Future Smart Home Research Articles

Flexible smart home design: Case study to design future smart home prototypes

Most Smart home literature is not realistic since technologies are implemented at the end of the design process without taking into account the working, physical, and spatial aspects. No comprehensive work has been done regarding spatial design modifications accommodating the lifestyle requirement of Smart Home inhabitants, especially in Sulaymaniyah, Iraq. To fill the knowledge gap the research establishes practical guidelines for designing- Flexible Smart Home Models based on holistic literature review and case study of design preferences using virtual reality experiment and a questionnaire survey. Results indicated higher technology acceptance and flexible spatial integration of public and private zones by younger sample (75 m2 prototype) compared to the older sample with 115 m2 prototype and less versatile space. It was concluded that the opportunity to change scale, form, relation, and configuration becomes simpler, quicker with more possibilities of arranging spaces in smart homes with the help of the hypothetical prototypes.

Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning.

Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.

SMARS: Sleep Monitoring via Ambient Radio Signals

We present the model, design, and implementation of SMARS, the first practical Sleep Monitoring system that exploits Ambient Radio Signals to recognize sleep stages and assess sleep quality. This will enable a future smart home that monitors daily sleep in a ubiquitous, non-invasive and contactless manner, without instrumenting the subject's body or the bed. The key enabler underlying SMARS is a statistical model that accounts for all reflecting and scattering multipaths, allowing highly accurate and instantaneous breathing estimation with best-ever performance achieved on commodity devices. On this basis, SMARS then recognizes different sleep stages, including wake, rapid eye movement (REM), and non-REM (NREM), which was previously only possible with dedicated hardware. We implement a real-time system on commercial WiFi chipsets and deploy it in 6 homes, resulting in 32 nights of data in total. Our results demonstrate that SMARS yields a median absolute error of 0.47 breaths per minute (BPM) and a 95 percent-tile error of only 2.92 BPM for breathing estimation, and detects breathing robustly even when a person is 10 meters away from the link, or behind a wall. SMARS achieves a sleep staging accuracy of 88 percent, outperforming the prevalent unobtrusive commodity solutions using bed sensor or UWB radar. The performance is also validated upon a public sleep dataset of 20 patients. By achieving promising results with merely a single commodity RF link, we believe that SMARS will set the stage for a practical in-home sleep monitoring solution.

Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications

The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable electronics. Gait reveals sensory information in daily life containing personal information, regarding identification and healthcare. Current wearable electronics of gait analysis are mainly limited by high fabrication cost, operation energy consumption, or inferior analysis methods, which barely involve machine learning or implement nonoptimal models that require massive datasets for training. Herein, we developed low-cost triboelectric intelligent socks for harvesting waste energy from low-frequency body motions to transmit wireless sensory data. The sock equipped with self-powered functionality also can be used as wearable sensors to deliver information, regarding the identity, health status, and activity of the users. To further address the issue of ineffective analysis methods, an optimized deep learning model with an end-to-end structure on the socks signals for the gait analysis is proposed, which produces a 93.54% identification accuracy of 13 participants and detects five different human activities with 96.67% accuracy. Toward practical application, we map the physical signals collected through the socks in the virtual space to establish a digital human system for sports monitoring, healthcare, identification, and future smart home applications.

Smart home design flexibility as an enabler of sustainability and the reception in Sulaiymaniah, Kurdistan Region-Iraq

Technological progress has become a key aspect of our well-being and impacts both our daily life and everyone’s use of architectural space. The recent decade has witnessed a rapid evolution of technology which paved the way for smart interior design solutions and stimulated the environment for designers. Studying the effect of integrating smart solutions into aspects of flexible and sustainable interior design. In general, one of the most important ways to achieve Sustainability is to develop the space flexibility by the integration of home automation technology into future living spaces to ensure more efficient use of space (adaptable buildings are likely to use the same amount of space and materials more efficiently, on average, over their entire life). Smart homes have different goals and end-users. A smart home has to be a house and a home to stay, but as the way of living changes within the house, certain changes in space are needed to satisfy the new lifestyle of the occupants. This study explores how flexible space can improve the sustainability of interior design by using Smart Technology and understand the level of acceptance of smart home technology and the design preferences by the people of Sulaymaniyah. A questionnaire survey was established based on the literature review of principles of flexibility, sustainability, and smart technology in interior design, collected raw data and perform statistical analysis to understand whether or not Smart home technology is suitable for the region and what would be the design preferences of people to make it more welcomed in the housing sector as a solution to attain more flexible and sustainable buildings. Results explained that there is a market for Smart Homes in the region of Sulaymaniyah with some modifications in the concepts of smart homes design. The younger group of samples is more accepting of the concept while the older group is also accepting, however, is not entirely comfortable with the complete integration of public and private zones of the future Smart homes.

Control framework for cooperative robots in smart home using bio-inspired neural network

In this paper, we present a model-free tracking controller for a cooperative mobile-manipulators, which are the cornerstone for future smart homes. The mobile-manipulators are common in industries as they are flexible and mobile, and there are several control frameworks for their industrial applications. However, these control algorithms do not consider the unique complex environment of a smart home, which requires a specialized and customized control. For example, elderly care robots in smart homes are not monitored by skilled users in that particular field, as the goals are different in the home environment, so the controller should also be different. The paper is centered around the tracking control problem of the mobile-manipulators to perform tasks individually and cooperatively. We used an optimization-driven approach to formulate an optimization problem that includes our set goals. We then used a zeroing neural network with beetle antennae search (ZNNBAS) algorithm to solve it. ZNNBAS is a nature-inspired metaheuristic algorithm inspired by the food searching nature of the beetles. It also incorporates the parallel-processing capability of ZNN, which further enhances the computational power and efficiency of the system. The paper also deals with the theoretical analysis of ZNNBAS and proves that it is stable and convergent. To test our algorithm, we used a simulated model of IIWA14 (KUKA LBR), mounted on the P3-DX mobile platform. We used two mobile-manipulators and tested them in the following scenarios: (1) Robots working independently, (2) Robots working cooperatively. The simulation results show that ZNNBAS was able to accurately and robustly complete the assigned tasks.

RFID AC Current Sensing Technique

The mechanism is described by which a passive UHF RFID tag coupled with a tuning circuit is integrated with a current transformer for sensing ac current in an electrical wire for smart power monitoring of individual appliances. A capacitance change in the tuning circuit results from a reverse bias voltage from the current transformer. The tuning circuit reactance is detected by a capacitance sensing RFID tag and the value is transmitted as a 5–bit sensor code which is directly related to the ac current drawn by an electrical load. The passive tag harvests energy and offers an innovative solution for energy management in future smart homes and for industry 4.0. As well as indicating current level, the technique can also be used to detect the ON and OFF state of an electrical device and is demonstrated to work for a rapidly switching load. The sensor is tuned for EPC Class 1 Generation 2 UHF RFID readers at 868 MHz.

Home energy management system based on task classification and the resident’s requirements

In this paper, an approach for home energy management system is introduced that is based on task classification. The problem is to find the best task activation plan regarding the resident’s requirements and appliances constraints, considering time of use pricing. We show that to have a best task activation plan it is necessary to specify four specific values for each task. In this regard, a quadratic utility function is derived based on the law of diminishing marginal utility in microeconomic. With the parameters such as sensitivity index and monetary equivalent value, each task’s utility function can be configured in accordance to the resident’s personal preferences. In this model, the concept of tasks interaction are also taken into account, for the first time. The problem is transformed to a mixed integer nonlinear programming problem, so that the available commercial solvers can successfully solve it in an acceptable solving time. The outputs of numerical examples show that they are reasonable and can be considered as optimal or near optimal results. The results indicate that the less sensitive the resident is to changing his/her desire task parameters, the more profit he/she will get. It is also shown that by increasing the monetary equivalent value, the task activation is shifted toward the times that are more preferable or has more favorite environment conditions. These results as well as the acceptable solving times, show that the proposed approach can be a promising model for home energy management systems in the future smart homes.

Realistic and Intelligent Management of Connected Storage Devices in Future Smart Homes Considering Energy Price Tag

Coordinated energy management plays a main role in increasing the performance and economic benefits of future smart homes. This article focuses on the energy management of a smart home equipped with a plug-in electric vehicle (PEV), household battery storage, and photovoltaics (PV), and it proposes an energy price tag (EPT) for all energy storage devices connected to the smart home system. A hybrid approach that consists of optimization and a rule-based prioritization is presented. The proposed algorithm establishes a priority order between the PEV, the household battery, and the imported power from the grid based on the EPT of energy sources. The proposed energy management algorithm seeks for the minimum overall energy cost for the smart home and PEV owner while satisfying household power demand and charging requirements of the storage devices. The performance of the energy management algorithm is examined on a typical smart home over a 24-h period based on time-varying grid electricity price.

Emerging Smart Home Technologies to Facilitate Engaging With Aging.

Engaging with aging (EWA), the ongoing process of an individual to identify resources and implement adaptive strategies to maximize quality of life, may be enhanced by advances in information technology and adaptive upgrades to the built environment. Smart home technologies (SmHT) introduce passive monitoring features into the residential infrastructure to promote older adults' ability to manage day-to-day living and age in place. This article provides an overview of current and emerging SmHT and discusses opportunities to leverage this technology for enhancing the capacity of older adults to engage with their own aging. There are opportunities to create smart homes that enhance physical and cognitive capacity for older adults, but there are also ethical and practical challenges that will inform the design of future smart home systems. [Journal of Gerontological Nursing, 45(12), 41-48.].

A-SEM: An adaptive smart energy management testbed for shiftable loads optimisation in the smart home

Managing the increment in energy demand can be solved by involving the demand side in order to increase energy consumption efficiency. However, increasing energy consumption efficiency tends to influence the user level of comfort. A research gap exists in the study of the energy efficiency and user comfort trade-off and in particular in providing low cost testbeds to study this phenomenon. This research discusses a low-cost testbed hardware design and the potential of the proposed Adaptive-Smart Energy Management Tool (A-SEM tool) to balance the level of comfort and energy consumption efficiency. We implement an adaptive energy limitation algorithm which uses up to 30 days historical data. The energy consumption is influenced by user behaviour which is monitored by the system sensors and energy limits are adapted for the provision of comfort. A smart home user is able to set the monthly energy consumption budget which determines the initial level of daily energy limitation. A-SEM performs real time monitoring and controlling, mainly considering shiftable loads evaluated at the testing stage. We test 3 possible conditions (possible modes of operation) and prove that our “adaptive limit” energy limitation algorithm is the most successful in balancing the level of comfort and energy efficiency. For a fixed budget and energy price, the proposed adaptive approach meets user level of comfort (our main priority) as well as achieving some energy savings. In our test the A-SEM tool provides user comfort, meets the monthly budget constraint and yet shows an energy saving of 2.62% which can increase or decrease depending on user behaviour. We present results showing energy saving levels, comfort levels and efficiency levels. The proposed A-SEM tool is low cost, implements an uncomplicated adaptive algorithm and therefore has the potential to be an affordable smart energy management system in future smart homes.

PERCEPTIONS, ACCEPTABILITY, EXPECTATIONS, AND CONCERNS OF SMART HOME TECHNOLOGIES AMONG OLDER ADULTS

Optimal aging in place has become a common preference among older adults to maintain identity and independence, thus smart home technologies are increasingly utilized to achieve these goals. However, disconnect may exist between potential technological benefit and perceptions of acceptability and usability (Lee & Coughlin, 2015). We assessed perceptions of adults aged 50+ (range 50-90 years) to analyze their priorities and ultimate acceptability of smart home technology. Data were collected through surveys, focus groups, and case study interviews. Three major themes emerged regarding smart home utilization: benefits, concerns, and expectations. Participants endorsed smart home technologies (e.g., sensors, telehealth devices) and identified benefits, such as the promotion of optimal aging (e.g., maintaining independence, staying active, safety). However, responses also reflected concerns about privacy, ease of use, and amount of control. Expectations regarding smart homes included more mobility, efficiency, and safety within the home. One participant described technology as having “options [that] are exhausting, but also exciting.” Survey responses (n=30) were analyzed to understand participants’ familiarity with smart home technologies, including: nanotechnology (10.7%), smart showers (42.9%), home sensors (70.4%), telehealth (74.1%), smart appliances (71.4%), personal sensors (81.5%), and voice-activated devices (96.4%). Additionally, respondents indicated their willingness to implement these technologies to maintain and/or improve their daily functioning: nanotechnology (53.8%), smart showers (28.6%), home sensors (66.6%), telehealth (81.5%), smart appliances (40.0%), personal sensors (55.5%), voice-activated devices (64.3%). Discussion focuses on the priorities and needs older adults express regarding technology utilization and the implications for person-centered design and implementation of future smart home technologies.

The Influence of Spatial Flexibility to improve Sustainability of Interior Design by Using Smart Technology (Case study –Future Smart home in Iraq)

Technological progress is an endless accelerating phenomenon. It becomes a major aspect of our wellbeing and influences our everyday life as well as our use of architectural spaces. A professional career in Interior Design is more challenging today than ever. It combines the creative, the aesthetic, the innovative, and the practical to create the important stages upon which people live their lives. This pragmatic approach found a considerable positive impact on enhancing overall building flexibility and sustainability by using smart technology. Literatures displayed that both terms –flexibility and sustainability -are used almost to the same extent. In general, developing space flexibility is one of the main ways to achieve Sustainability to ensure efficient space utilization. The objectives of this research is to explain how smart interior design can improve spatial flexibility of inner spaces and to evaluate whether flexibility had a positive impacts toward sustainability (case study: Iraqi houses). A literature review in the disciplines of, smart home technology, spatial flexibility and Interior design sustainability have been done, then we used virtual reality methods and self –assessment practical tools to improve better understanding and to test hypothesis. Results explained that smart design improve spatial flexibility by and ensure efficient space utilization (sustainable inner spaces).Keywords: Flexibility, Sustainability, Smart Technology, Smart Interior Design, Smart Homes

Filtering antennas for energy harvesting in wearable systems

AbstractWearable systems are important for future smart home care for the disabled and ageing population. One of the key challenges is the power supply. One solution is to develop an energy harvesting system suitable for providing power wirelessly. The matching network and filter in traditional energy harvesting system occupy lots of space, introduce additional losses, and reduce the total efficiency. This paper presents a novel filtering antenna suitable for wearable energy harvesting systems. The developed antenna has a broadband operation, harmonics suppression, compact size, and low cost. Firstly, a novel technique for broadband filtering antenna modeling and design in order to realize complex input impedance is presented. By adjusting the external quality factors and coupling coefficients of the resonators, complex impedance matching can be realized. Then, the theory of characteristic mode is used for the modal analysis of the filtering antenna. To verify the design concept, a C‐band circularly polarized filtering antenna with complex impedance is designed and fabricated. The results show that the impedance bandwidth is more than 14.5% with a peak gain higher than 8.5 dBi. The proposed antenna is conjugate matched to the rectifying circuit at 5.8 GHz and mismatched at the second harmonic frequency to suppress the harmonic reradiation, leading to a compact rectenna structure.

Exploring the Efficacy of Sparse, General-Purpose Sensor Constellations for Wide-Area Activity Sensing

Future smart homes, offices, stores and many other environments will increasingly be monitored by distributed sensors, supporting rich, context-sensitive applications. There are two opposing instrumentation approaches. On one end is full sensor saturation, where every object of interest is tagged with a sensor. On the other end, we can imagine a hypothetical, omniscient sensor capable of detecting events throughout an entire building from one location. Neither approach is currently practical, and thus we explore the middle ground between these two extremes: a sparse constellation of sensors working together to provide the benefits of full saturation, but without the social, aesthetic, maintenance and financial drawbacks. More specifically, we target a density of one sensor per room (and less), which means the average home could achieve full coverage with perhaps ten sensors. We quantify and characterize the performance of sparse sensor constellations through deployments across three environments and 67 unique activities. Our results illuminate accuracy implications across key spatial configurations important for enabling more practical, wide-area activity sensing.

A Data-Centric Cognitive Gateway with Distributed MIMO for Future Smart Homes

Gateways play very important roles in smart homes. With gateways that can effectively separate applications and physical devices, the smart home architecture can be simplified. In the future, when more and more wireless devices are deployed in smart homes, gateways are facing critical challenges to accommodate the variety and heterogeneity of devices while maintaining enough simplicity, full interoperability, high energy and spectrum efficiency, and uncompromised system security. In this article, we first study the-state-of-the-art techniques and solutions for smart home infrastructure and present both their advantages and disadvantages. Then we propose a data-centric cognitive gateway, which is simple, efficient, and secure for smart homes. In addition, we investigate the trade-off among deployment cost, routine maintenance overhead, and system security. Our experiments verify the efficiency of our proposed solutions.

A New Layered Architecture for Future Big Data-Driven Smart Homes

In this paper, a new layered architecture is proposed for big data-driven processing and management of future smart homes. The proposed Representational State Transfer (REST)-based architecture includes seven layers: physical, fog-computing, network, cloud-computing, service, session, and application for efficient data exchange and processing tasks of future smart homes. The smart home physical layer includes all the sensing technologies and smart devices within the smart home, which monitors the home environment and its residents. The data of these sensors will be sent to the smart home fog-computing layer that can do limited data storage and processing. Then, all the required data will be sent to the cloud-computing layer using smart home network layer. The cloud-computing layer provides the scalable solution for data processing and storage. The processed data in the cloud-computing layer will be provided as the data-driven services to different smart home and third-party (e.g., smart city) applications via smart home service layer. Based on the proposed architecture, the applications will utilize the session layer and RESTFUL APIs to use the data-driven services of the smart home. The proposed smart home architecture can provide a ubiquitous and shared data environment as the key aspect of Internet-of-Things systems.

A Security Protocol for Route Optimization in DMM-Based Smart Home IoT Networks

Smart home networks have been recognized as one of its representative important applications in the forthcoming 5G era. It is also expected that in 5G networks, future smart home services will be much powered by mobility management, which enables users to remotely access and control their in-home Internet of Things (IoT) sensors and appliances anywhere anytime any device. As a major solution, Distributed IP Mobility Management (DMM) can be considered because it addresses the limitation of the centralized approaches as well as its flat architecture is suit for 5G networks. Obviously, without being protected, mobility management can cause smart home systems to be vulnerable to various security threats. Especially, it is of paramount important to protect data traffic transmitted between user mobile devices and their in-home IoT appliances because they include users' sensitive and critical privacy information. Taking this into consideration, it is necessary to support secure route optimization, which allows the involved devices to directly communicate each other in secure way while minimizing possibility of information leakage during data transmission. According to our best knowledge, there is no study on securing route optimization for DMM networks. Motivated by this, we propose a secure route optimization protocol for DMM-based smart home systems. The proposed security protocol, composed of the route optimization initialization and handover phases, is designed to provide mutual authentication, key exchange, perfect forward secrecy, and privacy protection. Its security is thoroughly verified through the two formal security analysis tools, BAN-logic and Automated Validation of Internet Security Protocols and Applications (AVISPA). From the comparison analysis, it is shown that the proposed protocol is better than other standard protocols.

EH-HL: Effective Communication Model by Integrated EH-WSN and Hybrid LiFi/WiFi for IoT

Technological advances over the last decade in the field of wireless communications have resulted in the improvement of small and low cost sensor nodes outfitted with wireless communication abilities capable of establishing wireless sensor network (WSN). Due to the expansion of Internet of Things (IoT), there are many areas in IoT application where WSN applications are found. These applications generally impose severe constraints on the lifetime of the WSN, which is expected to last several years. It is necessary to diminish the overall energy consumption of the sensor node and to find an additional source of energy for achieving this objective. On the other hand, due to the imminent crisis of the radio frequency spectrum, light fidelity (LiFi) offers many key benefits and effective solutions for these issues that have been postured in the most recent decade. In this paper, we propose a novel EH-HL model for future smart homes and industries based on the integration of energy harvesting WSN (EH-WSN) and hybrid LiFi/WiFi communication techniques. The proposed model is capable of efficiently transmitting data at high speed for bidirectional multidevice and by harvesting energy, we provide the power to the sensor nodes. To synchronize multidevice transmissions, transmit data and provide low-cost wireless communication, we used the color beams of the red, green, and blue LEDs. The result of the evaluation shows that the hybrid communication scheme is proposed in the EH-HL model. It also offers superior performance and achieves a data rate of 25 Mb/s for multiaccess/multiusers.