CN110086518B - Elastic beam forming method based on multi-arm gambling machine in wireless sensor network - Google Patents
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Abstract
The invention relates to a multi-arm gambling machine-based elastic beam forming method, which comprises the following steps: m sensor nodes are arranged in the wireless sensor network; when there is fault sensor node interference, the set containing different sensors is defined as an arm, and then K groups of arms are shared, wherein K is 2m-2; initializing the selected times of each arm, the estimated value of each arm, and the probability of each arm being selected at the initial momenti1/K; randomly selecting one arm according to the selected probability, carrying out phase updating on all sensor nodes in the selected arm once according to a random grouping distributed beam forming method, increasing the selected times corresponding to the selected arm by one, updating a reward value corresponding to the selected arm, and updating an estimated value of the selected arm; updating the probability of each arm being selected according to a boltzmann distribution; and repeating the steps until the arms corresponding to all the normal sensor nodes are selected, and enabling the power of the signals sent by the sensor nodes to be optimal. The invention is suitable for the fault removal sensor.
Description
Technical Field
The invention belongs to the technical field of wireless communication networks, and particularly relates to an elastic beam forming method based on a multi-arm gambling machine in a wireless sensor network.
Background
In recent years, with rapid development of sensor technology, communication technology, information processing technology, and embedded technology, wireless sensor networks are increasingly widely used. The wireless sensor network can sense and collect information in a target area, and finally transmits the information to the user terminal, has wide application scenes, and is mainly applied to various fields such as area detection, medical monitoring, environment monitoring, industrial monitoring, military and national defense at present.
The wireless sensor network consists of a large number of cheap micro sensor nodes deployed in a monitoring area, and is a multi-hop self-organizing network system formed in a wireless communication mode, and the wireless sensor network aims to sense, collect and process information of a sensed object in a network coverage area through cooperation and send the information to an observer. In real-life situations, due to a bad and uncertain environment, battery power limitation, noise influence, malicious attack by an adversary, and the like, there are often some sensors with malicious behaviors in the network to interfere with the system. If these malicious sensors are not processed in a timely manner, data corruption, system instability, and energy loss may result. Therefore, how to design a strategy to exclude malicious sensors so as to ensure the correctness of all the sending signal sensors is an important problem faced by the wireless sensor network.
The beamforming technology is one of the technologies for improving the communication quality of the wireless sensor network. Beamforming is a signal processing technique for sensor arrays that achieves directional signal transmission or reception by causing signals at specific angles to undergo constructive interference while other signals undergo destructive interference, thereby achieving an increase in the power of the transmitted signal. In the wireless sensor network, signals transmitted by nodes can be concentrated to the direction of a receiving end through a beam forming technology, so that the power of the signals received by the receiving end is increased. Meanwhile, the beam forming technology evenly shares the energy consumption required by information transmission to a plurality of nodes participating in signal transmission, so that the energy consumption of the nodes is reduced, and the service life of the wireless sensor network is prolonged. However, if a faulty sensor participates in transmitting signals, it inevitably results in the signal power at the receiving end not being maximized, i.e., beamforming is not achieved.
The Multi-arm gambling machine (Multi-arm Bandit) problem belongs to a field of reinforcement learning, the main idea of which is to select one of a plurality of given arms, allocated different resources, according to a certain strategy for each player in order to find the best arm, maximizing the total prize in a series of trials. The method converts the beam forming problem of a sensor with a fault into the problem of a multi-arm gambling machine, performs multiple selections and updates the probability of the selected arm by constructing a proper instant reward, so that the selection probability of the optimal arm is increased to 1, and finally, the selected arm only contains normal sensor nodes each time, thereby eliminating the influence of the fault sensor and ensuring the maximization of the power of a signal received by a receiving end.
In the wireless sensor network, the requirement on the accuracy of data collection is high, and the sensor nodes need to transmit information to the user terminal accurately without errors, so that the interference of a faulty sensor cannot occur. In wireless sensor networks, a centralized approach was first proposed, involving a central node receiving all other node information and validating its status by analyzing the information. Tang and Chow propose an algorithm called neighborhood hidden conditional random field (NHCR) which determines whether a node is normal by receiving signal strength, frequency and signal delay. The method can relax the independent assumption of the hidden Markov model and can effectively provide reliable diagnosis results even under different networks. The method for classifying and managing the fault nodes based on the fuzzy rule is provided by using the method that Chanak and Banerjee detect the fault sensor nodes through the fault state. The scheme not only enhances the reusability of the fault node, overcomes the uncertainty problem, but also improves the overall performance of the network. To avoid reliance on central nodes, Lee and Choi propose a distributed fault detection algorithm in which each node determines its neighbor node states by comparison with its own state. The method relies on an Agnostic Diagnostic (AD) method of minimum domain knowledge to detect faulty nodes and can therefore be applied to various wireless sensor networks. The AD gradually identifies the failed node by describing the state of the node with a continuously updated correlation graph.
Therefore, how to design a method for identifying and avoiding a faulty sensor in a wireless sensor network is a very worthy problem.
Disclosure of Invention
Based on the above problems in the prior art, the present invention provides a dobby-based elastic beam forming method in a wireless sensor network.
In order to achieve the purpose, the invention adopts the following technical scheme:
a multi-arm gambling machine-based elastic beam forming method in a wireless sensor network comprises the following steps:
s1, m sensor nodes are arranged in the wireless sensor network; when there is faulty sensor node interference, a set containing different sensors is defined as an arm, and there are K groups of arms in common, where K is 2m-2; initializing the number of times each arm is selected N i0, estimated value Q of each armi(0) 1, the probability of each arm initial time being chosen is pi＝1/K；
S2, randomly selecting one arm according to the selected probability, carrying out phase updating on all sensor nodes in the selected arm once according to a random grouping distributed beam forming method, increasing the selected times corresponding to the selected arm by one, updating a reward value corresponding to the selected arm, and updating an estimated value of the selected arm;
s3, updating the selected probability of each arm according to the Boltzmann distribution;
and S4, repeating the steps S2 and S3 until the arms corresponding to all the normal sensor nodes are selected, and enabling the power of the signals sent by the sensor nodes to be optimal.
Preferably, all the sensor nodes in the selected arm perform a phase update according to a random grouping distributed beam forming method, including the following steps:
s21, initializing the phases of all sensor nodes in the selected arm;
s22, determining the grouping probability q, wherein q is more than 0 and less than 1; randomly dividing all sensor nodes into two groups G according to grouping probability q1And G2；
S23、G1The sensor nodes in the group respectively send signals to a receiving end in four time slots, and carry out phase offset according to the received feedback information; g2The sensor nodes in the group are respectivelyAnd transmitting signals to a receiving end in four time slots, wherein the phase offset in each time slot is zero.
As a preferred scheme, the performing phase shift according to the received feedback information includes: after receiving the feedback in the first time slot, adjusting the phase to pi; after receiving the feedback in the second time slot, the phase is adjusted to pi/2; after receiving the feedback in the third time slot, the phase is adjusted to-psi (n) -3 pi/2;
where ψ (n) ═ arctan ((1+ α (n))/(1- α (n))),
α(n)＝[(P(4n+2)-P(4n))/(P(4n+2)-P(4n+1))]，
p (4n), P (4n +1), and P (4n +2) respectively represent the signal power received by the receiving end of the first, second, and third time slots, where n is 1; for the phase offset of the fourth time slot, if the power value P (4n +3) received by the receiving end of the fourth time slot is greater than or equal to the power value P (4n) received at the initial moment of the iteration stage where the receiving end is located, the phase offset is 0; otherwise, the phase offset is π.
Preferably, the method for updating the reward value corresponding to the selected arm includes:
wherein, Pi(old) is the signal power generated by the node contained in the selected arm transmitting the signal to the receiving end without any phase offset; pi(new) updating the signal power generated by sending signals to the receiving end for the primary phase of the nodes contained in the selected arm; t' is greater than 0 and is constant.
Preferably, the method for updating the estimated value of the selected arm includes:
Qi(Ni)＝Qi(Ni-1)+β[Ri(Ni)-Qi(Ni-1)]，0＜β＜1。
preferably, the method for updating the probability of each arm being selected comprises:
preferably, the method for optimizing the power of the signal sent by the sensor node in step S4 includes: and randomly grouping the sensor nodes in the arm corresponding to all the normal sensor nodes, and adjusting the phase to perform multiple iterations according to the feedback of the receiving end until the power of the signal received by the receiving end reaches an optimal value.
Preferably, the method for optimizing the power of the signal sent by the sensor node in step S4 includes the following steps:
s41, initializing the phase of each sensor node in the arm corresponding to all normal sensor nodes;
s42, determining the grouping probability q, wherein q is more than 0 and less than 1; randomly dividing all sensor nodes into two groups G according to grouping probability q1And G2；
S43, each iteration comprises four time slots; g1The sensor nodes in the group respectively send signals to a receiving end in each time slot and carry out phase deviation according to the received feedback information; g2The sensor nodes in the group respectively send signals to a receiving end in each time slot, and the phase offset in each time slot is zero;
and S44, repeating the steps S42 and S43 until the power of the signal received by the receiving end reaches an optimal value.
Preferably, the sensor nodes are assigned to G1Probability of group q, assigned to G2Probability of group 1-q.
As a preferred scheme, the performing phase shift according to the received feedback information includes: after receiving the feedback in the first time slot, adjusting the phase to pi; after receiving the feedback in the second time slot, the phase is adjusted to pi/2; after receiving the feedback in the third time slot, the phase is adjusted to-psi (n) -3 pi/2;
where ψ (n) ═ arctan ((1+ α (n))/(1- α (n))),
α(n)＝[(P(4n+2)-P(4n))/(P(4n+2)-P(4n+1))]，
p (4n), P (4n +1), P (4n +2) respectively represent the signal power received by the receiving end of the first, second and third time slots in the nth iteration; for the phase offset of the fourth time slot, if the power value P (4n +3) received by the receiving end in the fourth time slot is greater than or equal to the power value P (4n) received at the initial moment of the iteration stage where the power value P is located, the phase offset is 0; otherwise, the phase offset is π.
Compared with the prior art, the invention has the beneficial effects that:
the elastic beam forming method based on the dobby in the wireless sensor network takes the set of different sensors as one arm in the dobby problem, selects the different sensors according to a certain probability, and finds the optimal arm, namely all normal sensors by using the thought of the dobby problem, thereby avoiding the influence of fault sensors, and finally perfectly coupling the signals of all normal sensors at the receiving end, thereby enabling the power of the signals sent by the sensor nodes to be optimal. The method is suitable for the wireless sensor network to eliminate the fault sensor in the system, improves the stability of the system and can improve the signal transmission power.
Drawings
Fig. 1 is a flowchart of a dobby-based elastic beam forming method in a wireless sensor network according to an embodiment of the present invention;
FIG. 2 is a graph of the probability of a normal sensor node being selected during an iteration of a dobby-based elastic beam forming method in a wireless sensor network according to an embodiment of the present invention;
FIG. 3 is a graph of optimal signal transmit power in an iterative process for a dobby-based elastic beamforming method in a wireless sensor network according to an embodiment of the present invention;
fig. 4 is a flowchart of an RG-DB algorithm in a dobby-based elastic beam forming method in a wireless sensor network according to an embodiment of the present invention;
fig. 5 is a diagram illustrating the operation of the RG-DB algorithm in each time slot during one iteration in the dobby-based elastic beam forming method in the wireless sensor network according to the embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention, the following description will explain the embodiments of the present invention with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, without inventive effort, other drawings and embodiments can be derived from them.
The invention provides an elastic beam forming method based on a multi-arm gambling machine in a wireless sensor network, aiming at solving the problem that the power of a signal sent by a sensor node cannot reach an optimal value at a receiving end due to the existence of a fault sensor and finding a node strategy capable of eliminating the fault sensor on the premise of ensuring the low energy consumption of the network; and a Random Grouping based Distributed beam forming method (or algorithm) (RG-DB algorithm for short) is also adopted to perform phase adjustment on the selected sensor nodes, and finally, the signal power sent by the sensor nodes can be received by a receiving end to the maximum extent.
The elastic beam forming method based on the multi-arm gambling machine in the wireless sensor network has the main idea that the problem of eliminating the faulty sensor is converted into the problem of selecting the optimal arm of the multi-arm gambling machine, the phase of the selected arm (a group of sensor nodes) is adjusted by using an RG-DB algorithm through constructing a proper instant reward, then the probability of selecting the arm is updated, and finally, the elimination of all the faulty sensor nodes can be realized, so that the power of a signal received by a receiving end is maximized. The above is an elastic beam forming algorithm (Multi-arm bundled sensitive beam forming) based on the Multi-arm gambling machine, which is called MAB-RB algorithm for short.
Aiming at the application characteristics of the sensor network, the invention establishes the following network model: in the wireless sensor network, m sensor nodes are provided, and each sensor node can send a signal As (t) to a receiving end, wherein A represents the amplitude of a transmission signal, and s (t) eiωtI is an imaginary unit, and ω is a carrier frequency; each sensor nodeAnd the phase offset of the receiver can be adjusted according to the feedback information of the receiver, and the transmitted signals are uniformly diffused in all directions.
It is assumed that each sensor node in the system has its own local oscillator that can be synchronized to the carrier frequency ω. Dividing time into time segments with iteration cycle T ═ Tx+Tr(TxIndicating the time period during which the sensor transmits a signal, TrRepresenting a period of time for which the node receives feedback information). In the RG-DB algorithm, each iteration includes four slots, i.e., the nth iteration occurs in the time interval [4nT,4nT +4T]During which time. One iteration of the operation is performed as shown in fig. 5. The signal will have a certain delay in propagation and some attenuation with the distance of propagation.
The RG-DB algorithm is mainly used in a wireless sensor network, sensor nodes are randomly divided into two groups according to grouping probability q in each iteration period, signals are sent in each time slot, and phase offset is carried out according to feedback information sent back by a receiving end. Through multiple iterations, all signals are finally perfectly coupled at the receiving end, and therefore signal power maximization of the receiving end is achieved. When there is a faulty sensor node interfering with the system, the set of different sensors is defined as an arm, and there are K sets of arms in common, where K is 2m-2 (except in the case of all normal sensors or all faulty sensors). At this point, the elastic beamforming problem translates into the MAB problem, the goal being to select the best arm that contains only normal sensors.
To adjust the phase offset of the corresponding sensor of the selected arm, a random packet distributed beamforming method (RG-DB) algorithm is employed. The following is a detailed description of the steps of the RG-DB algorithm, as shown in FIG. 4:
step A, network initialization, namely initializing the phase of each node in the network and determining the grouping probability q, wherein q is more than 0 and less than 1;
b, dividing all the sensor nodes into two groups, and dividing each node into a group G according to the probability of q1Probability of 1-q into group G2；
Step C, for group G1Each node in the network is towards the receiving end according to the corresponding phase of each time slotSending signals and receiving feedback information. As shown in fig. 5, the specific phase adjustment strategy is: the phase shift in the first slot is pi, the phase shift in the second slot is pi/2, and the phase shift in the third slot is-psi (n) -3 pi/2;
the calculation method of psi (n) is as follows:
ψ(n)＝arctan((1+a(n))/(1-α(n)))，
a(n)＝[(P(4n+2)-P(4n))/(P(4n+2)-P(4n+1))]and (P (4n), P (4n +1), and P (4n +2) respectively represent the signal power received by the receiving end in the first, second, and third time slots in the nth iteration. And, for group G1If the power value P (4n +3) received by the receiver in the third time slot is greater than or equal to the power value P (4n) received at the initial time of the iterative phase, the phase offset corresponding to the fourth time slot is 0, otherwise, it is pi.
Step D, group G2The sensor nodes in the system respectively send signals to a receiving end in each time slot, and the four time slots are not subjected to phase updating, namely the phase offset in each time slot is zero.
Step E, repeating the step B, C, D; after multiple cycles, the signal power at the receiving end can reach the optimal value.
Through the steps, the node phase offset can be adjusted, and finally the signal power of the receiving end can be maximized, so that the optimal beam forming is realized.
In addition, the MAB-RB algorithm mainly adopts a set of different sensors as one arm in the problem of the multi-arm gambling machine, selects the different sensors according to a certain probability, and finds the optimal arm (namely all normal sensors) by utilizing the idea of the problem of the multi-arm gambling machine, thereby avoiding the influence of a fault node.
Therefore, an appropriate real-time reward needs to be constructed and an arm estimation value Q is introducedi(Ni) To evaluate the performance of arm i, where NiIs the number of times arm i is selected. Specifically, as shown in fig. 1, the method for elastic beam forming based on a dobby in a wireless sensor network of the present embodiment includes the following steps:
step oneAnd initializing the network, wherein in the wireless sensor network comprising m sensor nodes, when the interference of the faulty sensor node exists, the total K is 2m-2 arms; initializing the number of times each arm is selected N i0, estimated value Q of each armi(0) 1 and the probability of each arm being chosen is pi＝1/K；
Randomly selecting one arm according to the selected probability, carrying out one-time phase updating on all sensor nodes in the selected arm according to a random grouping distributed beam forming method, increasing the selected times corresponding to the selected arm by 1, updating a reward value corresponding to the selected arm, and updating an estimated value of the selected arm; specifically, one arm is selected according to the probability, all sensor nodes contained in the arm perform one phase update according to the RG-DB algorithm (namely, steps A-D in the RG-DB algorithm are also called as one iteration), and then the corresponding selected times of the arm are increased by one;
by using
Calculating the corresponding reward value of the arm, wherein Pi(old) is the signal power generated by the node contained in the selected arm transmitting the signal to the receiving end without any phase offset; pi(new) updating the signal power generated by sending signals to the receiving end for the primary phase of the nodes contained in the selected arm; t' is greater than 0 and is a constant;
the method for updating the estimated value of the selected arm comprises the following steps:
Qi(Ni)＝Qi(Ni-1)+β[Ri(Ni)-Qi(Ni-1)](ii) a Beta is more than 0 and less than 1 and is a constant.
Step three, updating the selected probability of each arm (namely all arms) according to the Boltzmann distribution, wherein the method for updating the probability comprises the following steps:
And step four, repeating the step two and the step three, selecting the arm containing all normal nodes through multiple iterations, and enabling the sending signal power of the sensor node to reach an optimal value.
The method for enabling the power of the signal sent by the sensor node to reach the optimal value comprises the following steps:
s41, initializing the phase of each sensor node in the arm corresponding to all normal sensor nodes;
s42, determining the grouping probability q, wherein q is more than 0 and less than 1; randomly dividing all sensor nodes into two groups G according to grouping probability q1And G2(ii) a Sensor node assignment to G1Probability of group q, assigned to G2Probability of group 1-q;
s43, each iteration comprises four time slots; g1The sensor nodes in the group respectively send signals to a receiving end in each time slot and carry out phase deviation according to the received feedback information; g2The sensor nodes in the group respectively send signals to a receiving end in each time slot, and the phase offset in each time slot is zero; wherein, according to the received feedback information, the phase shift is performed, which comprises: after receiving the feedback in the first time slot, adjusting the phase to pi; after receiving the feedback in the second time slot, the phase is adjusted to pi/2; after receiving the feedback in the third time slot, adjusting the phase to-psi (n) -3 pi/2;
wherein ψ (n) ═ arctan ((1+ a (n))/(1-a (n)),
a(n)＝[(P(4n+2)-P(4n))/(P(4n+2)-P(4n+1))]，
p (4n), P (4n +1), P (4n +2) respectively represent the signal power received by the receiving end of the first, second and third time slots in the nth iteration; for the phase offset of the fourth time slot, if the power value P (4n +3) received by the receiving end in the fourth time slot is greater than or equal to the power value P (4n) received at the initial moment of the iteration stage where the power value P is located, the phase offset is 0; otherwise, the phase offset is π.
And S44, repeating the steps S42 and S43 until the power of the signal received by the receiving end reaches an optimal value.
The specific application example of the dobby-based elastic beam forming method in the wireless sensor network of the invention is as follows:
firstly, network topology and parameter setting, 8 sensors are set, including 6 normal sensors and 2 fault sensors, and the phase of the fault sensor always changes along with time and is uncontrollable; all sensors have the same attenuation magnitude vιA is 0.5, and the grouping probability q is 0.4; for simplicity of the embodiment, the number of arms is assumed to be known assuming that the number of faulty sensors is known
Secondly, initializing estimated value Q of each armi(0) Set the number of times N each arm is selected as 1i0 and the probability of each arm being chosen is pi＝1/K；
Randomly selecting one arm according to the probability, carrying out one iteration on nodes contained in the arm according to an RG-DB algorithm, and increasing one selected time corresponding to the arm; updating the reward, the estimation value and the selected probability corresponding to the arm according to a corresponding formula;
and fourthly, continuously iterating the third step, and finally obtaining that the probability of all correct sensors being selected tends to 1 as shown in fig. 2, and the signal power values of all normal sensors converge to the maximum value of 9dB as shown in fig. 3.
It follows that, finally, all normal sensor nodes, i.e. the optimal arm, are selected each time. At this point, the faulty sensor is eliminated and the signal transmission power reaches a maximum.
The foregoing has outlined rather broadly the preferred embodiments and principles of the present invention and it will be appreciated that those skilled in the art may devise variations of the present invention that are within the spirit and scope of the appended claims.
Claims (10)
1. A multi-arm gambling machine-based elastic beam forming method in a wireless sensor network is characterized by comprising the following steps:
s1, m sensor nodes are arranged in the wireless sensor network; when present, isWhen a fault sensor node is interfered, a set containing different sensors is defined as an arm, and K groups of arms are shared, wherein K is 2m-2; initializing the number of times each arm is selected Ni0, estimated value Q of each armi(0) 1, the probability of each arm initial time being chosen is pi＝1/K；
S2, randomly selecting one arm according to the selected probability, carrying out phase updating on all sensor nodes in the selected arm once according to a random grouping distributed beam forming method, increasing the selected times corresponding to the selected arm by one, updating a reward value corresponding to the selected arm, and updating an estimated value of the selected arm;
s3, updating the selected probability of each arm according to the Boltzmann distribution;
and S4, repeating the steps S2 and S3 until the arms corresponding to all the normal sensor nodes are selected, and enabling the power of the signals sent by the sensor nodes to be optimal.
2. The dobby-based elastic beam forming method in a wireless sensor network according to claim 1, wherein all sensor nodes in the selected arm perform one phase update according to a random grouping distributed beam forming method, comprising the steps of:
s21, initializing the phases of all sensor nodes in the selected arm;
s22, determining the grouping probability q, wherein q is more than 0 and less than 1; randomly dividing all sensor nodes into two groups G according to grouping probability q1And G2；
S23、G1The sensor nodes in the group respectively send signals to a receiving end in four time slots, and carry out phase offset according to the received feedback information; g2The sensor nodes in the group respectively send signals to a receiving end in four time slots, and the phase deviation in each time slot is zero.
3. The dobby-based elastic beam forming method in wireless sensor network according to claim 2, wherein the phase shifting according to the received feedback information comprises: after receiving the feedback in the first time slot, adjusting the phase to pi; after receiving the feedback in the second time slot, the phase is adjusted to pi/2; after receiving the feedback in the third time slot, the phase is adjusted to-psi (n) -3 pi/2;
where ψ (n) ═ arctan ((1+ α (n))/(1- α (n))),
α(n)＝[(P(4n+2)-P(4n))/(P(4n+2)-P(4n+1))]，
p (4n), P (4n +1), and P (4n +2) respectively represent the signal power received by the receiving end of the first, second, and third time slots, where n is 1; for the phase offset of the fourth time slot, if the power value P (4n +3) received by the receiving end of the fourth time slot is greater than or equal to the power value P (4n) received at the initial moment of the iteration stage where the receiving end of the fourth time slot is located, the phase offset is 0; otherwise, the phase offset is π.
4. The multi-arm gambling machine-based elastic beam forming method in the wireless sensor network according to claim 3, wherein the method for updating the reward value corresponding to the selected arm is as follows:
wherein, Pi(old) is the signal power generated by the node contained in the selected arm transmitting the signal to the receiving end without any phase offset; pi(new) updating the signal power generated by sending signals to the receiving end for the primary phase of the nodes contained in the selected arm; t' is greater than 0 and is constant.
5. The method of claim 4, wherein the method of updating the estimated value of the selected arm comprises:
Qi(Ni)＝Qi(Ni-1)+β[Ri(Ni)-Qi(Ni-1)]，0＜β＜1。
6. the multi-arm gambling machine based elastic beam forming method in wireless sensor network of claim 5, wherein the method for updating the probability of each arm being picked is as follows:
7. the dobby-based elastic beam forming method in wireless sensor network according to any one of claims 1 to 6, wherein the method for optimizing the power of the signals transmitted by the sensor nodes in step S4 is as follows: and randomly grouping the sensor nodes in the arm corresponding to all the normal sensor nodes, and adjusting the phase to perform multiple iterations according to the feedback of the receiving end until the power of the signal received by the receiving end reaches an optimal value.
8. The method for forming elastic beam based on dobby machine in wireless sensor network as claimed in claim 7, wherein the method for optimizing the power of the signal transmitted by the sensor node in step S4 comprises the following steps:
s41, initializing the phase of each sensor node in the arm corresponding to all normal sensor nodes;
s42, determining the grouping probability q, wherein q is more than 0 and less than 1; randomly dividing all sensor nodes into two groups G according to grouping probability q1And G2；
S43, each iteration comprises four time slots; g1The sensor nodes in the group respectively send signals to a receiving end in each time slot and carry out phase deviation according to the received feedback information; g2The sensor nodes in the group respectively send signals to a receiving end in each time slot, and the phase offset in each time slot is zero;
and S44, repeating the steps S42 and S43 until the power of the signal received by the receiving end reaches an optimal value.
9. The method of claim 8, wherein the sensor nodes are assigned to G1Probability of group q, assigned to G2Probability of group 1-q.
10. The method of claim 8, wherein the phase shifting according to the received feedback information comprises: after receiving the feedback in the first time slot, adjusting the phase to pi; after receiving the feedback in the second time slot, the phase is adjusted to pi/2; after receiving the feedback in the third time slot, the phase is adjusted to-psi (n) -3 pi/2;
where ψ (n) ═ arctan ((1+ α (n))/(1- α (n))),
α(n)＝[(P(4n+2)-P(4n))/(P(4n+2)-P(4n+1))]，
p (4n), P (4n +1), P (4n +2) respectively represent the signal power received by the receiving end of the first, second and third time slots in the nth iteration; for the phase offset of the fourth time slot, if the power value P (4n +3) received by the receiving end in the fourth time slot is greater than or equal to the power value P (4n) received at the initial moment of the iteration stage where the power value P is located, the phase offset is 0; otherwise, the phase offset is π.
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