CN114222291A - Wireless body area network data encryption method - Google Patents
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- H—ELECTRICITY
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- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/03—Protecting confidentiality, e.g. by encryption
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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- H04L25/00—Baseband systems
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- H04W12/04—Key management, e.g. using generic bootstrapping architecture [GBA]
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- H04W12/043—Key management, e.g. using generic bootstrapping architecture [GBA] using a trusted network node as an anchor
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention relates to a wireless body area network data encryption method, which belongs to the field of network data encryption and comprises the following steps: s1: acquiring an electrocardiosignal of t seconds, and forming an electrocardiosignal characteristic value lambda according to the R waveform of the electrocardiosignal; s2: performing near-body channel estimation by adopting a method based on LMMSE, VGG16 and a transform to obtain the amplitude A and the phase P of a channel; s3: quantizing the characteristic value lambda, the amplitude A and the phase P of the electrocardiosignal respectively to form three parameters which are used as encryption keys; s4: broadcasting the encryption key obtained in the step S3 to each node in the wireless body area network, and encrypting a plaintext sequence by each node by using a staggered stop-and-go stream encryption circuit according to the encryption key to form a ciphertext; s5: and returning to the step S1 to update the characteristic value lambda, the amplitude A and the phase P of the electric signal according to the interval period T.
Description
Technical Field
The invention belongs to the field of network data encryption, and relates to a wireless body area network data encryption method.
Background
As an important public application Network, a Body Area Network (BAN) has great application requirements in the field of electronic medical services, especially remote medical treatment, special crowd monitoring, community and home medical treatment, and the like. The BAN is a local area network which is connected with a plurality of wearable sensor nodes in a wireless mode to form a communication distance of no more than 2 meters, and each small sensor node can be attached to the body surface of a human body or implanted inside the human body and is used for collecting vital sign parameters such as blood pressure, heart rate, body temperature, blood oxygen saturation, electrocardio and the like.
The BAN technology can meet the requirements of medical health information, realize intelligent medical work of people, solve the problems of difficult and expensive medical examination, promote the interaction between individuals and medical staff, medical institutions and medical equipment, realize the management of personal health, namely the early treatment and health maintenance of diseases, and promote the informatization and the intellectualization of health management.
Due to the open nature of wireless channels, BANs are also exposed to security threats such as personal privacy disclosure, monitoring of transmitted information, frequent medical accidents, etc. With the development of informatization and sensor technology, how to solve the problem of user information security in medical application is a problem which needs to be solved urgently in the development process of BAN. If the requirements of users on data security and confidentiality and reliable transmission cannot be met, the application and development of BAN technology-related services are greatly hindered. Therefore, unlike a general wireless sensor network, security of user information is one of the main considerations in BAN applications.
At present, there are two main schemes for encrypting by using BAN channel characteristics, the first is to calculate the signal strength received through a wireless channel at a receiving end and generate a secret key according to the signal strength value. And secondly, frequency difference is increased by using a channel frequency hopping technology, so that continuous channel sampling is effectively decorrelated, and the information entropy of the generated key can be improved. However, the basic idea of both schemes is to generate a key by using the signal strength of the receiving end, the encryption method is simple, and if the distance from the eavesdropper to the receiving end and the distance from the sender to the receiving end are basically unchanged, a consistent key is generated, which may cause the security protection to fail.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method for encrypting data of a wireless body area network, which is used to encrypt data of each node in the body area network, and which can not only meet data processing efficiency of the body area network, but also ensure security of data transmission in the network.
In order to achieve the purpose, the invention provides the following technical scheme:
a wireless body area network data encryption method comprises the following steps:
s1: acquiring an electrocardiosignal of t seconds, and forming an electrocardiosignal characteristic value lambda according to the R waveform of the electrocardiosignal;
s2: performing near-body channel estimation by adopting a method based on LMMSE, VGG16 and a transform to obtain the amplitude A and the phase P of a channel;
s3: quantizing the characteristic value lambda, the amplitude A and the phase P of the electrocardiosignal respectively to form three parameters which are used as encryption keys;
s4: broadcasting the encryption key obtained in the step S3 to each node in the wireless body area network, and encrypting a plaintext sequence by each node by using a staggered stop-and-go stream encryption circuit according to the encryption key to form a ciphertext;
s5: and returning to the step S1 to update the characteristic value lambda, the amplitude A and the phase P of the electric signal according to the interval period T.
The key information in the method integrates the physiological parameter characteristics, the encryption time and the complexity are reduced, and the safety of network data transmission is ensured by periodically replacing the key. The method is adopted to encrypt the data of each node in the body area network, thereby not only meeting the data processing efficiency of the body area network, but also ensuring the safety of data transmission in the network.
Further, the calculation formula of the electrocardiosignal characteristic value lambda is
Wherein, tiThe interval time of the ith R wave and the (i + 1) th R wave is shown, and n is the number of the R waves of the electrocardiosignal in t seconds.
Further, the period T is 200-400 seconds and T is 10 seconds by combining the human body electrocardiosignal period.
Further, in step S2, firstly, a linear minimum mean square error estimation algorithm LMMSE is used to obtain a low-resolution channel characteristic, then the channel characteristic is input to a VGG16 convolutional neural network for feature extraction, the obtained feature map is sent to an RPN network for generating a candidate region, a frame regression correction anchor is used to obtain an accurate region, a pooling layer collects the input feature map and the region, extracts the candidate feature map after synthesizing the information, and sends the candidate feature map to a full-connection layer to obtain a final target judgment; on the basis of extracting the spatial features of the two-dimensional image by the RCNN, further extracting detail features by combining a Transformer model; after Feature dimensionality reduction, adding the Feature dimensionality reduction result and Spatial Positional Encoding (Spatial position Encoder) and inputting the result to an Encoder, and in order to embody information of an image in x and y dimensions, the DETR Decoder decodes N objects in parallel, wherein each Decoder has two inputs: output of Object Query or last Decoder, result of Encoder; two feedforward neural networks FFN are connected behind the last Decoder, and the detection frames and the types of the detection frames are respectively predicted; and outputting the near-body channel time-frequency response after calculation.
Further, in step S3, the characteristic values λ of the electrocardiographic signals are quantized to form N1Quantizing the amplitude A to form N2An initial value of the order linear feedback shift register quantizes the phase P to form N3Feeding back initial values of shift register in order linearity, and using the obtained three initial value parameters as encryption key, N1、N2And N3The number of bits of the shift register.
Further, the staggered stop-and-go stream encryption circuit in step S4 is composed of three stage-adjustable linear feedback shift registers, two and gates, a not gate, and an exclusive or gate, where:
the parameter obtained by quantizing the electrocardiosignal characteristic value lambda serves as an initial sequence of a first linear feedback shift register LFSR-1, a plaintext sequence serves as a driving clock of the first linear feedback shift register LFSR-1, the output end of the first linear feedback shift register LFSR-1 is connected with one input end of a first AND gate, and the plaintext sequence is input into the other input end of the first AND gate;
the signal output by the first AND gate is used as a driving clock of the second linear feedback shift register LFSR-2, and the parameter obtained by amplitude A quantization is used as an initial sequence of the second linear feedback shift register LFSR-2;
the output end of the first linear feedback shift register LFSR-1 is also connected with one input end of a second AND gate through the NOT gate, the other input end of the second AND gate inputs a plaintext sequence, a signal output by the second AND gate is used as a driving clock of a third linear feedback shift register LFSR-3, and a parameter obtained by phase P quantization is used as an initial sequence of the third linear feedback shift register LFSR-3;
and the signals output by the second linear feedback shift register LFSR-2 and the signals output by the third linear feedback shift register LFSR-3 are subjected to XOR processing by the XOR gate, and then a ciphertext is output.
Further, the polynomial expression of the first linear feedback shift register LFSR-1 is:
f1(X)＝X12+X11+X3+X+1
the polynomial expression of the second linear feedback shift register LFSR-2 is:
f2(X)＝X16+X12+X2+1
the polynomial expression of the third linear feedback shift register LFSR-3 is:
f3(X)＝X16+X12+X5+1。
further, the calculation formula of the linear minimum mean square error estimation algorithm is as follows:
wherein h ═ h1,h2,...hK]，hkFor the channel state estimated for the k-th time,
X＝diag(x1,x2,…,xK)，xKfor the k-th estimated training sequence, y is Xh + n, and n represents an independent and equally distributed white gaussian noise vector.
Further, in order to ensure the coordination and stability of the whole network, a coordinator is arranged in the network, when the network is initialized, the electrocardio node sends 10 seconds of electrocardio data and a leader sequence with a certain length, the coordinator receives the leader sequence, extracts the 10 seconds of electrocardio data and calculates an encryption key, and then the coordinator broadcasts the encryption key to all nodes.
The invention has the beneficial effects that: the wireless body area network data encryption method provided by the invention adopts a scheme of measuring a short training sequence for multiple times and adopting a better channel joint estimation algorithm to replace independent estimation, thereby increasing the consistency of the key. In the invention, the nodes do not need to bear larger calculation amount, low power consumption is easy to realize, and by randomly selecting the nodes, the channel parameter difference is larger due to the position change, the dynamically generated new key has larger change, and high-strength data encryption is realized.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a diagram of the method steps of the present invention;
FIG. 2 is a schematic diagram of R-wave intervals;
FIG. 3 is a diagram of an embodiment of a method for channel feature estimation based on artificial intelligence
FIG. 4 is a convolutional neural network structure in an embodiment
FIG. 5 is a diagram of a Transformer network architecture in an embodiment
FIG. 6 is a circuit configuration diagram of the feedback shift register LFSR-1;
FIG. 7 is a circuit diagram of a feedback shift register LFSR-2 in an exemplary embodiment;
FIG. 8 is a circuit diagram of a feedback shift register LFSR-3 in an exemplary embodiment;
fig. 9 is the interleaved stop-and-go stream cipher circuit.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
As shown in fig. 1, a method for encrypting data of a wireless body area network includes the following steps:
step 1: acquiring an electrocardiosignal of 10 seconds, forming an electrocardiosignal characteristic value lambda according to an R wave form of the electrocardiosignal, generally arranging a coordinator in a network, when the network is initialized, sending sampled electrocardio data of 10 seconds and a leader sequence (such as 63 bits) of a certain length by an electrocardio node, extracting the electrocardio data of 10 seconds after the coordinator receives the leader sequence, and marking the interval time of two adjacent R waves after the coordinator receives the leader sequence, wherein the interval time of two adjacent R waves is marked according to the R wave form of the electrocardiosignal as shown in figure 2
Calculating to obtain an electrocardiosignal characteristic value lambda, wherein tiAt the interval of the ith R wave and the (i + 1) th R waveAnd n is the number of the electrocardiosignal R waves within 10 s.
Step 2: performing channel estimation by using a neural network estimation algorithm to obtain the amplitude a and the phase P of the channel, as shown in fig. 3; in the direction, firstly, a linear minimum mean square error estimation algorithm (LMMSE) is adopted to obtain a low-resolution channel characteristic, then the low-resolution channel characteristic is input to a VGG16 convolutional neural network for feature extraction, and an obtained feature map is sent to an RPN network for generating a candidate region, as shown in fig. 4. The layer obtains a precise region by correcting the anchor by using frame regression. And collecting the input feature map and the input region by the pooling layer, extracting the candidate feature map after integrating the information, and sending the candidate feature map to a subsequent full-connection layer to obtain the final target judgment. And on the basis of the RCNN for extracting the spatial features of the two-dimensional image, further extracting detail features by combining with a transform model. After Feature dimensionality reduction, adding the Feature dimensionality reduction result and Spatial Positional Encoding (Spatial position Encoder) and inputting the result to an Encoder, and in order to embody information of an image in x and y dimensions, the DETR Decoder decodes N objects in parallel, wherein each Decoder has two inputs: object Query (or the output of the last Decoder), the other way is the result of the Endecoder. The last Decoder is followed by two FFNs (feed forward neural networks) that predict the detection box and its class, respectively, as shown in fig. 5. And outputting the near-body channel time-frequency response after calculation.
And step 3: respectively quantizing the characteristic values lambda of the electrocardiosignals to form N1Quantizing the amplitude A to form N2An initial value of the order linear feedback shift register quantizes the phase P to form N3Feeding back initial values of shift register in order linearity, and using the obtained three initial value parameters as encryption key, N1、N2And N3Is the bit number of the shift register; in this embodiment, 3 LFSRs based on λ, a and P are used as initialization sequences, because the three parameters can be quantized with different bit lengths at different times, the number of stages of the 3 LFSRs is adjustable. In this example, the characteristic value λ of the central electrical signal is quantized to 12 bits, i.e., a 12-step linear feedback shift register LFSR-1 is selected, the amplitude A is quantized to 16 bits, i.e., a 16-step linear feedback shift register LFSR-2 is selected, and the phase P is quantized to 16 bits, i.e., a 16-step linear feedback shift register LFSR-2 is selectedA linear feedback shift register LFSR-3;
and 4, step 4: broadcasting the encryption key obtained in the step 3 to each node in the wireless body area network by the coordinator, and encrypting the plaintext sequence phi (t) by each node according to the encryption key by using a staggered stop-and-go stream encryption circuit to form a ciphertext;
specifically, all sensor nodes initialize 12-bit values obtained by quantizing λ to obtain initial values of 12-order linear feedback shift registers LFSR-1, where the structure of LFSR-1 is shown in fig. 3, and 12 registers (X) are provided in each clock cycle12,…X1) The last register X performs shift calculation in turn0Data and register X in (1)1、X3And X11The value of (A) is XOR-ed and sent to the input end to be XOR-ed with an input bit of the original data and then sent to the register X11Thus, the polynomial expression of the 12 th order linear feedback shift register LFSR-1 is f1(X)＝X12+X11+X3+ X +1, its circuit is composed of 12D flip-flops, 12 multiplexers and 4 exclusive-or gates. The '0' port of each multiplexer is connected with initialization data, the '1' port is connected with the input end of a trigger, and 3 triggers X11、X3And X1Output data and flip-flop X of0The output data is subjected to exclusive OR and then subjected to exclusive OR with the input signal, and the exclusive OR is used as the input signal and fed back to the first multiplexer. I is11，…I0For 12 bits of initialization data sel is a mode set signal connected at the selection terminals of the 12 multiplexers. When sel is 0, I11，…，I0Setting a trigger value through a multiplexer to complete initialization; when sel is 1, the circuit enters a cyclic shift mode.
Similarly, all sensor nodes initialize the 16-bit value obtained after quantizing the amplitude A to obtain an initial value of a 16-step linear feedback shift register LFSR-2, wherein the LFSR-2 has a structure as shown in FIG. 7, and 16 registers (X) are arranged in each clock cycle16,…X1) The last register X performs shift calculation in turn0Data and register X in (1)2And X12The value of (A) is XOR-ed and sent to the input end to be XOR-ed with an input bit of the original data and then sent to the register X15Thus, the polynomial expression of the 16-stage linear feedback shift register LFSR-2 is f2(X)＝X16+X12+X2+ 1; the circuit is composed of 16D flip-flops, 16 multiplexers and 3 exclusive-OR gates.
Similarly, all sensor nodes initialize the 16-bit value obtained after the quantization of the phase P to obtain an initial value of a 16-step linear feedback shift register LFSR-3, where the structure of the LFSR-3 is shown in fig. 8, and 16 registers (X) are provided in each clock cycle16,…X1) The last register X performs shift calculation in turn0Data and register X in (1)5And X12The value of (A) is XOR-ed and sent to the input end to be XOR-ed with an input bit of the original data and then sent to the register X15Thus, the polynomial expression of the 15 th order linear feedback shift register LFSR-3 is f3(X)＝X16+X12+X5+1, its circuit is composed of 16D flip-flops, 16 multiplexers and 3 exclusive-or gates.
Referring to fig. 9, the staggered stop-and-go stream encryption circuit is composed of three stage-adjustable linear feedback shift registers, two and gates, a not gate and an exclusive or gate, wherein:
the parameter obtained by quantizing the electrocardiosignal characteristic value lambda serves as an initial sequence of a first linear feedback shift register LFSR-1, a plaintext sequence phi (t) serves as a driving clock of the first linear feedback shift register LFSR-1, the output end of the first linear feedback shift register LFSR-1 is connected with one input end of a first AND gate, and the plaintext sequence phi (t) is input into the other input end of the first AND gate;
the signal output by the first AND gate is used as a driving clock of the second linear feedback shift register LFSR-2, and the parameter obtained by amplitude A quantization is used as an initial sequence of the second linear feedback shift register LFSR-2;
the output end of the first linear feedback shift register LFSR-1 is also connected with one input end of a second AND gate through the NOT gate, the other input end of the second AND gate inputs a plaintext sequence phi (t), a signal output by the second AND gate is used as a driving clock of a third linear feedback shift register LFSR-3, and a parameter obtained by phase P quantization is used as an initial sequence of the third linear feedback shift register LFSR-3; that is, when the LFSR-1 output is "1", LFSR-2 is driven by the clock, and when the LFSR-1 output is "0", LFSR-3 is driven by the clock,
and the signal output by the second linear feedback shift register LFSR-2 and the signal output by the third linear feedback shift register LFSR-3 are processed by the XOR gate to output a ciphertext b (t). The generator has simple structure, easy realization and longer period, and adopts the staggered output with adjustable tap series, so that the tap sequence can not be deduced even if the plaintext ciphertext pair is known by the monitoring party, and the high-strength data encryption can be realized.
And finally, entering the step 5: and returning to the step 1 according to the period T for circular operation, thereby realizing the dynamic refreshing of the encryption key. In this embodiment, the period T is preferably 200 to 400 seconds, in combination with the period of the human body electrocardiosignal.
Compared with the traditional encryption method, the method not only can effectively reduce encryption time delay and operation complexity, but also has larger channel parameter difference, larger change of the dynamically generated new secret key, and realizes high-strength data encryption.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (9)
1. A wireless body area network data encryption method is characterized in that: the method comprises the following steps:
s1: acquiring an electrocardiosignal of t seconds, and forming an electrocardiosignal characteristic value lambda according to the R waveform of the electrocardiosignal;
s2: performing near-body channel estimation by adopting a method based on LMMSE, VGG16 and a transform to obtain the amplitude A and the phase P of a channel;
s3: quantizing the characteristic value lambda, the amplitude A and the phase P of the electrocardiosignal respectively to form three parameters which are used as encryption keys;
s4: broadcasting the encryption key obtained in the step S3 to each node in the wireless body area network, and encrypting a plaintext sequence by each node by using a staggered stop-and-go stream encryption circuit according to the encryption key to form a ciphertext;
s5: and returning to the step S1 to update the characteristic value lambda, the amplitude A and the phase P of the electric signal according to the interval period T.
2. The wireless body area network data encryption method of claim 1, wherein: the calculation formula of the electrocardiosignal characteristic value lambda is
Wherein, tiThe interval time of the ith R wave and the (i + 1) th R wave is shown, and n is the number of the R waves of the electrocardiosignal in t seconds.
3. The wireless body area network data encryption method of claim 2, wherein: and combining the human body electrocardiosignal period, wherein the period T is 200-400 seconds, and T is 10 seconds.
4. The wireless body area network data encryption method of claim 1, wherein: in the step S2, firstly, a linear minimum mean square error estimation algorithm LMMSE is adopted to obtain low-resolution channel characteristics, then the low-resolution channel characteristics are input into a VGG16 convolutional neural network for feature extraction, the obtained feature map is sent into an RPN for generating a candidate region, a frame regression correction anchor is used for obtaining an accurate region, a pooling layer collects the input feature map and the region, the candidate feature map is extracted after the information is synthesized, and the accurate region is sent into a full-connection layer to obtain final target judgment; on the basis of extracting the spatial features of the two-dimensional image by the RCNN, further extracting detail features by combining a Transformer model; after Feature dimensionality reduction, adding the space position Encoder Spatial Positional Encoding and inputting the result into an Encoder, and decoding N objects in parallel by using a DETR Decoder, wherein each Decoder has two inputs: output of Object Query or last Decoder, result of Encoder; two feedforward neural networks FFN are connected behind the last Decoder, and the detection frames and the types of the detection frames are respectively predicted; and outputting the near-body channel time-frequency response after calculation.
5. The wireless body area network data encryption method of claim 1, wherein: in step S3, the characteristic values λ of the electrocardiographic signals are quantized to form N1Quantizing the amplitude A to form N2An initial value of the order linear feedback shift register quantizes the phase P to form N3Feeding back initial values of shift register in order linearity, and using the obtained three initial value parameters as encryption key, N1、N2And N3The number of bits of the shift register.
6. The wireless body area network data encryption method of claim 1, wherein: in step S4, the staggered stop-and-go stream encryption circuit is composed of three stage-adjustable linear feedback shift registers, two and gates, a not gate, and an exclusive or gate, where:
the parameter obtained by quantizing the electrocardiosignal characteristic value lambda serves as an initial sequence of a first linear feedback shift register LFSR-1, a plaintext sequence serves as a driving clock of the first linear feedback shift register LFSR-1, the output end of the first linear feedback shift register LFSR-1 is connected with one input end of a first AND gate, and the plaintext sequence is input into the other input end of the first AND gate;
the signal output by the first AND gate is used as a driving clock of the second linear feedback shift register LFSR-2, and the parameter obtained by amplitude A quantization is used as an initial sequence of the second linear feedback shift register LFSR-2;
the output end of the first linear feedback shift register LFSR-1 is also connected with one input end of a second AND gate through the NOT gate, the other input end of the second AND gate inputs a plaintext sequence, a signal output by the second AND gate is used as a driving clock of a third linear feedback shift register LFSR-3, and a parameter obtained by phase P quantization is used as an initial sequence of the third linear feedback shift register LFSR-3;
and the signals output by the second linear feedback shift register LFSR-2 and the signals output by the third linear feedback shift register LFSR-3 are subjected to XOR processing by the XOR gate, and then a ciphertext is output.
7. The wireless body area network data encryption method of claim 1, wherein: the polynomial expression of the first linear feedback shift register LFSR-1 is:
f1(X)＝X12+X11+X3+X+1
the polynomial expression of the second linear feedback shift register LFSR-2 is:
f2(X)＝X16+X12+X2+1
the polynomial expression of the third linear feedback shift register LFSR-3 is:
f3(X)＝X16+X12+X5+1。
8. the wireless body area network data encryption method of claim 1, wherein: the linear minimum mean square error estimation algorithm has the following calculation formula:
wherein h ═ h1,h2,...hK]，hkFor the channel state estimated for the k-th time,
X＝diag(x1,x2,…,xK)，xKfor the k-th estimated training sequence, y is Xh + n, and n represents an independent and equally distributed white gaussian noise vector.
9. The wireless body area network data encryption method of claim 1, wherein: a coordinator is arranged in a network, when the network is initialized, an electrocardiogram node sends 10 seconds of electrocardiogram data and a leader sequence with a certain length, the coordinator receives the leader sequence and then extracts the 10 seconds of electrocardiogram data and calculates an encryption key, and then the coordinator broadcasts the encryption key to all nodes.
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