Synthetic Sensor Data Generation for Authentic IoT Device Emulation
Date
2021
Authors
Journal Title
Journal ISSN
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Publisher
Tartu Ülikool
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.
Description
Keywords
Internet of Things, Synthetic Data Generation, IoT System Testing, IoT Data Emulation