CN114828030B - WIFI coverage condition identification method, device and system based on traffic - Google Patents
WIFI coverage condition identification method, device and system based on traffic Download PDFInfo
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Abstract
The invention discloses a WIFI coverage condition identification method, device and system based on flow, belonging to the technical field of wireless mobile communication, wherein the method comprises the following steps: capturing WIFI data packets and acquiring data characteristics of the WIFI data packets under different network coverage conditions; comparing the difference degree of each data characteristic under different network coverage conditions, and taking the difference degree larger than a threshold value as a target data characteristic; constructing a machine learning model corresponding to the target data characteristics, and training the machine learning model by using the labeled WIFI data packet to obtain a WIFI coverage condition model; and acquiring a WIFI data packet under the current network, acquiring corresponding target data characteristics of the WIFI data packet, and inputting the target data characteristics under the current network into a WIFI coverage condition model to obtain the coverage condition of the network. The method and the device can accurately acquire the current network condition in real time, and greatly improve the accuracy and the stability of WIFI coverage condition identification.
Description
Technical Field
The invention belongs to the technical field of wireless mobile communication, and particularly relates to a WIFI coverage condition identification method, device and system based on flow.
Background
Along with the explosion of wireless internet and intelligent terminal, wireless network brings great convenience to people's life, but when bringing convenience, the problem of network quality always influences user's experience. The most common problem is coverage of WIFI, and once the coverage of WIFI is poor, a user can refuse to use WIFI, and even use traffic. The coverage condition of the network is judged by capturing the data packet in real time and through the model, the user can be reminded with low cost and high efficiency, and the network utilization experience of the user can be improved.
The WIFI coverage status identification is a technology for identifying the coverage status of the network environment where the data packet is located according to the characteristics of the real-time data packet. The WIFI coverage condition identification has very important significance in the fields of intelligent networks, everything interconnection and the like. Researchers of the intelligent network can realize network optimization through WIFI coverage condition identification; researchers in case of interconnection can study the interconnection of more devices through WIFI coverage status identification. WIFI coverage status identification is therefore a very important technology.
WIFI coverage status identification has been studied for a relatively long time. The initial researchers only complete the WIFI coverage status identification through the RSSI values. However, along with the complex changes of the environment and the continuous increase of the space, the RSSI value of each position cannot be accurately measured, and the WIFI coverage condition identification cannot be completed with low cost and high efficiency. Later, through ray tracing, the WIFI coverage condition of each position is realized through calculation by simulating an indoor environment, but the method can not realize dynamic WIFI coverage condition identification, and has low accuracy.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a WIFI coverage condition identification method, device and system based on flow, which aim to judge the coverage condition of a network by capturing data packets in real time and through a machine learning model, and can remind a user with low cost and high efficiency, thereby solving the technical problem that the prior art cannot identify the WIFI coverage condition efficiently and accurately.
In order to achieve the above object, according to one aspect of the present invention, there is provided a traffic-based WIFI coverage status identifying method, including:
s1: capturing a plurality of WIFI data packets under different network coverage conditions, marking the network coverage conditions of the WIFI data packets and acquiring the data characteristics of the WIFI data packets;
S2: comparing the difference degree of each data characteristic under different network coverage conditions, and taking the data characteristic with the difference degree larger than a threshold value as a target data characteristic; the data features include: the method comprises the steps of uploading packet rate, downloading packet rate, uplink rate, downlink rate, time delay, jitter, maximum time delay and minimum time delay;
S3: constructing a machine learning model corresponding to the target data characteristics, and training the machine learning model by using the labeled WIFI data packet to obtain a WIFI coverage condition model;
S4: and acquiring a WIFI data packet under the current network, acquiring corresponding target data characteristics of the WIFI data packet, and inputting the target data characteristics under the current network into the WIFI coverage condition model to obtain the coverage condition of the network.
In one embodiment, the S1 includes:
s11: capturing a plurality of WIFI data packets under different network coverage conditions, and marking the network coverage conditions of the WIFI data packets; the WIFI data packet is a pcap file;
S12: grouping the pcap files every other fixed number of data packets and converting the grouping into csv files suitable for being used in a machine learning model;
s13: and screening the valid fields in the csv file to calculate the data characteristics.
In one embodiment, the valid field includes at least: source IP, source port number, destination IP, destination port number, protocol, packet length, payload length, sequence number, acknowledgement byte and time;
The WIFI data comprise an uploading packet and a downloading packet, wherein the uploading packet is a data packet transmitted from the terminal equipment to the remote server, and the downloading packet is a data packet transmitted from the remote service area to the terminal equipment.
In one embodiment, the S2 includes:
S21: calculating cosine similarity of each data characteristic under different network coverage conditions;
S22: and taking the data characteristic corresponding to the cosine similarity larger than the threshold value as the target data characteristic.
In one embodiment, the step S21 includes:
Using the formula Calculating cosine similarity of each data characteristic under different network coverage conditions;
Wherein, A and B are the same length vector corresponding to the data features under the two different network coverage conditions, n is the vector length, A i is the ith element in the vector A, and B i is the ith element in the vector B.
In one embodiment, the network coverage condition includes: high coverage corresponding to-30 db and low coverage corresponding to-70 db.
In one embodiment, the machine learning model is a CNN model, a random forest model, or a decision tree model.
In a second aspect, the present invention provides a traffic-based WIFI coverage status identifying apparatus, including:
the data packet grabbing module is used for grabbing a plurality of WIFI data packets under different network coverage conditions, marking the network coverage conditions of the WIFI data packets and acquiring the data characteristics of the WIFI data packets;
The difference degree comparison module is used for comparing the difference degree of each data characteristic under different network coverage conditions and taking the data characteristic with the difference degree larger than a threshold value as a target data characteristic; the data features include: the method comprises the steps of uploading packet rate, downloading packet rate, uplink rate, downlink rate, time delay, jitter, maximum time delay and minimum time delay;
the network model building module is used for building a machine learning model corresponding to the target data characteristics, and training the machine learning model by using the labeled WIFI data packet to obtain a WIFI coverage condition model;
the coverage condition identification module is used for acquiring the WIFI data packet under the current network and obtaining the corresponding target data characteristic of the WIFI data packet, and inputting the target data characteristic under the current network into the WIFI coverage condition model to obtain the coverage condition of the network.
In a third aspect, the present invention provides a WIFI coverage status identification system based on traffic, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the method when executing the computer program.
In a third aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method.
In general, the above technical solutions conceived by the present invention, compared with the prior art, enable the following beneficial effects to be obtained:
1. The invention provides a WIFI coverage condition identification method based on flow, which is characterized in that the traditional method can only carry out manual statistics or static calculation, so that the result has volatility and uncertainty, and accurate judgment can not be given to complex situations; according to the invention, through capturing and analyzing the WIFI data packet in real time, a pre-trained machine learning model is put in, and the machine learning model is constructed aiming at the data characteristics with the difference degree larger than the threshold value under different network coverage conditions, so that the current network conditions can be accurately obtained in real time, and the accuracy and the stability of the identification of the WIFI coverage conditions are greatly improved.
2. According to the WIFI coverage condition identification method, the WIFI data packet is captured and preprocessed under different network coverage conditions, all the features are matched under different environments, the selected features are input into a pre-trained WIFI coverage condition model, the network condition of the data packet is identified, and therefore the WIFI coverage condition identification accuracy is greatly improved.
3. The invention can integrate three machine learning algorithms to select the result with highest recognition accuracy, minimum time complexity and most stable result, thereby greatly improving the usability of the system.
Drawings
Fig. 1 is a flowchart of a method for identifying a WIFI coverage status based on traffic according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of valid fields of data preprocessing according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a characteristic data frame after data preprocessing according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of feature computation (time delay) provided by an embodiment of the present invention;
FIG. 5 is a flow chart of feature selection provided by an embodiment of the present invention;
FIG. 6 is an illustration of features dissimilar provided by an embodiment of the present invention;
Fig. 7 is a schematic structural diagram of a WIFI coverage status recognition device based on traffic according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
As shown in fig. 1, the present invention provides a method for identifying WIFI coverage status based on traffic, including:
S1: capturing a plurality of WIFI data packets under different network coverage conditions, marking the network coverage conditions of the WIFI data packets and acquiring the data characteristics of the WIFI data packets;
S2: comparing the difference degree of each data characteristic under different network coverage conditions, and taking the data characteristic with the difference degree larger than a threshold value as a target data characteristic; the data features include: the method comprises the steps of uploading packet rate, downloading packet rate, uplink rate, downlink rate, time delay, jitter, maximum time delay and minimum time delay;
S3: constructing a machine learning model corresponding to the target data characteristics, and training the machine learning model by using the labeled WIFI data packet to obtain a WIFI coverage condition model;
s4: and acquiring a WIFI data packet under the current network, acquiring corresponding target data characteristics of the WIFI data packet, and inputting the target data characteristics under the current network into a WIFI coverage condition model to obtain the coverage condition of the network.
In one embodiment, S1 comprises:
s11: capturing a plurality of WIFI data packets under different network coverage conditions, and marking the network coverage conditions of the WIFI data packets; the WIFI data packet is a pcap file;
S12: grouping the pcap files every other fixed number of data packets, and converting the grouping into csv files suitable for being used in a machine learning model;
S13: the valid fields in the csv file are filtered to calculate the individual data characteristics.
In this embodiment, in two WIFI coverage cases (high coverage-30 db and low coverage-70 db), where the coverage situation is checked in real time by CellularZ software, the WIFI data packet is captured by using Wireshark software, and stored in a format of a pcap file, the data packets are grouped at fixed intervals, and effective fields are screened in each group, including: source IP, source port number, destination IP, destination port number, protocol, packet length, payload length, sequence number, acknowledgement byte, time, as shown in fig. 2. The data packets are divided into uploading packets and downloading packets, wherein the uploading packets are data packets transmitted from the terminal equipment to the remote server, and the downloading packets are data packets transmitted from the remote service area to the terminal equipment. Calculating features by screening the selected valid fields, including: the characteristics are combined into a csv format data frame, as shown in fig. 3. Specifically, the process of calculating the time delay is shown in fig. 4, and the time delay is converted into NumPy arrays shown in table 1. Specifically, in this embodiment, the fixed number is 150.
| Features (e.g. a character) | 0 | 0 | … |
| Upload packet rate | 3.1067 | 3.0888 | … |
| Download packet rate | 6.03079 | 5.996 | … |
| Uplink rate | 0.25946 | 0.41149 | … |
| Downlink rate | 1.62175 | 13.2996 | … |
| Time delay | 1.1522 | 1.1522 | … |
| Dithering | 5.06006 | 54.1318 | … |
| Maximum time delay | 2.40881 | 4.37549 | … |
| Minimum time delay | 0.019775 | 0.020019 | … |
TABLE 1
Wherein, the valid field includes:
Source IP: source port number of the local machine: port number for transmitting data packet of the local machine;
Destination IP: the IP address of the remote server transmitting the data packet;
Destination port number: port number for data packet transmission of remote server;
protocol: the network protocol used for the data packet transmission at this time;
Packet length: the number of bytes per packet;
payload length: fragment length of data packet in transmission process;
Sequence number: recording a serial number of a data packet transmission position in the TCP transmission process;
acknowledgement bytes: recording byte sequence numbers of data packets which are normally transmitted in the TCP transmission process;
time: the absolute time of arrival of the packet.
In one embodiment, the valid field includes at least: source IP, source port number, destination IP, destination port number, protocol, packet length, payload length, sequence number, acknowledgement byte and time;
The WIFI data comprise an uploading packet and a downloading packet, wherein the uploading packet is a data packet transmitted from the terminal equipment to the remote server, and the downloading packet is a data packet transmitted from the remote service area to the terminal equipment.
In one embodiment, S2 comprises:
S21: calculating cosine similarity of each data characteristic under different network coverage conditions;
S22: and taking the data characteristic corresponding to the cosine similarity larger than the threshold value as the target data characteristic.
In one embodiment, S21 includes:
Using the formula Calculating cosine similarity of each data characteristic under different network coverage conditions;
Wherein, A and B are the same length vector corresponding to the data features under the two different network coverage conditions, n is the vector length, A i is the ith element in the vector A, and B i is the ith element in the vector B.
Specifically, the above feature matching method is to compare cosine similarity between features, where the definition of cosine similarity is as follows:
Where A i and B i are the values of each point of the two curves.
When the cosine similarity is larger than 0.8, judging that the characteristic change is not obvious under different coverage conditions, otherwise, judging that the characteristic change is obvious under different coverage conditions. Specifically, the feature similarity determination flow is shown in fig. 5. In particular, fig. 6 illustrates examples of feature dissimilarity under different coverage conditions.
It should be noted that the selected characteristics are downlink rate, delay, jitter, and maximum delay. Table 2 shows the experimental results of different feature combinations, and the results of experiment 21 verify the effectiveness of feature selection.
TABLE 2
In one embodiment, the network coverage condition includes: high coverage and low coverage, high coverage corresponding to-30 db and low coverage corresponding to-70 db.
In one embodiment, the machine learning model is a CNN model, a random forest model, or a decision tree model.
The WIFI coverage condition identification method based on the flow provided by the invention can be applied to classrooms, laboratories, offices and other daily places, and compared with other methods for detecting the WIFI coverage condition, the WIFI coverage condition identification method based on the flow is lower in cost and higher in accuracy. In order to further explain the performance of the method provided by the invention, experiments are carried out in a laboratory under different network coverage conditions through android equipment, WIFI data packets are captured at different positions in the laboratory, the captured data packets are uploaded to a server, the data packets are transmitted back to the WIFI network coverage condition through remote processing, and an actual measurement result is taken as a reference to obtain the accuracy; 30 experiments were performed in total according to the above method, and the results obtained for each 10 times were averaged, and the obtained results are shown in table 3:
TABLE 3 Table 3
From table 3, the accuracy rate of the WIFI coverage condition identification method based on the flow provided by the invention can reach about 90% no matter which machine learning algorithm is used, and the method has higher availability.
As shown in fig. 7, the device for identifying WIFI coverage status based on traffic of the present invention further includes:
The data packet grabbing module is used for grabbing a plurality of WIFI data packets under different network coverage conditions, marking the network coverage conditions of the WIFI data packets and acquiring the data characteristics of the WIFI data packets;
The difference degree comparison module is used for comparing the difference degree of each data characteristic under different network coverage conditions and taking the data characteristic with the difference degree larger than a threshold value as a target data characteristic; the data features include: the method comprises the steps of uploading packet rate, downloading packet rate, uplink rate, downlink rate, time delay, jitter, maximum time delay and minimum time delay;
The network model building module is used for building a machine learning model corresponding to the target data characteristics, and training the machine learning model by using the labeled WIFI data packet to obtain a WIFI coverage condition model;
the coverage condition recognition module is used for acquiring the WIFI data packet under the current network and obtaining the corresponding target data characteristic of the WIFI data packet, and inputting the target data characteristic under the current network into the WIFI coverage condition model to obtain the coverage condition of the network.
A WIFI coverage condition identification system based on traffic comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the method when executing the computer program.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. The WIFI coverage condition identification method based on the traffic is characterized by comprising the following steps of:
s1: capturing a plurality of WIFI data packets under different network coverage conditions, marking the network coverage conditions of the WIFI data packets and acquiring the data characteristics of the WIFI data packets;
S2: comparing the difference degree of each data characteristic under different network coverage conditions, and taking the data characteristic with the difference degree larger than a threshold value as a target data characteristic; the data features include: the method comprises the steps of uploading packet rate, downloading packet rate, uplink rate, downlink rate, time delay, jitter, maximum time delay and minimum time delay;
S3: constructing a machine learning model corresponding to the target data characteristics, and training the machine learning model by using the labeled WIFI data packet to obtain a WIFI coverage condition model;
s4: collecting a WIFI data packet under a current network, acquiring corresponding target data characteristics of the WIFI data packet, and inputting the target data characteristics under the current network into the WIFI coverage condition model to obtain a network coverage condition;
The step S2 comprises the following steps: s21: calculating cosine similarity of each data characteristic under different network coverage conditions; s22: taking the data characteristic corresponding to the cosine similarity larger than the threshold value as the target data characteristic;
the S21 includes: using the formula Calculating cosine similarity of each data characteristic under different network coverage conditions; wherein, A and B are the same length vector corresponding to the data features under the two different network coverage conditions, n is the vector length, A i is the ith element in the vector A, and B i is the ith element in the vector B.
2. The traffic-based WIFI coverage status identification method according to claim 1, wherein S1 includes:
s11: capturing a plurality of WIFI data packets under different network coverage conditions, and marking the network coverage conditions of the WIFI data packets; the WIFI data packet is a pcap file;
S12: grouping the pcap files every other fixed number of data packets and converting the grouping into csv files suitable for being used in a machine learning model;
s13: and screening the valid fields in the csv file to calculate the data characteristics.
3. The traffic-based WIFI coverage status identification method of claim 2, wherein the valid field comprises at least: source IP, source port number, destination IP, destination port number, protocol, packet length, payload length, sequence number, acknowledgement byte and time;
The WIFI data comprise an uploading packet and a downloading packet, wherein the uploading packet is a data packet transmitted from the terminal equipment to the remote server, and the downloading packet is a data packet transmitted from the remote service area to the terminal equipment.
4. The traffic-based WIFI coverage status identification method of claim 1, wherein the network coverage status comprises: high coverage corresponding to-30 db and low coverage corresponding to-70 db.
5. A method for identifying traffic-based WIFI coverage status according to any of claims 1-4, wherein the machine learning model is a CNN model, a random forest model or a decision tree model.
6. A traffic-based WIFI coverage status identification apparatus, configured to perform the traffic-based WIFI coverage status identification method of claim 1, comprising:
the data packet grabbing module is used for grabbing a plurality of WIFI data packets under different network coverage conditions, marking the network coverage conditions of the WIFI data packets and acquiring the data characteristics of the WIFI data packets;
The difference degree comparison module is used for comparing the difference degree of each data characteristic under different network coverage conditions and taking the data characteristic with the difference degree larger than a threshold value as a target data characteristic; the data features include: the method comprises the steps of uploading packet rate, downloading packet rate, uplink rate, downlink rate, time delay, jitter, maximum time delay and minimum time delay;
the network model building module is used for building a machine learning model corresponding to the target data characteristics, and training the machine learning model by using the labeled WIFI data packet to obtain a WIFI coverage condition model;
the coverage condition identification module is used for acquiring the WIFI data packet under the current network and obtaining the corresponding target data characteristic of the WIFI data packet, and inputting the target data characteristic under the current network into the WIFI coverage condition model to obtain the coverage condition of the network.
7. A traffic-based WIFI coverage status identification system comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1to 5 when the computer program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5.
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| CN108810911A (en) * | 2018-06-04 | 2018-11-13 | 南京邮电大学 | A kind of low-power consumption WAN network planing method based on data mining |
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