Wireless environmental monitoring method and system based on internet of things

Abstract

The present invention relates to the field of wireless environmental monitoring. Provided are a wireless environmental monitoring method and system based on the Internet of Things. The wireless environmental monitoring method based on the Internet of Things comprises: S1, deploying a plurality of wireless sensor nodes for collecting environment parameters, which comprise temperature, humidity, and air quality; S2, the sensor nodes transmitting collected data to a gateway device by means of low-power wireless communication technology; and S3, the gateway device performing preliminary processing on the received data, and transmitting the data to a cloud server by means of the Internet. The stability and anti-interference capability of data transmission are ensured by using Zigbee communication technology, adaptive frequency-hopping technology, and a dual-antenna system. In terms of data processing, the system uses edge computing technology to perform partial data processing at the gateway device, thereby reducing the computational pressure on the cloud server. Moreover, the cloud server uses a distributed computing architecture, thereby achieving efficient processing of large-scale data and providing good scalability.

Description

A wireless environmental monitoring method and system based on the Internet of Things Technical Field

[0001] This invention relates to the field of wireless environmental monitoring technology, specifically to a wireless environmental monitoring method and system based on the Internet of Things. Background Technology

[0002] A wireless environmental monitoring method and system based on the Internet of Things (IoT) collects various environmental parameters, including temperature, humidity, and air quality, by deploying multiple wireless sensor nodes. The sensor nodes utilize IoT technology to wirelessly transmit the collected data to a central server. On the server side, advanced data processing and analysis algorithms are employed to filter, integrate, and mine massive amounts of environmental data, extracting valuable information. The system features intelligent early warning capabilities, promptly sending alerts to relevant personnel when monitored data exceeds preset thresholds. Furthermore, users can access monitoring data in real time via web pages or mobile applications, enabling them to monitor environmental conditions anytime, anywhere.

[0003] While this IoT-based wireless environmental monitoring system offers advantages such as real-time, accurate, and remote monitoring, it also has some drawbacks. First, the reliability of wireless transmission is affected by environmental factors, making it susceptible to signal interference and attenuation, especially in complex environments or over long distances, potentially leading to data loss or delays. Second, sensor nodes typically rely on battery power, which has a limited lifespan and requires regular maintenance and replacement, increasing operational costs. Third, the large volume of environmental monitoring data places significant pressure on cloud server storage and computing, potentially necessitating high-performance hardware and optimized data processing algorithms to ensure efficient system operation.

[0004] Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a wireless environmental monitoring method and system based on the Internet of Things (IoT), which solves the problem that the reliability of wireless transmission is affected by environmental factors; sensor nodes typically rely on battery power, which has a limited lifespan and requires regular maintenance and replacement, increasing operation and maintenance costs; and the large volume of environmental monitoring data places a heavy burden on the storage and computing power of cloud servers.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: a wireless environmental monitoring method based on the Internet of Things, comprising:

[0009] S1. Deploy multiple wireless sensor nodes to collect environmental parameters, including temperature, humidity, and air quality;

[0010] S2. The sensor node transmits the collected data to the gateway device through low-power wireless communication technology;

[0011] S3. The gateway device performs preliminary processing on the received data and transmits the data to the cloud server via the Internet;

[0012] S4. On the cloud server side, advanced data processing and analysis algorithms are used to filter, integrate, and mine the received environmental data;

[0013] S5. Extract valuable information and store it in the database. The system has an intelligent early warning function. When the monitored data exceeds the preset threshold, an alarm is sent to the relevant personnel.

[0014] S6. Users can access monitoring data in real time via web pages or mobile applications, providing a visual representation of the data in the form of charts and reports;

[0015] S7. By optimizing the data compression algorithm, reduce bandwidth consumption during data transmission, and integrate energy harvesting modules, such as solar panels, into the sensor nodes to extend the equipment's lifespan.

[0016] S8. During data transmission, anti-interference and signal enhancement technologies are employed to improve the reliability of data transmission;

[0017] S9. By utilizing edge computing technology, data is partially processed at the gateway device to reduce the computing pressure on the cloud server.

[0018] Preferably, the sensor node uses Zigbee communication technology to improve the stability and anti-interference capability of data transmission, and the sensor node is equipped with a low-power Bluetooth module to reduce power consumption.

[0019] Preferably, the energy harvesting module of the sensor node adopts a hybrid harvesting method of solar energy and environmental vibration energy to further extend battery life, and the gateway device is equipped with a dual-antenna system to improve signal reception sensitivity and transmission distance.

[0020] Preferably, AES encryption technology is used during the data transmission process to ensure data security and privacy.

[0021] Preferably, the cloud server adopts a distributed computing architecture to improve the system's scalability and processing power; the client application has data analysis and report generation functions to facilitate users' in-depth data mining and analysis; and the data transmission between the sensor nodes and the gateway device adopts adaptive frequency hopping technology to reduce signal interference.

[0022] Preferably, the environmental data processing algorithm includes anomaly detection and trend analysis to improve the accuracy of data analysis.

[0023] Preferably, the success rate of wireless data transmission can be measured by the signal reception power (P). r It is described by the signal-to-interference ratio (SIR):

[0024] Where: P t It is transmission power, G t and G r These represent the transmit and receive antenna gains, respectively; λ is the signal wavelength; d is the transmission distance; and SIR (signal-to-interference ratio) is:

[0025] Where: I is the interference signal power, N is the noise power, and the energy consumption of the sensor node mainly includes the energy consumption for data acquisition, processing, and transmission, which can be expressed as: E total =E sense +E proc +E trans

[0026] Among them: E sense It is the energy consumption for data acquisition, E proc It is the energy consumption for data processing, E trans This refers to the energy consumption for data transmission. For sensor nodes, the transmission energy consumption is E. trans It can be further subdivided into: E trans =P t ·t

[0027] Where: P t Where t is the transmission power and t is the transmission time, the data processing and analysis algorithm, on the cloud server side, the processing and analysis of environmental data can be described by the following formula: D(t) = f(D raw (t),θ)

[0028] Where: D(t) is the processed data, D raw (t) represents the original data, θ represents the parameter set of the algorithm, and f represents the data processing and analysis algorithm.

[0029] Anomaly detection algorithms can employ statistical models, such as those based on mean and standard deviation: Outlier = {x∈D(t)||x-μ|>kσ}

[0030] Where μ is the data mean, σ is the data standard deviation, and k is the detection threshold. The signal enhancement can be achieved by increasing the antenna gain or using a relay node.

[0031] Anti-interference technologies such as adaptive frequency hopping can avoid interference by adjusting the frequency: fnew =f current +Δf

[0032] Where: f new It is a new transmission frequency, f current This is the current transmission frequency, and Δf is the frequency offset. In edge computing, some data processing is completed at the gateway device to reduce the load on the cloud server: D edge (t)=g(D raw (t),φ)

[0033] Where: D edge (t) is the data after edge computing processing, φ is the parameter set of the edge computing algorithm, and g is the edge computing algorithm.

[0034] A wireless environmental monitoring system based on the Internet of Things includes wireless sensor nodes, an integrated energy harvesting module, a gateway device, a cloud server, and a user terminal.

[0035] (III) Beneficial Effects

[0036] This invention provides a wireless environmental monitoring method and system based on the Internet of Things (IoT). It has the following beneficial effects:

[0037] By employing Zigbee communication technology, adaptive frequency hopping technology, and a dual-antenna system, the stability and anti-interference capability of data transmission are ensured.

[0038] In terms of data processing, the system utilizes edge computing technology to perform some data processing at the gateway device, reducing the computing pressure on the cloud server. At the same time, the cloud server adopts a distributed computing architecture, which is efficient in processing large-scale data and has good scalability. During data transmission, AES encryption technology and multi-level data access permission management are used to ensure data security and privacy.

[0039] The intelligent early warning function can detect environmental anomalies in a timely manner and notify relevant personnel. The self-diagnostic function ensures the reliability and maintenance efficiency of the system. Users can access and monitor environmental data in real time through web pages or mobile applications. The data visualization display allows users to intuitively understand and analyze environmental conditions. The system is designed to be applicable to various scenarios such as agricultural, industrial and urban environmental monitoring, and can be linked with smart home devices to realize the automation of environmental control. Detailed Implementation

[0040] The technical solutions in the embodiments of the present invention have been clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] This invention provides a wireless environmental monitoring method and system based on the Internet of Things, comprising:

[0042] S1. Deploy multiple wireless sensor nodes to collect environmental parameters, including temperature, humidity, and air quality. The processing algorithm for the environmental data includes anomaly detection and trend analysis to improve the accuracy of data analysis.

[0043] S2. The sensor node transmits the collected data to the gateway device via low-power wireless communication technology. The sensor node uses Zigbee communication technology to improve data transmission stability and anti-interference capabilities. The sensor node is equipped with a low-power Bluetooth module to reduce energy consumption. The energy harvesting module of the sensor node adopts a hybrid harvesting method of solar energy and environmental vibration energy to further extend battery life. The gateway device is equipped with a dual-antenna system to improve signal reception sensitivity and transmission distance. The energy consumption of the sensor node mainly includes the energy consumption of data acquisition, processing, and transmission, which can be expressed as: E total =E sense +E proc +E trans

[0044] Among them: E sense It is the energy consumption for data acquisition, E proc It is the energy consumption for data processing, E trans This refers to the energy consumption for data transmission. For sensor nodes, the transmission energy consumption is E. trans It can be further subdivided into: E trans =P t ·t

[0045] Where: P t t is the transmission power, and t is the transmission time.

[0046] S3. The gateway device performs preliminary processing on the received data and transmits the data to the cloud server via the Internet. AES encryption technology is used during data transmission to ensure data security and privacy. The success rate of wireless data transmission can be measured by the signal reception power (P). r It is described by the signal-to-interference ratio (SIR):

[0047] Where: P t It is transmission power, G t and G r These represent the transmit and receive antenna gains, respectively; λ is the signal wavelength; d is the transmission distance; and SIR (signal-to-interference ratio) is:

[0048] Where: I is the interference signal power, and N is the noise power.

[0049] S4. On the cloud server side, advanced data processing and analysis algorithms are used to filter, integrate, and mine the received environmental data. The cloud server adopts a distributed computing architecture to improve the system's scalability and processing capabilities. The client application has data analysis and report generation functions, which facilitates users to perform in-depth data mining and analysis. The data transmission between the sensor nodes and the gateway device adopts adaptive frequency hopping technology to reduce signal interference.

[0050] S5. Extract valuable information and store it in the database. The system has an intelligent early warning function. When the monitored data exceeds a preset threshold, an alarm is sent to relevant personnel. The data processing and analysis algorithm, on the cloud server side, allows for the processing and analysis of environmental data, which can be described by the following formula: D(t)=f(D raw (t),θ)

[0051] Where: D(t) is the processed data, D raw (t) represents the original data, θ represents the parameter set of the algorithm, and f represents the data processing and analysis algorithm.

[0052] Anomaly detection algorithms can employ statistical models, such as those based on mean and standard deviation: Outlier = {x∈D(t)||x-μ|>kσ}

[0053] Where μ is the data mean, σ is the data standard deviation, and k is the detection threshold. The signal enhancement can be achieved by increasing the antenna gain or using a relay node.

[0054] Anti-interference technologies such as adaptive frequency hopping can avoid interference by adjusting the frequency: f new =f current +Δf

[0055] Where: f new It is a new transmission frequency, f current This is the current transmission frequency, and Δf is the frequency offset. In edge computing, some data processing is completed at the gateway device to reduce the load on the cloud server: D edge (t)=g(D raw (t),φ)

[0056] Where: D edge (t) is the data after edge computing processing, φ is the parameter set of the edge computing algorithm, and g is the edge computing algorithm.

[0057] S6. Users can access monitoring data in real time via web pages or mobile applications, providing a visual representation of the data in the form of charts and reports.

[0058] S7. By optimizing the data compression algorithm, bandwidth consumption during data transmission is reduced, and energy harvesting modules, such as solar panels, are integrated into the sensor nodes to extend the equipment's lifespan.

[0059] S8. During data transmission, anti-interference and signal enhancement technologies are employed to improve the reliability of data transmission.

[0060] S9. By utilizing edge computing technology, data is partially processed at the gateway device to reduce the computing pressure on the cloud server.

[0061] A wireless environmental monitoring system based on the Internet of Things includes wireless sensor nodes, an integrated energy harvesting module, a gateway device, a cloud server, and a user terminal.

[0062] Table 1: Comparison of Parameters between the Invention and Existing Technologies

[0063] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A wireless environment monitoring method based on Internet of Things, characterized in that, Comprise: S1, deploy multiple wireless sensor nodes for collecting environmental parameters, including temperature, humidity, air quality; S2, sensor nodes transmit collected data to gateway devices through low-power wireless communication technology; S3, gateway devices perform preliminary processing on received data and transmit data to cloud servers through the Internet; S4, in the cloud server, high-level data processing and analysis algorithms are used to filter, integrate and mine the received environmental data; S5, valuable information is extracted and stored in the database, and the system has intelligent early warning function, when the monitoring data exceeds the preset threshold, the alarm is sent to the relevant personnel; S6, users can access monitoring data in real time through web pages or mobile applications, and provide visual display of data in the form of charts and reports; S7, through optimized data compression algorithm, reduce bandwidth consumption in data transmission process, integrate energy collection module such as solar panel in sensor node, to prolong the service life of the equipment; S8, in the process of data transmission, anti-interference and signal enhancement technology is adopted to improve the reliability of data transmission; S9, use edge computing technology, part of the data is processed at the gateway device, to reduce the computing pressure of the cloud server. 2.The wireless environment monitoring method based on the Internet of Things according to claim 1, wherein: The sensor node uses Zigbee communication technology to improve the stability and anti-interference ability of data transmission, and the sensor node is equipped with low-power Bluetooth module to reduce energy consumption. 3.The wireless environment monitoring method based on the Internet of Things according to claim 1, characterized in that: The energy collection module of the sensor node adopts solar energy and environmental vibration energy mixed collection method to further prolong the battery life, and the gateway device is equipped with double antenna system to improve the sensitivity of signal reception and transmission distance. 4.The wireless environment monitoring method based on the Internet of Things according to claim 1, wherein: AES encryption technology is used in the process of data transmission to ensure the security and privacy of data. 5.The wireless environment monitoring method based on the Internet of Things according to claim 1, characterized in that: The cloud server adopts distributed computing architecture to improve the scalability and processing capacity of the system, the user terminal application has data analysis and report generation function, which is convenient for users to carry out deep data mining and analysis, and the data transmission between the sensor node and the gateway device adopts adaptive frequency hopping technology to reduce signal interference. 6.The wireless environment monitoring method based on the Internet of Things according to claim 1, characterized in that: The environmental data processing algorithm includes anomaly detection and trend analysis to improve the accuracy of data analysis. 7.The wireless environment monitoring method based on the Internet of Things according to claim 1, characterized in that: The transmission success rate of the wireless data can be described by a signal received power (P r ) and a signal to interference ratio (SIR): where: P is the transmitted power, Gt and Gr are the transmit and receive antenna gains, respectively, λ is the signal wavelength, d is the transmission distance, and the signal-to-interference ratio (SIR) is: t t r ​​​ Wherein: I is the interference signal power, N is the noise power, the energy consumption of the sensor node mainly includes the energy consumption of data collection, processing and transmission, which can be represented as: E total = E sense + E proc + E trans wherein: E sense is the data acquisition energy consumption, E proc is the data processing energy consumption, E trans is the data transmission energy consumption, energy consumption of the sensor node, transmission energy consumption E trans may be further subdivided into: E trans = P t · t where: P t is the transmission power, t is the transmission time, the data processing and analysis algorithm, the processing and analysis of environmental data at the cloud server end can be described by the following formula: D(t) = f(D raw (t), θ) where: D(t) is the processed data, D raw (t) is the raw data, Θ is the parameter set of the algorithm, f is the data processing and analysis algorithm The anomaly detection algorithm can use statistical model, such as mean and standard deviation based detection: Outlier = {x∈D(t)||x-μ∣>kσ} where: μ is the data mean, σ is the data standard deviation, k is the detection threshold, and the signal enhancement can be achieved by increasing the antenna gain or using a relay node: Anti-interference technology such as adaptive frequency hopping technology can avoid interference by adjusting frequency: f new = f current + Δf wherein: f new is the new transmission frequency, f current is the current transmission frequency, and Δf is the frequency offset. In edge computing, part of data processing is completed at the gateway device to reduce the load of the cloud server: D edge (t) = g(D raw (t),φ) where: D edge (t) is the data after edge computing processing, φ is the parameter set of the edge computing algorithm, and g is the edge computing algorithm.

8. An Internet of Things based wireless environment monitoring system characterized by: Wireless sensor node, integrated energy collection module, gateway device, cloud server, user terminal.

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