Guanglin Zhang

This book provides an in-depth discussion of the significance of energy management in microgrids, focusing on three key areas: multi-energy cooperative management in single and multi-microgrid systems, and energy management issues related to the Internet of Vehicles (IoV) in microgrids. In Part 1, the book emphasizes the collaborative management of multiple energy sources, considering electric, gas, hydrogen, and renewable energy for a single microgrid system. In Part 2, it addresses the challenges in multi-microgrid systems and proposes a scheduling scheme for multi-energy cooperative management. Finally, Part 3 investigates the energy management challenges posed by the Internet of Vehicles in microgrids, and proposes a management scheme for electric vehicles (EVs) within microgrids using deep reinforcement learning. Overall, the algorithms discussed in this book are essential for addressing the energy management challenges in microgrids.

This book provides a discussion of privacy in the following three parts: Privacy Issues in Data Aggregation; Privacy Issues in Indoor Localization; and Privacy-Preserving Offloading in MEC. In Part 1, the book proposes LocMIA, which shifts from membership inference attacks against aggregated location data to a binary classification problem, synthesizing privacy preserving traces by enhancing the plausibility of synthetic traces with social networks. In Part 2, the book highlights Indoor Localization to propose a lightweight scheme that can protect both location privacy and data privacy of LS. In Part 3, it investigates the tradeoff between computation rate and privacy protection for task offloading a multi-user MEC system, and verifies that the proposed load balancing strategy improves the computing service capability of the MEC system. In summary, all the algorithms discussed in this book are of great significance in demonstrating the importance of privacy.

This book provides a discussion of privacy in the following three parts: Privacy Issues in Data Aggregation; Privacy Issues in Indoor Localization; and Privacy-Preserving Offloading in MEC. In Part 1, the book proposes LocMIA, which shifts from membership inference attacks against aggregated location data to a binary classification problem, synthesizing privacy preserving traces by enhancing the plausibility of synthetic traces with social networks. In Part 2, the book highlights Indoor Localization to propose a lightweight scheme that can protect both location privacy and data privacy of LS. In Part 3, it investigates the tradeoff between computation rate and privacy protection for task offloading a multi-user MEC system, and verifies that the proposed load balancing strategy improves the computing service capability of the MEC system. In summary, all the algorithms discussed in this book are of great significance in demonstrating the importance of privacy.