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| Title | Machine Learning Frameworks In Iot Systems: A Survey, Case Study, And Future Research Directions |
|---|---|
| ID_Doc | 35990 |
| Authors | Chen Z.; Tian P.; Qian C.; Liao W.; Hussaini A.; Yu W. |
| Year | 2024 |
| Published | Smart Spaces: a volume in Intelligent Data-Centric Systems |
| DOI | http://dx.doi.org/10.1016/B978-0-443-13462-3.00015-7 |
| Abstract | The Internet of Things (IoT) and the creation of cyber-physical systems (CPS) have recently received much attention in both academia and industry due to the rapid growth and advance of information communication technology. IoT-based systems that incorporate a variety of smart devices have brought significant advancements in different application domains (smart city, smart transportation, etc.), aiming to increase the efficiency and reliability of the production and operation process. However, traditional data analytics techniques face significant difficulty due to the inherent complexity, heterogeneity, and system security of IoT-based systems. For example, the system administrator might have to aggregate multiple prediction results from various smart IoT devices to improve system performance. Artificial intelligence (AI) and machine learning/deep learning (ML/DL) are promising techniques for solving complex data analytics problems. In the past decade, researchers have proposed a variety of machine learning frameworks, including centralized machine learning, distributed machine learning, and decentralized machine learning. This chapter thoroughly investigates existing machine learning frameworks and proposes a three-dimensional problem space to classify the state-of-the-art machine learning frameworks over IoT application domains. Furthermore, we demonstrate one case study to illustrate potential security risks in the decentralized learning framework. Finally, we outline some research directions and unsolved challenges for future research. © 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. |
| Author Keywords | Cyber-physical systems (CPS); Decentralized learning; Deep learning; Distributed learning; Internet of Things (IoT); Machine learning |
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