Böcker av Kaishun Wu

This book introduces readers to the fundamentals of the cross-technology coexistence problem in heterogeneous wireless networks. It also highlights a range of mechanisms designed to combat this problem and improve network performance, including protocol design, theoretical analysis, and experimental evaluation.In turn, the book proposes three mechanisms that can be combined to combat the cross-technology coexistence problem and improve network performance. First, the authors present a fast signal identification method. It provides the basis for the subsequent protocol design and allows heterogeneous devices to adopt proper transmission strategies. Second, the authors present two cross-technology interference management mechanisms in both the time domain and the frequency domain, which can mitigate interference and increase transmission opportunities for heterogeneous devices, thus improving network performance. Third, they present a cross-technology communication mechanism basedon symbol-level energy modulation, which allows heterogeneous devices to transmit information directly without a gateway, improving transmission efficiency and paving the way for new applications in IoT scenarios. Lastly, they outline several potential research directions to further improve the efficiency of cross-technology coexistence. This book is intended for researchers, computer scientists, and engineers who are interested in the research areas of wireless networking, wireless communication, mobile computing, and Internet of Things. Advanced-level students studying these topics will benefit from the book as well.

This book constitutes the refereed post-conference proceedings of the 2nd International Conference on Edge Computing and IoT, ICECI 2021, held in December 2021 in Shenzhen, China. Due to COVID-19 pandemic the conference was held virtually. The explosion of the big data generated by ubiquitous edge devices motivates the emergence of applying machine learning systems for edge computing and Internet of Things (IoT) services. Machine learning techniques are delivering a promising solution to the industry for building IoT systems and to make innovation at a rapid pace. The 12 full papers of ICECI 2021 were selected from 26 submissions and present results and ideas in the area of edge computing and IoT.