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A decade and a half after the debut of pervasive computing, a large number of prototypes, applications, and interaction interfaces have emerged. However, there is a lack of consensus about the best approaches to create such systems or how to evaluate them. To address these issues, this paper aims to develop a performance evaluation framework for pervasive computing systems.

Based on the authors' experience in the Gator Tech Smart House – an assistive environment for the elderly, they established a reference scenario that was used to guide the analysis of the large number of systems they studied. An extensive survey of the literature was conducted, and through a thorough analysis, the authors derived and arrived at a broad taxonomy that could form a basic framework for evaluating existing and future pervasive computing systems.

A taxonomy of pervasive systems is instrumental to their successful evaluation and assessment. The process of creating such taxonomy is cumbersome, and as pervasive systems evolve with new technological advances, such taxonomy is bound to change by way of refinement or extension. This paper found that a taxonomy for something so broad as pervasive systems is very complex. It overcomes the complexity by focusing the classifications on key aspects of pervasive systems, decided purely empirically and based on the authors own experience in a real‐life, large‐scale pervasive system project.

There are currently no methods or frameworks for comparing, classifying, or evaluating pervasive systems. The paper establishes a taxonomy – a first step toward a larger evaluation methodology. It also provides a wealth of information, derived from a survey of a broad collection of pervasive systems.

The purpose of this paper is to present a novel model for inhabitant prediction in smart houses based on daily life activities. It uses data provided by non intrusive sensors and devices to predict the house occupant. The authors' model, named Behavior Classification Model (BCM), applies Support Vector Machines (SVM) classifier to learn the users' habits when they perform activities, and then predicts the user. BCM was tested using real data and compared with a frequency based approach. In this paper the authors present also their approach to improve the accuracy of BCM using SVM feature selection algorithm.

The model, named Behavior Classification Model (BCM), applies Support Vector Machines (SVM) classifier to learn the users' habits when they perform activities, and then predicts the user.

BCM was tested using real data and compared with a frequency based approach. In this paper the authors' also present their approach to improve the accuracy of BCM using SVM feature selection algorithm.

The paper is based on blind user recognition in smart homes.

The purpose of this paper is to study what motivates small businesses to engage in collective action, especially in high-tech industry.

Application domain is smart living industry, in which installation companies offer ICT-enabled solutions for smart home services. A survey was conducted among 140 small/medium installation companies in smart living industry which are members of a major Dutch branch organization. Structural Equation Modelling (SEM) was used to model and analyse data from the survey.

The paper found that different types of motivations do not significantly increase the collective orientation of small businesses. Moreover, the current involvement of small companies in smart living projects is not directly related to their collective orientation.

The lack of collaboration is resulting in market fragmentation and lack of technological interoperability. The paper advise policy makers to provide selective incentives to stimulate collaboration and to facilitate knowledge sharing on best practices and collective business cases.

The motivational factors for collective action between small businesses in high-tech sectors have rarely been studied. Although many studies addresses collective action issues on an individual level, the literature lacks an integration of existing theories into a set of testable hypotheses that aim at motivators and inhibitors of inter-organizational collective action.