Hugo de Man Professor Katholieke Universiteit Leuven Senior Research Fellow IMEC The steady evolution of hardware, software and communications technology is rapidly transforming the PC- and dot.com world into the world of Ambient Intelligence (AmI). This next wave of information technology is fundam- tally different in that it makes distributed wired and wireless computing and communication disappear to the background and puts users to the foreground. AmI adapts to people instead of the other way around. It will augment our consciousness, monitor our health and security, guide us through traffic etc. In short, its ultimate goal is to improve the quality of our life by a quiet, reliable and secure interaction with our social and material environment. What makes AmI engineering so fascinating is that its design starts from studying person to world interactions that need to be implemented as an int- ligent and autonomous interplay of virtually all necessary networked electronic intelligence on the globe. This is a new and exciting dimension for most elect- cal and software engineers and may attract more creative talent to engineering than pure technology does. Development of the leading technology for AmI will only succeed if the engineering research community is prepared to join forces in order to make Mark Weiser s dream of 1991 come true. This will not be business as usual by just doubling transistor count or clock speed in a microprocessor or increasing the bandwidth of communication."

This book describes model-based development of adaptive embedded systems, which enable improved functionality using the same resources. The techniques presented facilitate design from a higher level of abstraction, focusing on the problem domain rather than on the solution domain, thereby increasing development efficiency. Models are used to capture system specifications and to implement (manually or automatically) system functionality. The authors demonstrate the real impact of adaptivity on engineering of embedded systems by providing several industrial examples of the models used in the development of adaptive embedded systems.

This book introduces a generic and systematic design-time/run-time methodology for handling the dynamic nature of modern embedded systems, without adding large safety margins in the design. The techniques introduced can be utilized on top of most existing static mapping methodologies to deal effectively with dynamism and to increase drastically their efficiency. This methodology is based on the concept of system scenarios, which group system behaviors that are similar from a multi-dimensional cost perspective, such as resource requirements, delay, and energy consumption. Readers will be enabled to design systems capable to adapt to current inputs, improving system quality and/or reducing cost, possibly learning on-the-fly during execution. Provides an effective solution to deal with dynamic system design Includes a broad survey of the state-of-the-art approaches in this domain Enables readers to design for substantial cost improvements (e.g. energy reductions), by exploiting system scenarios Demonstrates how the methodology has been applied effectively on various, real design problems in the embedded system context

This book describes model-based development of adaptive embedded systems, which enable improved functionality using the same resources. The techniques presented facilitate design from a higher level of abstraction, focusing on the problem domain rather than on the solution domain, thereby increasing development efficiency. Models are used to capture system specifications and to implement (manually or automatically) system functionality. The authors demonstrate the real impact of adaptivity on engineering of embedded systems by providing several industrial examples of the models used in the development of adaptive embedded systems.

Hugo de Man Professor Katholieke Universiteit Leuven Senior Research Fellow IMEC The steady evolution of hardware, software and communications technology is rapidly transforming the PC- and dot.com world into the world of Ambient Intelligence (AmI). This next wave of information technology is fundam- tally different in that it makes distributed wired and wireless computing and communication disappear to the background and puts users to the foreground. AmI adapts to people instead of the other way around. It will augment our consciousness, monitor our health and security, guide us through traffic etc. In short, its ultimate goal is to improve the quality of our life by a quiet, reliable and secure interaction with our social and material environment. What makes AmI engineering so fascinating is that its design starts from studying person to world interactions that need to be implemented as an int- ligent and autonomous interplay of virtually all necessary networked electronic intelligence on the globe. This is a new and exciting dimension for most elect- cal and software engineers and may attract more creative talent to engineering than pure technology does. Development of the leading technology for AmI will only succeed if the engineering research community is prepared to join forces in order to make Mark Weiser's dream of 1991 come true. This will not be business as usual by just doubling transistor count or clock speed in a microprocessor or increasing the bandwidth of communication.

This book introduces a generic and systematic design-time/run-time methodology for handling the dynamic nature of modern embedded systems, without adding large safety margins in the design. The techniques introduced can be utilized on top of most existing static mapping methodologies to deal effectively with dynamism and to increase drastically their efficiency. This methodology is based on the concept of system scenarios, which group system behaviors that are similar from a multi-dimensional cost perspective, such as resource requirements, delay, and energy consumption. Readers will be enabled to design systems capable to adapt to current inputs, improving system quality and/or reducing cost, possibly learning on-the-fly during execution. Provides an effective solution to deal with dynamic system design Includes a broad survey of the state-of-the-art approaches in this domain Enables readers to design for substantial cost improvements (e.g. energy reductions), by exploiting system scenarios Demonstrates how the methodology has been applied effectively on various, real design problems in the embedded system context