A Novel User Behavior Prediction Model Based on Automatic Annotated Behavior Recognition in Smart Home Systems

User behavior prediction has become a core element to Internet of Things(IoT)and received promising attention in the related fields.Many existing IoT systems(e.g.smart home systems)have been deployed various sensors and the user’s behavior can be predicted through the sensor data.However,most of the existing sensor-based systems use the annotated behavior data which requires human intervention to achieve the behavior prediction.Therefore,it is a challenge to provide an automatic behavior prediction model based on the original sensor data.To solve the problem,this paper proposed a novel automatic annotated user behavior prediction(AAUBP)model.The proposed AAUBP model combined the Discontinuous Solving Order Sequence Mining(DVSM)behavior recognition model and behavior prediction model based on the Long Short Term Memory(LSTM)network.To evaluate the model,we performed several experiments on a real-world dataset tuning the parameters.The results showed that the AAUBP model can effectively recognize behaviors and had a good performance for behavior prediction.

A Solution-Based Analysis of Attack Vectors on Smart Home Systems

The development and wider adoption of smart home technology also created an increased requirement for safe and secure smart home environments with guaranteed privacy constraints. In this paper, a short survey of privacy and security in the more broad smart-world context is first presented. The main contribution is then to analyze and rank attack vectors or entry points into a smart home system and propose solutions to remedy or diminish the risk of compromised security or privacy. Further, the usability impacts resulting from the proposed solutions are evaluated. The smart home system used for the analysis in this paper is a digital- STROM installation, a home-automation solution that is quickly gaining popularity in central Europe, the findings, however, aim to be as solution independent as possible.

Design of Smart Home System Based on ZigBee Technology and R&D for Application

In this paper, a smart home system based on ZigBee technology is designed. The system includes home network, home server and mobile terminal. The program is highly scalable and cost-effective. This paper developed the home server-side application based on MFC technology and the mobile terminal application. The mobile client can remotely control home devices and query the running state, electric energy information and historical data of home devices. At the same time, the home server-side application can store electric energy information and electricity consumption of home devices. Combined with household distributed photovoltaic generation system, the system can be applied to home energy management system. Through running tests and application, the results show that the system has realized basic functions of smart home and achieved the desired design goals.

Novel plug-configure-play ZigBee-based smart home system

The key purpose of a smart home system is to provide people with a better indoor life experience using the technology of Internet of Things. However,there are some limitations which make the current smart home system impractical,such as high cost,complex installation,poor flexibility and maintainability. In this paper,a novel plug-configure-play ZigB ee-based smart home system is proposed to provide repeatable use and improve flexibility and maintainability with the reductions of cost and complexity,which can be customized and reconfigured without redevelopment. In this system,new sensors can be flexibly added through different interfaces on the ZigB ee nodes and the sensor network layer is transparent to the users. Therefore,by using our method,users can customize and use the smart home system simply by configuring the sensors information via software on the application layer.

The Design of Internet of Things Smart Home System based on S3C2440 and Zigbee

In order to achieve the smart home system for remote management and wireless transmission function in the smart home networking system design, the article will combined with ARM technology and ZigBee wireless network technology based on samsung＇s $3C2440 chip and some related peripheral devices to form into home gateway of the system control platform, and the home control network using the internal ZigBee wireless network technologies. Practice has proved that the system is stable, is worth promoting.

A Smart Home System Design Based on GSM

This paper introduces a mobile phone short message control method in smart home based on GSM (Global System for Mobile communications). AT commands and the system structure is illustrated in detail. The hardware system including STC89C51 and TC35i is presented. The software framework is also analyzed clearly in this paper. In addition, some other potential application areas and its direction of development in future are given at last.

Development of Novel Smart Home System based on ZigBee Technique and Embedded Control System

In this paper, we report the development of novel smart home system based on ZigBee technique and embedded control system. In a lot of ZigBee technology applied in smart home system at the same time, how to view of the existing network terminal equipment, control and management of household field research also in rapid development. Terminal equipment mainly includes joining the network and control commands to send coordinator system combines the environment parameter data acquisition and serial port communication, and other functions. The simulation directly shows the effectiveness of our system visually and numerically.

A Home Automation Control System Based on LabVIEW

The objective of this project is to explore the possibility of using X-10 and LabVIEW to control the device in the house. Based on the serial port communication of LabVIEW, the X-10 module can be programmed by using the X-10 commands. Through the power line, all the devices connected to the socket will be controlled. Without replacing the existing wire, it must be an easy control system for the user who has no experience in electronics or communication engineering. Actually, this is a quite practical X-10 home automation system.

On the Security of Smart Home Systems:A Survey

Among the plethora of IoT(Internet of Things)applications,the smart home is one of the fastest-growing.However,the rapid development of the smart home has also made smart home systems a target for attackers.Recently,researchers have made many efforts to investigate and enhance the security of smart home systems.Toward a more secure smart home ecosystem,we present a detailed literature review on the security of smart home systems.Specifically,we categorize smart home systems’security issues into the platform,device,and communication issues.After exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security standards.Finally,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.

A Smart Home Energy Monitoring System Based on Internet of Things and Inter Planetary File System for Secure Data Sharing

Energy demand will continue to rise as a result of predicted population growth. In this work, a user-friendly home energy monitoring system based on IoT is described, which is capable of collecting, analyzing, and displaying data. Users register their sensors and devices on the monitoring platform. PostgreSQL and Elasticsearch databases are used to store the resulting measurements. In a smart home, the wireless sensor ACS712 was used to monitor the flow of electricity (current and voltage) for a household device. The user can share data about electricity consumption and costs with a third party via the private IPFS (InterPlanetary File System) network. A third party can download all the energy consumption data for a device or many devices from the platform for 1 day, 3 months, 6 months, and 1 year. The studies on the development of energy-efficient technology for home devices benefit greatly from the gathered data. For security in the system, it is preferred to run Keyrock Idm, Wilma Pep Proxy, and Orion Context Broker in HTTPS mode, and MQTTS is used to retrieve sensor data. The experimental results showed that the energy monitoring system accurately records voltage, current, active power, and the total amount of power used and offers low-cost solutions to the users using household devices in a day.

基于NodeMCU和Home Assistant的智能家居控制系统

为了提高家居生活的舒适性和便利性,开发了一套智能家居控制系统。该系统采用节点微控制单元(NodeMCU)作为基础硬件,负责收集传感器数据并操控相关执行器件。同时,该系统利用消息队列遥测传输协议(MQTT)和WiˉFi技术构建数据传输链路。此外,该系统利用开源的Home Assistant系统建立了后台服务端,以实现数据的图形化展示。用户可通过安卓移动设备实时监控家居环境状况,并对智能设备实施控制。该系统经过严格测试,表现稳定,显著提高了家居环境的智能化程度。

Informing policy makers in developing countries:Practices and limitations of geriatric home medication review in Malaysia-A qualitative inquiry

Objective:To explore existing practices and challenges in the delivery of geriatric home medication review(HMR).The study was part of a larger study aimed to offer solution to expand the range of geriatric HMR.Methods:This study employed qualitative exploratory design through semi-structured individual in-depth interviews with the public pharmacists involved in the delivery of geriatric HMR at public hospitals.The purpose of the interviews was to explore challenges faced by them in the delivery of geriatric HMR.Results:Based on the emerging themes from the qualitative data,the study reveals that geriatric HMR in Malaysia is integrated as part of multidisciplinary home care visits,encompassing a diverse patient population with various healthcare needs.However,it faces challenges such as the lack of outcome monitoring,formal training,and workforce constraints.Despite these hurdles,there is a pressing need for the expansion of this service to better serve the community,and collaboration with community pharmacists holds potential to broaden its scope.Ultimately,the findings suggest that pharmacist-led HMR is both warranted and feasible within the Malaysian healthcare context.In order to optimize medicine-use among older people living in the community,approaches for expanding geriatric HMR services in Malaysia must be developed.Conclusions:This study holds profound implications as it attempts to illuminate policy makers in developing countries,enabling them to formulate effective HMR plans.By considering the challenges highlighted within this research,policy makers can design a comprehensive HMR service that caters adeptly to the healthcare needs of the mass population.

Which Neighborhoods Have Easier Access to Online Home Delivery Services?A Spatiotemporal Accessibility Analysis in Nanjing,China

The rise in online home delivery services(OHDS)has had a significant impact on how urban services are supplied and used in recent years.Studies on the spatial accessibility of OHDS are emerging,but few is known about the temporal dimension of OHDS accessibility as well as the geographic and socioeconomic differences in the spatiotemporal accessibility of OHDS.This study measures the spatiotemporal accessibility of four types of OHDS,namely leisure,fresh and convenient,medical,and catering services.The geographic and socioeconomic disparities in the spatiotemporal accessibility of these four types of OHDS are then identified using spatial statistical methods and the Kruskal-Wallis test(K-W test).The case study in Nanjing,China,suggests that:1)spatiotemporal accessibility better reflects the temporal variation of OHDS accessibility and avoids overestimation of OHDS accessibility when only considering its spatial dimension.2)The spatiotemporal accessibility of OHDS varies geographically and socioeconomically.Neighborhoods located in the main city or neighborhoods with higher housing prices,higher population density,and higher point of interest(POI)mix have better OHDS spatiotemporal accessibility.Our study contributes to the understanding of OHDS accessibility from a spatiotemporal perspective,and the empirical insights can assist policymakers in creating intervention plans that take into account variations in OHDS spatiotemporal accessibility.

Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.

LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks.The proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly recognition.The model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and falls.This study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.

Determinants of Home Deliveries by Pregnant Mothers in Lumbo Chabbobboma Zone of Gwembe District in Zambia

Background: Home deliveries is still high globally at 42% WHO 2022, due to high home deliveries, maternal death is also high at 43% globally. In sub-Sahara region home deliveries still high. Giving birth at health facilities in most of sub-Saharan African countries Zambia inclusive is still a challenge whereby more than 51% of first-time mothers give birth at home and this gives a risk of high maternal and perinatal deaths. Therefore Reducing number of home deliveries is important to improve maternal and perinatal health issues. In this study, the aim was to investigate the determinants of home deliveries by pregnant mothers in the Luumbo zone of Gwembe district, Zambia. Purpose: Access to skilled care and facilities with capacity to provide emergency and newborn care is critical to reduce maternal death. In Zambia 42% of women still deliveries from home, suggesting a persistent challenge for women to seek, reach, and receive quality maternity care. This study aimed investigate the determinants of home deliveries by pregnant mothers in Luumbo zone of Gwembe district, Zambia. Methods: The study was conducted among postnatal mothers who came for postnatal care at 6 weeks in Luumbo Chabbobboma clinic in Gwembe district southern province of Zambia. This was a descriptive cross-sectional study where a Simple random sampling technique was used to select 105 women of childbearing age who attended postnatal and had a recent delivery. Data were collected using a researcher-administered structured questionnaire to identify determinants of home deliveries in Luumbo Chabbobboma zone. Data analysis was done using SPSS computer software version 27.0. Both descriptive and inferential (chi-square test) analyses were performed and statistical significance was taken at α ≤ 0.05. Results: The results show that 46 (43.8%) respondents were in the age bracket 20 - 29 years. Of the 105 respondents included in the study, 24 (22.9%) of them delivered from home. The results show that high maternal age (p = 0.03), occupation (p = 0.024), distance to the facility (p = 0.014), means of transportation (p = 0.023), multiparity (p = 0.01), timing and number of ANC visits (p Conclusion: From this population. The major reason why women still deliver at home was long distance to the nearest facility. To reduce maternal and perinatal mortality access to health facilities by pregnant women needs to be improved. There should also be active engagement of the traditional and religious institutions in the area.

Neuroarchitecture Analysis in the Mis Abuelitos Nursing Home in Cochachinche, Huanuco, Peru - A Secondary Publication

Neuroarchitecture is a set of characteristics grouped into principles that seek to generate a certain behavior when applied.These principles will vary according to the type of user for whom the design is intended.In this research,these neuroarchitectural principles are identified in the nursing home Mis Abuelitos in Cochachinche,Huánuco.The home was designed and built with notions of nature and the Andean while emphasizing the occupants are the elderly.With this purpose,qualitative research was carried out using two sequential criteria,the first was to identify what these principles are and the second was to recognize the principles within the area.The principles that are assertively used for the elderly occupants were selected.The study observation was performed with observation and photographic sheets and was analyzed with ATLAS.ti processing software.It was obtained that the three neuroarchitectural principles are present in the nursing home which are 67%of the recommended characteristics for the elderly.

Application of horticulture therapy as a health intervention for elderly people in nursing homes:a scoping review

Objective:To review the scope of interventional studies on horticultural therapy(HT)applied to elderly people in nursing institutions to support the efficient implementation of HT among this target group.Methods:In accordance with the scoping review framework proposed by the Joanna Briggs Institute(JBI),the Pub Med,JSTOR,Web of Science,CNKI,and Wanfang databases were searched.Data from the retrieved literature were summarized and analyzed.Results:In all,18 studies were included in this review.The target population groups of interventions included self-supporting elderly people,cognitively impaired elderly people,elderly people with negative emotions,and elderly people with frailty living in nursing institutions.HT interventions,including planting,craft activities,derivative activities,and outdoor viewing activities,are implemented indoors,outdoors,or in mixed settings.The most common duration of the intervention was 8 weeks,the most common frequency was once per week,and the most common session duration was 60 min.Conclusions:The measurements used in HT interventions included assessments of physical,psycho-mental,and social health;quality of life;and activity effects.Future studies should include partially dependent groups and completely dependent groups of elderly individuals,interventions that last at least 6 months,scientifically designed activity intensity and safety-guarantee plans,and outcomeevaluation indicators such as compliance and intervention benefits.

Home control system based on ZigBee wireless sensor networks

A new scheme of the home control system based on ZigBee wireless sensor networks is presented. The design and development of the software and hardware of the proposed system are given. In addition to the basic data acquisition and processing functions, the gateway supports the Bluetooth-based local interface and the general packet radio service （GPRS）-based remote interface. Users on the client service side can use a pocket PC or notebook PC to achieve real-time data acquisition and control instruction implementation, or remotely control the home control system through a mobile phone by sending a short message. The Labview graphical development environment is adopted to create PDA applications running on pocket PCs and monitoring platform established on notebook PCs. Except for the gateway, other nodes in the system work in sleep mode most of the time on the system, and thus it improves the lifetime of the whole system efficiently.

A Hybrid Intrusion Detection System for Smart Home Security Based on Machine Learning and User Behavior

With technology constantly becoming present in people’s lives, smart homes are increasing in popularity. A smart home system controls lighting, temperature, security camera systems, and appliances. These devices and sensors are connected to the internet, and these devices can easily become the target of attacks. To mitigate the risk of using smart home devices, the security and privacy thereof must be artificially smart so they can adapt based on user behavior and environments. The security and privacy systems must accurately analyze all actions and predict future actions to protect the smart home system. We propose a Hybrid Intrusion Detection (HID) system using machine learning algorithms, including random forest, X gboost, decision tree, K -nearest neighbors, and misuse detection technique.