DAIS – Distributed Artificial Intelligent Systems

In recent years, technological developments in consumer electronics and industrial applications have been advancing rapidly. More and more, small, networked devices can collect and process data anywhere. This Internet of Things (IoT) is a revolutionary change for many sectors like building, automotive, digital industry, energy, etc. As a result, the amount of data being generated at the Edge level will increase dramatically, resulting in higher network bandwidth requirements. In the meantime, with the emergence of novel applications, lower latency of the network is required. The new paradigm of edge computing (EC) provides new solutions by bringing resources closer to the user, keeps sensitive & private data on devices, and provides low latency, energy efficiency, and scalability compared to cloud services while reducing the network bandwidth.

At the same time, there is an increasing need to use Artificial Intelligence (AI) at the edge.  Today, AI applications based on machine learning (especially deep learning algorithms) are fuelled by advances in models, processing power, and big data. The developments of AI applications mostly require the processing of data in centralized cloud locations. Hence, they cannot be used for applications where milliseconds matter in safety-critical applications, such as in autonomous vehicles. Similarly, there are high temporal requirements for processing either online or offline for face recognition and speech translation applications. In addition to speed, edge computing offers security benefits due to wider data distribution at the edge level. Reducing the distance data has to travel for processing means decreasing the opportunities for trackers and hackers to intercept it during transmission and preserves its privacy. With more data remaining at the network edges, central servers are also less likely to become targets for cyberattacks.