This chapter focused on providing the latest knowledge about IoT with prominence on application issues, intelligent sensors, enabling technologies, communication technology, and protocols. This chapter explores the interconnection among cloud computing, the internet of things (IoT), and machine learning. It delves into how integrating these technologies can enable intelligent and data-driven applications in various domains for smart cities. It also describes the challenges and opportunities presented by combining cloud computing, IoT, and machine learning and highlights the novel approaches, techniques, and frameworks that leverage this convergence. The convergence of cloud computing, the internet of things (IoT), and machine learning has opened up new opportunities for creating intelligent and efficient systems.
Top1. Introduction
The Internet of Things (IoT) has brought about a significant transformation in the way people interact with their surroundings. It has established a network that connects various objects and devices to the Internet, allowing them to communicate effortlessly and exchange data. This interconnectedness has resulted in the exponential growth of data generated by IoT devices, presenting challenges and opportunities for businesses and industries (Soni & Kumar, 2022). This section focused on the background and inspiration. One of the key challenges in harnessing the potential of IoT is efficiently processing and analyzing the massive amounts of data generated. Traditional data analysis methods often fall short in coping with the volume, velocity, and variety of IoT data. This is where cloud computing and machine learning techniques come into play. Cloud computing offers a scalable and flexible infrastructure that caters specifically to the storage, processing, and analysis of data generated by the Internet of Things (IoT). It provides a platform that can be easily adjusted to accommodate the varying requirements and demands of managing IoT data. By leveraging cloud resources, organizations can overcome the limitations of on-premises infrastructure and effectively handle the ever-increasing data volumes. This research explores the potential of combining cloud computing and machine learning in the context of IoT to enhance efficiency, optimized resource allocation, and decision-making (Schmitt et al., 2020).
The objectives of this research paper are threefold. Firstly, we aim to provide an overview of IoT, cloud computing, and machine learning, highlighting their strengths and challenges. Secondly, we aim to explore the architecture and integration of machine learning algorithms in cloud-based IoT systems. Lastly, focus on investigating the potential benefits of this intelligent cloud-based approach in terms of enhanced efficiency and decision-making in various application domains.
1.1. Research Challenges
Despite the numerous advantages of IoT and the availability of cloud computing resources, several challenges hinder the efficient utilization of IoT data and the application of machine learning techniques. These challenges give rise to the following problem statement:
Processing and analyzing the vast amount of data generated by IoT devices present substantial challenges due to the volume and speed at which the data is generated. Traditional on-premises infrastructure often faces difficulties in effectively managing the scale and intricacy of IoT data, resulting in processing bottlenecks and delays in obtaining valuable insights.
Many organizations face limitations in scaling their infrastructure to accommodate the growing number of IoT devices and the associated data. The lack of scalability may hinder the ability to handle peak loads, resulting in performance degradation effectively and decreased operational efficiency.
Time-sensitive applications in IoT demand real-time decision-making capabilities. However, traditional data analysis approaches may not be able to provide timely insights, preventing organizations from making proactive decisions and responding promptly to critical events or anomalies.
IoT data is often characterized by its heterogeneity and complexity, making it challenging to extract meaningful information. Different types of sensors, varying data formats, and data quality issues introduce complexities that traditional analytical approaches may struggle to address.
IoT devices typically have limited computational resources, storage capacity, and power constraints. Deploying complex machine learning models directly on resource-constrained devices may not be feasible, necessitating computation offloading to cloud-based resources.