1,721,022 research outputs found
What Would You Ask to Your Home if It Were Intelligent? Exploring User Expectations about Next-Generation Homes
Full text linkAmbient Intelligence (AmI) research is giving birth to a multitude of futuristic home scenarios and applications; however a clear discrepancy between current installations and research-level designs can be easily noticed. Whether this gap is due to the natural distance between research and engineered applications or to mismatching of needs and solutions remains to be understood. This paper discusses the results of a survey about user expectations with respect to intelligent homes. Starting from a very simple and open question about what users would ask to their intelligent homes, we derived user perceptions about what intelligent homes can do, and we analyzed to what extent current research solutions, as well as commercially available systems, address these emerging needs. Interestingly, most user concerns about smart homes involve comfort and household tasks and most of them can be currently addressed by existing commercial systems, or by suitable combinations of them. A clear trend emerges from the poll findings: the technical gap between user expectations and current solutions is actually narrower and easier to bridge than it may appear, but users perceive this gap as wide and limiting, thus requiring the AmI community to establish a more effective communication with final users, with an increased attention to real-world deploymen
Rule-based Intelligence for Domotic Environments
No full textIn the last years Home Automation systems gained new momentum, permeating many human-related environments, from homes to hospitals. The rapid evolution of such systems showed several interoperability pitfalls and a generally insufficient support for advanced user-home interaction. To tackle these emerging issues, recent research works defined the concept of Intelligent Domotic Environments (IDEs) where different automation systems, appliances and devices are integrated into a single powerful environment, capable of providing Ambient Intelligence (AmI) functionalities. IDEs represent one of the first attempts to define a framework for AmI environments based on off-the-shelf domotic systems. This paper contributes to extend IDE capabilities by supporting basic intelligence requirements through a rule-based reasoning mechanism. Starting from a formal model of IDE elements (DogOnt), rules are defined to evaluate environment properties. Property checking is done both off-line, for structural properties, i.e., properties involving the physical structure and configuration of the IDE, and on-line for properties dependent on current IDE states. Two rule languages, SWRL and JenaRules, are considered for rule formalization and their reasoning performance is evaluated by comparing two different rule engines, namely Jess and Jena. Results show that rule-based reasoning can deal with quite complex property checking, effectively addressing basic intelligence for IDEs and providing the basis for more advanced behaviors such as user adaptation and proactive interactio
spChains: A Declarative Framework for Data Stream Processing in Pervasive Applications
Full text linkPervasive applications rely on increasingly complex streams of sensor data continuously captured from the physical world. Such data is crucial to enable applications to ``understand'' the current context and to infer the right actions to perform, be they fully automatic or involving some user decisions. However, the continuous nature of such streams, the relatively high throughput at which data is generated and the number of sensors usually deployed in the environment, make direct data handling practically unfeasible. Data not only needs to be cleaned, but it must also be filtered and aggregated to relieve higher level algorithms from near real-time handling of such massive data flows. We propose here a stream-processing framework (spChains), based upon state-of-the-art stream processing engines, which enables declarative and modular composition of stream processing chains built atop of a set of extensible stream processing blocks. While stream processing blocks are delivered as a standard, yet extensible, library of application-independent processing elements, chains can be defined by the pervasive application engineering team. We demonstrate the flexibility and effectiveness of the spChains framework on two real-world applications in the energy management and in the industrial plant management domains, by evaluating them on a prototype implementation based on the Esper stream processo
DogOnt as a viable seed for semantic modeling of AEC/FM
Full text linkEnergy consumption and performance assessment of Smart Cities must consider different levels and various sub-domains. A comprehensive energy profile of a city, in fact, should work at the city, district, and building levels. At the same time and for each level, it should take into account both electrical and thermal consumptions, and gather these information from a plethora of different sensors and from various stakeholders (i.e., citizens, utilities, policy makers, and energy providers). Current modeling approaches for this context address each level and domain separately, thus preventing a structured and comprehensive approach to a unified energy representation. Moreover, current approaches make it difficult to keep the consistency between the energetic data through levels, sub-domains, and across stakeholders. Starting from an analysis of ontologies at the state-of-the-art, this paper shows how DogOnt can be used as a foundation towards a shared and unified model for such a context. DogOnt was firstly developed in 2008 and withstands over 8 years of usage without major failures and shortcomings. We discuss successful design choices and adaptations, which kept the model up-to-date and increasingly adopted in such a mid-term time frame for energy representation in Smart Cities
Design Recommendations for Smart Energy Monitoring: a Case Study in Italy
Full text linkIn the era of green energy and smart grids, the ability to access energy information and effectively analyze such data to extract key performance indicators is a crucial factor for successful building management. Energy data can in fact be exploited both in long-term policy adaptation and in shorter term habits modification, providing the basis for stable improvements of the overall efficiency of buildings and dwellings. To reach the ambitious goal of actually improving how buildings consume energy, four main challenges emerge from literature: (a) lack of skills and experience of energy managers, (b) complex and disparate data sets, which are currently blocking decision making processes, (c) mostly-manual work-flows that struggle to find relevant information into overwhelming streams of data sourced by monitoring systems, and (d) lack of collaborations between organizational departments. This paper provides deeper insights on these challenges, by investigating the kind of analysis currently performed by energy managers (in Italy) and the expectations they have if required to reason about systems that will be available within the next five years, and proposes design recommendations for next generation energy intelligence system
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