Real-Time Locating System with Kafka

How’s the craic?
2021/03/03

This is another blog about Raspberry PI, and today I want to show how I did a simple Kafka cluster demo using the Inky pHAT. It’s kind of a continuation from my previous blog, Kafka at the edge with Raspberry PI.

If you’re not familiar with Kafka, I suggest you have a look at my previous post What is Kafka? before, and you can have a look at how I created the Kafka cluster here.

The Inky pHAT is an add-on board for Raspberry Pi, that has a low-energy, high-falutin, electronic paper (ePaper / eInk / EPD) display for your Pi, in three different color schemes: red/black/white, yellow/black/white, and black/white.
You can learn more about Inky pHAT here and check the API here.

Real-Time Locating System (RTLS) enables identifying and tracking the location of objects or people in real-time. It is used everywhere in transportation and logistics across industries.

Use Cases

• Real-time alerting on a single event: Monitor assets and people and send an alert to a controller, mobile app, or any other interface if an issue happens.
• Continuous real-time aggregation of multiple events: Correlation data while it is in motion. Calculate average, enforce business rules, apply an analytic model for predictions on new events, or any other business logic.
• Batch analytics on all historical events: Take all historical data to find insights, e.g., for analyzing issues of the past, planning future location requirements, or training analytic models.

This is not an exhaustive list.

A postmodern RTLS requires an open architecture and high scalability and of course, the implementations can rely on Kafka.

Idea

A simple Raspberry Pi Kafka example where I can use one node to get the open Dublin Bus data and display it in real-time on the other node using Inky phat.

For the producer part, you can use my Micronaut Sense hat example as a start point and just change to use Dublin Bus data instead. You can get the full Micronaut Kafka Producer code on my GitHub.

https://data.smartdublin.ie/dataset/gtfs-r-real-time-passenger-information
https://developer.nationaltransport.ie/api-details#api=gtfsr&operation=gtfsr

For the consumer part, you can use my Quarkus example. You can get the full Quarkusl Kafka Consumer code on my GitHub.

Results

Kafka can be deployed as a single broker in a vehicle and a global Kafka infrastructure can spread to multiple cloud providers, regions, countries, or even continents and integrate with tens or hundreds of factories or other edge locations.

Curiosity

1- Dublin bus is already using an e-ink display to show bus stop data.

2- I did a demo for real-time data from connected vehicles some time ago.

3- I won a prize at Europe’s First government-funded Blockchain Hackathon with the idea of an app to track medical devices combining Kafka and BlockChain.

Links

https://towardsdatascience.com/tracking-nyc-citi-bike-real-time-utilization-using-kafka-streams-1c0ea9e24e79

https://eng.lyft.com/a-new-real-time-map-matching-algorithm-at-lyft-da593ab7b006

https://github.com/ds4es/real-time-units-gps-tracking