creates challenges with enormous technical, societal, economic, and political consequences. Consequently, a wide range of researchers from academia and industry, as well as businesses, government agencies, and cities, are exploring this exciting technology from three main perspectives: scientific theory, engineering design, and the user experience.Motivated by this more holistic view, the research community has moved its focus from the system to the end user. This shift aims to empower users by providing them with the knowledge required to understand and control their environment, as well as by offering new accessible and interactive interfaces that go beyond the traditional desktop.With this in mind, this special issue of Computer presents five recent research and deployment case studies. Two of the articles project our readers into futurist scenarios: one imagines nanotechnologies' penetration into embedded computing and electronics, while the other discusses the extent to which neuroscience will drive future IoT development. The remaining three articles offer detailed insight into technological solutions that are unleashing new forms of AI and programming constructs, and discuss their societal impact through end-user empowerment. All of the articles are at the forefront of the user-centered design approach.

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This paper outlines an approach that we are taking for elder-care applications in the smart home, involving cognitive errors and their compensation. Our approach involves high level modeling of daily activities of the elderly by breaking down these activities into smaller units, which can then be automatically recognized at a low level by collections of sensors placed in the homes of the elderly. This separation allows us to employ plan recognition algorithms and systems at a high level, while developing stand-alone activity recognition algorithms and systems at a low level. It also allows the mixing and matching of multi-modality sensors of various kinds that go to support the same high level requirement.Currently our plan recognition algorithms are still at a conceptual stage, whereas a number of low level activity recognition algorithms and systems have been developed. Herein we present our model for plan recognition, providing a brief survey of the background literature. We also present some concrete results that we have achieved for activity recognition, emphasizing how these results are incorporated into the overall plan recognition system.