Advanced Intelligent Environments

With nearly 80 million baby boomers in the United States just reaching their sixties, the demand for senior-oriented devices and services will explode in the coming years. Managing the increasing health-care costs for such a population requires developing technologies that will allow seniors to maintain active, independent lifestyles. Pervasive computing pervasive computing environments, such as smart homes, bundle assistive technologies assistive technology and specially designed architectural and home furnishing elements provide health-care and well-being services to its residents. However, for such environments to be commercially viable, we require a system that allows technology to be easily utilized and included as it enters the market place. Also we require new technology to be introduced in a plug-and-play fashion, and applications that are developed by programmers, not system integrators. The Gator Tech Smart House, a full-size, free-standing residential home located in the Oak Hammock Retirement Community in Gainesville, Florida, is an example of this kind of assistive environment. It uses the Atlas sensor network platform, an enabling technology that combines a hardware platform and software middleware, making the Gator Tech Smart House a truly programmable pervasive computing space.

If we are designing for digital homes then we are not designing for humans? How do we truly design for real people? consumer Experience Architecture (CEA) provides an actionable framework for the development, design, and production of products and services specifically centered around human needs, desires and frames of understanding. This chapter dismantles CEA into its essential components, exploring real-world examples and illustrations. Finally the chapter challenges the reader to expand current development practices by looking toward science fiction or other cultural inputs as possible laboratories or inspirations for future designs.

The realization of the vision of ambient intelligence requires developments both at infrastructure and application levels. As a consequence of the former, physical spaces are turned into intelligent AmI environments, which offer not only services such as sensing, digital storage, computing, and networking but also optimization, data fusion, and adaptation. However, despite the large capabilities of AmI environments, people’s interaction with their environment will not cease to be goal-oriented and task-centric. In this chapter, we use the notions of ambient ecology to describe the resources of an AmI environment and activity spheres to describe the specific ambient ecology resources, data and knowledge required to support a user in realizing a specific goal. In order to achieve task-based collaboration among the heterogeneous members of an ambient ecology, first one has to deal with this heterogeneity, while at the same time achieving independence between a task description and its respective realization within a specific AmI environment. Successful execution of tasks depends on the quality of interactions among artifacts and among people and artifacts, as well as on the efficiency of adaptation mechanisms. The formation of a system that realizes adaptive activity spheres is supported by a service-oriented architecture, which uses intelligent agents to support adaptive planning, task realization and enhanced human–machine interaction, ontologies to represent knowledge and ontology alignment mechanisms to achieve adaptation and device independence. The proposed system supports adaptation at different levels, such as the changing configuration of the ambient ecology, the realization of the same activity sphere in different AmI environments, the realization of tasks in different contexts, and the interaction between the system and the user.

This chapter deals with the design of multimodal information systems in the framework of ambient intelligence. Its agent architecture is based on KUP, an alternative to traditional software architecture models for human–computer interaction. The KUP model is accompanied by an algorithm for choosing and instantiating interaction modalities. The model and the algorithm have been implemented in a platform called PRIAM, with which we have performed experiments in pseudo-real scale.

The intention of this work is the investigation of the performance of an automatic emotion recognizer using biologically motivated features, comprising perceived loudness features proposed by Zwicker, robust RASTA-PLP features, and novel long-term modulation spectrum-based features. Single classifiers using only one type of features and multi-classifier systems utilizing all three types are examined using two-classifier fusion techniques. For all the experiments the standard Berlin Database of Emotional Speech comprising recordings of seven different emotions is used to evaluate the performance of the proposed multi-classifier system. The performance is compared with earlier work as well as with human recognition performance. The results reveal that using simple fusion techniques could improve the performance significantly, outperforming other classifiers used in earlier work. The generalization ability of the proposed system is further investigated in a leave-out one-speaker experiment, uncovering a strong ability to recognize emotions expressed by unknown speakers. Moreover, similarities between earlier speech analysis and the automatic emotion recognition results were found.

In order to act in urban environments an individual accesses various types of knowledge, such as memories, spatial strategies and also information from the environment so as to develop plans and make decisions. This chapter will investigate the nature of spatial knowledge acquisition in an environmental setting by comparing performance in a task where the participants learnt the environment using spatial assistance either from a map or from a . It outlines the early results of an empirical experiment which evaluated participants’ spatial knowledge acquisition for orientation and distance estimation tasks in a large-scale urban environmental setting. The initial findings of the experiment highlight the fact that participants performed worse in distance estimation tasks than map participants and that their errors for complex routes were high. We will conclude by analysing the results of this experiment in terms of the specific types of knowledge afforded by mobile maps and the implications for spatial learning in urban environments.

This chapter is the extended work of the paper titled “Genetic Algorithm for Data Aggregation Trees in Wireless Sensor Networks”, appeared in Proceedings of the Third International Conference on Intelligent Environments (IE), Ulm, Germany, September 24–25, 2007. presents a genetic algorithm (GA) to generate balanced and energy-efficient data aggregation spanning trees for wireless sensor networks. In a data gathering round, a single best tree consumes lowest energy from all nodes but assigns more load to some sensors. As a result, the energy resources of heavily loaded nodes will be depleted earlier than others. Therefore, a collection of trees need to be used that balance load among nodes and consume less energy. The proposed GA takes these two issues in generating aggregation trees. The GA is simulated in an open-source simulator, J-sim. The simulation results show that proposed GA outperforms a few other data aggregation tree-based approaches in terms of extending network lifetime.

Technological enhancements aid development and research in smart homes and . The temporal nature of data collected in a smart environment provides us with a better understanding of patterns that occur over time. Predicting events and detecting anomalies in such data sets is a complex and challenging task. To solve this problem, we suggest a solution using temporal relations. Our temporal pattern discovery algorithm, based on Allen’s temporal relations, has helped discover interesting patterns and relations from data sets. We hypothesize that machine learning algorithms can be designed to automatically learn models of resident behavior in a and, when these are incorporated with temporal information, the results can be used to detect anomalies. We describe a method of discovering temporal relations in data sets and applying them to perform anomaly detection on the frequently occurring events by incorporating information shared by the activity. We validate our hypothesis using empirical studies based on the data collected from real resident and virtual resident (synthetic) data.

Service-oriented architecture (SOA) promises an elegant model that can easily handle dynamic and heterogeneous environments such as pervasive computing systems. However, in reality, frequent failures of resource-poor and low-cost sensors greatly diminish the guarantees on reliability and availability expected from SOA. To provide a framework for building fault-resilient, service-oriented pervasive computing systems, we present a solution that combines a virtual sensor framework with WS-Pro/ASCT, a service composition mechanism. The use of virtual sensors enhances the availability of services, while the service composition solution ensures that the system can efficiently adapt to changes and failures in the environment. This approach also includes a novel probe-based monitoring technique for proactive collection of performance data and a Finite Population Queuing System Petri Net (FPQSPN) for modeling the performance of composed services.

The chapter is about a form of networked urbanism distributed in east London, consisting of a system of infrastructural and leisure clusters of cellular units, combining as a connective tissue of bridges and islands, adapting, and negotiating as both a physical and an informational network. Embedded with self-learning behavioral and responsive systems, it allows for an intelligent choreography of soft programmatic spaces to create new leisure experiences, negotiating the changing effects of time, weather, programmatic, and crowd-dynamical inputs, extending parametric processes to drive urban performance.

This chapter addresses a shift in financial spatial thought and a disconnection from contemporary architectural thought and practice. It establishes the concept of the totality of space, an idea and practice unconsciously defined and designed by banking institutions as their business practices and operations evolved due to advancements in communication and information technology, banking law, and managerial theory, through the analysis of the banking industry in the United States. It suggests that architecture needs to address the totality of space and cooperate with such corporations in order to produce conscious, and thus more effective, design results. Ultimately, this chapter addresses the emerging need for a macroscale level of design of a financial institution’s space, in parallel to the microscale level, where the architectural tradition is currently focused.