Data scientists spend much time with data cleaning tasks, and this is especially important when dealing with data gathered from sensors, as finding failures is not unusual (there is an abundance of research on anomaly detection in sensor data).

In the field of plant disease protection, many approaches exist, but all acknowledge the necessity of fast and accurate identification of the source in order to make the most efficient applications.

This whitepaper provides an overview of edge computing technology, use cases and existing market developments.

Edge computing locates computing and storage resources at the edge of the network, with the intention of getting data and analytics to the right place, at the right time. It decentralises data processing and avoids non-essential data transmission.

Comparison of Edge and Swarm Computing

Industry and academia shift from the single cloud provider paradigm to cloud federations and alternative models, which orchestrate heterogeneous resources, such as Mobile Edge Computing and Fog Computing.

With the development of new system solutions that integrate traditional cloud computing with the edge/fog computing paradigm, dynamic optimization of service execution has become a challenge due to the edge computing resources being more distributed and dynamic.

Coordinated fog (edge) and cloud computing systems are expected to expedite the evolution of smart farming toward openness and data sharing while making agriculture economically sustainable for smaller farms.

In general, one of the complexities of large simulations is related to the usage of the heterogeneous computational resources that are needed to execute them. The definition of workflows, usually linked to concrete orchestrations solutions, has reduced most of that complexity.

Accurate digital twinning of the global challenges (GC) leads to computationally expensive coupled simulations.