28,374 research outputs found
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Personalized Ambience: An Integration of Learning Model and Intelligent Lighting Control
The number of households and offices adopting automation system is on the rise. Although devices and actuators can be controlled through wireless transmission, they are mostly static with preset schedules, or at different times it requires human intervention. This paper presents a smart ambience system that analyzes the userâs lighting habits, taking into account different environmental context variables and user needs in order to automatically learn about the userâs preferences and automate the room ambience dynamically. Context information is obtained from Yahoo Weather and environmental data pertaining to the room is collected via Cubesensors to study the userâs lighting habits. We employs a learning model known as the Reduced Error Prune Tree (REPTree) to analyze the usersâ preferences, and subsequently predicts the preferred lighting condition to be actuated in real time through Philips Hue. The system is able to ensure the userâs comfort at all time by performing a closed feedback control loop which checks and maintains a suitable lighting ambience at optimal level
The smart home in the mind and in the practice of digital natives. The case of âSapienzaâ University
Smart home e giovani: quale la percezione? La presente indagine pilota, effettuata da un gruppo di studiosi dellâUniversitaĚ Sapienza di Roma mira ad analizzarne i risultati, rappresentando una ricognizione essenziale di quello che eĚ lâuniverso dei giovani in relazione al mondo smart e alla domotica. LâAteneo Sapienza sposa appieno la sfida lanciata da Horizon 2020 con il progetto ReStart4Smart, un laboratorio pratico in cui poter conoscere e sperimentare, fare ricerca e innovare, condividere e divulgare, tanto problemi quanto, e piuĚ possibile, soluzioni ambientali ed abitative. Chi sono realmente i nativi digitali? E qual eĚ il loro livello di conoscenza della smart home? Quali i valori e quali i comportamenti concreti in relazione allâutilizzo intelligente delle nuove tecnologie
On the Integration of Adaptive and Interactive Robotic Smart Spaces
Š 2015 Mauro Dragone et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the userâs acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree â to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving usersâ needs,but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.Peer reviewe
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
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