GPThingSim: A IoT Simulator Based GPT Models Over an Edge-Cloud Environments

Abstract

The Internet of Things encapsulates a vision of a world in which billions of objects, called connected devices, are endowed with intelligence, communication capabilities, and integrated sensing and actuation capabilities. They often outsource their storage and computing needs to more powerful resources in the cloud. In Edge computing, it is the device itself that collects and processes data and must make real-time decisions. A model that combines the edge and the cloud allows for instant processing of large amounts of data, making devices increasingly intelligent. One fundamental aspect of Artificial Intelligence, particularly Machine Learning (ML), is that it requires a substantial amount of data to learn. The GPT model can be adapted to operate within a cloud environment, which enables instant processing of large quantities of data. In this article, our approach relies on a hybrid of Cloud computing and Edge computing, merging the best features of both approaches by using Edge for real-time processing and the cloud for storage and large-scale data analysis. We will explore the possibilities of integrating the GPT model into IoT devices. This extension allows devices to offer processing capabilities at the local level, which can be extremely beneficial for many applications. We propose a platform for simulating connected objects augmented by the LSTM neural network model to predict the energy consumption of IoT devices in a smart city scenario.