1_36_Grenzkontrollen_SmartFreightBorders

Overview of Borderstations
Borderstation Dashboard
Workflow to send license plates from Camera and submit to Cumulocity
Apama event processing rule to assign a Truck to an Lane
Overall Architecture
Prototype Architecture

Inspiration

Improving the border control of goods into the country and prevent traffic jams. The goal is to combine data from logistic companies (truck licence plate, type of goods, delivery date etc.) with camera scans on the high way to be able to prioritize trucks at the border.

Inspired existing smart border solutions and research around it like: https://www.europarl.europa.eu/RegData/etudes/STUD/2017/596828/IPOL_STU(2017)596828_EN.pdf https://ec.europa.eu/home-affairs/what-we-do/policies/borders-and-visas/smart-borders_en

What it does

The system is triggered by cameras on the highway scanning the licence plates of bypassing trucks. This data is checked against a system with the actual transportation data provided by the logistic companies. In our case an Amazon DynamoDb is used to simulate this system. When the data is found our real time analytics engine processes the data and gives the truck a priority based on the importance of the goods (e.g. medical supplies => priority 1, food/groceries priority => 2, others => priority 3). Based on these informations the system assigns a lane to the truck for the border control. Not pre-registered trucks will automatically get a low priority. This information could be displayed on screens for the drivers or send to them via SMS. (e.g. if you have priority 1 please used the lane 2) The border station employees get an overview about the trucks on the different lanes and know in advance what has to be checked. The system will automatically fill the lanes so that you can have "fast lanes" for important goods and spread the customs load evenly over the other lanes.

How we built it

The solution is based on Webmethods.io, Cumulocity IoT and Apama

To simulate a camera we used an MQTT client connected to Cumulocity and to simulate the truck data provided by the logistics companies we used Amazon's DynamoDB.

Additionally we used Nexmo to send out driver notification SMS.

Challenges we ran into

Missing knowledge of already existing systems with logistics/truck data that could be integrated.

What we learned

A lot about smart border projects that are onging inside the EU and challanges coming with it.

What's next for 1_36_Grenzkontrollen_SmartFreightBorders

Integration of real cameras with licence plate detection into Cumulocity
Integration of real logistic data systems
Use Case analysis. e.g. Mobile app needed for drivers and border employees etc.
Improve algorithm for lane assignment to allow for for input variables (e.g. trusted companies)