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.

Blockchain-based DDoS attack mitigation protocol for device-to-device interaction in smart home

Smart home devices are vulnerable to a variety of attacks.The matter gets more complicated when a number of devices collaborate to launch a colluding attack(e.g.,Distributed-Denial-of-Service(DDoS))in a network(e.g.,Smart home).To handle these attacks,most studies have hitherto proposed authentication protocols that cannot necessarily be implemented in devices,especially during Device-to-Device(D2D)interactions.Tapping into the potential of Ethereum blockchain and smart contracts,this work proposes a lightweight authentication mechanism that enables safe D2D interactions in a smart home.The Ethereum blockchain enables the implementation of a decentralized prototype as well as a peer-to-peer distributed ledger system.The work also uses a single server queuing system model and the authentication mechanism to curtail DDoS attacks by controlling the number of service requests in the system.The simulation was conducted twenty times,each with varying number of devices chosen at random(ranging from 1 to 30).Each requester device sends an arbitrary request with a unique resource requirement at a time.This is done to measure the system's consistency across a variety of device capabilities.The experimental results show that the proposed protocol not only prevents colluding attacks,but also outperforms the benchmark protocols in terms of computational cost,message processing,and response times.

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

Smart Home IoT Privacy and Security Preservation via Machine Learning Techniques

The development and use of Internet of Things(IoT)devices have grown significantly in recent years.Advanced IoT device characteristics are mainly to blame for the wide range of applications that may now be achieved with IoT devices.Corporations have begun to embrace the IoT concept.Identifying true and suitable devices,security faults that might be used for bad reasons,and administration of such devices are only a few of the issues that IoT,a new concept in technological progress,provides.In some ways,IoT device traffic differs from regular device traffic.Devices with particular features can be classified into categories,irrespective of their function or performance.Ever-changing and complex environments,like a smart home,demand this classification scheme.A total of 41 IoT devices were employed in this investigation.To build a multiclass classification model,IoT devices contributed 13 network traffic parameters.To further preprocess the raw data received,preprocessing techniques like Normalization and Dataset Scaling were utilized.Feature engineering techniques can extract features from the text data.A total of 117,423 feature vectors are contained in the dataset after stratification,which are used to further improve the classification model.In this study,a variety of performance indicators were employed to show the performance of the logiboosted algorithms.Logi-XGB scored 80.2%accuracy following application of the logit-boosted algorithms to the dataset for classification,whereas Logi-GBC achieved 77.8%accuracy.Meanwhile,Logi-ABC attained 80.7%accuracy.Logi-CBC,on the other hand,received the highest Accuracy score of 85.6%.The accuracy of Logi-LGBM and Logi-HGBC was the same at 81.37%each.Our suggested Logi-CBC showed the highest accuracy on the dataset when compared to existing Logit-Boosted Algorithms used in earlier studies.

Power Scheduling with Max User Comfort in Smart Home:Performance Analysis and Tradeoffs

The smart grid has enabled users to control their home energy more effectively and efficiently.A home energy management system(HEM)is a challenging task because this requires the most effective scheduling of intelligent home appliances to save energy.Here,we presented a meta-heuristic-based HEM system that integrates the Greywolf Algorithm(GWA)and Harmony Search Algorithms(HSA).Moreover,a fusion initiated on HSA and GWA operators is used to optimize energy intake.Furthermore,many knapsacks are being utilized to ensure that peak-hour load usage for electricity customers does not surpass a certain edge.Hybridization has proven beneficial in achieving numerous objectives simultaneously,decreasing the peak-to-average ratio and power prices.Widespread MATLAB simulations are cast-off to evaluate the routine of the anticipated method,Harmony GWA(HGWA).The simulations are for a multifamily housing complex outfitted with various cool gadgets.The simulation results indicate that GWA functions better regarding cost savings than HSA.In reputes of PAR,HSA is significantly more effective than GWA.The suggested method reduces costs for single and ten-house construction by up to 2200.3 PKR,as opposed to 503.4 in GWA,398.10 in HSA and 640.3 in HGWA.The suggested approach performed better than HSA and GWA in PAR reduction.For single-family homes in HGWA,GWA and HSA,the reduction in PAR is 45.79%,21.92%and 20.54%,respectively.The hybrid approach,however,performs better than the currently used nature-inspired techniques in terms of Cost and PAR.

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.

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.

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.

I Smart Home移动通信系统设计

智能家居技术正蓬勃发展,为紧跟信息技术发展前沿,文章利用labview开发了I smart home智能家居中控系统,通过串口与控制器进行通信。开发了基于Android的手机控制客户端,通过wifi移动通信技术实现了对控制器输出的控制。通过外接LED进行实验测试,结果表明技术路线正确可行。

Smart home architecture based on event-driven DPWS～+

With the advances of electronic information technology and computer network, especially the embedded technology, smart home is no more just a vision but being practical. The interoperability of heterogeneous devices and flexibility of devices＇ usage are two key problems that challenge the implementation of smart home. To deal with these two issues, this paper proposes an event-driven service oriented architecture using device profile for web services （DPWS）. DPWS inherits the advantages of the traditional web services in achieving interoperability without dependence on platform, while improving service discovery and security as well as being optimized for deploying on resource constrained devices. By providing a visual interface for describing a service workflow （SW）, the user can easily customize the actions of devices by services composition. Devices automatically cooperate without user＇s intervention to complete required business logic. This is achieved by fully exploiting the eventing capabilities on DPWS enabled home devices. Finally, a home theater scenario is given to illustrate the event driven mechanism for the SW in the proposed smart home framework.

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 Novel Probabilistic Hybrid Model to Detect Anomaly in Smart Homes

Anomaly detection in smart homes provides support to enhance the health and safety of people who live alone.Compared to the previous studies done on this topic,less attention has been given to hybrid methods.This paper presents a two-steps hybrid probabilistic anomaly detection model in the smart home.First,it employs various algorithms with different characteristics to detect anomalies from sensory data.Then,it aggregates their results using a Bayesian network.In this Bayesian network,abnormal events are detected through calculating the probability of abnormality given anomaly detection results of base methods.Experimental evaluation of a real dataset indicates the effectiveness of the proposed method by reducing false positives and increasing true positives.

TelG Mote: A Green Wireless Sensor Node Platform for Smart Home and Ambient Assisted Living

The wireless sensor network （WSN） consists of sensor nodes that interact with each other to collectively monitor environmental or physical conditions at different locations for the intended users. One of its potential deployments is in the form of smart home and ambient assisted living （SHAAL）to measure patients or elderly physiological signals, control home appliances, and monitor home. This paper focuses on the development of a wireless sensor node platform for SHAAL application over WSN which complies with the IEEE 802.15.4 standard and operates in 2.4 GHz ISM （industrial, scientific, and medical） band. The initial stage of SHAAL application development is the design of the wireless sensor node named TelG mote. The main features of TelG mote contributing to the green communications include low power consumption, wearable, flexible, user-friendly, and small sizes. It is then embedded with a self-built operating system named WiseOS to support customized operation. The node can achieve a packet reception rate （PRR） above 80% for a distance of up to 8 m. The designed TelG mote is also comparable with the existing wireless sensor nodes available in the market.

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.

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.

上下文感知在Smart Home系统中的应用

针对Smart Home环境的复杂性问题,提出了利用上下文技术来提高其智能化的方法。阐述了在普适环境下的上下文在智能家居环境中的重要性,介绍了上下文和上下文感知计算的定义,采用了基元组的形式定义了不同的上下文信息;提出了基于上下文感知的Smart Home系统模型,并对其功能进行了详细的描述;在Smart Home系统中,应用该模型对具体的上下文实例进行了描述和推理。

An Investigative Study for Smart Home Security: Issues, Challenges and Countermeasures

Home security should be a top concern for everyone who owns or rents a home. Moreover, safe and secure residential space is the necessity of every individual as most of the family members are working. The home is left unattended for most of the day-time and home invasion crimes are at its peak as constantly monitoring of the home is difficult. Another reason for the need of home safety is specifically when the elderly person is alone or the kids are with baby-sitter and servant. Home security system i.e. HomeOS is thus applicable and desirable for resident’s safety and convenience. This will be achieved by turning your home into a smart home by intelligent remote monitoring. Smart home comes into picture for the purpose of controlling and monitoring the home. It will give you peace of mind, as you can have a close watch and stay connected anytime, anywhere. But, is common man really concerned about home security? An investigative study was done by conducting a survey to get the inputs from different people from diverse backgrounds. The main motivation behind this survey was to make people aware of advanced HomeOS and analyze their need for security. This paper also studied the necessity of HomeOS investigative study in current situation where the home burglaries are rising at an exponential rate. In order to arrive at findings and conclusions, data were analyzed. The graphical method was employed to identify the relative significance of home security. From this analysis, we can infer that the cases of having kids and aged person at home or location of home contribute significantly to the need of advanced home security system. At the end, the proposed system model with its flow and the challenges faced while implementing home security systems are also discussed.

Internet of Things Cloud Framework for Smart Homes

Smart appliances and renewable energy resources are becoming an integral part of smart homes. Nowadays, home appliances are communicating with each other with home short-range home area gateways, using existing network communication protocols such as ZigBee, Bluetooth, RFID, and WiFi. A Gateway allows homeowners and utilities to communicate remotely with the appliances via long-range communication networks such as GPRS, WiMax, LTE, and power liner carrier. This paper utilizes the Internet of Things （IoT） concepts to monitor and control home appliances. Moreover, this paper proposes a framework that enables the integration and the coordination of Human-to-Appliance, Utility-to- Appliance, and Appliance-to-Appliance. Utilizing the concepts of Internet of Things leads to one standard communication protocols, TCP/IPV6, which overcomes the many diverse home area networks and neighborhood area networks protocols. This work proposes a cloud based framework that enables the IoTs integration and supports the coordination between devices, as well as with device-human interaction. A prototype is designed, implemented, and tested to validate the proposed solution.

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.