A review of smart home applications based on Internet of Things

With the advancement of Information and Communication Technology (ICT) and the proliferation of sensor technologies, the Internet of Things (IoT) is now being widely used in smart home for the purposes of efficient resource management and pervasive sensing. In smart homes, various IoT devices are connected to each other, and these connections are centered on gateways. The role of gateways in the smart homes is significant, however, its centralized structure presents multiple security vulnerabilities such as integrity, certification, and availability. To address these security vulnerabilities, in this paper, we propose a blockchain-based smart home gateway network that counters possible attacks on the gateway of smart homes. The network consists of three layers including device, gateway, and cloud layers. The blockchain technology is employed at the gateway layer wherein data is stored and exchanged in the form blocks of blockchain to support decentralization and overcome the problem from traditional centralized architecture. The blockchain ensures the integrity of the data inside and outside of the smart home and provides availability through authentication and efficient communication between network members. We implemented the proposed network on the Ethereum blockchain technology and evaluated in terms of standard security measures including security response time and accuracy. The evaluation results demonstrate that the proposed security solutions outperforms over the existing solutions.

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A blockchain-based smart home gateway architecture for preventing data forgery

This paper presents an effective solution based on speech recognition to provide elderly people, patients and disabled people with an easy control system. The goal is to build a low-cost system based on speech recognition to easily access Internet of Things (IoT) devices installed in smart homes and hospitals without relying on a centralized supervisory system. The proposed system used a Raspberry Pi board to control home appliances through wireless with smartphones. The main purpose of this system is to facilitate interactions between the user and home appliances through IoT communications based on speech commands. The proposed framework contribution uses a hybrid Support Vector Machine (SVM) with a Dynamic Time Warping (DTW) algorithm to enhance the speech recognition process. The proposed solution is a machine learning-based system for controlling smart devices through speech commands with an accuracy of 97%. The results helped patients and elderly people to access and control IoT devices that are compatible with our system using speech recognition. The proposed speech recognition system is flexible with scalability and availability in adapting to existing smart IoT devices, and it provides privacy in managing patient devices. The research provides an effective method to integrate our systems among medical institutions to help elderly people and patients.

Development of Smart Healthcare System Based on Speech Recognition Using Support Vector Machine and Dynamic Time Warping

One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO2, humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO2 concentration measurement for detecting and monitoring room´s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg–Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO2 concentrations from obtained measurements of temperature (indoor, outdoor) T
 i, T
 o and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO2, T and rH with the aim to indirectly predict CO2 leading to the elimination of CO2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (>95%) within the short-term and long-term experiments for learned ANN (1.6.2015–30.6.2015). For learned ANN (1.2.2014–27.2.2014) was verified worse method accuracy (>60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO2 for ADL monitoring in SHC.

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Monitoring of the daily living activities in smart home care

People with severe disabilities may have difficulties when interacting with their home devices due to the limitations inherent to their disability. Simple home activities may even be impossible for this group of people. Although much work has been devoted to proposing new assistive technologies to improve the lives of people with disabilities, some studies have found that the abandonment of such technologies is quite high. This work presents a new assistive system based on eye tracking for controlling and monitoring a smart home, based on the Internet of Things, which was developed following concepts of user-centered design and usability. With this system, a person with severe disabilities was able to control everyday equipment in her residence, such as lamps, television, fan, and radio. In addition, her caregiver was able to monitor remotely, by Internet, her use of the system in real time. Additionally, the user interface developed here has some functionalities that allowed improving the usability of the system as a whole. The experiments were divided into two steps. In the first step, the assistive system was assembled in an actual home where tests were conducted with 29 participants without disabilities. In the second step, the system was tested with online monitoring for seven days by a person with severe disability (end-user), in her own home, not only to increase convenience and comfort, but also so that the system could be tested where it would in fact be used. At the end of both steps, all the participants answered the System Usability Scale (SUS) questionnaire, which showed that both the group of participants without disabilities and the person with severe disabilities evaluated the assistive system with mean scores of 89.9 and 92.5, respectively.

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A Human–Machine Interface Based on Eye Tracking for Controlling and Monitoring a Smart Home Using the Internet of Things

Current availability of interactive technologies infrastructure such as internet bandwidth, increased processing power and connectivity through mobile devices at affordable costs have led to new concepts related to human living. Smart cities, smart life and internet of things etc. are few such evolving research domains. A prominent concept among these is ‘Smart Home’, which involves automation and interactive technologies. This paper proposes a multi-functional ‘Smart Home Automation System’ (SHAS), where users can use voice-commands to control their home-appliances and gadgets, for different functionalities and purposes. The proposed system can be adapted to a user's voice and recognise the voice-commands, independent of the speaker's personal characteristics such as accent. The system is aimed to be cost-effective, flexible and robust. The voice command recognition is achieved using a dedicated hardware module and an Arduino micro-controller board for commands processing and control. Performance evaluation is carried out by developing a multi-functional miniature prototype of the SHAS. Results of the experiments conducted are quite promising. The prototype SHAS can be used for converting existing homes into smart homes at relatively low cost and with convenience.

Paridhi Toshniwal

Papers

A voice-controlled multi-functional Smart Home Automation System