A Framework to Improve Energy Efficient Behaviour at Home through Activity and Context Monitoring (original) (raw)
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The promotion of changes in users' behaviors with the aim of saving energy consumption in public buildings is a complex task that requires the use of multiple technologies. In this sense, context-aware technologies such as Wireless Sensor Networks and Real-Time Locating Systems, along with the use of Collaborative Learning, Virtual Organizations of Agents and Social Computing, provide a great potential for the development of serious games that foster the acquisition of good energy and healthy habits among workers and users in the public building. This paper presents the development of a serious game to change the users' behaviors when using resources in public buildings using CAFCLA, a framework that allows the integration of multiple technologies that facilitate both context-awareness and social computing.
Energy Efficiency in Public Buildings through Context-Aware Social Computing
Sensors, 2017
The challenge of promoting behavioral changes in users that leads to energy savings in public buildings has become a complex task requiring the involvement of multiple technologies. Wireless sensor networks have a great potential for the development of tools, such as serious games, that encourage acquiring good energy and healthy habits among users in the workplace. This paper presents the development of a serious game using CAFCLA, a framework that allows for integrating multiple technologies, which provide both context-awareness and social computing. Game development has shown that the data provided by sensor networks encourage users to reduce energy consumption in their workplace and that social interactions and competitiveness allow for accelerating the achievement of good results and behavioral changes that favor energy savings.
Distributed Social Data Collection for Home Energy Management
2013
The work described in this paper is part of a system that tackles the energy wasting problem in households through the use of recommender systems. Its ultimate goal is to recommend personalized strategies to each user so that he can reduce energy spendings in his house. This paper is focused on the initial steps of this system:data collection. Data collection is performed in two different modes: offline and online. Offline data collection is based on a series of questions regarding user houses; this questionnaire was disseminated as a Facebook application, and was comprised of several questions divided in four main groups: household characteristics; electrical devices' characteristics; energy consumption habits; and environmental awareness. Online data collection is performed by energy meters installed in households, which send consumption data to the system; this data has great value for the personalized recommendation generation, as it provides finer grained information regarding energy consumption. The results at this point prove that the combination of energy consumption data and the knowledge of the house characteristics represent an good ground to build the recommendations for the users and by that increase their energy savings. The next step will consist in the interpretation of the collected data trying to find groups of users with the same profile of energy consumption.
Context-aware applications using personal sensors
Proceedings of the Second International Conference on Body Area Networks BodyNets, 2007
Context-aware applications require the development of convenient frameworks. Effective mobility requires that mobile applications can integrate new sensors or new types of information. This is not possible within traditional applications, because a redesign phase in necessary. We describe in this article an agent-based framework supporting sensors' data fusion and context-aware information exchanges. An ontology-based representation of data is used. Exchanges inbetween components are carried out within so-called virtual knowledge communities. An application has been designed within this framework ('Wake me up', taking place in the metro). It makes use of wearable sensor, transmitters and cell phones. The wearable sensor is used to determine the state of a user. Transmitters provide geographical information, and cell phones are used as personal assistants.
Energy Consumption Information Services for Smart Home Inhabitants
Lecture Notes in Computer Science, 2010
We investigate services giving users an adequate insight on his or her energy consumption habits in order to optimize it in the long run. The explored energy awareness services are addressed to inhabitants of smart homes, equipped with smart meters, advanced communication facilities, sensors and actuators. To analyze the potential of such services, a game at a social network Facebook has been designed and implemented, and the information about players' responses and interactions within the game environment has been collected and analyzed. The players have had their virtual home energy usage visualized in different ways, and had to optimize the energy consumption basing on their own perceptions of the consumption information. Evaluations reveal, in particular, that users are specifically responsive to information shown as a real-time graph and as costs in Euro, and are able to produce and share with each other policies for managing their smart home environments.
A system for energy savings in an ambient intelligence environment
… and Communication on …, 2011
This work presents an Ambient Intelligence system that targets energy consumption awareness and savings. The system was deployed at the School of Science and Technology of the International Hellenic University and follows a three-layer approach. The first layer hosts devices (currently smart plugs, sensor boards and smart clampers) suited for the purpose. The second layer, namely the aWESoME middleware (a WEb Service MiddlewarE), resolves interoperability issues on the first layer, by universally exposing all actuator functions and sensor data through Web Services. Finally, a prototype application, named iDEALISM, has been developed to reside on the topmost layer. iDEALISM presents and manages all heterogeneous devices in the same place, enabling users to make comparisons, and take informed decisions on saving energy.
Context-aware recommender system for multi-user smart home
International Journal of Electrical and Computer Engineering (IJECE), 2023
Smart home is one of the most important applications of the internet of things (IoT). Smart home makes life simpler, easier to control, saves energy based on user's behavior and interaction with the home appliances. Many existing approaches have designed a smart home system using data mining algorithms. However, these approaches do not consider multiusers that exist in the same location and time (which needs a complex control). They also use centralized mining algorithm, then the system's efficiency is reduced when datasets increase. Therefore, in this paper, we firstly build a context-aware recommender system that considers multi-user's preferences and solves their conflicts by using unsupervised algorithms to deliver useful recommendation services. Secondly, we improve smart home's responsive using parallel computing. The results reveal that the proposed method is better than existing approaches.
Context-Aware Recommender System by Merging Wireless Sensors and Mobile Data
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Mobile social network is an important aspect in mobile computing.It includes sensor networks that can enable applications such as context-aware music players, health applications and video screens. Context-aware computing is a computing paradigm that is restricted to a class of mobile systems, which senses physical environment around it and adapts to its behavior [1]. Context-aware system isthe component of a computing paradigm which is recently getting into the trend of Artificial Intelligence [1]. It is also a component of ubiquitous computing or pervasive computing environment.TheThreemain aspects of contextawarenessare as follows: where you are, who you are with, and what resources are nearby. Location is a primary capability in context-awareness. Location-aware systemsdo not capture things of interest that are changing. Context-aware in contrast generally includes nearby people, lighting, devices, network availability, noise level, and even the social situation, e.g., whether y...
Enabling Location-Aware Pervasive Computing Applications for the Edlerly
2003
The RERC Center on Aging at the University of Florida is dedicated to creating smart environments and assistants to enable elderly persons to live a longer and a more independent life at home. By achieving this goal, technology will increase the chances of successful aging despite an ailing Health Care system (e.g. Medicaid). One of the essential services required to maximize the intelligence of a smart environment is an indoor precision tracking system. Such system allows the smart home to make proactive decisions to better serve its occupants by enabling context-awareness instead of being solely reactive to their commands. This paper presents our hands-on experience and lessons learnt from our first phase work to build up a smart home infrastructure for the elderly. We review location tracking technology and describe the rational behind our choice of the emerging ultrasonic sensor technology. We give an overview of the House of Matilda (an in-laboratory mock up house) and describe our design of a precision in-door tracking system. We also describe an OSGi-based robust framework that abstracts the ultrasonic technology into a standard service to enable the creation of tracking based applications by third party, and to facilitate the collaboration among various devices and other OSGi services. Finally, we describe three pervasive computing applications that use the location-tracking system, which we have implemented in Matilda's house.
Pervasive CSCW for smart spaces communities
2012 IEEE International Conference on Pervasive Computing and Communications Workshops, 2012
Future pervasive environments will take into consideration not only individual users' interest, but also social relationships. In today's scenarios, the trend is to make use of collective intelligence, where the interpretation of context information can be harnessed as input for pervasive systems. Therefore, social CSCW applications represent new challenges and possibilities in terms of use of group context information for adaptability and personalization in pervasive computing. The objective of this paper is to present two enterprise scenarios that support collaboration and adaption capabilities through pervasive communities combined with social computing. Collaborative applications integrated with pervasive communities can increase the activity's quality of the end user in a wide variety of tasks.