609 research outputs found
Development of Economic Water Usage Sensor and Cyber-Physical Systems Co-Simulation Platform for Home Energy Saving
In this thesis, two Cyber-Physical Systems (CPS) approaches were considered to reduce residential building energy consumption. First, a flow sensor was developed for residential gas and electric storage water heaters. The sensor utilizes unique temperature changes of tank inlet and outlet pipes upon water draw to provide occupant hot water usage. Post processing of measured pipe temperature data was able to detect water draw events. Conservation of energy was applied to heater pipes to determine relative internal water flow rate based on transient temperature measurements. Correlations between calculated flow and actual flow were significant at a 95% confidence level. Using this methodology, a CPS water heater controller can activate existing residential storage water heaters according to occupant hot water demand. The second CPS approach integrated an open-source building simulation tool, EnergyPlus, into a CPS simulation platform developed by the National Institute of Standards and Technology (NIST). The NIST platform utilizes the High Level Architecture (HLA) co-simulation protocol for logical timing control and data communication. By modifying existing EnergyPlus co-simulation capabilities, NIST’s open-source platform was able to execute an uninterrupted simulation between a residential house in EnergyPlus and an externally connected thermostat controller. The developed EnergyPlus wrapper for HLA co-simulation can allow active replacement of traditional real-time data collection for building CPS development. As such, occupant sensors and simple home CPS product can allow greater residential participation in energy saving practices, saving up to 33% on home energy consumption nationally
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 user’s lighting habits, taking into account different environmental context variables and user needs in order to automatically learn about the user’s 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 user’s lighting habits. We employs a learning model known as the Reduced Error Prune Tree (REPTree) to analyze the users’ preferences, and subsequently predicts the preferred lighting condition to be actuated in real time through Philips Hue. The system is able to ensure the user’s comfort at all time by performing a closed feedback control loop which checks and maintains a suitable lighting ambience at optimal level
Low-Cost Architecture for an Advanced Smart Shower System Using Internet of Things Platform
Wastage of water is a critical issue amongst the various global crises. This
paper proposes an architecture model for a low-cost, energy efficient SMART
Shower system that is ideal for efficient water management and be able to
predict reliably any accidental fall in the shower space. The sensors in this
prototype can document the surrounding temperature and humidity in real time
and thereby circulate the ideal temperature of water for its patron, rather
than its reliance on predictive values . Three different scenarios are
discussed that can allow reliably predicting any accidental fall in the shower
vicinity. Motion sensors, sound sensors and gesture sensors can be used to
compliment prediction of possible injuries in the shower. The integration with
the Internet of Things (IoT) platform will allow caretakers to monitor the
activities in the shower space especially in the case of elderly individuals as
there have been reported cases of casualties in the slippery shower space. The
proposed proof-of-concept prototype is cost effective and can be incorporated
into an existing system for the added precedence of safety and convenience. The
intelligent system is conserving water by optimizing its flow temperature and
the IoT platform allows real time monitoring for safety
Probing IoT-based consumer services: ‘Insights’ from the Connected Shower
This paper presents findings from the deployment of a technology probe – the
connected shower – and implications for the development of ‘living services’ or autonomous
context-aware consumer-oriented IoT services that exploit sensing to gain consumer ‘insight’ and
drive personalised service innovation. It contributes to the literature on water sustainability and the
potential role and barriers to the adoption of smart showers in domestic life. It also contributes to
our understanding of context, which enables user activity to be discriminated and elaborated
thereby furnishing the ‘insight’ living services require for their successful operation.
Problematically, however, our study shows that context is not a property of sensor data. Rather
than provide contextual insights into showering, the sensor data requires contextualisation to
discriminate and elaborate user activity. Thus, in addition to examining the potential of the
connected shower in everyday life, we consider how sensor data is contextualised through the
doing of data work and the relevance of its interactional accomplishment and organisation to the
design of living services
IoTBeholder: A Privacy Snooping Attack on User Habitual Behaviors from Smart Home Wi-Fi Traffic
With the deployment of a growing number of smart home IoT devices, privacy leakage has become a growing concern. Prior work on privacy-invasive device localization, classification, and activity identification have proven the existence of various privacy leakage risks in smart home environments. However, they only demonstrate limited threats in real world due to many impractical assumptions, such as having privileged access to the user's home network. In this paper, we identify a new end-to-end attack surface using IoTBeholder, a system that performs device localization, classification, and user activity identification. IoTBeholder can be easily run and replicated on commercial off-the-shelf (COTS) devices such as mobile phones or personal computers, enabling attackers to infer user's habitual behaviors from smart home Wi-Fi traffic alone. We set up a testbed with 23 IoT devices for evaluation in the real world. The result shows that IoTBeholder has good device classification and device activity identification performance. In addition, IoTBeholder can infer the users' habitual behaviors and automation rules with high accuracy and interpretability. It can even accurately predict the users' future actions, highlighting a significant threat to user privacy that IoT vendors and users should highly concern
Active aging in place supported by caregiver-centered modular low-cost platform
Aging in place happens when people age in the residence of their choice, usually their homes because
is their preference for living as long as possible. This research work is focused on the
conceptualization and implementation of a platform to support active aging in place with a particular
focus on the caregivers and their requirements to accomplish their tasks with comfort and supervision.
An engagement dimension is also a plus provided by the platform since it supports modules to make
people react to challenges, stimulating them to be naturally more active. The platform is supported
by IoT, using low-cost technology to increment the platform modularly. Is a modular platform capable
of responding to specific needs of seniors aging in place and their caregivers, obtaining data regarding
the person under supervision, as well as providing conditions for constant and more effective
monitoring, through modules and tools that support decision making and tasks realization for active
living. The constant monitoring allows knowing the routine of daily activities of the senior. The use
of machine learning techniques allows the platform to identify, in real-time, situations of potential
risk, allowing to trigger triage processes with the older adult, and consequently trigger the necessary
actions so that the caregiver can intervene in useful time.O envelhecimento no local acontece quando as pessoas envelhecem na residência da sua escolha,
geralmente nas suas próprias casas porque é a sua preferência para viver o máximo de tempo possÃvel.
Este trabalho de investigação foca-se na conceptualização e implementação de uma plataforma de
apoio ao envelhecimento ativo no local, com particular enfoque nos cuidadores e nas suas
necessidades para cumprir as suas tarefas com conforto e supervisão. Uma dimensão de engajamento
também é um diferencial da plataforma, pois esta integra módulos de desafios para fazer as pessoas
reagirem aos mesmos, estimulando-as a serem naturalmente mais ativas. A plataforma é suportada
por IoT, utilizando tecnologia de baixo custo para incrementar a plataforma de forma modular. É uma
plataforma modular capaz de responder à s necessidades especÃficas do envelhecimento dos idosos no
local e dos seus cuidadores, obtendo dados relativos à pessoa sob supervisão, bem como fornecendo
condições para um acompanhamento constante e mais eficaz, através de módulos e ferramentas que
apoiam a tomada de decisões e realização de tarefas para a vida ativa. A monitorização constante
permite conhecer a rotina das atividades diárias do idoso, permitindo que, com a utilização de técnicas
de machine learning, a plataforma seja capaz de detetar em tempo real situações de risco potencial,
permitindo desencadear um processo de triagem junto do idoso, e consequentemente despoletar as
ações necessárias para que o prestador de cuidados possa intervir em tempo útil
Advancing Data Collection, Management, and Analysis for Quantifying Residential Water Use via Low Cost, Open Source, Smart Metering Infrastructure
Urbanization, climate change, aging infrastructure, and the cost of delivering water to residential customers make it vital that we achieve a higher efficiency in the management of urban water resources. Understanding how water is used at the household level is vital for this objective.Water meters measure water use for billing purposes, commonly at a monthly, or coarser temporal resolutions. This is insufficient to understand where water is used (i.e., the distribution of water use across different fixtures like toilets, showers, outdoor irrigation), when water is used (i.e., identifying peaks of consumption, instantaneous or at hourly, daily, weekly intervals), the efficiency of water using fixtures, or water use behaviors across different households. Most smart meters available today are not capable of collecting data at the temporal resolutions needed to fully characterize residential water use, and managing this data represents a challenge given the rapidly increasing volume of data generated. The research in this dissertation presents low cost, open source cyberinfrastructure (datalogging and data management systems) to collect and manage high temporal resolution, residential water use data. Performance testing of the cyberinfrastructure demonstrated the scalability of the system to multiple hundreds of simultaneous data collection devices. Using this cyberinfrastructure, we conducted a case study application in the cities of Logan and Providence, Utah where we found significant variability in the temporal distribution, timing, and volumes of indoor water use. This variability can impact the design of water conservation programs, estimations and forecast of water demand, and sizing of future water infrastructure. Outdoor water use was the largest component of residential water use, yet homeowners were not significantly overwatering their landscapes. Opportunities to improve the efficiency of water using fixtures and to conserve water by promoting behavior changes exist among participants
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