Abstract
The wide range of IoT devices and wireless devices used in healthcare, hospitals and enterprises generates a large volume of digital data that must be processed, analysed and stored. Owing to the small processing capacity of these devices, the data generated cannot be processed on-board. Therefore, we suggest offloading this data to an efficient server. Time-critical applications cannot rely on the availability of cloud servers since they are in a remote location. The paper examines algorithms such as Deep Reinforcement Learning for Online Computation Offloading (DROO), coordinate descent, adaptive boosting, and then implements the K-nearest neighbour time critical optimisation algorithm as a fog offloading network topology. The offloading decision is based on the cost function, which includes latency, memory consumption and model accuracy. The topology implementing K-NN can be trained quickly and offers almost 99% accuracy when it comes to data offloading. Based on the comparative analysis, it excels over other machine learning approaches.
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