IoT Platforms & Ecosystems

Cloudflight co-creates IoT Platforms and Ecosystems with a variety of well-known companies. We are the partner and provider throughout the whole journey and accompany the business and IT value chain from the IT and cloud infrastructure to the edge device and security concept. This enables our customers to run leading IoT platforms and pilot extraordinary communities.

Project Case
Airport Ground Assets IoT

Large airports are operating over 1.000 flights per day, each day.

Many unforeseeable events destroy plans, they have long-lasting effects over the day and need on-demand planning. Internet-of-Things for all ground assets delivers precise information to automate this planning. We created an intelligent and scalable system for managing airport ground assets, analyzing, optimizing and predicting their usage and performing optimal route and resource planning while respecting process requirements.


The raw geographic location of ground based assets is, in itself, only halfway useful. The aggregation and generation of domain knowledge driven data is key to the efficient use of the available tracking data.

By having an accurate, realistic and fast simulation of these processes an optimal selection of planning algorithms and asset or staff numbers can be achieved without the need of expensive and time consuming real world trials.

Consider the following scenario: Your task is to bring an empty dolly of a specific type to a certain location.

What kind of information do you need?

  • The exact location of a dolly with a specific ID?
  • The location of the next available dolly?
  • The route to a dolly on the way to your target location that has the shortest deviation from the optimal route?

Or a route and dolly proposal by an intelligent system that takes into account all these factors and others as the task deadlines of you and your peers or the predicted availability of assets at locations?


Allocating too much hardware to a system is a waste of resources, allocating not enough leads to performance problems. Therefore modern systems need to grow with the size of the problem they need to tackle, an approach called scalable infrastructure. We solved this by deploying our micro-service IoT platform in an on-premise hosted cloud and connecting to other airport services.

In order to enable the access to the system on a variety of devices we developed two different interfaces:

  • A mobile friendly web user interface which enables the access form virtually any web enabled modern hardware, ranging from desktop computers to mobile phones and tablets.
  • In order to leverage existing user interface and facilitate the integration into the existing infrastructure we developed a REST interface that enables the the integration into legacy software.

The services provided by the system can be roughly put into 4 categories:

1. Current System Status

This module provides a real time view of the location and status of all ground assets. Furthermore it enables the querying of aggregated data (e.g. the amount of specific assets within certain geo-fences) or the search for assets matching certain criterias (e.g. all assets of type X that are close to location Y and are not in use).

  • See real time status and location of assets
  • Search for assets matching domain specific criterias
  • Aggregate data for high level overviews
2. Order Fulfillment & Planning

Manually searching for assets needed for the fulfillment of specific orders (e.g. bring dolly of type X to location Y) is a tedious and error prone process. The system thus provides an automatic scheduling algorithm that searches for matching assets while considering various constraints and optimization factors (e.g. the optimal route, the predicted amount of dollies at a location, time constraints on the order or orders fulfilled by other operators)

  • Support fulfillment of orders
  • Provide scheduling and route planning algorithms
  • Optimizing of multiple, potentially conflicting criterias (shortest route vs. optimal schedule)
3. Statistics and Predictions

A real world application that tracks thousands of devices does need to provide an aggregated system view for an efficient assessment. These aggregations are highly domain specific and depend on the processes and workflows present at the airport. By analyzing the historic aggregated data and by knowing the problem domain specific processes  we can provide a comparison of the current system state to the state of previous time intervalsThis enables not only the early detection of deviations from usual patterns (and thus early proactive corrective measures) but also the prediction of future states and potential problems

  • Aggregated system views 
  • Analyze historical data
  • Detect deviation from norm
  • Predict future problems
4. Simulation

In any sufficiently large system certain questions about the system can not be easily answered without running a controlled experiment in the system. In the case of ground asset management such a question could be: “Do I need 10.000 dollies? Can I use less?” or “What would happen if I move X dollies of type Y to location Z at night?” These questions can of course be answered by running these experiments in the real world – a very expensive and time consuming processA simulation environment that keeps track of ground asset locations, staff location and disposition and order planning and fulfillment can be used to test “What if?” scenarios with a few mouse clicks – saving time and money.

  • Controlled experiments in simulation environment
  • Quickly and cheap test scenarios

Can you imagine that these systems are not limited to airport operations?