Smart Home Sensor Research Articles

Urban planning and building smart cities based on the Internet of Things using Big Data analytics

The rapid growth in the population density in urban cities demands that services and an infrastructure be provided to meet the needs of city inhabitants. Thus, there has been an increase in the request for embedded devices, such as sensors, actuators, and smartphones, leading to considerable business potential for the new era of the Internet of Things (IoT), in which all devices are capable of interconnecting and communicating with each other over the Internet. Thus, Internet technologies provide a way of integrating and sharing a common communication medium. With this knowledge, in this paper, we propose a combined IoT-based system for smart city development and urban planning using Big Data analytics. We propose a complete system consisting of various types of sensor deployment, including smart home sensors, vehicular networking, weather and water sensors, smart parking sensors, and surveillance objects. A four-tier architecture is proposed that includes 1) Bottom tier-1, which is responsible for IoT sources and data generation and collection, 2) Intermediate tier-1, which is responsible for all types of communication between, for instance, sensors, relays, base stations, and the Internet, 3) Intermediate tier 2, which is responsible for data management and processing using a Hadoop framework, and 4) Top tier, which is responsible for application and usage of the data analysis and the results generated. The system implementation consists of various steps that begin with data generation and move to collection, aggregation, filtration, classification, preprocessing, computing and decision making. The proposed system is implemented using Hadoop with Spark, voltDB, Storm or S4 for real time processing of the IoT data to generate results to establish the smart city. For urban planning or city future development, the offline historical data are analyzed with Hadoop using MapReduce programming. IoT datasets generated by smart homes, smart parking weather, pollution, and vehicle data sets are used for analysis and evaluation. This type of system with full functionality does not currently exist. Similarly, the results demonstrate that the proposed system is more scalable and efficient than existing systems. Moreover, system efficiency is measured in terms of throughput and processing time.

Modeling patterns of activities using activity curves

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve, which represents an abstraction of an individual’s normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics.

Automated Cognitive Health Assessment From Smart Home-Based Behavior Data.

Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behavior in the home and predicting clinical scores of the residents. To accomplish this goal, we propose a clinical assessment using activity behavior (CAAB) approach to model a smart home resident's daily behavior and predict the corresponding clinical scores. CAAB uses statistical features that describe characteristics of a resident's daily activity performance to train machine learning algorithms that predict the clinical scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years. We obtain a statistically significant correlation ( r=0.72) between CAAB-predicted and clinician-provided cognitive scores and a statistically significant correlation ( r=0.45) between CAAB-predicted and clinician-provided mobility scores. These prediction results suggest that it is feasible to predict clinical scores using smart home sensor data and learning-based data analysis.

Mobile agent-based cross-layer anomaly detection in smart home sensor networks using fuzzy logic

Despite the rapid advancements in consumer electronics, the data transmitted by sensing devices in a smart home environment are still vulnerable to anomalies due to node faults, transmission errors, or attacks. This affects the reliability of the received sensed data and may lead to the incorrect decision making at both local (i.e., smart home) and global (i.e., smart city) levels. This study introduces a novel mobile agent-based cross-layer anomaly detection scheme, which takes into account stochastic variability in cross-layer data obtained from received data packets, and defines fuzzy logic-based soft boundaries to characterize behavior of sensor nodes. This cross-layer design approach empowers the proposed scheme to detect both node and link anomalies, and also effectively transmits mobile agents by considering the communication link-state before transmission of the mobile agent. The proposed scheme is implemented on a real testbed and a modular application software is developed to manage the anomaly detection system in the smart home. The experimental results show that the proposed scheme detects cross-layer anomalies with high accuracy and considerably reduces the energy consumption caused by the mobile agent transmission in the poor communication link-state situations.

Smart Blueprints: How Simple Sensors Can Collaboratively Map Out Their Own Locations in the Home

Off-the-shelf home automation technology is making it easier than ever for people to convert their own homes into smart homes. However, manual configuration is tedious and error-prone. In this article, we present and compare a family of solutions that automatically generate a map of the home and the devices within it using data from the smart home sensors themselves (e.g., light and motion sensors). These solutions can be used to automatically configure home automation systems or to automatically produce an intuitive map-like interface for visualizing sensor data and interacting with controllers. We call our approach Smart Blueprints because it automatically maps out the unique configuration of each smart home. We demonstrate the Smart Blueprints using a variety of sensor combinations, including light sensors, motion sensors, and magnetometers deployed on the doors and/or windows of the home. For evaluation of each combination on sensor-map generation, we deployed more than 200 sensors in seven different houses at different locations and compared the ability to use a variety of techniques to map out the configuration. We show that, in almost all houses, our system can automatically narrow the configuration down to 1--5 candidates per home using only one week of collected data.

Design of smart home sensor visualizations for older adults.

Smart home sensor systems provide a valuable opportunity to continuously and unobtrusively monitor older adult wellness. However, the density of sensor data can be challenging to visualize, especially for an older adult consumer with distinct user needs. We describe the design of sensor visualizations informed by interviews with older adults. The goal of the visualizations is to present sensor activity data to an older adult consumer audience that supports both longitudinal detection of trends and on-demand display of activity details for any chosen day. The design process is grounded through participatory design with older adult interviews during a six-month pilot sensor study. Through a secondary analysis of interviews, we identified the visualization needs of older adults. We incorporated these needs with cognitive perceptual visualization guidelines and the emotional design principles of Norman to develop sensor visualizations. We present a design of sensor visualization that integrate both temporal and spatial components of information. The visualization supports longitudinal detection of trends while allowing the viewer to view activity within a specific date. Appropriately designed visualizations for older adults not only provide insight into health and wellness, but also are a valuable resource to promote engagement within care.

Design of smart home sensor visualizations for older adults.

Smart home sensor systems provide a valuable opportunity to continuously and unobtrusively monitor older adult wellness. However, the density of sensor data can be challenging to visualize, especially for an older adult consumer with distinct user needs. We describe the design of sensor visualizations informed by interviews with older adults. The goal of the visualizations is to present sensor activity data to an older adult consumer audience that supports both longitudinal detection of trends and on-demand display of activity details for any chosen day. The design process is grounded through participatory design with older adult interviews during a six-month pilot sensor study. Through a secondary analysis of interviews, we identified the visualization needs of older adults. We incorporated these needs with cognitive perceptual visualization guidelines and the emotional design principles of Norman to develop sensor visualizations. We present a design of sensor visualization that integrate both temporal and spatial components of information. The visualization supports longitudinal detection of trends while allowing the viewer to view activity within a specific date.CONCLUSIONS: Appropriately designed visualizations for older adults not only provide insight into health and wellness, but also are a valuable resource to promote engagement within care.

Sensor Node Design and Building of Multimedia Network in Smart Home

With the requirements of multimedia service increasing in people's life, sensor modules such as microphone, camera are added in the smart home's sensor network, and the acquisition and processing of a large amount of information media such as audio, image and video is becoming a significant characteristics of smart home. The paper focuses on solving the following technical problems: the building of Zigbee multimedia network, the Design and selection of multimedia sensor node. These provide the basic network platform and the core technical support for the building of smart home.

The Encyclopedia of elder care: the comprehensive resource on geriatric health and social care

This interdisciplinary clinical reference encompasses more than 310 current entries on a broad range of topics related to geriatrics and geriatric care across multiple health care disciplines. The third edition reflects the many advances in geriatrics that have occurred since the publication of the second edition in 2006. It contains the updated, evidence-based contributions of more than 260 nationally recognised geriatric healthcare professionals regarding elder-care concerns relating to society, community, caregiving, and the individual. Completely new entries address Geriatric Care Models (acute care for elders, Project BOOST), Geriatric Health Issues (diarrhea in adults, prolonged hospitalisation, frailty, fronto-temporal dementia, re-hospitalisation, acute urinary retention, personality and aging, primary palliative care), Technology and Aging (Smart home sensors, Telehealth, Surveillance technology), and more. The Encyclopedia is organised alphabetically and includes links to important Web-based resources and Apps. Clinical topics comprehensively address diagnosis, treatment, and disease management. Health care clinicians across the continuum will find this reference-the only one of its kind--to be a valuable guide to making appropriate referrals to social service providers, and social service professionals will be well informed by highly accessible descriptions of diagnoses, clinical syndromes, and care management.

Automated assessment of cognitive health using smart home technologies

The goal of this work is to develop intelligent systems to monitor the wellbeing of individuals in their home environments. This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve=0.80, g-mean=0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained.

Data reconciliation in a smart home sensor network

This paper describes a data-driven approach to sensor data validation. The data originates from a network of sensors embedded in an indoor environment such as an office, home, factory, public mall or airport. Data analysis is performed to automatically detect events and classify activities taking place within the environment. Sensor failure and in particular intermittent failure, caused by electrical interference, undermines the inference processes. PCA and CCA are compared for detecting intermittent faults and masking such failures. The fault detection relies on models built from historical data. As new sensor observations are collected the model is updated and compared to that previously estimated, where a difference is indicative of a failure.

MEMS Tornado to Challenge Next Generation Packaging

In the last decade multiple MEMS devices entered Tornado (avalanche sales growth), adopting along the way the IC packaging technologies with a “twist”. Consumer applications for MEMS devices exploded, with sensor shipments increasing from 10M units in 2007 to over 2B units in 2011. This explosion resulted from Steve Jobs vision converting a cell phone into a powerful computer. Computing power enabled creative user interfaces, such as touch sensitive screens and auto landscape-portrait rotation, opening a Tornado for MEMS. As more complex MEMS devices are finding its way to cell phones, they will be creating packaging challenges. An emergence of even a bigger forthcoming Tornado is surfacing.History shows that all major technologies coming to market enabled advancement of world-changing productivity and balance of power. At a recent conference, a VP of Maxim Integrated Products stated that we have entered the 3rd technical revolution which will free humans for creative work through a fusion of computing, communication and sensing. This will enable MEMS component market to catch up with the semiconductor market ($300B).At the 2010 MEMS Technology Summit conference at Stanford University, Bosch presented a vision for 7 trillion devices driven by Sensory Swarms connected to the Internet to serve 7 billion people in 2017 (in 2010 there were already 5 billion mobile phone subscribers). This vision translates to 1,000 sensors per average person in 2017. We are already seeing high end applications supported by large number of MEMS sensors in advanced cars, smart homes, smart phones and medical diagnostics, to mention a few.In 2010 HP outlined a vision for CeNSE, a Central Nervous System for the Earth, which expected to deploy a trillion nano sensors and actuators. Processing the sensor information will require increasing the size of Internet 1,000 times; creating huge demand for computing and generating by 2013 a $70B global market for sensing systems, and $290B market for value added sensing services. As device volumes start approaching a trillion units a year, packaging challenges will be created by:Complexity of emerging MEMS devices. The required “twists” to IC packaging will be in the areas such as low package stress, chemical and fluidic media compatibility, optical performance, etc.Cost pressure. Due to a sheer size of expected market, packaging industry may expect very large changes. Addressing these challenges will likely have a big impact on the packaging industry.

Genetic algorithm and pure random search for exosensor distribution optimisation

The positioning, amount(s) and field of view(s) of exosensors are a fundamental characteristic of a smart home environment. Contemporary smart home sensor distribution is aligned to either a a total coverage approach b a human assessment approach. These methods for sensor arrangement are not data driven strategies, are unempirical, and frequently irrational. Little research has been conducted in relation to optimal resource allocation in smart homes environments. This study aimed to generate globally optimal sensor distributions for a smart home replica-kitchen using two distinct methodologies, namely a genetic algorithm (GA) and a pure random search algorithm (PRS), to ascertain which method is appropriate for this task. GA outperformed PRS consistently, with a coverage percentage that encapsulated an average of 43.6% more inhabitant spatial frequency data. The results of this study indicate that GA provides more optimal solutions than PRS for exosensor distributions in a smart home environment.

Pure random search for ambient sensor distribution optimisation in a smart home environment

Smart homes are living spaces facilitated with technology to allow individuals to remain in their own homes for longer, rather than be institutionalised. Sensors are the fundamental physical layer with any smart home, as the data they generate is used to inform decision support systems, facilitating appropriate actuator actions. Positioning of sensors is therefore a fundamental characteristic of a smart home. Contemporary smart home sensor distribution is aligned to either a) a total coverage approach; b) a human assessment approach. These methods for sensor arrangement are not data driven strategies, are unempirical and frequently irrational. This Study hypothesised that sensor deployment directed by an optimisation method that utilises inhabitants' spatial frequency data as the search space, would produce more optimal sensor distributions vs. the current method of sensor deployment by engineers. Seven human engineers were tasked to create sensor distributions based on perceived utility for 9 deployment scenarios. A Pure Random Search (PRS) algorithm was then tasked to create matched sensor distributions. The PRS method produced superior distributions in 98.4% of test cases (n=64) against human engineer instructed deployments when the engineers had no access to the spatial frequency data, and in 92.0% of test cases (n=64) when engineers had full access to these data. These results thus confirmed the hypothesis.

Sensor selection to support practical use of health‐monitoring smart environments

AbstractThe data mining and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties in living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. One question that frequently arises, however, is how many smart home sensors are needed and where should they be placed in order to accurately recognize activities? We employ data mining techniques to look at the problem of sensor selection for activity recognition in smart homes. We analyze the results based on six datasets collected in five distinct smart home environments. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 339–351 DOI: 10.1002/widm.20This article is categorized under: Application Areas > Science and Technology

Efficient mobile sensor authentication in smart home and WPAN

Currently, it is rapidly increasing convergence services based on various mobile devices with sensors like Smart Home. Specifically the mobility of the sensors in Smart Home merged with wireless sensor networks (WSN) brings security issues such as re-authentication and tracing the node movement. We extend our novel and efficient node authentication and key exchange protocol that support Irregular distribution. Compared with previous protocols, our protocol has only a third of communication and computational overhead. We expect our protocol to be the efficient solution that increases the lifetime of sensor network.