Internet of Things (IoT) services have grown substantially in recent years. Consequently, IoT
service providers (SPs) are emerging in the market and competing to offer their services …

Recent years have witnessed the progressive integration between IoT (Internet of Things)
devices and the cloud server, which promotes the efficiency and interoperability of IoT …

Federated learning (FL) is a distributed machine learning technique that enables model
development on user equipments (UEs) locally, without violating their data privacy …

Federated learning (FL) as a new decentralized learning/computing technique has potential
advantages (eg, accelerating computation task processing and protecting user privacy) for …

IoT rule engines are important middlewares that allow users to easily create custom trigger-
action programs (TAPs) and interact with the physical world. Users expect their TAPs to give …

Even the AI has been widely used and significantly changed our life, deploying the large AI
models on resource limited edge devices directly is not appropriate. Thus, the model split …

IoT application development usually involves separate programming at the device side and
server side. While separate programming style is sufficient for many simple applications, it is …

This chapter presented a discussion on the measurement and evaluation of the quality of IoT
applications. We noted that IoT applications may be human-in-the-loop or autonomic …

IoT application development usually involves separate programming at the device side and
server side. While separate programming style is sufficient for many simple applications, it is …

Cloud computing is a crucial concept in contemporary computing, providing adaptable and
expandable resources to accommodate the changing demands of different applications …