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Smart environments are technology augmented physical ecosystems, which have been pervasively and non-invasively instrumented to become perceptive and responsive. These enriched capabilities are possible thanks to the existence of infrastructures that enable the acquisition and fusion of data coming from a wide variety of both virtual sources and ubiquitous sensing devices (wireless sensor networks, personal devices, smart objects, etc.). On top of this data, user-centric paradigms aim at learning about the ecosystem’s activity, in order to adapt and personalize the offering of features and applications that may better fit the needs and preferences of its dwellers. In a complementary way, environment-centric paradigms exploit information in pursuit of a holistic understanding of the smart space’s status and its evolution, through situation analysis techniques, to enhance decision making of global management services. With this context in mind, the present Special Issue brings together recent advances on tools, activity recognition, applications and service design for Personalization and Situation Awareness in Smart Environments. The Special Issue contains both extended versions of selected papers presented at the International Workshop on Smart Environments and Ambient Intelligence 2013 (SEnAmI 2013) and submissions accepted from open call. After a three-round review process, in which three referees reviewed each submitted paper, eight papers were finally selected for publication. The first paper, ‘‘System-Level Approach to the Design of Ambient Intelligence Systems based on Wireless Sensor and Actuator Networks’’ by U.D. Atmojo et al., lies within the architectural solutions to deal with smart environments information. The authors propose a system-level programming language to develop a middleware-free Ambient Intelligence system, in order to overcome the problems of designing distributed systems on top of Wireless Sensor and Actor Networks (WSANs). The three following papers explore different aspects of activity/behavior recognition in smart environments.‘‘Bioinspired relevant interaction modeling in Cognitive crowd management’’ by S. Chiappino et al., describes an Intelligent Video Surveillance (IVS) system based on a bioinspired model of human reasoning and consciousness that applies Cognitive Systems to anomaly detection and management. Thereafter, ‘‘CRAFFT: An activity prediction model based on Bayesian networks’’ by E. Nazerfard and D.J. Cook, details and validates an activity prediction model that relies on Bayesian networks together with a two-step inference process to predict and label the activities and to anticipate the start time of the next one. The paper entitled ‘‘Using implicit user feedback to balance energy consumption and user comfort of computer screens’’ by P. Jaramillo et al., presents a dynamically adaptive proximity controller to balance energy consumption and user comfort of computer screens in office environments. Two application-oriented papers are next included. ‘‘Building a smart campus to support ubiquitous learning’’ by Y. Atif et al., describes a social community platform for university campuses that involves learners, experts and physical resources. The proposed model of smart campus, A. M. Bernardos (&) Universidad Politécnica de Madrid, Madrid, Spain e-mail: abernardos@grpss.ssr.upm.es

Special issue on “Personalization and situation awareness in smart environments”