A method for dynamic adjustment of multi-node wireless sensing network topology

Abstract

This application discloses a method for dynamically adjusting the topology of a multi-node wireless sensing network, specifically relating to the field of detection equipment. The method comprises: transmitting a detection data packet through a transmitting end, and obtaining the detection data packet received by each receiving end; determining the signal reception strength of the received detection data packet, and using the signal reception strength to generate a signal reception strength sequence for each receiving end; determining the degree of fluctuation of the signal reception strength sequence for each receiving end, and adjusting the wireless sensing network topology based on the similarity of the fluctuation degree. The method can explore potential sensing links in a multi-node sensing network, thereby significantly improving the richness of sensing data, enabling wireless sensing applications to better adapt to dynamically changing actual sensing scenarios and complex sensing environments.

Description

Technical Field

[0001] The present application relates to the field of detection equipment, and in particular to a method for dynamically adjusting the topology of a multi-node wireless sensing network. Background Art

[0002] With the continuous development of wireless communication technology, ubiquitous radio waves have become a vital medium for communication in human society. As a key wireless local area network (WLAN) communication technology, Wi-Fi offers advantages such as high portability, strong scalability, and low cost. Beyond wireless communication, wireless sensing technology, which uses electromagnetic waves as a sensing medium, has seen rapid development in recent years. Compared to specialized sensors, wireless sensing technology offers advantages such as low intrusion and ubiquity. In current wireless sensing research, Wi-Fi sensing technology, due to its low cost and widespread availability, has broad application scenarios.

[0003] As research deepens, Wi-Fi sensing technology faces a series of challenges. Because Wi-Fi devices often use omnidirectional antennas and have low signal bandwidth, their sensing performance and accuracy are poor. Furthermore, due to noise and indoor multipath effects, Wi-Fi sensing technology exhibits poor stability in diverse sensing environments. To mitigate these challenges, a sensing network can be built by adding heterogeneous Wi-Fi sensing nodes for collaborative sensing. By exploring dynamic, collaborative heterogeneous wireless sensing technologies, the applicability of current wireless sensing research can be improved in increasingly complex sensing environments, laying the foundation for the practical application of wireless sensing technology.

[0004] However, current research on multi-device wireless sensing faces the following challenges: Most work focuses on sensing tasks in specific scenarios, often with fixed sensing device locations, fixed participation, homogeneous hardware information, and a fixed overall sensing network structure. This makes it difficult to adapt to the complex sensing conditions of real-world scenarios, where device locations and participation vary dynamically, hardware is heterogeneous, and the sensing network changes dynamically. Wireless sensing platform research is needed to bridge the gap between theory and application. However, current wireless sensing platforms are relatively old in development, support a limited number of devices, have complex workflows, and fail to account for dynamic node changes, making them difficult to adapt to complex sensing environments. Summary of the Invention

[0005] The main purpose of this application is to provide a method for dynamically adjusting the topology of a multi-node wireless sensing network, aiming to solve the problem that existing multi-device wireless sensing devices are difficult to adapt to complex sensing environments.

[0006] To achieve the above-mentioned objectives, the present application provides a method for dynamically adjusting the topology of a multi-node wireless sensing network. The wireless sensing network topology includes a transmitting end and at least two receiving ends. The method includes: transmitting a detection data packet through the transmitting end, and obtaining the detection data packet received by each receiving end respectively; determining the signal reception strength of the received detection data packet, and using the signal reception strength to generate a signal reception strength sequence for each receiving end; setting a sliding window, determining the frequency value of the RSSI element appearing in the signal reception strength sequence of each sliding window; using the frequency value corresponding to the RSSI element, generating a frequency vector for each sliding window; determining the degree of fluctuation between the frequency vectors of all two adjacent sliding windows in each receiving end, and combining all the fluctuation degrees to obtain the fluctuation sequence of each receiving end; determining the degree of similarity between the fluctuation sequence of each receiving end and the fluctuation sequences of other receiving ends; and adjusting the transmitting end and the receiving end according to the degree of similarity.

[0007] Optionally, the RSSI element includes multiple RSSI values, determining the frequency value of the RSSI element appearing in the signal reception strength sequence of each sliding window, and using the frequency value corresponding to the RSSI element to generate a frequency vector for each sliding window, including: determining the frequency value of each RSSI value appearing in the signal reception strength sequence of each sliding window, combining the frequency values ​​corresponding to all RSSI values ​​in each sliding window, and obtaining the frequency vector of each sliding window.

[0008] Optionally, the RSSI element is represented as: ,in, is the maximum possible RSSI value, is the RSSI value.

[0009] Alternatively, the degree of similarity is determined by the Euclidean distance between the two fluctuation series.

[0010] Optionally, the transmitting end and the receiving end are adjusted according to the degree of similarity, including: when the Euclidean distance between the two fluctuation sequences is greater than a preset threshold, the transmitting end is adjusted to the receiving end, and the peak value of each fluctuation sequence is obtained, and the receiving end with the largest peak value is adjusted to the new transmitting end.

[0011] Optionally, the degree of fluctuation is determined by the Euclidean distance between the frequency vectors of two sliding windows.

[0012] Optionally, the formula for the fluctuation sequence at each receiving end is as follows:

[0013]

[0014] Where, is the ith fluctuation sequence, is the number of sliding windows, is the degree of fluctuation, and is the frequency vector of two adjacent sliding windows.

[0015] Optionally, before determining the degree of similarity between each fluctuation sequence and other fluctuation sequences, the method also includes: smoothing each fluctuation sequence and making the length of each fluctuation sequence the same through an interpolation resampling method; and normalizing the fluctuation sequence after interpolation resampling to a preset interval.

[0016] Optionally, the preset interval is 0-1.

[0017] To achieve the above-mentioned purpose, the present application also provides a multi-node wireless sensing network topology dynamic adjustment device, the wireless sensing network topology includes a transmitting end and at least two receiving ends, and the device includes: an acquisition module, which is used to transmit a detection data packet through the transmitting end and obtain the detection data packet received by each receiving end respectively; a signal reception strength sequence generation module, which determines the signal reception strength of the received detection data, and uses the signal reception strength to generate a signal reception strength sequence for each receiving end; a frequency vector generation module, which sets a sliding window and determines the frequency value of the RSSI element appearing in the signal reception strength sequence of each sliding window; uses the frequency value corresponding to the RSSI element to generate the frequency vector of each sliding window; a fluctuation sequence generation module, which determines the degree of fluctuation between the frequency vectors of all two adjacent sliding windows in each receiving end, and combines all fluctuation degrees to obtain the fluctuation sequence of each receiving end; an adjustment module, which determines the degree of similarity between the fluctuation sequence of each receiving end and the fluctuation sequence of other receiving ends, and adjusts the transmitting end and the receiving end according to the similarity.

[0018] Compared with the prior art, the present invention has the following advantages:

[0019] The multi-node wireless sensing network topology dynamic adjustment method of the present invention can infer the relative position of the human body in the sensing network by calculating the similarity of RSSI signal fluctuations of different terminals, thereby dynamically adjusting the topology of the network; by switching between the transmitting end and the receiving end, it can explore potential sensing links in the multi-node sensing network, thereby greatly improving the richness of sensing data, so that wireless sensing applications can better adapt to dynamically changing actual sensing scenarios and complex sensing environments; compared with traditional variance-based signal fluctuation degree calculation methods or smoothing filtering-based outlier removal methods, by vectorizing the signal reception strength of the receiving end and determining the fluctuation of the vector through Euclidean distance, vectorized fluctuation is achieved, which can achieve stable and reliable fluctuation feature calculation while retaining all RSSI information without causing sudden changes in numerical values ​​that affect subsequent calculations. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a structural diagram of a wireless sensing network topology in a multi-node wireless sensing network topology dynamic adjustment method of this application;

[0021] Figure 2 This is a flowchart of a method for dynamically adjusting the topology of a multi-node wireless sensing network according to the present application;

[0022] Figure 3 This is a structural diagram of the wireless sensing network topology in Example 1 of the present application;

[0023] Figure 4 This is a schematic diagram of data processing at different stages obtained in Example 1 of the present application;

[0024] Figure 5 This is a diagram illustrating the principle of different signal characteristics caused by different Fresnel zones in a multi-node wireless sensing network topology dynamic adjustment method of this application;

[0025] Figure 6 This is a diagram of the wireless sensing network topology adjustment process in a multi-node wireless sensing network topology dynamic adjustment method of this application.

[0026] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0027] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0028] The first embodiment of the present invention provides a method for dynamically adjusting the topology of a multi-node wireless sensing network. Figure 1 As shown, the wireless sensing network topology includes a transmitter and at least two receivers, and the transmitter and receiver are equipped with hardware network card devices and software drivers that can read sensing data; Figure 2 As shown, the specific steps include:

[0029] Step S1: transmitting a probe data packet through the transmitting end, obtaining the probe data packet received by each receiving end; determining the received signal strength (RSSI) of the received probe data packet, and using the received signal strength to generate a signal strength sequence for each receiving end;

[0030] In this embodiment, the signal reception strengths of all the detection data packets received by each receiving end are calculated, and the signal reception strengths of all the detection data packets are combined to obtain a signal reception strength sequence S for each receiving end.

[0031] Step S2: setting a sliding window, determining the frequency value of the RSSI element appearing in the signal reception strength sequence of each sliding window; using the frequency value corresponding to the RSSI element, generating a frequency vector for each sliding window;

[0032] Exemplarily, the RSSI element includes multiple RSSI values, and the RSSI element is represented as:

[0033]

[0034] Where, is the maximum possible RSSI value.

[0035] Specifically, set the length to The sliding window is set and the step length is , and continuously step the sliding window in the signal reception strength sequence of each receiving end. The signal reception strength sequence S in each sliding window is:

[0036]

[0037] Where, is the length of the sliding window.

[0038] Determine the frequency value of each RSSI value in the signal reception strength sequence of each sliding window, that is, Each value in Frequency of occurrence in The calculation method is:

[0039]

[0040] Where, Represents a counting function.

[0041] Combine the frequency values ​​corresponding to all RSSI values ​​in each sliding window to obtain the frequency vector of each sliding window :

[0042]

[0043] Then the frequency vector combination of all sliding windows can construct a vector sequence :

[0044]

[0045] Where, is the number of sliding windows;

[0046] Step S3: Determine the degree of fluctuation between the frequency vectors of all two adjacent sliding windows in each receiving end. , and combine all fluctuation degrees to obtain the fluctuation sequence of each receiving end : The degree of fluctuation is determined by the Euclidean distance between the frequency vectors of two sliding windows, and the formula of the fluctuation sequence R is as follows:

[0047]

[0048] Where, and is the frequency vector of two adjacent sliding windows, is the ith fluctuation sequence.

[0049] In this embodiment, the signal reception strength at the receiving end is vectorized, and the fluctuation of the vector is determined by Euclidean distance, thereby realizing vectorized fluctuation. This can achieve stable and reliable fluctuation feature calculation while retaining all RSSI information without causing drastic changes in values ​​and affecting subsequent calculations.

[0050] Furthermore, since different wave sequences have different lengths and heights and contain random noise, in order to better match the waveform characteristics between wave sequences, before determining the similarity between each wave sequence and other wave sequences in step S3, the wave sequences are processed as follows:

[0051] Each fluctuation sequence is smoothed, and the length of each fluctuation sequence is made the same through interpolation resampling method; the fluctuation sequence after interpolation resampling is normalized to 0-1.

[0052] Step S4: determining the similarity between the fluctuation sequence of each receiving end and the fluctuation sequence of other receiving ends, and adjusting the wireless sensing network topology according to the similarity.

[0053] Specifically, the similarity is determined by the Euclidean distance between the two fluctuation sequences. When , the transmitter is adjusted to the receiver, and the peak value of each fluctuation sequence is obtained, and the receiver with the largest peak value is adjusted to the new transmitter; if the Euclidean distance between the two fluctuation sequences is less than or equal to the preset threshold , no topology adjustment is performed.

[0054] In this embodiment, based on the Fresnel zone theory, the signal change characteristics caused by human activities in different Fresnel zones in a multi-node perception network can reflect its relative position. The RSSI signal in the sliding window is vectorized and the Euclidean distance between adjacent vectors is calculated to characterize the degree of signal fluctuation, so as to judge the relative position of the current target in the perception network and dynamically adjust the network topology.

[0055] The multi-node wireless sensing network topology dynamic adjustment method of the present invention is described below with specific examples.

[0056] The wireless sensing network topology consists of three terminal devices equipped with a Qualcomm AR9380 network card, POP!_OS 22.04, and the Atheros CSI Tool. These are deployed indoors. One device is the transmitter (Tx), and the other two are the receivers (Rx1 and Rx2). Each device is 0.8 meters above the ground and arranged in an equilateral triangle with a spacing of 2 meters. All three devices are connected to the same local area network (LAN). Data forwarding within the LAN forwards these data to a terminal, which can be a computer and serves as the central control device. This terminal processes the data from the three devices and sends commands and controls them, as shown in Figure 3.

[0057] Step S1: The transmitter Tx transmits a detection data packet at a rate of 500 packets / s. The central control device obtains the detection data packets received by each receiving end Rx1 and Rx2 respectively; determines the signal receiving strength of the detection data packet received by the receiving end Rx1, and uses the signal receiving strength to generate the signal receiving strength sequence of the receiving end Rx1. Similarly, we get the signal receiving strength sequence of the receiving end Rx2 ,See Figure 4 a;

[0058] Step S2, set the length Set the step length for a sliding window of 50 is 50, then the signal receiving strength sequence in each sliding window is expressed as:

[0059]

[0060] In the Atheros CSI Tool, RSSI is represented as an unsigned 8-bit integer with a possible range of 0 to 255. Therefore, the RSSI element is represented as:

[0061]

[0062] Determine the frequency value of each RSSI value in the signal reception strength sequence of each sliding window, that is, Each value in Frequency of occurrence in The calculation method is:

[0063]

[0064] Combine the frequency values ​​corresponding to all RSSI values ​​in each sliding window to obtain the frequency vector of each sliding window :

[0065]

[0066] Then the frequency vector combination of all sliding windows can construct a vector sequence :

[0067]

[0068] Where, is the number of sliding windows;

[0069] Step S3: Calculate the Euclidean distance between the frequency vectors of all two adjacent sliding windows to characterize the fluctuation degree of RSSI data. , and combine all the fluctuation degrees in each receiving end to obtain the fluctuation sequence of the receiving end Rx1 and the fluctuation sequence of the receiving end Rx2 ,See Figure 4 b. The fluctuation degree R of both receiving ends is calculated by the following formula:

[0070]

[0071] Where, and is the frequency vector of two adjacent sliding windows.

[0072] Step S4, the window size of the SG filter is set to 20, the order is set to 2, and the fluctuation sequence is filtered by the SG filter. and volatility series Smoothing is performed, as shown in FIG4c; and the length of each fluctuation sequence is made the same through interpolation resampling method; the fluctuation sequence after interpolation resampling is normalized to 0-1, as shown in FIG4c. Figure 4 As shown in d.

[0073] Step S5, calculate the fluctuation sequence and volatility series Euclidean distance between According to the Fresnel zone theory, The smaller the value, the more similar the two data are. Figure 5As shown in a, when human activity occurs near Tx, it will affect both Fresnel zones 1 and 2, resulting in similar signal fluctuation characteristics; on the contrary, the greater the difference, the more likely the activity is to occur near a certain Rx, as shown in Figure 5 As shown in b, there will be significant differences in the signal fluctuation characteristics of the two Fresnel zones; when When it is greater than 3.2, adjust the transmitter Tx to the receiver and Figure 4 b Get the peak value of each fluctuation sequence, such as Figure 6 As shown, the receiving end Rx with the largest peak value is adjusted to the new transmitting end; if the Euclidean distance between the two fluctuation sequences is less than or equal to the preset threshold , no topology adjustment is performed.

[0074] A second embodiment of the present invention provides a multi-node wireless sensing network topology dynamic adjustment device. The wireless sensing network topology includes a transmitting end and at least two receiving ends. The device includes an acquisition module, a signal reception strength sequence generation module, a frequency vector generation module, a fluctuation sequence generation module, and an adjustment module. The acquisition module is configured to transmit a probe data packet through the transmitting end and obtain the probe data packet received by each receiving end.

[0075] a signal reception strength sequence generation module, which determines the signal reception strength of the received detection data and generates a signal reception strength sequence for each receiving end;

[0076] The frequency vector generation module sets a sliding window for each signal reception strength sequence and determines the frequency value of the RSSI element in each sliding window; and generates the frequency vector of each sliding window using the frequency value corresponding to the RSSI element;

[0077] The fluctuation sequence generation module determines the fluctuation degree between the frequency vectors of all two adjacent sliding windows at each receiving end and combines all the fluctuation degrees to obtain the fluctuation sequence of each receiving end;

[0078] The adjustment module determines the similarity between the fluctuation sequence of each receiving end and the fluctuation sequence of other receiving ends, and adjusts the wireless sensing network topology according to the similarity.

[0079] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for dynamically adjusting the topology of a multi-node wireless sensing network, characterized in that: The wireless sensing network topology includes a transmitting end and at least two receiving ends, and the method includes: Transmitting a detection data packet through the transmitting end, and respectively obtaining the detection data packet received by each of the receiving ends; Determining the signal reception strength of the received detection data packet, and generating a signal reception strength sequence for each of the receiving ends using the signal reception strength; Set a sliding window and determine the frequency value of the RSSI element appearing in the signal reception strength sequence of each sliding window; Generate a frequency vector for each sliding window using the frequency value corresponding to the RSSI element; Determine the degree of fluctuation between the frequency vectors of all two adjacent sliding windows at each receiving end, and combine all the fluctuation degrees to obtain the fluctuation sequence of each receiving end; Determine how similar the fluctuation sequence of each receiver is to the fluctuation sequences of other receivers; adjusting the transmitting end and the receiving end according to the similarity; The RSSI element includes a plurality of RSSI values, and determining a frequency value of the RSSI element appearing in a signal reception strength sequence of each sliding window, and generating a frequency vector for each sliding window using the frequency value corresponding to the RSSI element, includes: Determine the frequency value of each RSSI value in the signal reception strength sequence of each sliding window, combine the frequency values ​​corresponding to all RSSI values ​​in each sliding window, and obtain the frequency vector of each sliding window; The degree of fluctuation is determined by the Euclidean distance between the frequency vectors of two sliding windows. The formula of the fluctuation sequence of each receiving end is as follows: Where, is the ith fluctuation sequence, is the number of sliding windows, and is the frequency vector of two adjacent sliding windows, The degree of fluctuation.

2. The method for dynamically adjusting the topology of a multi-node wireless sensing network according to claim 1, characterized in that: The RSSI element is represented by: ,in, is the maximum possible RSSI value, is the RSSI value.

3. The method for dynamically adjusting the topology of a multi-node wireless sensing network according to claim 1, characterized in that: The degree of similarity is determined by the Euclidean distance between the two fluctuation sequences.

4. The method for dynamically adjusting the topology of a multi-node wireless sensing network according to claim 3, wherein: The adjusting the transmitting end and the receiving end according to the similarity degree includes: When the Euclidean distance between two fluctuation sequences is greater than a preset threshold, the transmitting end is adjusted to the receiving end, and the peak value of each fluctuation sequence is obtained, and the receiving end with the largest peak value is adjusted to the new transmitting end.

5. The method for dynamically adjusting the topology of a multi-node wireless sensing network according to claim 1, characterized in that: Before determining the degree of similarity between each fluctuation sequence and other fluctuation sequences, the method further includes: Smoothing each of the fluctuation sequences and making the lengths of each of the fluctuation sequences the same by an interpolation resampling method; Normalize the interpolation and resampling fluctuation series to the preset range.

6. The method for dynamically adjusting the topology of a multi-node wireless sensing network according to claim 5, characterized in that: The preset interval is 0-1.

7. A multi-node wireless sensing network topology dynamic adjustment device, characterized in that: The wireless sensing network topology includes a transmitting end and at least two receiving ends, and the device includes: An acquisition module, configured to transmit a detection data packet through the transmitting end and respectively acquire the detection data packet received by each of the receiving ends; a signal reception strength sequence generating module, which determines the signal reception strength of the received detection data and generates a signal reception strength sequence for each of the receiving ends using the signal reception strength; A frequency vector generation module sets a sliding window and determines the frequency value of the RSSI element in the signal reception strength sequence of each sliding window; and generates a frequency vector for each sliding window using the frequency value corresponding to the RSSI element; The fluctuation sequence generation module determines the fluctuation degree between the frequency vectors of all two adjacent sliding windows at each receiving end and combines all the fluctuation degrees to obtain the fluctuation sequence of each receiving end; an adjustment module, determining a similarity between the fluctuation sequence of each receiving end and the fluctuation sequence of other receiving ends, and adjusting the transmitting end and the receiving end according to the similarity; The RSSI element includes a plurality of RSSI values, and determining a frequency value of the RSSI element appearing in a signal reception strength sequence of each sliding window, and generating a frequency vector for each sliding window using the frequency value corresponding to the RSSI element, includes: Determine the frequency value of each RSSI value in the signal reception strength sequence of each sliding window, combine the frequency values ​​corresponding to all RSSI values ​​in each sliding window, and obtain the frequency vector of each sliding window; The degree of fluctuation is determined by the Euclidean distance between the frequency vectors of two sliding windows. The formula of the fluctuation sequence of each receiving end is as follows: Where, is the ith fluctuation sequence, is the number of sliding windows, and is the frequency vector of two adjacent sliding windows, The degree of fluctuation.

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