3,579 research outputs found
Recommended from our members
Energy Demand Shifting in Residential Households: The Interdependence between Social Practices and Technology Design
Emerging energy technologies, such as smart meters and solar photovoltaic systems (solar PV), are changing our relationship to energy. There is increasing evidence that households with solar PV on their roof tend naturally to shift their energy consumption in time to match their local generation, but what do people actually do to achieve this and how ICT can support them to optimize their consumption? In this paper we present a year-long user study to understand social practices around laundry routines and local energy generation. We highlight four challenges for the next generation of home energy management systems
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
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
DeePLT: Personalized Lighting Facilitates by Trajectory Prediction of Recognized Residents in the Smart Home
In recent years, the intelligence of various parts of the home has become one
of the essential features of any modern home. One of these parts is the
intelligence lighting system that personalizes the light for each person. This
paper proposes an intelligent system based on machine learning that
personalizes lighting in the instant future location of a recognized user,
inferred by trajectory prediction. Our proposed system consists of the
following modules: (I) human detection to detect and localize the person in
each given video frame, (II) face recognition to identify the detected person,
(III) human tracking to track the person in the sequence of video frames and
(IV) trajectory prediction to forecast the future location of the user in the
environment using Inverse Reinforcement Learning. The proposed method provides
a unique profile for each person, including specifications, face images, and
custom lighting settings. This profile is used in the lighting adjustment
process. Unlike other methods that consider constant lighting for every person,
our system can apply each 'person's desired lighting in terms of color and
light intensity without direct user intervention. Therefore, the lighting is
adjusted with higher speed and better efficiency. In addition, the predicted
trajectory path makes the proposed system apply the desired lighting, creating
more pleasant and comfortable conditions for the home residents. In the
experimental results, the system applied the desired lighting in an average
time of 1.4 seconds from the moment of entry, as well as a performance of
22.1mAp in human detection, 95.12% accuracy in face recognition, 93.3% MDP in
human tracking, and 10.80 MinADE20, 18.55 MinFDE20, 15.8 MinADE5 and 30.50
MinFDE5 in trajectory prediction
Home Energy Management System and Internet of Things: Current Trends and Way Forward
Managing energy in the residential areas has becoming essential with the aim of cost saving, to realize a practical approach of home energy management system (HEMS) in the area of heterogeneous Internet-of-Thing (IoT) devices. The devices are currently developed in different standards and protocols. Integration of these devices in the same HEMS is an issue, and many systems were proposed to integrate them efficiently. However, implementing new systems will incur high capital cost. This work aims to conduct a review on recent HEMS studies towards achieving the same objectives: energy efficiency, energy saving, reduce energy cost, reduce peak to average ratio, and maximizing user's comfort. Potential research directions and discussion on current issues and challenges in HEMS implementation are also provided
Design and implementation of an open framework for ubiquitous carbon footprint calculator applications
As climate change is becoming an important global issue, more and more people are beginning to pay attention to reducing greenhouse gas emissions. To measure personal or household carbon dioxide emission, there are already plenty of carbon footprint calculators available on the web. Most of these calculators use quantitative models to estimate carbon emission caused by a user\u27s activities. Although these calculators can promote public awareness regarding carbon emission due to an individual\u27s behavior, there are concerns about the consistency and transparency of these existing CO2 calculators. Apart from a small group of smart phone based carbon footprint calculator applications, most of the existing CO2 calculators require users to input data manually. This not only provides a poor user experience but also makes the calculation less accurate. The use of a standard framework for various carbon footprint application developments can increase the accuracy of overall calculations, which in turn may increase energy awareness at the individual human level. We aim for developing a carbon footprint calculation framework that can serve as a platform for various carbon footprint calculator applications. Therefore, in this paper, we propose a platform-agnostic Open Carbon Footprint Framework (OCFF) that will provide the necessary interfaces for software developers to incorporate the latest scientific knowledge regarding climate change into their applications. OCFF will maintain a clouded knowledge base that will give developers access to a dynamic source of computational information that can be brought to bear on real-time sensor data. Based on the OCFF platform, we developed a Ubiquitous Carbon Footprint Calculator application (UCFC) that allows the user to be aware of their personal carbon footprint based on their ubiquitous activity and act accordingly. The major contribution of this paper is the presentation of the quantitative model of the platform along with the entire design and implementation of UCFC application. We also present the results, analysis, and findings of an extensive survey that has been conducted to find users’ awareness of increased carbon footprint, feature requirements, and expectations and desires to alleviate CO2 emissions by using a footprint calculator. The design of UCFC application incorporates the analysis and inferences of the survey results. We are also developing a fuel efficient mobile GPS application for iPhone suggesting the greenest/most fuel efficient route to the user. In this paper, we also point out some important features of such an application
- …