Embedded Systems and Internet of Things Foundations

Abstract

The convergence of embedded systems and the Internet of Things (IoT) has accelerated the development of tiny machine learning (TinyML) real-time intelligence, which is delivered on ultra-low-power devices. Thus, this chapter examines the architecture, design methodologies, and emerging roles of embedded systems within the broader IoT ecosystem. This chapter provides a foundational understanding of embedded systems, establishing the components and structures of embedded systems, putting embedded system architectures into the context of IoT architectures, and understanding the increasing roles of embedded systems in improving intelligent edge computing. A detailed examination of embedded architectures, real-time operating systems, and performance limitations provided discourses on the trade-offs designers face between the outcome of embedded systems for real-world applications and the intentional consequences of designs. In the architectural context of IoT, smart services will realise their full potential when they leverage reliable, communicating sensors and actuators included in their design, and direct human intervention at the history collection void. A closer examination of the IoT architecture with its layers opened up communication protocols with embedded sensors and actuators, which are the main drivers combining the edges to connect. Considering the context of embedded IoT, the editors analyzed data processing, secure communication, and deployment to the real world. Based on this discussion of traditional embodied intelligence, the chapter will consider IoT and the challenges of deploying artificial intelligence (AI) as a service, and review the literature in which academic researchers and industry organizations are engaged in deploying TinyML on constrained hardware. A focused discussion follows on how embedded platforms facilitate the execution of machine learning models, with considerations for tools such as TensorFlow Lite Micro. Design methodologies are analyzed and critiqued with a focus on the principles of hardware-software co-design, performance optimization, and real-time processing. A subset of the findings employed in this chapter is based on benchmarking studies and inference-time analysis performed across a wide range of microcontroller platforms. Finally, examples have been provided with an application focus ranging from smart homes, industrial automation, and healthcare to show how the trends are being harnessed. Future directions of the field, namely sustainable edge AI, interoperability, and on-device learning, are provided before concluding. This chapter illustrates that embedded systems and IoT represent the foundation for the next generation of intelligent systems.