Smart Computing and Self-Adaptive Systems

This volume examines open problems in the growing fields of smart computing and self-adaptive technology. Its scope includes machine learning, artificial intelligence, deep learning, robotics, cloud and fog computing, and data mining, as well as newer intelligent applications built on these research areas.

The chapters cover how self-adaptation architectures can be implemented in smart devices, and how adaptive models apply to smart cities and self-driving cars. Other topics include decentralized adaptive computing at network edges, energy-aware AI systems, machine-to-machine (M2M) networks, sensors, data analytics, and the algorithms and tools used to engineer self-adaptive systems. The book presents self-healing and self-adaptation as the basis for fully automatic future technologies and discusses the computational abilities of smart systems.

It also presents tools and techniques for data management and data integration aimed at improving the efficiency of big data. A dedicated chapter looks ahead to the future of self-stabilizing and self-adaptive systems of systems. Application areas include automation, robotics, medical and biomedical sciences, agricultural science, and healthcare.

The book is intended for researchers and graduate students in machine learning, information technology, and artificial intelligence.