CN112533134B - Wireless sensor network safety positioning method based on double detection - Google Patents

Wireless sensor network safety positioning method based on double detection Download PDF

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CN112533134B
CN112533134B CN202011230278.XA CN202011230278A CN112533134B CN 112533134 B CN112533134 B CN 112533134B CN 202011230278 A CN202011230278 A CN 202011230278A CN 112533134 B CN112533134 B CN 112533134B
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CN112533134A (en
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张文安
石清波
史秀纺
洪榛
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Zhejiang University of Technology ZJUT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination

Abstract

A wireless sensor network safety positioning method based on double detection is characterized in that an RSSI (received signal strength indicator) logarithmic attenuation model is established in real time; the unknown node collects the RSSI value of the received signal strength of the anchor node in the communication range and processes the data; converting the RSSI value into the distance between the unknown node and the anchor node through a logarithmic attenuation model; detecting malicious nodes through the difference of the RSSI value variance; comparing the distance variance between the malicious node and the non-malicious node to perform secondary detection to see whether the malicious node needs to be reserved or not; calculating the position of an unknown node by using the reserved anchor node to perform a weighted least square algorithm; and upgrading the located unknown node to an unknown node after the anchor node continues to be located. The invention introduces cooperative positioning, so that the information between unknown nodes can be fully utilized, the requirement on the distribution density of the anchor nodes is greatly reduced, and the positioning precision is further improved.

Description

Wireless sensor network safety positioning method based on double detection
Technical Field
The invention relates to the field of node safety positioning, in particular to a wireless sensor network safety positioning method based on double detection.
Background
Object localization is an important issue in the context of wireless communication networks, as location information is useful for many applications, such as monitoring and tracking of people in buildings or in emergency situations (i.e. during fires, smoke events, dark periods and earthquakes), patient monitoring in hospitals and homes, mobile robot tracking, location detection of products in warehouses, monitoring and tracking of workers in construction sites, automatic control of equipment, etc. To determine the target location, RSSI information is more widely used because most radios have RSSI circuitry built in.
The uncooperative positioning method only allows communication between the anchor nodes and the target source, so when the available anchor nodes are few, enough information may not be available to determine the position of the target source, and the uncooperative positioning method is popularized to the cooperative positioning method, so that the information between unknown nodes can be fully utilized, the requirement on the distribution density of the anchor nodes is greatly reduced, and the positioning accuracy is further improved.
Due to the characteristics of openness, limited resources and the like of the wireless sensor network, the positioning process of the unknown node is extremely easy to be attacked from the inside or the outside of the network, the sensor node is likely to be attacked by an adversary to become a malicious node, the malicious node destroys the positioning process by tampering with the measurement information, and further reduces the positioning accuracy of the unknown node, so that safety measures need to be taken to ensure that the positioning network can cope with the attacks.
Disclosure of Invention
The invention aims to solve the defects of the prior art, a safety weighted least square algorithm is applied to a cooperative positioning network to realize positioning of large-area unknown nodes by using a small number of anchor nodes, and a safety weighted least square positioning method based on double detection is provided.
The invention is realized by the following technical scheme:
a wireless sensor network safety positioning method based on double detection comprises the following steps:
(1) measuring an environment attenuation index n of a place where the current moment is located, and establishing an RSSI logarithmic attenuation model, wherein the steps are as follows:
(1.1) setting a reference node X1Receive 2 neighbor anchor nodes X2And X3The RSSI value of (1);
(1.2) substitution
Figure GDA0002771776880000021
After subtraction of the two formulas, the environmental attenuation index n and the RSSI can be found0Irrelevant; RSSIiIs a reference node X1Receiving the signal strength, RSSI, of the ith neighbor anchor node0RSSI value received at 1m, d0＝1m，diIs a reference node X1And X2、X3Actual measured distance therebetween;
(1.3) averaging multiple measurement groups to obtain the environment attenuation factor n of the current time location, and obtaining the RSSI logarithmic attenuation model as RSSI ═ RSSI0-10nlog10(di)；
(2) The unknown node respectively collects the number N of anchor nodes in a communication range and p RSSI values received by aiming at each anchor node, the communication radius is x meters, and the communication radius is expressed as:
Figure GDA0002771776880000022
wherein the content of the first and second substances,
Figure GDA0002771776880000027
p RSSI values representing the ith anchor node received by the unknown node;
(3) if the number N of the anchor nodes in the communication range is more than or equal to 3, switching to the step (4), if not, expanding the communication range, and then switching to the step (2);
(4) converting each acquired RSSI value into a corresponding distance through a logarithmic attenuation model and calculating the distance variance of each anchor node, wherein the calculation formulas are respectively as follows:
Figure GDA0002771776880000023
Figure GDA0002771776880000024
wherein the RSSI0Expressed as RSSI value received at 1m distance from unknown node, RSSI' is the value in step (2)The collected RSSI value of the unknown node received from the anchor node in the communication range, n is a decay index,
Figure GDA0002771776880000025
p RSSI signal values of the ith anchor node received by the unknown node are respectively converted into corresponding distance variances according to the attenuation model;
(5) eliminating a small probability value from the acquired RSSI value through a Gaussian filtering model, and then averaging the processed RSSI, wherein the average calculation formula is as follows:
Figure GDA0002771776880000026
wherein N is the number of anchor nodes in the communication range,
Figure GDA0002771776880000031
the sum of p RSSI signal values of the ith anchor node received by the unknown node;
(6) converting each RSSI mean value into a corresponding distance between nodes according to a logarithmic decay model, and expressing a calculation formula as follows:
Figure GDA0002771776880000032
wherein the RSSI0The RSSI value is represented as the RSSI value received at a position 1m away from the unknown node, and is the RSSI mean value of N anchor nodes in the communication range received by the unknown node, and N is an attenuation index;
(7) according to the difference of the RSSI variances, malicious nodes existing in the communication range are detected, and the steps of detecting the malicious nodes are as follows:
(7.1) firstly, calculating the noise standard deviation of the RSSI value of each anchor node, wherein the calculation formula is as follows:
Figure GDA0002771776880000033
it can be simplified to that,
Figure GDA0002771776880000034
where argmin |. is a function of the value of a given expression up to its maximum value, Var (d)i1,di2,…,dip) P RSSI signal values of the ith anchor node received by the unknown node are respectively converted into corresponding distance variances according to the attenuation model; the distance variance of the value converted into the corresponding distance by the ith anchor node according to the RSSI mean value after data processing in a logarithmic decay model is expressed, and the specific derivation of the expression is derived in detail in the step (9); n in the simplified expression is an environmental attenuation factor,
Figure GDA0002771776880000035
the distance between the ith anchor node in the step (6) and the unknown node is obtained through conversion according to the attenuation model;
(7.2) calculating σ for each Anchor nodettThe value is compared with a threshold value lambda sigma if sigmatt<The lambda sigma is marked as a non-malicious node; otherwise, marking as a malicious node; wherein σ represents a noise standard deviation of the non-malicious node;
(8) if the number of the malicious nodes detected in the step (7) is equal to 0, switching to a step (9); if not, performing secondary detection, comparing the distance variance of the malicious nodes with that of the non-malicious nodes, if the distance variance of the malicious nodes is smaller than that of the non-malicious nodes, reserving the malicious nodes, and if the number of the anchor nodes is M, then turning to the step (9);
(9) according to attenuation model
Figure GDA0002771776880000036
The distance of each anchor node is weighted by the variance to obtain a weighting matrix W, and the calculation formula of the distance variance is as follows:
(9.1) assuming that the distance estimate is a random variable, the cumulative distribution function CDF is given by:
Figure GDA0002771776880000041
wherein Q (·) is a Q function,
Figure GDA0002771776880000042
σ denotes the noise standard deviation of the non-malicious nodes, diFor distance estimation, γ is expressed as a random variable.
(9.2)diThe probability density function PDF is expressed as:
Figure GDA0002771776880000043
(9.3) thus obtained, di、
Figure GDA0002771776880000044
The variance of (d) is expressed as:
Figure GDA0002771776880000045
Figure GDA0002771776880000046
(9.4) from this, the weighting matrix is:
Figure GDA0002771776880000047
(10) determining the position coordinates of the unknown nodes according to a weighted least square algorithm, wherein the calculation formula of the weighted least square algorithm is as follows:
Figure GDA0002771776880000048
the coordinate of the unknown node is t ═ q1,q2]T；
Wherein the content of the first and second substances,
Figure GDA0002771776880000049
Figure GDA00027717768800000410
wherein the content of the first and second substances,
Figure GDA00027717768800000411
coordinate values representing the ith anchor nodes x and y respectively,
Figure GDA00027717768800000412
the distance between the ith anchor node in the step (6) and the unknown node is obtained through conversion according to the attenuation model;
(11) upgrading the located unknown nodes into anchor nodes to participate in the location of the rest of the unknown nodes;
the entity involved in the invention comprises an anchor node, an unknown node and a malicious node, wherein the anchor node is a node with known position coordinates, the unknown node is a common node to be positioned, the position coordinates of the unknown node are unknown, the malicious node is a node disguised as the unknown node, the normal communication among the nodes is interfered, namely the node capable of initiating an attack, the unknown node and the anchor node communicate and implement positioning under normal conditions, and if the malicious node exists and participates in the communication, the communication content is wrong and not credible.
Further, in the step (7), the specific content of the method for detecting the malicious node is as follows: noise standard deviation values of malicious nodes and non-malicious nodes due to spoofing attack are respectively expressed as
Figure GDA0002771776880000051
And sigma, so that the noise standard deviation of the RSSI value of the malicious node is larger than that of the RSSI value of the non-malicious node, and whether the anchor node is the malicious node or not can be detected according to the noise standard deviation.
Further, in the step (8), in the secondary detection, when the variance of the spoofing attack is small, the distance variance of the malicious node is smaller than the variance of the non-malicious node, and the malicious node is missed to be detected or the non-malicious node is misused based on the RSSI variance alone, which affects the overall positioning performance of the algorithm. Therefore, secondary detection is provided, the distance variance between the malicious node and the non-malicious node is compared, and if the distance variance of the malicious node is smaller than that of the non-malicious node, the malicious node is reserved.
Further, in the step (9), based on the distance weighting, since the relationship between RSSI and d in the log-attenuated model is non-linear, any change in the received signal power affects the distance estimation in a non-linear manner, and based on the model, the distance estimation close to the target node is more robust to noise than the distance estimation far away from the target node, so that different estimated distance variance formulas corresponding to different received power levels in the presence of the same environmental noise in the model are calculated.
The wireless sensor network safety positioning method based on double detection can detect and position malicious nodes, and has the following advantages that:
1. the system adopts cooperative positioning, utilizes the received signal strength information between the unknown nodes and the unknown nodes, greatly reduces the requirement on the distribution density of the anchor nodes, and further improves the positioning accuracy. In the case of fewer anchor nodes or sparsely distributed anchor nodes, all sensor nodes can be positioned.
2. The safety weighting least square method for double detection is provided, under the condition of deception attack, malicious node detection and high-precision position estimation can be achieved, and when the deception attack variance is small, the poor positioning performance caused by the omission of malicious nodes or the misuse of non-malicious nodes can be avoided.
Drawings
Fig. 1 is a general flowchart of a wireless sensor network security positioning method based on dual detection;
FIG. 2 shows σattComparing the position estimation error accumulation distribution function with the graph at 3 hours;
FIG. 3 is aattComparing the 8-hour position estimation error accumulation distribution function with a graph;
Detailed Description
The following detailed description of the invention is provided in connection with the accompanying drawings:
referring to fig. 1 to 3, a method for wireless sensor network security location based on dual detection includes the following steps:
(1) measuring an environment attenuation index n of a place where the current moment is located, and establishing an RSSI logarithmic attenuation model, wherein the steps are as follows:
(1.1) setting a reference node X1Receiving 2 neighbor anchor nodes X2And X3The RSSI value of (1);
(1.2) substitution
Figure GDA0002771776880000061
After subtraction of the two formulas, the environmental attenuation index n and the RSSI can be found0Irrelevant; RSSI (received Signal Strength indicator)iIs a reference node X1Receiving the signal strength, RSSI, of the ith neighbor anchor node0RSSI value received at 1m, d0＝1m，diIs a reference node X1And X2、X3Actual measured distance therebetween;
(1.3) averaging multiple measurement groups to obtain the environment attenuation factor n of the current time location, and obtaining the RSSI logarithmic attenuation model as RSSI ═ RSSI0-10nlog10(di)；
(1) The unknown node respectively collects the number N of anchor nodes in a communication range and p RSSI values received by aiming at each anchor node, the communication radius is x meters, and the communication radius is represented as:
Figure GDA0002771776880000062
wherein the content of the first and second substances,
Figure GDA0002771776880000063
p RSSI values representing the ith anchor node received by the unknown node;
(2) if the number N of the anchor nodes in the communication range is more than or equal to 3, switching to the step (4), if not, expanding the communication range, and then switching to the step (2);
(3) converting each acquired RSSI value into a corresponding distance through a logarithmic attenuation model and calculating the distance variance of each anchor node, wherein the calculation formulas are respectively as follows:
Figure GDA0002771776880000064
Figure GDA0002771776880000065
wherein, RSSI0The RSSI is represented as the RSSI value received at a position 1m away from the unknown node, the RSSI' is the RSSI value of the anchor node in the communication range received by the unknown node collected in the step (2), n is a decay index,
Figure GDA0002771776880000071
p RSSI signal values of the ith anchor node received by the unknown node are respectively converted into corresponding distance variances according to the attenuation model;
(4) eliminating small probability values of the acquired RSSI values through a Gaussian filter model, and then averaging the processed RSSI values, wherein the average value calculation formula is as follows:
Figure GDA0002771776880000072
wherein N is the number of anchor nodes in the communication range,
Figure GDA0002771776880000073
the sum of p RSSI signal values of the ith anchor node received by the unknown node;
(5) converting each RSSI mean value into a corresponding distance between nodes according to a logarithmic decay model, and expressing a calculation formula as follows:
Figure GDA0002771776880000074
wherein, RSSI0Represented as the RSSI value received at 1m from the unknown node, asThe RSSI mean value of N anchor nodes in a communication range received by a known node, wherein N is an attenuation index;
(6) according to the difference of the RSSI variances, malicious nodes existing in the communication range are detected, and the steps of detecting the malicious nodes are as follows:
(7.1) firstly, calculating the noise standard deviation of the RSSI value of each anchor node, wherein the calculation formula is as follows:
Figure GDA0002771776880000075
it can be simplified to that,
Figure GDA0002771776880000076
where argmin |. is a function of the value of a given expression up to its maximum value, Var (d)i1,di2,…,dip) P RSSI signal values of the ith anchor node received by the unknown node are respectively converted into corresponding distance variances according to the attenuation model; the distance variance of the value converted into the corresponding distance by the ith anchor node according to the RSSI mean value after data processing in a logarithmic decay model is expressed, and the specific derivation of the expression is derived in detail in the step (9); n in the simplified expression is an environmental attenuation factor,
Figure GDA0002771776880000077
the distance between the ith anchor node in the step (6) and the unknown node is obtained through conversion according to the attenuation model;
(7.2) calculating σ for each Anchor nodettThe value is compared with a threshold value lambda sigma if sigmatt<The lambda sigma is marked as a non-malicious node; otherwise, marking as a malicious node; wherein σ represents a noise standard deviation of the non-malicious node;
(7) if the number of the malicious nodes detected in the step (7) is equal to 0, switching to a step (9); if not, performing secondary detection, comparing the distance variance of the malicious nodes with that of the non-malicious nodes, if the distance variance of the malicious nodes is smaller than that of the non-malicious nodes, reserving the malicious nodes, and if the number of the anchor nodes is M, then turning to the step (9);
(8) according to attenuation model
Figure GDA0002771776880000081
The distance of each anchor node is weighted by the variance to obtain a weighting matrix W, and the calculation formula of the distance variance is as follows:
(9.1) assuming that the distance estimate is a random variable, the cumulative distribution function CDF is given by:
Figure GDA0002771776880000082
wherein Q (·) is a Q function,
Figure GDA0002771776880000083
σ denotes the noise standard deviation of the non-malicious nodes, diFor distance estimation, γ is expressed as a random variable;
(9.2)dithe probability density function PDF is expressed as:
Figure GDA0002771776880000084
(9.3) thus obtained, di、
Figure GDA0002771776880000085
The variance of (a) is expressed as:
Figure GDA0002771776880000086
Figure GDA0002771776880000087
(9.4) from this, the weighting matrix is:
Figure GDA0002771776880000088
(9) determining the position coordinates of the unknown nodes according to a weighted least square algorithm, wherein the calculation formula of the weighted least square algorithm is as follows:
Figure GDA0002771776880000089
the coordinate of the unknown node is t ═ q1,q2]T；
Wherein the content of the first and second substances,
Figure GDA00027717768800000810
Figure GDA0002771776880000091
wherein the content of the first and second substances,
Figure GDA0002771776880000092
coordinate values representing the ith anchor nodes x and y respectively,
Figure GDA0002771776880000093
the distance between the ith anchor node in the step (6) and the unknown node is obtained through conversion according to the attenuation model;
(10) upgrading the positioned unknown nodes into anchor nodes to participate in positioning of other unknown nodes;
referring to fig. 1, the method for safely positioning a wireless sensor network based on dual detection firstly establishes a logarithmic attenuation model, then an unknown node acquires RSSI (received signal strength indicator) values of anchor nodes in a communication range, processes the data, and then converts the RSSI values into distances d between the unknown node and the anchor nodes through the logarithmic attenuation modeliThen, malicious nodes are detected through the difference of the RSSI value variances, the distance variances of the malicious nodes and the non-malicious nodes are compared to carry out secondary detection to see whether the malicious nodes need to be reserved or not, the reserved anchor nodes are used for carrying out weighted least squares to calculate the positions of the unknown nodes, and finally the positioned unknown nodes are upgraded to the anchor nodesThe point continues to locate the unknown node after it.
FIG. 2 is aattThe position estimation error accumulation distribution function is compared with the graph at 3. The proposed safe weighted least squares method of double detection is compared with a safe weighted least squares algorithm and a weighted least squares algorithm. Wherein SSWLS is a safe weighted least square method for double detection, SWLS is a safe weighted least square method, and WLS is a weighted least square method.
FIG. 3 is aattThe 8-hour position estimation error accumulation distribution function is compared with a graph. The proposed safe weighted least squares method of double detection is compared with a safe weighted least squares algorithm and a weighted least squares algorithm. Wherein SSWLS is a safe weighted least square method for double detection, SWLS is a safe weighted least square method, and WLS is a weighted least square method.

Claims (4)

1. A wireless sensor network safety positioning method based on double detection is characterized in that: the method comprises the following steps:
(1) measuring an environment attenuation index n of a place where the current moment is located, and establishing an RSSI logarithmic attenuation model, wherein the steps are as follows:
(1.1) setting a reference node X1Receiving 2 neighbor anchor nodes X2And X3The RSSI value of (1);
(1.2) substitution
Figure FDA0002764967510000011
After subtraction of the two formulas, the environmental attenuation index n and the RSSI can be found0Irrelevant; RSSIiIs a reference node X1Receiving the signal strength, RSSI, of the ith neighbor anchor node0RSSI value received at 1m, d0＝1m，diIs a reference node X1And X2、X3Actual measured distance therebetween;
(1.3) averaging multiple measurement groups to obtain the environment attenuation factor n of the current time location, and obtaining the RSSI logarithmic attenuation model as RSSI ═ RSSI0-10nlog10(di)；
(2) The unknown node respectively collects the number N of anchor nodes in a communication range and p RSSI values received by aiming at each anchor node, the communication radius is x meters, and the communication radius is represented as:
Figure FDA0002764967510000012
wherein the content of the first and second substances,
Figure FDA0002764967510000013
p RSSI values representing the ith anchor node received by the unknown node;
(3) if the number N of the anchor nodes in the communication range is more than or equal to 3, switching to the step (4), if not, expanding the communication range, and then switching to the step (2);
(4) converting each acquired RSSI value into a corresponding distance through a logarithmic attenuation model and calculating the distance variance of each anchor node, wherein the calculation formulas are respectively as follows:
Figure FDA0002764967510000014
Figure FDA0002764967510000015
wherein the RSSI0The RSSI is represented by the RSSI value received at a position 1m away from the unknown node, the RSSI' is the RSSI value of the anchor node in the communication range received by the unknown node collected in the step (2), n is a decay index,
Figure FDA0002764967510000016
p RSSI signal values of the ith anchor node received by the unknown node are respectively converted into corresponding distance variances according to the attenuation model;
(5) eliminating a small probability value from the acquired RSSI value through a Gaussian filtering model, and then averaging the processed RSSI, wherein the average calculation formula is as follows:
Figure FDA0002764967510000021
wherein N is the number of anchor nodes in the communication range,
Figure FDA0002764967510000022
the sum of p RSSI signal values of the ith anchor node received by the unknown node;
(6) converting each RSSI mean value into a corresponding distance between nodes according to a logarithmic decay model, and expressing a calculation formula as follows:
Figure FDA0002764967510000023
wherein the RSSI0The RSSI value is represented as the RSSI value received at a position 1m away from the unknown node, and is the RSSI mean value of N anchor nodes in the communication range received by the unknown node, and N is an attenuation index;
(7) according to the difference of the RSSI variances, malicious nodes existing in the communication range are detected, and the steps of detecting the malicious nodes are as follows:
(7.1) firstly, calculating the noise standard deviation of the RSSI value of each anchor node, wherein the calculation formula is as follows:
Figure FDA0002764967510000024
it can be simplified to that,
Figure FDA0002764967510000025
where argmin |. is a function of the value of a given expression up to its maximum value, Var (d)i1,di2,…,dip) P RSSI signal values of the ith anchor node received by an unknown node are respectively converted into corresponding distance variances according to a decay model; expressed as the corresponding value of the ith anchor node converted into the corresponding distance according to the RSSI mean value after data processing in a logarithmic decay modelThe distance variance, the concrete derivation of the expression has detailed derivation in step (9); n in the simplified expression is an environmental attenuation factor,
Figure FDA0002764967510000026
the distance between the ith anchor node in the step (6) and the unknown node is obtained through conversion according to the attenuation model;
(7.2) calculating σ for each Anchor nodettThe value is compared with a threshold value lambda sigma if sigmatt<The lambda sigma is marked as a non-malicious node; otherwise, marking as a malicious node; wherein σ represents a noise standard deviation of the non-malicious node;
(8) if the number of the malicious nodes detected in the step (7) is equal to 0, switching to a step (9); if not, performing secondary detection, comparing the distance variance of the malicious nodes with that of the non-malicious nodes, if the distance variance of the malicious nodes is smaller than that of the non-malicious nodes, reserving the malicious nodes, and if the number of the anchor nodes is M, then turning to the step (9);
(9) according to attenuation model
Figure FDA0002764967510000031
The distance of each anchor node is weighted by the variance to obtain a weighting matrix W, and the calculation formula of the distance variance is as follows:
(9.1) assuming that the distance estimate is a random variable, the cumulative distribution function CDF is given by:
Figure FDA0002764967510000032
wherein Q (·) is a Q function,
Figure FDA0002764967510000033
σ denotes the noise standard deviation of the non-malicious nodes, diFor distance estimation, γ is expressed as a random variable;
(9.2)dithe probability density function PDF is expressed as:
Figure FDA0002764967510000034
(9.3) thus obtained, di、
Figure FDA0002764967510000035
The variance of (d) is expressed as:
Figure FDA0002764967510000036
Figure FDA0002764967510000037
(9.4) from this, the weighting matrix is:
Figure FDA0002764967510000038
(10) determining the position coordinates of the unknown nodes according to a weighted least square algorithm, wherein the calculation formula of the weighted least square algorithm is as follows:
Figure FDA0002764967510000039
the coordinate of the unknown node is t ═ q1,q2]T；
Wherein the content of the first and second substances,
Figure FDA00027649675100000310
Figure FDA0002764967510000041
wherein the content of the first and second substances,
Figure FDA0002764967510000042
individual watchCoordinate values of the ith anchor node x and y,
Figure FDA0002764967510000043
the distance between the ith anchor node in the step (6) and the unknown node is obtained through conversion according to the attenuation model;
(11) and upgrading the positioned unknown node into an anchor node to participate in positioning of other unknown nodes.
2. The wireless sensor network security positioning method based on double detection as claimed in claim 1, characterized in that: in the step (7), the specific content of the method for detecting the malicious node is as follows: due to spoofing attack, the noise standard deviation values of the malicious nodes and the non-malicious nodes are respectively represented as sum, so that the noise standard deviation of the RSSI values of the malicious nodes is greater than that of the RSSI values of the non-malicious nodes, and whether the anchor node is the malicious node or not can be detected according to the noise standard deviation.
3. The wireless sensor network security location method based on dual detection as claimed in claim 1 or 2, characterized in that: in the step (8), in the secondary detection, when the variance of the spoofing attack is small, the distance variance of the malicious node is smaller than the variance of the non-malicious node, and the malicious node is missed to be detected or the non-malicious node is misused due to the RSSI variance which is purely based, so that the overall positioning performance of the algorithm is influenced; therefore, secondary detection is provided, the distance variance between the malicious node and the non-malicious node is compared, and if the distance variance of the malicious node is smaller than that of the non-malicious node, the malicious node is reserved.
4. The wireless sensor network security location method based on dual detection as claimed in claim 1 or 2, characterized in that: in the step (9), based on the distance weighting, since the relationship between RSSI and RSSI in the log attenuation model is nonlinear, any change in the received signal power affects the distance estimation in a nonlinear manner, and based on the model, the distance estimation close to the target node is more robust to noise than the distance estimation far away from the target node, so that different estimated distance variance formulas corresponding to different received power levels in the presence of the same environmental noise in the model are calculated.
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