108,635 research outputs found
A Framework to Improve Energy Efficient Behaviour at Home through Activity and Context Monitoring
[EN]Real-time Localization Systems have been postulated as one of the most appropriated
technologies for the development of applications that provide customized services. These systems
provide us with the ability to locate and trace users and, among other features, they help identify
behavioural patterns and habits. Moreover, the implementation of policies that will foster energy
saving in homes is a complex task that involves the use of this type of systems. Although there are
multiple proposals in this area, the implementation of frameworks that combine technologies and
use Social Computing to influence user behaviour have not yet reached any significant savings in
terms of energy. In this work, the CAFCLA framework (Context-Aware Framework for Collaborative
Learning Applications) is used to develop a recommendation system for home users. The proposed
system integrates a Real-Time Localization System and Wireless Sensor Networks, making it possible
to develop applications that work under the umbrella of Social Computing. The implementation
of an experimental use case aided efficient energy use, achieving savings of 17%. Moreover, the
conducted case study pointed to the possibility of attaining good energy consumption habits in the
long term. This can be done thanks to the systemâs real time and historical localization, tracking and
contextual data, based on which customized recommendations are generated.European Commision (EC). Funding H2020/MSCARISE. Project Code: 64179
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
NILM techniques for intelligent home energy management and ambient assisted living: a review
The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.AgĂȘncia financiadora:
Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve
01/SAICT/2018/39578
Fundação para a CiĂȘncia e Tecnologia through IDMEC, under LAETA:
SFRH/BSAB/142998/2018
SFRH/BSAB/142997/2018
UID/EMS/50022/2019
Junta de Comunidades de Castilla-La-Mancha, Spain:
SBPLY/17/180501/000392
Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project):
TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio
Scenarios for Educational and Game Activities using Internet of Things Data
Raising awareness among young people and changing their behavior and habits concerning energy usage and the environment is key to achieving a sustainable planet. The goal to address the global climate problem requires informing the population on their roles in mitigation actions and adaptation of sustainable behaviors. Addressing climate change and achieve ambitious energy and climate targets requires a change in citizen behavior and consumption practices. IoT sensing and related scenario and practices, which address school children via discovery, gamification, and educational activities, are examined in this paper. Use of seawater sensors in STEM education, that has not previously been addressed, is included in these educational scenaria
Large UK retailers' initiatives to reduce consumers' emissions: a systematic assessment
In the interest of climate change mitigation, policy makers, businesses and non-governmental organisations have devised initiatives designed to reduce in-use emissions whilst, at the same time, the number of energy-consuming products in homes, and household energy consumption, is increasing. Retailers are important because they are at the interface between manufacturers of products and consumers and they supply the vast majority of consumer goods in developed countries like the UK, including energy using products. Large retailers have a consistent history of corporate responsibility reporting and have included plans and actions to influence consumer emissions within them.
This paper adapts two frameworks to use them for systematically assessing large retailersâ initiatives aimed at reducing consumersâ carbon emissions. The Framework for Strategic Sustainable Development (FSSD) is adapted and used to analyse the strategic scope and coherence of these initiatives in relation to the businessesâ sustainability strategies. The ISM âIndividual Social Materialâ framework is adapted and used to analyse how consumer behaviour change mechanisms are framed by retailers. These frameworks are used to analyse eighteen initiatives designed to reduce consumer emissions from eight of the largest UK retail businesses, identified from publicly available data.
The results of the eighteen initiatives analysed show that the vast majority were not well planned nor were they strategically coherent. Secondly, most of these specific initiatives relied solely on providing information to consumers and thus deployed a rather narrow range of consumer behaviour change mechanisms. The research concludes that leaders of retail businesses and policy makers could use the FSSD to ensure processes, and measurements are comprehensive and integrated, in order to increase the materiality and impact of their initiatives to reduce consumer emissions in use. Furthermore, retailers could benefit from exploring different models of behaviour change from the ISM framework in order to access a wider set of tools for transformative system change
The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey
The Internet of Things (IoT) is a dynamic global information network
consisting of internet-connected objects, such as Radio-frequency
identification (RFIDs), sensors, actuators, as well as other instruments and
smart appliances that are becoming an integral component of the future
internet. Over the last decade, we have seen a large number of the IoT
solutions developed by start-ups, small and medium enterprises, large
corporations, academic research institutes (such as universities), and private
and public research organisations making their way into the market. In this
paper, we survey over one hundred IoT smart solutions in the marketplace and
examine them closely in order to identify the technologies used,
functionalities, and applications. More importantly, we identify the trends,
opportunities and open challenges in the industry-based the IoT solutions.
Based on the application domain, we classify and discuss these solutions under
five different categories: smart wearable, smart home, smart, city, smart
environment, and smart enterprise. This survey is intended to serve as a
guideline and conceptual framework for future research in the IoT and to
motivate and inspire further developments. It also provides a systematic
exploration of existing research and suggests a number of potentially
significant research directions.Comment: IEEE Transactions on Emerging Topics in Computing 201
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