A User-Centred Methodology to Design and Simulate Smart Home Environments and Related Services

openI progressi nelle tecnologie di automazione e comunicazione all'interno degli edifici residenziali offrono molti interessanti vantaggi per lo sviluppo delle Smart Home, come l'aumento di efficienza energetica, il miglioramento il comfort per gli abitanti e la riduzione dei costi operativi per il proprietario. L'aggregazione e la condivisione dei dati all'interno delle reti possono essere garantite dal moderno approccio denominato Internet delle cose (IoT) e supportati dalle nuove tecnologie dell'informazione e della comunicazione (ICT). Tali tecnologie si stanno evolvendo e le abitazioni stanno diventando luoghi tecnologici popolati da una moltitudine di dispositivi in grado di raccogliere una grande quantità di dati e di cooperare in modo intelligente per controllare tutti i dispositivi connessi, come gli elettrodomestici, l'illuminazione, i sistemi di riscaldamento, ecc. Da un lato, l’intelligenza crescente dei dispositivi connessi produce una grande quantità di dati; dall'altro lato, la complessità di tali dati crea difficoltà di classificazione, trasmissione ed interpretazione delle informazioni utili. Entrambi gli aspetti possono ridurre drasticamente i potenziali vantaggi e limitare la diffusione dei cosiddetti dispositivi “smart”. Mentre a livello aziendale già esistono soluzioni di automazione affermate ed ampiamente utilizzate, le applicazioni per le abitazioni private sono ancora di difficile diffusione a causa della mancanza di standard di comunicazione e della presenza di dispositivi e sistemi altamente eterogenei e quindi di difficile integrazione. Inoltre, anche quando la connessione tra due dispositivi viene stabilita, renderli interoperabili è un’altra grande sfida a causa delle differenze nelle modalità di funzionamento e della difficoltà di integrazione dell'interfaccia. Infatti, le Smart Home non consentono ancora una elevata interoperabilità e gli studi fatti sono spesso fortemente orientati alla tecnologia e concentrati sulle potenzialità dei singoli sottosistemi, trascurando i benefici per gli utenti finali. A tale scopo, questo lavoro definisce un modello di gestione delle informazioni per ambienti domestici intelligenti con lo scopo di supportare la progettazione e la simulazione dei dispositivi “smart” nonché dei servizi sviluppati. Tale modello considera diverse tipologie di dispositivi, le relazioni esistenti tra loro, i flussi informativi e le modalità di interazione dell’utente per modellare correttamente l'ambiente e definirne il comportamento. Il modello sviluppato supporta la progettazione della Smart Home ed è in grado di simulare le funzionalità dei dispositivi con lo scopo finale di valutare i benefici dei servizi forniti.The advances in home automation and communication technologies offer several attractive benefits for the modern smart home, such as increased energy efficiency, improved residential comfort and reduced operative costs for the homeowner. Data aggregation and sharing within the networks can be guaranteed by modern Internet of Things (IoT) approaches and supported by available Information and Communication Technologies (ICT) tools. Such technologies are evolving and the private houses are becoming technological places populated by a multitude of devices able to collect a huge quantity of data and to cooperate in an intelligent way to control different domains, from household appliances to lighting or heating and ventilation. On one hand, the rising intelligence of smart devices makes a large amount of data available; on the other hand, data complexity creates difficulties in classifying, transmitting and interpreting essential data. Both aspects may drastically reduce the potential advantages and limit the diffusion smart devices. While in building automation proven solutions already exist, tailored applications for private houses and integration among heterogeneous devices and systems are still challenging due to the lack of standards and the variety of adopted communication protocols and data model schemas. Furthermore, even when the device connection and consolidation are achieved, making them cooperate in an interoperable way is another big challenge due to differences in usage paradigms, operation modes and interface integration. In fact, Smart Homes still lack of high interoperability and researches are often strongly technology-oriented and focused on single sub-system potentialities neglecting the expected benefits for the final users. For this purpose, the presented research defines an information management model for the smart home environment to support design and simulation of its devices as well as the enabled services. Such a model considers different device typologies, their mutual relationships, the information flows and the user interaction modalities in order to properly model the environment and define its behavior. It supports the design of the smart home by simulating the devices’ functionalities and estimating the expected performances.INGEGNERIA MECCANICA E GESTIONALEopenCapitanelli, AndreaCapitanelli, Andre

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

State-of-the-art in Power Line Communications: from the Applications to the Medium

In recent decades, power line communication has attracted considerable attention from the research community and industry, as well as from regulatory and standardization bodies. In this article we provide an overview of both narrowband and broadband systems, covering potential applications, regulatory and standardization efforts and recent research advancements in channel characterization, physical layer performance, medium access and higher layer specifications and evaluations. We also identify areas of current and further study that will enable the continued success of power line communication technology.Comment: 19 pages, 12 figures. Accepted for publication, IEEE Journal on Selected Areas in Communications. Special Issue on Power Line Communications and its Integration with the Networking Ecosystem. 201

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Advancing Water Resources Systems Modeling Cyberinfrastructure to Enable Systematic Data Analysis, Modeling, and Comparisons

Water resources systems models aid in managing water resources holistically considering water, economic, energy, and environmental needs, among others. Developing such models require data that represent a water system’s physical and operational characteristics such as inflows, demands, reservoir storage, and release rules. However, such data is stored and described in different formats, metadata, and terminology. Therefore, Existing tools to store, query, and visualize modeling data are model, location, and dataset-specific, and developing such tools is time-consuming and requires programming experience. This dissertation presents an architecture and three software tools to enable researchers to more readily and consistently prepare and reuse data to develop, compare, and synthesize results from multiple models in a study area: (1) a generalized database design for consistent organization and storage of water resources datasets independent of study area or model, (2) software to extract data out of and populate data for any study area into the Water Evaluation and Planning system, and (3) software tools to visualize online, compare, and publish water management networks and their data for many models and study areas. The software tools are demonstrated using dozens of example and diverse local, regional, and national datasets from three watersheds for four models; the Bear and Weber Rivers in the USA and the Monterrey River in Mexico

Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance

Analysis and assessment of a knowledge based smart city architecture providing service APIs

Abstract The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs. Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented

Cross-domain interoperability using federated interoperable semantic IoT/Cloud testbeds and applications: The FIESTA-IoT approach

This work is funded by the European Commission under the EU-H2020 Project Grant ”Federated Interoperable Semantic IoT/cloudTestbeds andApplications (FIESTA)” with the Grant Agreement No. CNECT-ICT-643943

Taxonomy, Semantic Data Schema, and Schema Alignment for Open Data in Urban Building Energy Modeling

Urban Building Energy Modeling (UBEM) is a critical tool to provide quantitative analysis on building decarbonization, sustainability, building-to-grid integration, and renewable energy applications on city, regional, and national scales. Researchers usually use open data as inputs to build and calibrate UBEM. However, open data are from thousands of sources covering various perspectives of weather, building characteristics, etc. Besides, a lack of semantic features of open data further increases the engineering effort to process information to be directly used for UBEM as inputs. In this paper, we first reviewed open data types used for UBEM and developed a taxonomy to categorize open data. Based on that, we further developed a semantic data schema for each open data category to maintain data consistency and improve model automation for UBEM. In a case study, we use three popular open data to show how they can be automatically processed based on the proposed schematic data structure using large language models. The accurate results generated by large language models indicate the machine-readability and human-interpretability of the developed semantic data schema