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Abstrak :
In this book, contributors provide insights into the latest developments of Edge Computing/Mobile Edge Computing, specifically in terms of communication protocols and related applications and architectures. The book provides help to Edge service providers, Edge service consumers, and Edge service developers interested in getting the latest knowledge in the area. The book includes relevant Edge Computing topics such as applications; architecture; services; inter-operability; data analytics; deployment and service; resource management; simulation and modeling; and security and privacy. Targeted readers include those from varying disciplines who are interested in designing and deploying Edge Computing.;

Abstrak :
This book presents solutions to the problems arising in two trends in mobile computing and their intersection: increased mobile traffic driven mainly by sophisticated smart phone applications; and the issue of user demand for lighter phones, which cause more battery power constrained handhelds to offload computations to resource intensive clouds (the second trend exacerbating the bandwidth crunch often experienced over wireless networks). The authors posit a new solution called spectrum aware cognitive mobile computing, which uses dynamic spectrum access and management concepts from wireless networking to offer overall optimized computation offloading and scheduling solutions that achieve optimal trade-offs between the mobile device and wireless resources. They show how in order to allow these competing goals to meet in the middle, and to meet the promise of 5G mobile computing, it is essential to consider mobile offloading holistically, from end to end and use the power of multi-radio access technologies that have been recently developed. Technologies covered in this book have applications to mobile computing, edge computing, fog computing, vehicular communications, mobile healthcare, mobile application developments such as augmented reality, and virtual reality. - Gives readers valuable insights into the future of mobile computing and communication; - Touches on wireless technologies such as 5G, mobile edge computing (MEC), mobile cloud services, and cognition-based networking; - Provides examples throughout the book to provide insight into real world scenarios.

Abstrak :
This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme. There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results.