Synthetic Sensor Data Generation for Authentic IoT Device Emulation

Abstract

The Internet of Things (IoT) is the concept of connecting everyday physical objects to the internet and making them capable of identifying and communicating with other devices. The rise of the Internet of Things in the past few decades is commendable. This significant growth has resulted in IoT becoming a budding industry that provides a variety of products, services, and systems and therefore requires quality assurance. These systems are very complex in nature and are used in real-world environments. For these reasons, IoT systems and devices are obliged to testing. The implicit heterogeneity of IoT systems, which typically consist of many thousands of interacting actuators, sensors, and people, makes this an incredibly challenging task. A very important aspect when testing Internet of Things systems at the system level is to include the behavior exhibited by local entities such as IoT devices. However, physically deploying such a huge number of IoT devices is not always feasible in terms of cost, resource availability, and time. To solve this particular problem, it is essential to make use of data generators for synthetically generating IoT data that can emulate the behavior of real IoT devices. Motivated by this challenge, this thesis focuses on evaluating open source tools for generating real-like IoT data based on existing captured data traces, weighing their pros and cons, and finally proposing an approach that would perform more efficiently to achieve the required goal as well as be reusable. In addition, the proposed approach is evaluated by generating synthetic data for some existing datasets and the results are compared.