The feature extraction capabilities of Machine Learning (ML) models have led to their wide adoption in a large variety of sectors: from anomaly detection for machinery, to user clustering and behavioral prediction, market trends predictions, or the analysis of text, sound, and image data.

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.