CN114449444B - Cross-intelligent portable equipment association method based on WiFi-BLE signal passive sniffing - Google Patents
Cross-intelligent portable equipment association method based on WiFi-BLE signal passive sniffing Download PDFInfo
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Abstract
The invention provides a cross-intelligent portable equipment association method based on WiFi-BLE signal passive sniffing, which realizes the association of WiFi and BLE equipment carried by a user at the same time, and comprises the following steps: (1) WiFi-BLE dual signal sniffing: (2) multi-stage filtering-based signal processing: (3) dynamic time warping based device association: and randomly resampling the signal intensity value sequences of the BLE equipment to enable the signal sampling rates of different BLE equipment to be similar, respectively carrying out centering treatment on the two signal intensity value sequences of WiFi and BLE, calculating the distances of the two signal intensity value sequences of the two equipment by using a dynamic time warping method, and comparing the distances of the two sequences to obtain a correlation result. According to the invention, based on the habit that a user carries two types of equipment with the user, the characteristics of the dynamic change similarity of the two types of signals are extracted by analyzing the WiFi signal generated by the mobile intelligent terminal and the BLE signal generated by the intelligent wearing equipment, so that the mobile intelligent terminal and the intelligent wearing equipment can be accurately associated.
Description
Technical Field
The invention relates to the field of user equipment association, in particular to a cross-intelligent portable equipment association method based on WiFi-BLE signal passive sniffing.
Background
With the development of mobile communication technology and the popularization of mobile devices, intelligent portable devices are widely used in the production and living of people. The commonly used intelligent portable equipment mainly comprises two types of mobile intelligent terminals (such as mobile phones, tablets and the like) and intelligent wearable equipment, and in order to effectively manage and control the two types of equipment, the two types of equipment need to be associated so as to construct a user equipment map. After the two types of equipment are associated, the method can be applied to the scenes of mutual authentication of equipment, theft prevention of equipment and the like.
Aiming at the fact that the cross-intelligent portable equipment association method is still in a starting stage at present, the related technology mainly comprises two types: based on specific software that the mobile intelligent terminal can install intelligent wearable equipment, the corresponding intelligent wearable equipment is identified through analysis of network traffic generated by the mobile intelligent terminal, but the software cannot be associated when the network traffic is not generated; the second type is to perform joint positioning based on WiFi signals and BLE signals, but the application scene is the positioning of a user mobile intelligent terminal, the user is required to actively install corresponding software and deploy a large number of wireless AP hot spots or Bluetooth positioning beacons, and the association is realized by adopting a positioning technology, but the accurate association can not be realized under the condition of multiple users due to certain error caused by limited positioning precision. Therefore, the existing method has the problem that the assumption is too high in requirement, all the users are required to actively participate in the equipment association process, and the accurate cross-intelligent portable equipment association can not be realized by passively silently collecting data.
Consider that mobile smart terminals typically use WiFi access networks, while smart wearable devices use BLE to connect with mobile smart terminals. Moreover, both types of devices typically move together with the user, resulting in similar WiFi signal and BLE signal strength variations. The two classes of devices can thus be associated by analysis of the variation in the strength of the two classes of wireless signals.
Disclosure of Invention
How to efficiently and accurately correlate the mobile intelligent terminal and the intelligent wearable equipment is an important problem to be solved urgently, a user equipment spectrogram can be constructed through equipment correlation, and powerful support is provided for equipment management and control. Aiming at the problem that the existing cross-intelligent portable equipment association method is high in assumption requirement, the method calculates the similarity of WiFi and BLE signal intensity changes of two types of equipment based on a dynamic time warping method under the condition that active participation of a user is not needed, and therefore the two types of equipment are associated.
In order to achieve the above object, the technical scheme of the present invention is as follows: a method of cross-smart portable device association based on passive sniffing of WiFi-BLE signals, the method comprising the steps of:
(1) WiFi-BLE dual signal sniffing: the method comprises the steps of deploying sniffing equipment according to the field and equipment attributes, and determining an effective sniffing range of the sniffing equipment;
(2) Signal processing based on multistage filtering: classifying the acquired WiFi and BLE signal messages according to the equipment addresses, extracting signal intensity values, and carrying out noise filtration on a signal intensity value sequence by utilizing multistage filtering;
(3) Device association based on dynamic time warping: and randomly resampling the signal intensity value sequences of the BLE equipment to enable signal sampling rates of different BLE equipment to be similar, respectively carrying out centering treatment on the two signal intensity value sequences of WiFi and BLE, calculating the DTW distances of the signal intensity value sequences of the two equipment by using a dynamic time warping method, and finally obtaining a correlation result by comparing the DTW distances between the two equipment.
Further, the step (1) of WiFi-BLE dual signal sniffing specifically includes:
(11) Sniffer devices deploying WiFi signals and BLE signals. Selecting a proper position to place sniffing equipment according to the actual sniffing environment;
(12) The effective sniff range of the device is determined. The sniffing device is tested, the effective sniffing range of the sniffing device in the current sniffing environment is judged, the subsequent signal sniffing result is more accurate and effective after the effective sniffing range is determined, and more devices can be deployed according to the range, so that the sniffing coverage range is enlarged.
Further, the multistage filtering method in step (2) includes:
(21) Analyzing WiFi and BLE signal messages obtained by sniffing, respectively classifying the mobile intelligent terminal and the intelligent wearable device according to the IP address and the Bluetooth broadcast address, and then extracting an RSS value and a corresponding timestamp in the messages to form an RSS sequence;
(22) The RSS sequence is filtered by using a low-pass filter Butterworth filter and an arithmetic average filter to weaken interference of jitter noise and impact noise, so that calculation of a follow-up DTW value is more accurate.
Further, the device association method in step (3) includes:
(31) From the RSS sequence set of all BLE devices, two device RSS sequences are selected at will, and a sampling rate threshold is calculated;
(32) Based on the sampling rate threshold, randomly sampling BLE equipment sequences with more RSSs, enabling the sampling rate to be close to that of another BLE equipment, calculating a DTW value with the RSS sequences of the target WiFi equipment, randomly sampling for 1000 times, and taking an average value as a final DTW value;
(33) For BLE equipment with a small RSS number, directly calculating the DTW value of the sequence of the BLE equipment and the RSS sequence of the target WiFi equipment;
(34) Comparing the two DTW values, and selecting BLE equipment with a small DTW value as candidate equipment;
(35) For n BLE devices, two devices are arbitrarily selected from the n BLE devices, and then C (n, 2) combinations are obtained to obtain C (n, 2) candidate BLE devices, a candidate BLE device sequence is formed, and the mode in the sequence is selected as the final association result of the target WiFi device.
Before calculating the DTW values in the steps (32) and (33), the two sequences are subjected to a centering process, that is, the average value of the RSS values of the sequences is subtracted from each RSS value in the sequences, so that errors in the comparison result of the DTW values due to the difference of the RSS average values of different devices can be avoided through the centering operation.
Compared with the prior art, the invention has the following advantages:
1. the device association is carried out in a passive signal sniffing scene without actively installing software by a user, so that the device association method has practical feasibility;
2. the signal sequence is processed by low-pass filtering and arithmetic average filtering, so that the interference caused by noise is effectively reduced;
3. aiming at the problems that different BLE equipment broadcast message sampling rates are unequal and the distribution is uneven and can influence the DTW calculation, the random resampling and the centralizing processing are utilized, and the calculated DTW value enables the correlation result to be more accurate.
4. The intelligent wearable device and the mobile phone are associated based on the dynamic change consistency of the BLE signal and the WiFi signal, a good association result is obtained, and the monitoring and control device can be effectively assisted by a monitoring and control department.
Drawings
FIG. 1 is a schematic diagram of a related scenario based on passive signal sniffing according to the present invention;
FIG. 2 is a flow chart of a method associated with passive signal sniffing in accordance with the present invention;
fig. 3 is a schematic diagram of a data packet format of a WiFi signal and a BLE broadcast signal according to the present invention;
fig. 4 is a flowchart of an association algorithm based on dynamic time warping according to the present invention.
Detailed Description
The following describes specific embodiments of the present invention in detail with reference to the drawings.
In this embodiment, a method for associating a cross-intelligent portable device based on passive sniffing of WiFi-BLE signals is provided, where the association scenario is shown in fig. 1, where signal sniffing devices are deployed in the scenario, the effective sniffing range of the sniffing devices is tested, then two types of signals of WiFi and BLE are sniffed, the signal intensity value sequences of the two types of devices are extracted by analyzing the sniffed messages, then the signal sequences are processed by using multistage filtering, and finally DTW values of the two types of signal sequences are calculated based on a dynamic time warping method, so that accurate association between the WiFi device and the BLE device is achieved.
Example 1: as shown in fig. 2, the cross-intelligent portable device association method based on the passive sniffing of the WiFi-BLE signal comprises three steps, namely WiFi-BLE dual signal sniffing, signal processing based on multistage filtering and device association based on a dynamic time warping method: the method is characterized by comprising the following steps:
step (1), wiFi-BLE double signal sniffing,
1.1 sniffing device deploying WiFi and BLE signals
The WiFi signal sniffing equipment used in the invention is a notebook of the Ubuntu system and a wireless network card supporting monitor mode, and when the wireless network card is used for sniffing, the wireless network card is firstly required to be set to the monitor mode. Then, considering the grabbing of the convenient data, the channel of the wireless router providing the network service in the sniffing place can be fixed, so that the sniffing channel of the wireless network card can be set, and finally, the sniffing of the WiFi signal is performed by using the Tcpdump command, wherein the specific commands are as follows: the tcpdump-i network card name-w stores the file name; the sniffing equipment of the BLE broadcast signals is a computer of a Windows system and a Nordic nRF51 Dongle chip, and a plug-in corresponding to the chip is required to be installed in Wireshark software and then sniffing and packet grabbing are carried out.
In a given scene, the intensities of the two types of signals are mainly related to the distance between the user equipment and the sniffing equipment, so that the intensity variation trend of the two types of signals obtained by sniffing is the same, the sniffing equipment of the two types of signals needs to be arranged close, but due to the limitations of equipment size, signal interference and the like, in the invention, the interval between the two types of sniffing equipment is about 20 cm. In order to reduce the interference of objects in a scene on signals, sniffing equipment is generally arranged at a higher position, and in consideration of the convenience of deployment, a USB extension line can be used for connecting a computer and then a wireless network card and a Nordic chip are deployed at corresponding positions. In node arrangement, we find that the larger the distance change rate between two kinds of devices and the sniffing node is, the larger the change rate of the signal intensity is caused, so that the signal difference between different devices is more obvious, and the association precision can be improved. Therefore, when the sniffing node is arranged, the sniffing node needs to be as close as possible to the line in which the user movement direction is located.
1.2 determining sniffing device effective Range
After the sniffing equipment is deployed, the sniffing equipment needs to be tested, and the effective sniffing range of the sniffing equipment in the current sniffing scene is judged, namely the signal intensity RSS of the sniffed equipment and the change trend of the distance d between the equipment are tested in the range, so that the change trend of the distance d between the sniffed equipment meets the formula:
RSS＝A-10nlog(d)
where a is the signal strength when the sniffing node and the user equipment are 1 meter apart and n is the path loss index. The specific test method is that users approach or depart from sniffing nodes at different distances, extract RSS values obtained by sniffing, and make a line graph so as to observe whether the change trend of the line graph accords with a formula. After determining the effective sniffing range of the sniffing device, the sniffing coverage can be enlarged by deploying a plurality of sniffing nodes.
Step (2), signal processing based on multi-stage filtering,
2.1 sniffing Signal message resolution
In this step, the pcap message obtained by sniffing needs to be parsed, so as to classify traffic and obtain RSS sequences and corresponding timestamps in WiFi signals and BLE broadcast signals. Aiming at the WiFi signals, the IP address of each device is obtained by analyzing the IP message header, so that traffic classification is carried out according to the IP address. Then, the RSS value and the corresponding time stamp of the device are obtained by analyzing the radio tap message header and the pcap data packet header; and aiming at the BLE broadcast signals, the broadcast addresses of all the devices are obtained by analyzing the btle message header, so that traffic classification is carried out according to the broadcast addresses. And then obtaining the RSS value and the corresponding time stamp of the equipment by analyzing the ble message header and the pcap data packet header. The packet formats of the two types of messages are shown in fig. 3.
2.2 RSS sequence multi-level filtering
After the message is parsed, each device obtains an RSS sequence, and due to the influence of factors such as surrounding environment and user movement, the RSS sequences of the two types of devices both contain some jitter noise and impact noise, and the two types of devices need to be filtered. In order to deal with these noises, we compare existing filtering methods, including arithmetic average filtering, recursive average filtering, median average filtering, weighted recursive average filtering, first-order lag filtering, clipping anti-shake filtering, low-pass filtering, etc. Then, a method of first performing low-pass filtering and then performing arithmetic average filtering is selected. The low-pass filter selects a Butterworth filter, and the filter can effectively filter dithering noise. In the context of the present invention, the user equipment may be considered to have a small jitter amplitude and a relatively high speed. The cut-off frequency of the Butterworth filter may be set to 0.3. Because some impact noise in the acquired signals needs to be processed, the data also needs to be processed by an arithmetic average filtering method, and the invention sets the parameter value to be 2, namely, calculates the average value of two continuous values.
Step (3), equipment association based on a dynamic time warping method,
after filtering signals of two types of equipment, the similarity of the signal intensity changes of the two types of equipment needs to be calculated and compared. Referring to fig. 4, the specific steps of the association method are as follows:
3.1 sample Rate threshold calculation
Two devices are arbitrarily selected from the BLE device set W (W j ，w k ) Its corresponding RSS sequence is expressed asComparing the number of RSS values of the two sequences, if there is +.>The sampling rate threshold isOn the contrary, let(s)>
3.2 random sampling
Random sampling of sequences with large number of RSS values, no matter what the assumption isThe sampling sequence is thenFor sequences->Generates a random number between 0 and 1 when the random number is less than or equal to the samplingAt the rate threshold rate, the RSS is added to the sampling sequence +.>Otherwise, discarding. Traversal sequence->Then a sampling sequence is obtained>
3.3 calculating DTW value
In obtaining a sampling sequenceAfterwards, the DTW value of the sequence and the RSS value sequence of the device mi to be associated in the WiFi device set is calculated, expressed as +.>Before calculating the DTW value, it is necessary to center the two sequences, i.e. subtracting the RSS mean of a single RSS sequence from all RSS of that sequence. Repeating 3.2 steps 1000 times to obtain 1000 times of adopted sequences, calculating 1000 times of DTW values, and taking the average value as +.>Is a final result of (a).
3.4 comparing DTW to obtain single association result
Computing another BLE device RSS sequence with a smaller number of RSS valuesWith the device m to be associated i Similarly, the DTW value of the RSS sequence of (1) is required to be centered, expressed as +.>Then, compare->Anda device with a small DTW value is selected as a result of a single association.
3.5 traversing the equipment set to obtain a final association result
2 devices are selected from the BLE device set W at will, C (n, 2) combinations are shared, for each combination, 3.1 to 3.4 steps are repeated, for each mobile intelligent terminal device mi to be associated, C (n, 2) association results are obtained, and the mode in the association result list is selected as the final association result with the target WiFi device.
It should be noted that the above-mentioned embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, and the equivalent substitutions or alternatives made on the basis of the above-mentioned technical solutions are all included in the scope of the present invention.
Claims (2)
1. A method of cross-smart portable device association based on passive sniffing of WiFi-BLE signals, the method comprising the steps of:
(1) WiFi-BLE dual signal sniffing: the method comprises the steps of deploying sniffing equipment according to the field and equipment attributes, and then determining an effective sniffing range of the sniffing equipment;
(2) Signal processing based on multistage filtering: classifying the acquired WiFi and Bluetooth low energy (Bluetooth Low Energy, BLE) signal messages according to equipment, extracting signal intensity values, and carrying out noise filtration on a signal intensity value sequence by utilizing multistage filtering;
(3) Device association based on dynamic time warping: randomly resampling the signal intensity value sequences of the BLE equipment to enable signal sampling rates of different BLE equipment to be similar, respectively carrying out centering treatment on the two signal intensity value sequences of WiFi and BLE, calculating the distance between the two signal intensity value sequences of the two equipment by using a dynamic time warping method (Dynamic Time Warping, DTW), and finally obtaining a correlation result by comparing the DTW distances between the two equipment;
wherein, the step (3) is based on the device association of dynamic time warping, and specifically comprises:
(31) Two devices are selected at will from the set of BLE devices, the RSS quantity contained in the RSS sequences of the two BLE devices is compared, and a sampling threshold is calculated;
(32) Based on the sampling threshold, randomly sampling BLE equipment sequences with more RSSs, enabling the sampling frequency of the BLE equipment sequences to be similar to that of another BLE equipment, calculating a DTW value with the RSS sequences of the WiFi signals of the target mobile intelligent terminal, randomly sampling for 1000 times, and taking an average value as a final DTW value;
(33) Calculating a DTW value of a BLE equipment sequence with a small RSS quantity and a RSS sequence of a WiFi signal of a target mobile intelligent terminal;
(34) Comparing the two DTW values, and selecting BLE equipment with small DTW as candidate equipment;
(35) For n BLE equipment sets, two pieces of equipment are arbitrarily selected from the n BLE equipment sets, C (n, 2) combinations exist, the 4 steps (31) -34) are repeated, C (n, 2) candidate BLE equipment are obtained, and the mode in the candidate BLE equipment sequences is selected as the final association result of the WiFi equipment;
in the steps (32) (33), the RSS sequence needs to be centered before the DTW value is calculated, that is, each RSS value in the sequence is subtracted by the average value of the RSS values of the sequence.
2. A method for associating a cross-smart portable device based on passive sniffing of WiFi-BLE signals according to claim 1, wherein step (2) is based on multi-stage filtered signal processing, and specifically comprises:
(21) Analyzing two types of signal messages obtained by sniffing, respectively classifying WiFi equipment (namely a mobile intelligent terminal) and BLE equipment (namely intelligent wearing equipment) according to an IP address and a Bluetooth broadcast address, and then extracting signal strength values (Received Signal Strength, RSS) and corresponding time stamps in the messages to form an RSS sequence;
(22) The RSS sequence is multi-stage filtered using a low pass filter Butterworth filter and an arithmetic average filter to attenuate the interference of the dither noise and the impact noise.
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