Helvar鈥檚 smart lighting infrastructure can best be described as an IoT framework where apart from enabling the core lighting functionality, the network is also responsible for sending data generated by luminaires, sensors, and other devices. An example of the evolution in technology is the development of a reliable high-speed data network which forms the basis of the analytics platform. Naturally, the platform also presents new challenges to be tackled as it scales up. This year we have defined three subcategories inside our challenge that present real-world problems related to smart buildings and intelligent lighting.

First category

The first category in our challenge聽asks the question:聽how fast can we set聽up the network?聽Typical lighting installations in large buildings can contain hundreds and thousands of devices.聽The network聽begins聽to transmit data only when it聽has been聽installed and configured.聽However, it is important to know exactly where the devices are in the building聽in order to聽extract any meaningful insights. The current process is to聽physically聽go through聽every space in the building, identify the devices and locate them on a floorplan聽using聽a mobile application. This聽quickly becomes a time-intensive task as the size of the network聽increases.聽

We would like to see if the participants can design automation algorithms that speed this entire process up.聽Instead of manually identifying聽all聽the devices, is it then possible to detect a few devices throughout the building and聽utilise聽sensor data to locate the rest?聽The basic assumption to be made here is that co-located sensors聽theoretically send聽similar measurements and therefore聽the data should enable us to聽approximately locate a cluster of devices.聽Ideally,聽we should be able to聽reduce the聽number of devices聽which need聽to be identified聽while achieving the highest聽accuracy聽in determining the locations of聽the聽rest of the聽devices聽on聽the聽floor. The dataset will include聽fully聽anonymised聽floorplans聽with all device locations聽and聽time-series of聽their聽events.聽We will also provide an additional test dataset聽where聽the participants can聽showcase their approaches.


Second category

The second category is all about creating applications and services for smart buildings. Let鈥檚 assume that all devices have been identified with their locations on a floor plan. We would like to see if it is possible to detect the busiest and quietest paths in the building. This is a very useful example of how we can use sensor data to understand building dynamics. In large buildings for example, the flow of the occupants could change according to the time of the day, which allows our clients to gain a deeper understanding of how their premises are being used. We will provide a similar dataset to the previous category, which will contain the location and events from all the sensors on a floorplan.

Third category聽

For the last category in our challenge, we have recorded data at Helvar HQ鈥檚 garage. On a busy day, we observe normal vehicular traffic flow in and out of the garage as well as employees riding their bicycles to work. People also tend to walk around in the garage when entering or exiting from their vehicles, to go to the nearest elevator or stairwell. The garage at Helvar HQ already contains a network of ActiveAhead devices in which each luminaire is equipped with a motion sensor. We decided to augment the set up by placing multiple audio sensors around the garage to capture a second stream of information as well as motion data. The challenge is to identify what is actually happening in the garage. We call it the 鈥楳ystery Garage鈥.

Remember, even though we would like to see聽strong聽accuracy聽scores on datasets, it is more about the quality and聽innovativeness聽of聽the approach and how you reason聽about聽your algorithms聽and present the results.聽We are excited about what the participants will come up with.聽Good luck!