The Solution: NATIX Network
To solve the privacy issue, NATIX has developed a patent-pending AI technology that is the easiest way to make any camera smart and 100% privacy compliant. It’s so effective that international organizations such as E.ON, City of the Hague, and Deutsche Telekom are already using NATIX AI Technology.
NATIX Network aims to combine NATIX’s patent-pending AI technology with the world’s 45 billion existing cameras (in smartphones, drones, cars, and IP cameras) to create the largest crowd-sourced camera network ever. Almost any camera, anywhere can run the AI software, collect metadata (with the highest grade of privacy compliance) and populate a Decentralized Dynamic Map (DDMap). The camera's owners earn crypto in return as the real-time data is monetized to support real-time applications. As a result, NATIX Network creates a new crowdsourced geospatial data economy powered by the “Internet of Cameras”.
It’s a solution that reduces infrastructure costs to almost $0 for Data Consumers (as the cameras are owned by private individuals) and is 100% privacy compliant.
NATIX Network nodes deliver data to consumers through API services. The payment for data is divided between iLand NFT owners and data mining nodes
DDMap is the byproduct of NATIX Network and it serves citizens, businesses, and municipalities with a wide range of real-time data such as crowd size, available parking spots, pothole locations, and more.
DDMap consists of two main layers:
- Permanent and transient static layers - this includes the static road map, roadside infrastructure, and Landmarks.
- Dynamic and highly dynamic layer - This includes dynamic information such as road conditions, traffic conditions and parking spot availability.
The first layer does not need frequent updating and is mainly provided by mapping companies such as Mapbox. The second layer, however, requires frequent updates of the information and is the main focus of NATIX Network.
The Decentralized Dynamic Map Layers - An illustration of the dynamic (changing) and static map layers.