time you boot up the application it’s there again as if nothing happened. Another rather obvious advantage is speed and independence from an Internet connection. Storing data on a local drive is just faster than sending it to the cloud and back. And when you store your data locally, your application works with or without an Internet connection. This also means, you have full control over all your data, especially regarding who shall be able to access it… Finally, this reduces cloud costs.

For many databases (e.g. SQLite, PostgreSQL or MySQL), you need to know SQL. And for managing complex datasets, you need to be really good at it. Some like it, but for many developers this is annoying. Also, you might worry about SQL injection attacks.

Advantages of ObjectBox

ObjectBox is a database uniquely designed to sit on IoT and Mobile devices and fix these issues.

Firstly, you do not formulate your queries in SQL. ObjectBox comes with easy APIs allowing you to use the constructs and syntax native to the language you are using anyway. In this way ObjectBox is a NoSQL database. Secondly, benchmarks show that ObjectBox is faster than alternatives. So, even if you’d like to insert the data of a lot of sensors simultaneously, you don’t need to worry about speed anymore. This has the nice side effect that more speed means less CPU and thus is resourceful in every respect. Last not least, because ObjectBox is designed for small devices, it just needs about 1 MB of disk space. If you need something smaller, you can check out the ObjectBox client for Azure Sphere.

If you want to use ObjectBox in your next Raspberry Pi project, here is how you get started:

Installation

We’ll talk about the Go version of ObjectBox here. There are bindings for C, Java and Swift as well – and we will publish articles in the future about them too.

First, you need to have access to the shell (i.e. command line) of your Raspberry Pi. It does not matter if you’re connected via SSH or directly, just make sure that your Raspberry Pi has a working internet connection. Then, just execute the following command to get started right away:

This creates a folder named go in your home directory with the following subdirectories:

  • go with the binaries of Go 1.12.5 for ARMv6l
  • objectbox with the shell script update-objectbox.sh you can execute to easily update ObjectBox upon a new release
  • projects/objectbox-go-test mainly with the file main.go which contains a tiny demo application

Note: The script will install libobjectbox.so globally (confirm by typing “y”), so it might prompt you to enter your root password.

Working with ObjectBox Go

The first tool you will come across is objectbox-gogen. It generates the correct Go files out of a Go structure to make it usable with ObjectBox. Assuming your main.go looks something like this:

As long as you only change the program’s logic, you don’t need to do anything. If you change the model, simply execute objectbox-gogen -source main.go to update the auto-generated files for the ObjectBox model interface.

After that, you can execute the commands specified above to run and execute this source. The setup script does this automatically for you. As a result, you’ll get a directory called objectbox with the files data.mdb and lock.mdb; these are the underlying database files and they will be reused when you execute the program again.

Summary

So, that’s basically it! Setting up ObjectBox and using ObjectBox is easy. Check it out yourself and please share your thoughts with us; we appreciate any feedback. 🙂

Until we release the next part of this tutorial discussing the features of ObjectBox in Go more thoroughly, feel free to check out the fully-fledged demo application in its official repository. Also, if you’d like to learn more about all other features of ObjectBox Go, have a look at the official documentation!

If you encounter any problems during this process, please reach out to us. The best way is to ask a question and tag it “ObjectBox” on Stack Overflow. Finally, we would love hearing your thoughts on ObjectBox via TwitterFacebook, or Mail (contact [at] objectbox . io).

What is Edge Computing?

What is Edge Computing?

Today, over 90 percent of enterprise data is sent to the cloud. In the next years, this number will drop to just 25 percent according to Gartner. The rest of the data is not going anywhere. It is being stored and used locally, on the device it was created on – cars, trains, phones, cameras etc. This is edge computing. At least in our pragmatic definition.

Obviously, this is cutting the discussion short. With edge consortia springing up like mushrooms, there is no lack of overlapping definitions around the terms Edge Computing and Fog Computing.

 

From mist to fog to edge – a short overview 

To bring some light into the terminology mess: The terms “mist” and “cloud” constitute the ends of a continuum.In our definition, the edge covers everything from cloud to any end device, however tiny and limited it may be. In this definition, there really is only the cloud and the edge.

However, some authors additionally use the terms fog computing and mist computing.

Mist covers the computing area that takes place on really tiny, distributed, and outspread devices, e.g. humidity or temperature sensors. To make it a bit more tangible: These devices generally are too small to run an operating system locally. They just generate data and send that data to the network.

As opposed to mist computing, the cloud refers to huge centralized data centers. The terms “fog” and “edge” fall within this continuum and – depending on whose definition you follow – can be used interchangeably.

Form edge to cloud and back: History repeating itself

If these terms seem familiar to you, that is probably because edge computing is just another cycle in a series of computing developments. Computing has seen constant turns between centralized and distributed computing over the decades, and with recent developments in hardware capacity, we’re again entering into a decentralized cycle.

 

One to rule them all?

Neither the cloud nor the edge is a solution for all cases. As always it depends. There are cases, where the edge makes more sense than the cloud and vice versa. Most cases however, do need both.

 

Read why Android developers should care about Edge Computing.

 

ObjectBox Python Alpha

ObjectBox Python Alpha

As of today, you’ll be able to start using ObjectBox from the comfort of Python.

Whether you’re building an IoT solution, a desktop utility or a webservice, you’ll find the ObjectBox database handy to help you with data persistence on the edge. Among other features, ObjectBox provides implicit ACID transactions so there’s one less thing you have to worry about.

Let’s take a look at an example to see how ObjectBox lets you work with your data using Python:

Aside from x64 CPUs, ObjectBox supports ARMv6 and ARMv7, allowing you to benefit from a super-fast and scalable database on IoT devices, for example, the Raspberry Pi family (from the minimalistic Pi Zero to the high-spec Pi 3B+). You can also target desktop and server apps running on Linux, MacOS, or Windows using Python.

ObjectBox Python: How to get started

While this is still an early release in Python, it’s based on the proven ObjectBox core so you can already start building your applications today. Just install using

and head over to our GitHub repo at http://github.com/objectbox/objectbox-python to see some examples.

Your feedback helps us to improve ObjectBox, please share your thoughts and comments 🙂

Which edge consortia are there?

Which edge consortia are there?

There is no lack of consortia defining terms around edge computing – it’s a lot like the Judean People’s Front against the People’s Front of Judea. After a year of battle, the most prominent edge consortium emerging currently seem to be EdgeX under the umbrella of the Linux Foundation – fully open source, while also largely supported and driven by Dell, who initiated it. Other notable players trying to get a foothold in this space is the Deutsche Telekom with MobiledgeX and HPE with Edge Worx. A European counterpart, ECCE, formed in spring 2019 and might be worthwhile watching, as it is supported by many industry players like e.g. KUKA, Intel, and Huawei.

ObjectBox EdgeX Release (Beta)

ObjectBox EdgeX Release (Beta)

ObjectBox now runs on the EdgeX Foundry IoT Edge Platform. Utilizing ObjectBox’ speed and ease of use, EdgeX users can now compute millions of data points on the edge with minimal latency. ObjectBox’ small footprint of less than 1MB makes the database uniquely optimized for high performance on IoT edge devices and gateways, as well as fog nodes. Combining the speed and size advantages of ObjectBox on the EdgeX platform, we empower companies to analyze more data locally on the machine, faster than ever before.

As an object oriented database, ObjectBox is the perfect solution for structuring data within complex data models, from artificial intelligence and machine learning applications, as well as image recognition software that is beginning to be used more commonly on the edge.

With ObjectBox-backed EdgeX we’re bringing the efficiency, performance and small footprint of ObjectBox to all EdgeX applications. It is fully compatible, so you can use it as a drop-in replacement. And if you call against existing REST or Go EdgeX APIs, you do not need to change the code. Our version is based on the latest sources leading to  EdgeX 1.0, which is scheduled for June. And once EdgeX 1.0 is finalized, we will be ready to upgrade the ObjectBox Edition to 1.0 right away.

Get started with ObjectBox EdgeX

The simplest way to get started is to fetch the latest docker-compose.yml and start the containers:

You can check the status of your running services by going to http://localhost:8500/. At this point, you have the REST services running at their respective ports, available to access from your EdgeX applications.

Find more details and sources in our GitHub repo at  https://github.com/objectbox/edgex-objectbox.

If you’re new to EdgeX, you can visit the EdgeX Foundry IoT Edge Platform home page to find out more.

Feedback

Last not least, we are always happy to hear from you. Post any questions you may have on stack overflow tagged ObjectBox. Please share your thoughts on ObjectBox EdgeX with us via TwitterFacebook, or Mail (contact [at] objectbox . io).