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.

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.

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.

Improving Smart Home Security via MQTT: Maximizing Data Privacy and Device Authentication Using Elliptic Curve Cryptography

The rapid adoption of Internet of Things(IoT)technologies has introduced significant security challenges across the physical,network,and application layers,particularly with the widespread use of the Message Queue Telemetry Transport(MQTT)protocol,which,while efficient in bandwidth consumption,lacks inherent security features,making it vulnerable to various cyber threats.This research addresses these challenges by presenting a secure,lightweight communication proxy that enhances the scalability and security of MQTT-based Internet of Things(IoT)networks.The proposed solution builds upon the Dang-Scheme,a mutual authentication protocol designed explicitly for resource-constrained environments and enhances it using Elliptic Curve Cryptography(ECC).This integration significantly improves device authentication,data confidentiality,and energy efficiency,achieving an 87.68%increase in data confidentiality and up to 77.04%energy savings during publish/subscribe communications in smart homes.The Middleware Broker System dynamically manages transaction keys and session IDs,offering robust defences against common cyber threats like impersonation and brute-force attacks.Penetration testing with tools such as Hydra and Nmap further validated the system’s security,demonstrating its potential to significantly improve the security and efficiency of IoT networks while underscoring the need for ongoing research to combat emerging threats.

Application and Future Prospects of IoT Technology in Smart Homes

The integration of the Internet of Things(IoT)in smart homes significantly enhances home automation by offering advanced operational efficiency,energy management,and security.This paper explores the evolution of IoT from simple remote control systems to sophisticated ecosystems capable of autonomous decision-making,driven by advancements in sensor technology,machine learning algorithms,and broadband connectivity.We analyze the application of IoT in key areas such as smart lighting,security,and energy management,highlighting how these technologies optimize home functionality and user experience.Additionally,the paper addresses the main challenges facing IoT implementation.Looking forward,the paper discusses the future of IoT in smart homes,emphasizing the integration of AI and edge computing,and the potential market and social impacts.The findings suggest that despite existing challenges,the future of IoT in smart homes is promising,with significant implications for enhancing the intelligence and efficiency of home environments.

An Integrated Model of Smart Home Applications User Experience Based on Cognitive Dissonance

This study constructs an integrated model of user experience in smart home applications(apps)to deeply explore the impact of cognitive dissonance on users’emotional responses,subsequent behaviors,and experiential outcomes.The research emphasizes the importance of addressing emotional management in the design and development of smart home apps.The findings indicate that emotional response plays a critical mediating role in the user experience of these apps,offering new insights for further optimization.By understanding users’emotional reactions and behavioral patterns under cognitive dissonance,developers can more effectively improve interface design and enhance the overall user experience.

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.

Systematic Analysis of Safety and Security Risks in Smart Homes

The revolution in Internet of Things(IoT)-based devices and applications has provided smart applications for humans.These applications range from healthcare to traffic-flow management,to communication devices,to smart security devices,and many others.In particular,government and private organizations are showing significant interest in IoT-enabled applications for smart homes.Despite the perceived benefits and interest,human safety is also a key concern.This research is aimed at systematically analyzing the available literature on smart homes and identifying areas of concern or risk with a view to supporting the design of safe and secure smart homes.For this systematic review process,relevant work in the most highly regarded journals published in the period 2016–2020(a section of 2020 is included)was analyzed.A final set of 99 relevant articles(journal articles,book sections,conference papers,and survey papers)was analyzed in this study.This analysis is focused on three research questions and relevant keywords.The systematic analysis results and key insights will help researchers and practitioners to make more informed decisions when dealing with the safety and security risks of smart homes,especially in emergency situations.

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.

Analysis Markov Delay Control Strategy for Smart Home Systems

with the development of science and technology, smart home systems require better, faster to meet the needs of human. In order to achieve this goal, the human-machine-items all need to interact each other with understand, efficient and speedy. Cps could unify combination with the human-machine-items; realize the interaction between the physical nformation and the cyber world. However, information interaction and the control task needs to be completed in a valid time. Therefore, the transform delay control strategy becomes more and more important. This paper analysis Markov delay control strategy for smart home systems, which might help the system decrease the transmission delay.

The Development of Intelligent Home Remote Monitoring System based on B/S Framework

This paper deeply studies the intelligent Home embedded system based on B/S architecture. Firstly, analysis requirement of Home function based on the existing software development mode, and proposed a system framework of intelligent Home system, and designed the communication scheme of the whole Home system, also gives the system each function module realization. This paper achieve two basic service system based on Intelligent Home through a series of analysis and design： outdoor remote intelligent Home monitoring service and the indoor intelligent management service.

Smart Home Based on Wireless Sensor-Actuator Networks

Wireless sensor-actuator networks can bring flexibility to smart home.We design and develop a smart home prototype using wireless sensor-actuator network technology to realize environmental sensing and the control of electric appliances.The basic motivation of our solution is to utilize the collaboration among a mass of low-cost sensor nodes and actuator nodes to make life convenient.To achieve it,we design a novel system architecture with assembled component modules.In particular,we address some key technical challenges:1) Field-Programmable Gate Array (FPGA) Implementation of Adaptive Differential Pulse Code Modulation (ADPCM) for audio data;2) FPGA Implementation of Lempel Ziv Storer Szymanski (LZSS) for bulk data;3) combination of complex control logic.Finally,a set of experiments are presented to evaluate the performance of our solution.

Cognitive Emotion Model for Eldercare Robot in Smart Home

Based on the smart home and facial expression recognition, this paper presents a cognitive emotional model for eldercare robot. By combining with Gabor filter, Local Binary Pattern algorithm(LBP) and k-Nearest Neighbor algorithm(KNN) are facial emotional features extracted and recognized. Meanwhile, facial emotional features put influence on robot's emotion state, which is described in AVS emotion space. Then the optimization of smart home environment on the cognitive emotional model is specially analyzed using simulated annealing algorithm(SA). Finally, transition probability from any emotional state to a state of basic emotions is obtained based on the cognitive reappraisal strategy and Euclidean distance. The simulation and experiment have tested and verified the effective in reducing negative emotional state.

Zigbee-based new approach to smart home

Smart home is a promising solution to improving the quality of people＇s life. Much work has been done in the field, but most of these solutions are just based on home gateway, leaving much to be improved. One of its defects is the relatively high energy consuming and its radiation, and the other is that it is not available to the old home appliances which fail to access the internet. Full use of the low energy consuming characteristic of the Zigbee wireless sensor network, a completely new smart home solution is put forward in this paper. Without need of a home gateway and any modification for the currently used family appliances, the method uses the Zigbee coordinator as the central controller and the controllers of appliances as the end devices of Zigbee. It can realize a comfortable and smart home. Experiments show that the scheme proposed is feasible and it will be no doubt to be able to improve the quality of people＇s daily life.

Design and Implementation of a Novel HomePlug-Based Solution for Low Cost and High Performance Smart Home Networking

As the smart home is the end-point power consumer, it is the major part to be controlled in a smart micro grid. There are so many challenges for implementing a smart home system in which the most important ones are the cost and simplicity of the implementation method. It is clear that the major share of the total cost is referred to the internal controlling system network; although there are too many methods proposed but still there is not any satisfying method at the consumers＇ point of view. In this paper, a novel solution for this demand is proposed, which not only minimizes the implementation cost, but also provides a high level of reliability and simplicity of operation; feasibility, extendibility, and flexibility are other leading properties of the design.

Multi-objective reconfigurable production line scheduling for smart home appliances

In a typical discrete manufacturing process,a new type of reconfigurable production line is introduced,which aims to help small-and mid-size enterprises to improve machine utilization and reduce production cost.In order to effectively handle the production scheduling problem for the manufacturing system,an improved multi-objective particle swarm optimization algorithm based on Brownian motion(MOPSO-BM)is proposed.Since the existing MOPSO algorithms are easily stuck in the local optimum,the global search ability of the proposed method is enhanced based on the random motion mechanism of the BM.To further strengthen the global search capacity,a strategy of fitting the inertia weight with the piecewise Gaussian cumulative distribution function(GCDF)is included,which helps to maintain an excellent convergence rate of the algorithm.Based on the commonly used indicators generational distance(GD)and hypervolume(HV),we compare the MOPSO-BM with several other latest algorithms on the benchmark functions,and it shows a better overall performance.Furthermore,for a real reconfigurable production line of smart home appliances,three algorithms,namely non-dominated sorting genetic algorithm-II(NSGA-II),decomposition-based MOPSO(dMOPSO)and MOPSO-BM,are applied to tackle the scheduling problem.It is demonstrated that MOPSO-BM outperforms the others in terms of convergence rate and quality of solutions.