Context and Challenges

The IoT devices’ computing capabilities and their proximity to the generated data can be explored to complement the cloud, pushing the frontier of applications, data storage and services to the edge of the network.


By presenting an opportunity for data processing to occur closer to the source, edge computing promotes a significant decrease of the volume of data that needs to be moved to the cloud, consequently improving request latency and quality of service.


5G is promising ubiquitous connectivity, with high-speed access, low latency and massive connectivity, i.e. a network that scales to handle the ever increasing number of connected devices.


Along with the expansion of digitisation, the emergence of heterogeneous and intelligent devices in everyday life has led to a massive increase in the number of available data assets. However, the sensitive nature of data and their value are increasingly recognized, as data are processed to uncover patterns that drive business value.


The current market demand presents two major challenges to the existing platform:


    • to increase scalability to unprecedented levels;
    • to guarantee the necessary privacy to most data sources. 


In order to increase the platform’s scalability, AIDA will allow moving some of the phases of the chain to the extremes of the system, potentially beyond the physical or even administrative control of the owner. In order to guarantee the privacy of data sources, AIDA will explore emerging federated machine learning techniques, in addition to other techniques.


The first is the need to further distribute the platform components to achieve greater levels of scalability, by leveraging the increasing edge computing capacity made available by the IoT and the imminent large-scale deployment of 5G cellular technology. This would mean expanding the platform to operate beyond trusted administrative domains. 


The second is conciliating privacy and confidentiality guarantees to handle increasingly more input data sources, with the extraction of valuable information from the data that can be trusted, an issue exacerbated considering multiple data owners or across administrative domains.