CN107728107A - Passive cognitive method based on wireless sensor network - Google Patents

Abstract

A kind of passive cognitive method based on wireless sensor network, establish the loss model of radio signal propagation, the method perceived with wireless sensor network received signal strength indicator, the concrete condition of signal fluctuation is analyzed, the influence of error is reduced using the error analysis method based on statistics intermediate value, perception purpose is realized in this, as important criterion.Test result indicates that can effectively perceive out into the people in wireless network coverage, this for needing the place of security protection to have great significance more from now on.

Description

Passive passive sensing method based on wireless sensor network

technical field

The invention relates to the application of wireless network technology, in particular to a passive passive sensing method based on a wireless sensor network.

Background technique

In the field of wireless communication, wireless sensor technology is considered as a promising technology and has received extensive attention. Wireless sensor nodes are small in size, low in energy consumption, and can communicate with adjacent nodes or base stations, so they are used in many fields, such as remote monitoring, disaster early warning, military applications, and health monitoring. In these applications, we can use the fluctuating effect of people on the radio frequency signal of the wireless sensor network to perceive the people appearing in the coverage area of the wireless sensor network. When people are in the range covered by the network, the human body will affect the wireless signal link, causing the receiving strength of the link signal to fluctuate. By analyzing the change of this fluctuation, the purpose of perception can be achieved. Because people who enter the wireless sensor area will not carry any tags (such as RFID radio frequency tags), we can think that the perceived person has no information source, or simply referred to as passive; and when the person himself is perceived Sometimes, it is difficult to perceive by oneself, and it is usually passive. Utilizing the above two points, passive passive perception can be realized, which has broad prospects in security defense and intelligent monitoring.

The team of inventors conducted research on the status quo of wireless sensor networks sensing in target areas. It is insensitive to the environment, and external light and heat changes have minimal impact on wireless sensor networks, which makes it more widely used. The definition of passive passive sensing for wireless sensor networks and the way of passive passive sensing are studied: when a wireless sensor network is placed in the target area, when someone enters, it will definitely affect the received signal of the nodes in the area, making the received signal strength There are fluctuations, and perception is realized by analyzing such fluctuations.

In the use of passive sensing technology, the existing technology will pre-measure the threshold range of the unmanned state in the test environment. However, it is inevitable to be interfered by the outside world in the process of obtaining the threshold range. And under the interference of other electronic equipment and human body, the signal received by the receiving end will appear jumping fluctuations, which increases the instability and randomness of the measured value, resulting in inaccurate test results. The present invention strives to improve the passive perception Accuracy.

Contents of the invention

The technical problem to be solved by the present invention is to provide a passive passive sensing method based on a wireless sensor network, to design an algorithm that finally achieves the sensing purpose according to the fluctuation of the received signal strength indication of the wireless sensor network, and to improve the algorithm , to reduce errors caused by randomness and instability.

In the wireless sensor network-based passive passive sensing method, in a space with a plurality of wireless sensor network nodes that can communicate with each other, each wireless sensor node sends a data packet by broadcasting under normal working conditions, and the remaining nodes receive the data packet respectively. Data packet; Described data packet comprises RSSI (the signal strength indication that Received Signal StrengthIndication receives) value, it is characterized in that: judge whether there is personnel to enter in the network coverage area according to the steps that are carried out in the following order:

The first step. Collect the RSSI value of each sensor node in a stable state. The state where there are no people in the network coverage area and the sensor nodes are all working normally is regarded as a stable state. The RSSI value of each sensor node is expressed in the following matrix:

The row label of each element indicates the sensor node number, and the column label indicates the number received by the sensor node from other sensor nodes. When the row label and the column label are the same, the element value is 0. n indicates the maximum number of sensor nodes, and n is not less than 2;

Second step. Calculate the received signal strength value R(k) of each sensor node, and the letter k represents the kth sensor node;

Step 3. Take out the maximum value R(k) _max and the minimum value R(k) _min in the received signal strength value R(k) of the sensor node, and perform subtraction to obtain a difference Δ=R(k) _max －R(k) _min , and take the absolute value of the difference |Δ|;

The fourth step. Construct a normal distribution function with a mean value of 0 and a variance of |Δ|;

Step 5. When detecting, repeat the above steps. When the fluctuation range is greater than |Δ|, it is considered that someone has entered the network coverage area.

A calculation method of R(k) is: in the second step above, the mean value R(k) of the received signal strength of the sensor node is calculated according to the following method:

In order to further improve the accuracy, in the second step above, the received signal strength value R(k) of the sensor node is preprocessed according to the following method:

First, sort n measured values RSSI _m (wherein, m=1, 2, ..., n) to obtain the median RSSI _zz of the sequence, and calculate according to the following formula when distinguishing n as an odd number or an even number;

Then, calculate the weight α _m of each measurement value, and use the following formula for operation:

in,

Finally, each measurement value is multiplied by a weighting factor to obtain the final weighted average RSSI _f as the signal strength of the node:

The present invention proposes a new passive passive perception algorithm, and further improves the accuracy of character perception on this basis, that is, adopts the median filtering method to reduce the error influence caused by randomness and instability, so that the measured value is closer to to the real value. The experimental results show that the algorithm can effectively perceive people entering the target area, and significantly improve the perception accuracy of the existing technology, which is of great significance for the automatic control of some places that require security in the future.

Description of drawings

Figure 1 is a screenshot of the statistics interface of measuring forward gross errors.

Figure 2 is a screenshot of the two-way coarse error statistics interface,

Figure 3 is a schematic diagram of the distribution of nodes.

Fig. 4 is a schematic diagram of the perception result curve.

detailed description

Below in conjunction with accompanying drawing, the present invention is further described:

When someone appears in the area to be detected, the wireless link between some sensor nodes will change. We define the link value RSSI between sensors to be a random variable conforming to Gaussian distribution. Therefore, we first collect the RSSI value in a stable state as a passive passive sensing fluctuation threshold standard. At present, many wireless radio frequency manufacturers support reading the RSSI value through software or equipment. When the subsequent obtained RSSI value is consistent with our preset When the fluctuation threshold standard does not match, it can be judged that someone has entered the sensing area, and the purpose of sensing security can be achieved.

In a space with n wireless sensor nodes that can communicate with each other (n is at least greater than or equal to 2), each wireless sensor node in normal working state sends a data packet by broadcasting, and the other nodes receive the data packet respectively. For example, after the wireless sensor network is initialized, the nodes in the wireless sensor network may broadcast and send data packets one by one, and the sensors of other nodes independently receive the sent data packets, and the data packets include the RSSI values of each sensor node. Follow the steps in the following order to determine whether there is someone entering the network coverage area:

The first step. It is required that there is no human activity in the network coverage area, and the RSSI value of each sensor node is collected in a steady state. In the matrix of sensor nodes

In , the value on the main diagonal is represented by 0 because the nodes cannot receive and send spontaneously.

The second step. Usually, the calculation of the intensity signal value of each sensor node adopts the arithmetic mean algorithm:

In the formula, the letter k represents the kth sensor node.

However, in the unmanned state, under the interference of non-line-of-sight propagation, multipath propagation, and other electronic equipment and human body, the signal received by the receiving end will fluctuate in jumps, which increases the instability and randomness of the measured value. sex. External factors will make the pre-test results unstable, resulting in errors in perception. To this end, we can adopt an error analysis method based on the statistical median to process the received RSSI value to reduce the error caused by randomness and instability.

Specifically: A. sort n measured values RSSI _m (wherein, m=1, 2, ..., n) to obtain the median RSSI _zz of the sequence, and calculate according to the following formula when distinguishing n as odd or even;

B. Calculate the weight α _m of each measured value, and use the following formula for calculation:

in,

In the formula, max() is the function of taking the maximum value. In order to distinguish the signal containing the error from the median value of the sequence, the mean value of the square of the difference between each measured signal value and the median value is used as the threshold T. When the square of the difference is greater than the threshold value, the weight The value is determined by the square of the difference, otherwise it is determined by the threshold value, the greater the difference between the measured value and the median value, the smaller the weight value, and the closer the measured value is to the median value, the larger the weight value.

C. Multiply each measured value with the weighting coefficient to obtain the final weighted average RSSI _f as the signal strength of the node:

Then, the third step. Take out the maximum value R(k) _max and the minimum value R(k) _min in the received signal strength value R(k) of the sensor node, and perform subtraction to obtain a difference Δ=R(k ) _max - R(k) _min and take the absolute value of the difference |Δ|;

The fourth step. Construct a normal distribution function with a mean value of 0 and a variance of |Δ|;

Step 5. When detecting, repeat the above steps. When the fluctuation range is greater than |Δ|, it is considered that someone has entered the network coverage area.

Compare this method with the traditional mean filtering method: we assume that there are 100 signal strength measurement data, and randomly select 2 to 20 data from them to add coarse errors (mixing Gaussian noise with a mean value of 0 and a standard deviation of 8dB) , and the rest of the data are assumed to be 40dB. We can see in Figure 1 that when the error value is 2.5 times the real signal value, the relative error ratio of the statistical median filter is low; in Figure 2 we randomly set the error value to 1/ 3 or 2.5 times, the abscissa represents the proportion of gross error in all measured data, and the ordinate represents the relative error after filtering. It can be seen from the figure that when the proportion of gross error is small, the performance of median filter is very close to that of mean filter, but when the proportion of gross error is greater than 10%, statistical median filter has obvious advantages.

From the above analysis, it can be seen that compared with the traditional error processing method, the error analysis method using the statistical median filter can obtain more accurate processing results and make the measurement data more real. Therefore, before performing passive passive sensing, choose to use The error analysis method of the statistical median is used as the error processing method of the received signal strength indication.

Experimental verification

Experimental verification is carried out in a rectangular enclosed area of 3m×4.5m. The specific distribution of nodes is shown in Figure 3 (here 6 nodes are taken as an example). Using the function of sensor RF manufacturers to directly measure RSSI from the receiving end, we first test Obtain a set of data when no one enters, and process the data according to the method of statistical median filtering to obtain the following set of data:

Through calculation, R1=-20.37, R2=-20.27, R3=-21.61, R4=-23.34, R5=-24.24, R6=-23.95. where |Δ| = 3.97.

When someone appears, repeat the above steps and get the following results:

Through calculation, R1'=-29.37, R2'=-28.42, R3'=-31.39, R4'=-36.32, R5'=-34.85, R6'=-37.90 and |Δ| ^l =9.48. Perception results can be shown in Figure 4.

It can be seen from Figure 4 that when no one enters the coverage area of the wireless sensor network, the RSSI value distribution of the nodes is relatively concentrated; and when someone breaks into the coverage area, the RSSI values between nodes change, the distribution is relatively discrete, and the received signal strength indicator fluctuates larger. Therefore, it can be concluded that someone has broken into the coverage area of the wireless sensor network at this time, and the purpose of perception can be achieved.

Based on the broad application prospects of wireless sensor networks in people's lives and the huge demand for target perception in modern society, the present invention uses the method of wireless sensor network receiving signal strength indication for sensing, analyzes the specific situation of signal fluctuations, and Use this as an important criterion to achieve the purpose of perception. The test results show that people or things entering the coverage area of the wireless network can be effectively sensed, which is of great significance for some places that need security in the future.

Claims (3)

1. A passive passive sensing method based on a wireless sensor network. In a space with multiple wireless sensor network nodes that can communicate with each other, each wireless sensor node sends a data packet by broadcasting under normal working conditions, and the remaining nodes respectively Receive this data packet; Described data packet comprises RSSI value, it is characterized in that: according to the step of following order judge whether there is personnel to enter in the network coverage area: The first step. Collect the RSSI value of each sensor node in a stable state. The state where there are no people in the network coverage area and the sensor nodes are working normally is regarded as a stable state. The RSSI value of each sensor node is expressed in the following matrix: The row label of each element indicates the sensor node number, and the column label indicates the number received by the sensor node from other sensor nodes. When the row label and the column label are the same, the element value is 0. n indicates the maximum number of sensor nodes, and n is not less than 2; Second step. Calculate the received signal strength value R(k) of each sensor node, and the letter k represents the kth sensor node; Step 3. Take out the maximum value R(k) _max and the minimum value R(k) _min in the received signal strength value R(k) of the sensor node, and perform subtraction to obtain a difference Δ=R(k) _max －R(k) _min , and take the absolute value of the difference |Δ|; The fourth step. Construct a normal distribution function with a mean value of 0 and a variance of |Δ|; Step 5. When detecting, repeat the above steps. When the fluctuation range is greater than |Δ|, it is considered that someone has entered the network coverage area. 2. The passive sensing method according to claim 1, characterized in that: in the above-mentioned second step, the received signal strength value R (k) of the sensor node is calculated according to the following method: <mrow><mi>R</mi><mrow><mo>(</mo><mi>k</mi><mo>)</mo></mrow><mo>=</mo><mfrac><mrow><msubsup><mi>&amp;Sigma;</mi><mrow><mi>m</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></msubsup><msub><mi>R</mi><mrow><mi>k</mi><mi>n</mi></mrow></msub></mrow><mrow><mi>n</mi><mo>-</mo><mn>1</mn></mrow></mfrac><mo>.</mo></mrow> 3. The passive sensing method according to claim 1 or 2, characterized in that: in the above-mentioned second step, the received signal strength value R (k) of the sensor node is preprocessed according to the following method: First, sort n measured values RSSI _m (wherein, m=1, 2, ..., n) to obtain the median RSSI _zz of the sequence, and calculate according to the following formula when distinguishing n as an odd number or an even number; Then, calculate the weight α _m of each measurement value, and use the following formula for operation: <mrow><msub><mi>&amp;alpha;</mi><mi>m</mi></msub><mo>=</mo><mfrac><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><mi>max</mi><mo>{</mo><mi>T</mi><mo>,</mo><msup><mrow><mo>(</mo><msub><mi>RSSI</mi><mi>m</mi></msub><mo>-</mo><msub><mi>RSSI</mi><mrow><mi>z</mi><mi>z</mi></mrow></msub><mo>)</mo></mrow><mn>2</mn></msup><mo>}</mo></mrow></mfrac><mrow><msubsup><mi>&amp;Sigma;</mi><mrow><mi>m</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></msubsup><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><mi>max</mi><mo>{</mo><mi>T</mi><mo>,</mo><msup><mrow><mo>(</mo><msub><mi>RSSI</mi><mi>m</mi></msub><mo>-</mo><msub><mi>RSSI</mi><mrow><mi>z</mi><mi>z</mi></mrow></msub><mo>)</mo></mrow><mn>2</mn></msup><mo>}</mo></mrow></mfrac></mrow></mfrac><mo>,</mo></mrow> in, <mrow><mi>T</mi><mo>=</mo><mfrac><mrow><msubsup><mi>&amp;Sigma;</mi><mrow><mi>m</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></msubsup><msup><mrow><mo>(</mo><msub><mi>RSSI</mi><mi>m</mi></msub><mo>-</mo><msub><mi>RSSI</mi><mrow><mi>z</mi><mi>z</mi></mrow></msub><mo>)</mo></mrow><mn>2</mn></msup></mrow><mi>n</mi></mfrac><mo>,</mo></mrow> Finally, each measurement value is multiplied by a weighting factor to obtain the final weighted average RSSI _f as the signal strength of the node: <mrow><msub><mi>RSSI</mi><mi>f</mi></msub><mo>=</mo><msubsup><mi>&amp;Sigma;</mi><mrow><mi>m</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></msubsup><msub><mi>&amp;alpha;</mi><mi>m</mi></msub><mo>&amp;CenterDot;</mo><msub><mi>RSSI</mi><mi>m</mi></msub><mo>.</mo></mrow>