Abstract
The safety monitoring system has been used to monitor and manage engineering safety operation. The application scope of the safety monitoring system is very wide. It has a wide range of applications in the fields of pipeline safety monitoring, electrical safety monitoring and household safety monitoring. This article studied the application process of the household safety monitoring system. Many home safety accidents are caused by inadequate monitoring of safety problems. Therefore, it is very important to establish a household safety monitoring system. Traditional home safety monitoring systems only rely on cameras for safety monitoring, and the traditional home safety monitoring system uses too few sensors. With the continuous development of wireless sensor network (WSN) technology, it is possible to build a sensor node network, but provides real-time information for home security monitoring to the greatest extent. This article compared the home safety monitoring system based on the WSN technology of artificial intelligence (AI) with the traditional home safety monitoring system. The experimental results showed that in the large-scale home environment, the average monitoring accuracy of the traditional home security monitoring system and the home security monitoring system based on the WSN technology of AI was 77.76 and 89.36%, respectively. In the small-scale home environment, the average monitoring accuracy of the traditional home safety monitoring system and the home safety monitoring system based on the WSN technology of AI were 87.63 and 94.43%, respectively. Monitoring accuracy refers to the accuracy of the household safety monitoring system in detecting safety issues. Therefore, the application of the WSN technology based on artificial intelligence to the home safety monitoring system can effectively improve the accuracy of home safety monitoring.
1 Introduction
With the rapid development of the economy, people have new requirements for the working and living environment. The safety of traditional Chinese residential or office environments is very low. Every year, many families encounter fires, electricity leakage, and other situations, which also lead to many family safety accidents. In the first half of 2015, various types of domestic fires, gas explosions, gas poisoning, building collapses, and other residential safety accidents occurred in China, resulting in a total economic loss of at least 35 billion yuan, with at least 20,000 deaths and injuries. In traditional families, people lack a safety monitoring system. Due to people’s negligence or accident, many safety problems have occurred in many life scenarios, such as forgetting to close the gas valve when sleeping and forgetting to close the hot kettle when boiling water. The safety monitoring system can reflect the monitored object in real time, visually, and truly. It can replace manual monitoring for a long time in harsh environments and record it through a video recorder. With the development of Internet-of-Things technology, smart home appliances based on sensors have been designed. People can obtain all kinds of information about the family through sensors, including dangerous information, which provides people with a safe, reliable, and intelligent living environment. The development and application of sensors in home safety monitoring also experienced two stages. The first stage is single sensor or simple multiple sensors. Although some home safety information can be obtained through sensors, the fields involved are not comprehensive, leading to obvious defects in the home safety monitoring system. Multi-sensor refers to the collection, aggregation, or combination of information collected and provided by multiple sensors. The second stage is wireless sensor network (WSN), which monitors the home environment in all aspects by combining many sensor nodes into a network structure. With the development of wireless sensor technology, WSNs can provide real-time monitoring data in the home environment. With the help of artificial intelligence (AI) technology, data are analyzed to intelligently judge the safety of the home environment, thus improving the effect of home safety monitoring. Therefore, this article has research significance, applying WSNs to household safety monitoring systems to improve household safety monitoring effectiveness and ensure people’s residential safety.
In people’s lives, hidden safety problems can be seen everywhere. Many people have conducted in-depth research on the construction of safety monitoring systems. Wu proposed a time series analysis method of the hidden Markov model. He realized pipeline safety monitoring by analyzing the reflection frequency of light in the pipeline, which could effectively improve the accuracy of pipeline safety monitoring [1]. Wang’s research pointed out that the train central control system was the main condition to ensure the safety of train traffic. Based on the traditional train control system, he introduced topological space to describe the track and time of train movement, so as to accurately monitor the train operation [2]. Adamo’s study pointed out that the dam was an important barrier to prevent floods. He effectively ensured the safety of the people around the dam through safety monitoring of the rock characteristics of the dam and the impact resistance of the dam, and timely repaired when the dam was damaged [3]. Chavhan’s research pointed out that drugs were very important for people’s health. However, there were many phenomena of counterfeiting and cheating in drugs. Through the drug safety monitoring system, the production process and sales channels of drugs were monitored in real time, which could ensure the safety of drugs [4]. The security monitoring system has a wide range of applications and can achieve accurate security monitoring in various fields, but it lacks the use of sensors to obtain data information.
With the continuous development of the Internet-of-Things technology, data collection can be realized by using sensors. Relevant researchers have formed a network structure of multiple sensor nodes and applied them to the security monitoring system. Among them, Abdulkarem applied WSNs to structural health monitoring. Compared with traditional wired systems, structural health monitoring based on WSNs could effectively improve the safety of cities [5]. Salman improved the traditional single-sensor intelligent alarm system. He built an adaptive alarm system for WSNs, using sensors on the development kit to collect ambient light colors to implement an environmental adaptive alarm system, which could improve the sensitivity of the alarm system [6]. Islam and Rahaman proposed a medical monitoring system based on the environment of the Internet of Things, which coordinated the work of multiple sensors, including room-temperature sensor, heartbeat sensor, and body temperature sensor. WSNs could obtain more comprehensive medical data [7]. The application of WSN technology to the security monitoring system can obtain more detailed monitoring information through sensors, but it lacks intelligent analysis of the monitoring system.
In order to effectively analyze household safety monitoring, this article adopted the method of setting up a control group for safety monitoring and analysis. Sensors can provide accurate and direct physical information. This article used wireless communication technology to form a network structure of many sensors and applied them to the home security monitoring system. Through AI technology, the acquired data were analyzed for security. The home safety monitoring system based on AI WSN technology was compared with the traditional home safety monitoring system. The results showed that the home safety monitoring system based on WSN technology could improve the comprehensiveness of monitoring.
2 Application method of safety monitoring system
With the continuous progress of information technology, people’s life and communication technology are closely linked. Information technology has changed people’s lifestyle, and the living environment has also changed dramatically. The traditional residential communication equipment is simple and can only realize simple communication between residential buildings. Because of the poor communication technology, the traditional residents cannot realize the home safety monitoring, which makes it difficult to transmit the traditional residence safety information to people in time. Intelligent technology is constantly developing, and smart homes can realize intelligent communication between people and the home environment. Through the monitoring system, the house can be monitored in real time and safely to create a safe and comfortable home environment. The structural model of the household safety monitoring system is shown in Figure 1.
Structural model of household safety monitoring system.
In Figure 1, the structural model of home safety monitoring is described. Through monitoring equipment, the main home safety information is obtained, including kitchen, bedroom, corridor, balcony, living room, and elevator. Through the Internet, the monitoring information of all parts of the home is integrated to achieve remote monitoring of home safety.
The security monitoring system is a network system built with a large number of monitors, which realizes diversified intelligent security monitoring by integrating and analyzing the information of each monitor [8,9]. Now, home life is full of modern elements, and various intelligent devices provide people with great convenience in life. However, security issues also arise. In the modern home environment, people are prone to security problems in their homes due to work reasons or the decline in security awareness. For example, when people forget to close the gas valve while cooking, it is easy to cause gas poisoning or fire.
The household safety monitoring system covers a wide range of areas, including anti-theft security, fire prevention, and electricity prevention. For example, if there is an abnormal situation in the home, the owner can be notified in time. The composition and content of toxic gas in the room can be detected automatically. The traditional home safety monitoring system relies on sensor equipment to realize the safety monitoring of a single home device. The traditional home safety monitoring system can monitor the subsystems of the home environment, but it can not realize the association between multiple home subsystems.
2.1 WSN technology
The Internet of Things refers to the use of various sensing devices to obtain external environmental information, which realizes the exchange of collected information with the help of communication protocols to achieve the role of information exchange [10,11]. The core of information acquisition of the Internet of Things is the sensor device, which is an embedded physical device with automatic detection and control functions [12]. The sensor has a wide range of applications. It can be used to obtain household environmental information and be applied to household safety monitoring system.
WSN is a distributed sensor network. In WSNs, the network node terminal can obtain the external environment information and can interconnect all the acquired information [13,14]. The structure model of WSN is shown in Figure 2.
Structure model of WSN.
In Figure 2, the structure model of WSN is described. WSN includes sensor nodes and sink nodes. Sensor nodes can be installed in every corner of the home environment. According to the characteristics of sensors, the monitoring of different home information can be realized. The aggregation node can collect and sort out the data obtained by multiple sensor nodes, which is conducive to the comprehensive analysis of WSN data.
Wireless sensor node itself is a small embedded system with certain storage and processing capabilities. However, the most important thing is to obtain external environmental information. Wireless sensor nodes can store and forward the information forwarded by the surrounding nodes in addition to acquiring information by themselves.
The sink node has very strong storage and communication capabilities. By connecting with multiple sensor nodes, information communication between sensors is realized, and the status of sensor nodes can also be monitored. The collected data information is integrated, and finally, the resource data are uploaded.
The structural model of sensor nodes is shown in Figure 3.
Structural model of sensor nodes.
In Figure 3, the structure model of sensor nodes is described. The sensor node is structurally divided into three modules, namely, data acquisition area, data processing area, and data transmission area.
WSNs realize information exchange and resource sharing between different sensor nodes by setting a unified communication protocol [15]. The physical layer provides very simple modulated signals. The network layer performs routing selection. The transport layer performs data flow transmission control. The application layer transmits the collected information to the user. WSN is a network structure composed of numerous sensor nodes, which has the functions of data monitoring, control, and communication. Due to the large amount of node information in WSNs, higher quality transmission protocols are needed. WSNs are significantly different from ordinary wireless ad hoc networks. The WSN has a wider coverage of nodes and can achieve a wider range of home security monitoring. WSNs have the ability to node self-organization. It can adjust itself according to the needs of home monitoring and has higher reliability, which can accurately collect and process complex data.
The WSN has a distributed structure, which can carry out all-round real-time monitoring of the home environment and provide accurate information for home security monitoring.
2.2 ZigBee communication technology
The core of WSN technology is communication technology, and wireless communication is the basis for information exchange between different sensor nodes [16]. In recent years, communication technology has developed rapidly. Although traditional Bluetooth technology can achieve wireless communication between data, it is of high cost and has low transmission rate.
ZigBee is a low-cost and low-complexity communication technology, and the communication structure formed by ZigBee is very similar to WSNs, which can be well applied to WSNs [17]. As a new communication technology, ZigBee has the following characteristics.
The communication frequency band is flexible and can communicate information in multiple frequency bands, and the response speed is fast and can respond to real-time information in time. ZigBee has a huge network capacity and can support more than ten thousand nodes of information communication, which ensures a large coverage of communication information. ZigBee’s communication process is very safe. During the data transmission process, there is a three-layer data check mechanism to ensure that there is no error in the information transmission.
In the home safety monitoring system, information acquisition needs to rely on sensors, and the working frequency bands of different sensors are different. In order to ensure the normal communication of household safety monitoring information, ZigBee can divide the original signal into multiple sub-signals and adaptively allocate the signal frequency band to ensure a high signal recognition accuracy.
The WSN takes ZigBee communication technology as the core and can realize home safety monitoring. The characteristics of ZigBee communication technology and other communication technologies are shown in Table 1.
Comparison of characteristics between ZigBee communication technology and other communication technologies
| Communication technology | Wireless local area network | Bluetooth | ZigBee |
|---|---|---|---|
| Application area | Smart device online | Short-distance information transmission | Real-time monitoring |
| Response speed | Slow | Slow | Fast |
| Transmission distance | Far away | Near | Far |
| Use cost | High | Higher | Low |
In Table 1, the characteristics of ZigBee communication technology are compared with other communication technologies. Compared with Bluetooth and WLAN, ZigBee communication technology has faster response speed, longer transmission distance, and lower use cost, which can be effectively applied to the field of real-time monitoring.
In the home safety monitoring system, there are many factors that affect the safety of the house. The sensor covers a wide range. The WSN based on ZigBee communication should be designed for the distribution of sensor nodes in combination with household environmental factors, so as to improve the scalability of the entire household safety monitoring system and the comprehensiveness of real-time monitoring data.
2.3 AI
AI is a branch of computer science, which simulates the logic of the human brain in dealing with complex transactions to conduct intelligent research on complex problems. AI technology is developing more and more rapidly, which has a wide range of applications in data intelligent processing, image recognition, robot, and other fields [18,19]. The home security monitoring system using WSN technology has very complex sensor data. The safety monitoring system needs not only to collect data, but also to intelligently analyze the data collected by sensors and evaluate the safety of the home environment.
Artificial neural network is a common use method in AI technology. It has super-high data recognition and analysis ability after training and learning model data [20]. The structure model of the artificial neural network is shown in Figure 4.
Structure model of artificial neural network.
In Figure 4, the structure model of the artificial neural network is described. It has a three-layer structure and can realize intelligent analysis of the safety monitoring data.
Artificial neural network is an algorithmic mathematical model that imitates the behavioral characteristics of animal neural networks and carries out distributed parallel information processing. Based on the complexity of the system, the artificial neural network can process information by adjusting the relationship between a large number of internal nodes. Artificial neural networks can be used to analyze the data in the home safety monitoring system to make intelligent safety decisions.
There are two kinds of convergence results and convergence of neural networks. The convergence result is that the result calculated by the neural network is generally 1 or 0. Convergence can understand whether it can produce 1 or 0 or the probability of producing 1 or 0 after calculation by a neural network. The time complexity of an artificial neural network determines the training/prediction time of the model.
In the household safety monitoring system, the data set obtained by the sensor is
where r represents the result of artificial neuron processing safety monitoring data.
The processing result in formula (1) is processed by activation function to obtain:
where function
In the home safety monitoring system, it is often necessary to predict and analyze the safety situation according to the environmental information of the home, so as to timely respond to the dangers that occur. Back-propagation neural network has two modes: forward information transmission and reverse error feedback, which can optimize and predict nonlinear data. When the back-propagation neural network is applied to the household safety monitoring system, the error can be expressed as:
where
The structure of the neural network is adjusted according to the size of the error H. The adjustment process is as follows:
where
When the error reaches the acceptable range, it indicates that the trained reverse neural network has excellent safety monitoring capability. The specific process is shown as follows:
where h represents the maximum acceptable range of error.
AI technology has excellent information optimization ability. AI can intelligently analyze the data obtained by the safety monitoring system and effectively improve the effect of safety monitoring.
3 Application experiment of safety monitoring system
3.1 Construction of safety monitoring system evaluation system
Smart home security monitoring can obtain environmental information on the furniture in real time, operate smart devices remotely, and monitor the people at home. In this article, WSN technology was applied to home security monitoring system. In order to effectively analyze the performance comparison between the home safety monitoring system based on WSN and the traditional home safety monitoring system, this article conducted a questionnaire survey on 250 professional safety monitoring personnel, mainly analyzing the indicators they think can evaluate the effect of home safety monitoring. A total of 250 questionnaires were distributed, of which 200 were valid and 50 were invalid, the invalid questionnaire is due to the fact that the investigator did not provide a data answer, or the content of the answer was unrelated to the survey content. The household safety monitoring and evaluation indicators are shown in Table 2.
Household safety monitoring and evaluation indicators
| Serial number | Index | Effective quantity | Percentage |
|---|---|---|---|
| 1 | Response time | 36 | 18 |
| 2 | Monitoring stability | 44 | 22 |
| 3 | Monitoring comprehensiveness | 56 | 28 |
| 4 | Monitoring accuracy | 64 | 32 |
In Table 2, the household safety monitoring and evaluation indicators are described. A total of four evaluation indicators were calculated, including response time, monitoring stability, monitoring comprehensiveness, and monitoring accuracy. The highest percentage of monitoring accuracy is 32. The highest percentage of monitoring comprehensiveness is 28, and the lowest percentage of response time is 18.
In order to better reflect the effect of home safety monitoring, it is necessary to monitor key areas in the home environment. In this article, 50 houses were randomly selected for the experiment. The key areas of household monitoring are shown in Table 3.
Key areas of household monitoring
| Serial number | Region | Sensor duty ratio (%) |
|---|---|---|
| 1 | Shower room | 16 |
| 2 | Bedroom | 14 |
| 3 | A living room | 10 |
| 4 | Kitchen | 24 |
| 5 | Balcony | 11 |
| 6 | Stair case | 7 |
| 7 | Grocery | 18 |
In Table 3, the key areas of household monitoring are described. A total of seven key areas for household monitoring were counted, of which the sensor proportion in the kitchen area was up to 24%, and the sensor proportion in the stairwell area was at least 7%.
3.2 Experimental design of safety monitoring system application
The control group adopted the traditional home safety monitoring system, with a small number of sensors and no data exchange between sensors. The experimental group applied the WSN technology based on AI to the home safety monitoring system. The comparison points of the two home safety monitoring methods are as follows: response time, monitoring stability, monitoring comprehensiveness, and monitoring accuracy. In order to fully compare the monitoring effects of the traditional home safety monitoring system and the system in this article, multiple sets of iterative experiments are set up to eliminate the measurement error.
When the household safety monitoring system is running, due to the influence of its own system and household environmental factors, there may be some errors in data measurement. Therefore, this article set up iterative experiments to eliminate errors through multiple iterative experiments. The final result was the average of the results of multiple iterations.
In the process of home safety monitoring, the effect of home safety monitoring was affected by the residential area. Therefore, this article divided the experimental housing into large-scale home environments and small-scale home environments. The area of a large-scale residential environment is greater than or equal to 120 square meters, and the area of a small-scale residential environment is less than 120 square meters.
4 Application results of safety monitoring system
4.1 Response time
Response time refers to the time from the acquisition of data to the response of the system. Response time can greatly improve the performance of home safety monitoring. When the home environment is complex, the home safety monitoring system needs to respond in time to ensure the safety of the home environment. The response time of home safety monitoring systems based on WSN technology of AI and traditional home safety monitoring system is compared. The comparison results are shown in Figure 5.
Response time comparison results: (a) response time of large-scale home environment and (b) response time of small-scale home environment.
In Figure 5a, the response time comparison of two home safety monitoring systems in the large-scale home environment is described. Among them, the response time of the traditional home safety monitoring system reaches a minimum of 228 ms in the second time and the maximum of 256 ms in the fourth time, with an average response time of 241.6 ms. The response time of the home safety monitoring system based on the WSN technology of AI has significantly shortened, reaching a minimum of 80 ms in the first time and a maximum of 96 ms in the second time, with an average response time of 85.2 ms. The response time of the home safety monitoring system designed in this article is shorter than that of the traditional home safety monitoring system. This is because the home security monitoring system in this article uses WSN technology, and the acquisition and processing speed of home information is fast. In Figure 5b, the response time comparison of two home safety monitoring systems in a small-scale home environment is described. The response time of the traditional home safety monitoring system reaches a minimum of 100 ms the fourth time and a maximum of 168 ms the first time, with an average response time of 133.2 ms. The response time of the home safety monitoring system based on the WSN technology of AI reaches a minimum of 24 ms the third time and a maximum of 32 ms the fifth time, with an average response time of 28 ms. The response time of the system in this article fluctuates slightly in five experiments, while the response time of the traditional home safety monitoring system fluctuates greatly, so the monitoring response of the system in this article is more stable. Therefore, the home safety monitoring system based on WSN technology of AI can effectively reduce the response time of the system and improve the efficiency of home safety monitoring. Home safety monitoring systems are closely related to people’s life safety, and the shortest possible response time is helpful for the timely occurrence of home safety alerts.
4.2 Monitoring stability
Monitoring stability refers to the anti-interference ability of the home safety monitoring system, and whether it has stable home safety monitoring ability when the home environment changes or the sensor equipment works for a long time. By testing the stability of the household safety monitoring system in different levels of noise data, it is possible to set the noise data ratio to a gradient between 10 and 50% to analyze the stability of the household safety monitoring system. The monitoring stability of home safety monitoring systems based on WSN technology of AI and traditional home safety monitoring system is compared. The comparison results are shown in Figure 6.
Comparison results of monitoring stability: (a) monitoring stability of the large-scale household environment and (b) monitoring stability of the small-scale household environment.
In Figure 6a, the monitoring stability comparison of two home safety monitoring systems in the large-scale home environment is described. The monitoring stability of the traditional household safety monitoring system reaches a minimum of 84% the second time and a maximum of 92% the third time, with an average monitoring stability of 88%. The monitoring stability of the home safety monitoring system based on the WSN technology of AI reaches a minimum of 94% the first time and a maximum of 97% the fourth time, with an average monitoring stability of 95.6%. In Figure 6b, the monitoring stability comparison of two home safety monitoring systems in a small-scale home environment is described. It is obvious that the monitoring stability of the home safety monitoring system based on the WSN technology of AI is higher than that of the traditional home safety monitoring system. Among them, the monitoring stability of the traditional household safety monitoring system reaches the lowest of 87% the fourth time and the highest of 93% the fifth time, with an average monitoring stability of 90%. The average monitoring stability of the home safety monitoring system based on the WSN technology of AI is 97%. The monitoring stability of the home safety monitoring system based on the WSN technology of AI reaches a minimum of 95% the fifth time and a maximum of 99% the third time. Therefore, the application of WSN technology to the home safety monitoring system can significantly improve the monitoring stability of the home monitoring system.
4.3 Monitoring comprehensiveness
The household environment is generally complex, and there are many factors that cause household hazards. The kitchen, bathroom, and other areas need comprehensive monitoring. The home safety monitoring system based on the WSN technology of AI and the traditional home safety monitoring system is compared comprehensively. The comparison results are shown in Figure 7. The housing area of a large-scale home environment is more than 200 square meters. There are 11 sensors in large-scale home environments, including door magnetic sensors, gas sensors, temperature, and humidity sensors. The housing area of a small-scale home environment is between 15 and 30 square meters.
Comparison results of monitoring comprehensiveness: (a) monitoring comprehensiveness of large-scale household environment and (b) monitoring comprehensiveness of small-scale household environment.
In Figure 7a, the comprehensive comparison of the two home safety monitoring systems in the large-scale home environment is described. Among them, the monitoring comprehensiveness of the traditional household safety monitoring system reaches a minimum of 64% the third time and a maximum of 73% the fourth time, with an average monitoring comprehensiveness of 68.8%. The monitoring comprehensiveness of the home safety monitoring system based on the WSN technology of AI reaches a minimum of 82% the third time and a maximum of 87% the fifth time, with an average monitoring comprehensiveness of 84.8%. In Figure 7b, the comprehensive comparison of the two home safety monitoring systems in a small-scale home environment is described. The monitoring comprehensiveness of the traditional household safety monitoring system reaches a minimum of 72% the third time and a maximum of 78% the fourth time, with an average monitoring comprehensiveness of 74.8%. The monitoring comprehensiveness of the home safety monitoring system based on the WSN technology of AI reaches a minimum of 86% the third time and a maximum of 92% the fifth time, with an average monitoring comprehensiveness of 88.8%. WSN technology is applied to the home security monitoring system, and the coverage of home monitoring is improved by building a sensor node network. Therefore, the home safety monitoring system based on WSN technology of AI can effectively improve the comprehensiveness of home safety monitoring.
4.4 Monitoring accuracy
In household safety monitoring, the effect of safety monitoring is largely determined by the accuracy of household monitoring. The monitoring accuracy of home safety monitoring systems based on WSN technology of AI, and traditional home safety monitoring system is compared. The comparison results are shown in Figure 8.
Comparison results of monitoring accuracy: (a) monitoring accuracy of large-scale household environment and (b) monitoring accuracy of small-scale household environment.
In Figure 8a, the monitoring accuracy comparison of two home safety monitoring systems in the large-scale home environment is described. The monitoring accuracy of the home safety monitoring system based on the WSN technology of artificial intelligence is higher than that of the traditional home safety monitoring system throughout the experiment. Among them, the monitoring accuracy of the traditional household safety monitoring system reaches 70.8% the 10th time, 84.6% the 6th time, and the average monitoring accuracy is 77.76%. The monitoring accuracy of the home safety monitoring system based on the WSN technology of AI reaches the lowest of 85.2% the third time and the highest of 94.3% the fifth time, with an average monitoring accuracy of 89.36%. In Figure 8b, the monitoring accuracy comparison of two home safety monitoring systems in the small-scale home environment is described. The monitoring accuracy of the traditional household safety monitoring system reaches 80.1% the second time and 94.2% the seventh time, with an average monitoring accuracy of 87.63%. The monitoring accuracy of the home safety monitoring system based on the WSN technology of AI reaches the lowest of 90.3% the fourth time and the highest of 98.8% the seventh time, with an average monitoring accuracy of 94.43%. Therefore, a home safety monitoring system based on WSN technology of artificial intelligence can effectively improve the accuracy of home safety monitoring.
5 Conclusions
The home safety monitoring system is highly comprehensive. The design of smart computing, wireless communication, and other technologies is very important for home safety. Many home safety accidents have been caused by inadequate monitoring of the home environment. The traditional home monitoring system mainly used the Internet-of-Things technology and used a small number of sensor equipment to monitor the home environment, but the overall monitoring effect was poor. In this article, the WSN technology was applied to the home safety monitoring system, which formed an all-round monitoring through numerous sensor nodes. ZigBee communication technology was used to improve the effect of information transmission, and AI was used to intelligently analyze the data of home monitoring. In this article, the evaluation system of the safety monitoring system was constructed, and the home safety monitoring system based on the WSN technology of AI was compared with the traditional home safety monitoring system. The results showed that the home security monitoring system based on the WSN technology of AI had higher monitoring stability and comprehensive detection and effectively reduced the response time of the system. The home safety monitoring system used WSN technology to greatly improve the monitoring effect of the home environment and ensure the safety of the home environment. However, the number of iterations set in the comparison of the two home monitoring systems in this article is small, which can not reflect the real home safety monitoring effect. Therefore, more groups of home monitoring system experiment iterations have been set, which is the direction of future research.
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Funding information: This work was supported by Hainan Provincial Natural Science Foundation of China, project number: 621RC611 and Scientific research funding project of Hainan Vocational University of Science and Technology, project number: HKKY2022ZD-03.
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Conflict of interest: The authors declare that there is no conflict of interest with any financial organizations regarding the material reported in this manuscript.
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Data availability statement: Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
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