A Comprehensive Review of Optimisation Techniques in Machine Learning for Edge Devices

Abstract

Hundreds of billions of connected IoT devices will populate the earth in future. The environment interacts with devices with restricted resources. Machine learning models will be applied in these devices to analyse the behaviour of sensor data and deliver better predictions. With high-level linked devices, network congestion is a problem. As a result of introducing optimisation into machine learning, computations can now be conducted on edge devices, reducing network congestion. Many researchers are interested in optimisation as part of machine learning. We continue to face obstacles in adopting optimisation approaches in machine learning as the quantity of data and the machine learning complexity grows. Many studies have been proposed to improve machine learning optimisation techniques and solve optimisation difficulties. The major goal of this research is to learn more about machine learning optimisation strategies that ensure the execution of these models in IoT. The first step is to conduct a survey of machine learning techniques utilised in Internet of Things. Second, issues in using optimisation in machine learning are discussed, as well as a review of several optimisation approaches used in machine learning models. Finally, some of the open challenges in machine learning optimisation are discussed.