Machine Learning in Edge computing for IoT
As IoT devices become more widespread, creating more and more data, it is no longer credible that cloud computing can absorb and process all of the data, analysis and decision-making involved. Whether in terms of bandwidth, computing power, or algorithmic adaptability, new architectures and new machine learning (ML) techniques need to emerge to meet these new IoT needs. Edge computing is one of the recent techniques that allows some of the processing to be performed locally before being sent to the Cloud for analysis. Using an edge processor it is possible to move part of the intelligence and adaptability from the Cloud directly to the local IoT mesh network. However, the computing power and power requirements of such edge devices remains a limitation for ML frameworks meaning that ML techniques must be optimized or custom designed.