Kaishun Wu – författare

This book discusses how machine learning and the Internet of Things (IoT) are playing a part in smart control of underwater environments, known as Internet of Underwater Things (IoUT).

This book constitutes the refereed post-conference proceedings of the 15th EAI International Conference on Quality, Reliability, Security and Robustness in Heterogeneous Networks, QShine 2020, held in November 2020.

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.

This brief presents the novel PHY layer technique, attachment transmission, which provides an extra control panel with minimum overhead. In addition to describing the basic mechanisms of this technique, this brief also illustrates the challenges, the theoretical model, implementation and numerous applications of attachment transmission. Extensive experiments demonstrate that attachment transmission is capable of exploiting and utilizing channel redundancy to deliver control information and thus it can provide significant support to numerous higher layer applications. The authors also address the critical problem of providing cost-effective coordination mechanisms for wireless design. The combination of new techniques and implementation advice makes this brief a valuable resource for researchers and professionals interested in wireless penetration and communication networks.

This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring.This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical  non-linear traffic correlation modelling-basedurban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale.This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT). Advanced level students studying these topics  benefit from this book as well.

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.