Distributed fault-tolerant beamforming method, device and medium based on random grouping

Abstract

The present invention discloses a distributed fault-tolerant beamforming method, device and medium for wireless sensor networks based on random grouping, belonging to the technical field of wireless communication networks, and the method comprises the following steps: selecting a sensor subset containing a plurality of sensor combinations by probability according to a credibility value; randomly grouping all sensor nodes in the set, and adjusting the phase for iteration according to feedback information from a receiving end, wherein the feedback information is signal power; updating the credibility value and selection probability of each sensor subset, and iterating through multiple rounds until the selection probability of the optimal subset is 1, and the signal power received by the receiving end reaches the optimal value. The present invention can be applied to wireless sensor networks to improve signal transmission power, and has robustness against cyber attacks inside and outside the system, thereby ensuring low energy consumption of the entire network.

Description

[Technical field]

[0001] The invention belongs to the technical field of wireless communication networks, and in particular relates to a distributed fault-tolerant beamforming method for wireless sensor networks based on random grouping. [Background technology]

[0002] In recent years, with the rapid development of sensor technology, communication technology, information processing technology and embedded technology, wireless sensor networks have gradually been widely used. Wireless sensor networks can sense and collect information in the target area and finally transmit the information to the user terminal. This technology has a wide range of application scenarios, mainly used in many fields such as regional detection, environmental monitoring, industrial monitoring, military defense, etc.

[0003] A wireless sensor network is an intelligent network that uses a large number of sensors with specific functions to transmit information through wireless communication in a self-organized form and work together to complete a specific task. It can detect, sense and collect information in the network monitoring area in real time, and process the collected information before transmitting it to the user terminal. For example, the data collected in the residential area is sent to a distant receiver through a wireless remote meter reading system. Due to the limitations of sensor hardware resources in the network and the constraints of the surrounding environment, the signal power sent by a single node is limited, and the signal will be attenuated to a certain extent during the propagation process; at the same time, the signals will interfere with each other, further weakening the signal power. Therefore, the signal power received by the user terminal is often weaker than that of the transmitter. So, how to design a network protocol to maximize the signal power at the receiving end is an important issue facing wireless sensor networks.

[0004] Beamforming is one of the technologies to improve the communication quality of wireless sensor networks. Beamforming is a signal processing technology for sensor arrays. It achieves directional transmission or reception of signals by making signals at specific angles undergo constructive interference and other signals undergo destructive interference, thereby achieving power enhancement or long-distance communication of the transmitted signal. Distributed beamforming is a form of cooperative communication in which two or more transmitting nodes send the same information and control its phase to reassemble the received signal at the receiving end. In wireless sensor networks, beamforming technology can be used to concentrate the signals sent by sensors in the direction of the receiving end to increase the signal power received at the receiving end. At the same time, beamforming technology evenly distributes the energy consumption required for transmitting information to multiple nodes participating in sending signals, thereby reducing the energy consumption of nodes and enhancing the life of wireless sensor networks. Mudumbai et al. first proposed a distributed beamforming scheme by adjusting the phase based on 1-bit feedback from the receiving end. Bucklew et al. improved on this work by allowing sensors to independently send signals with random offsets in each iteration, which not only increases the convergence speed but also improves energy utilization. Humanen et al. sent frames instead of single symbols and had the transmitter add slightly different phase corrections to each symbol in the frame, resulting in an algorithm that outperformed previous methods in terms of convergence speed. Kumar et al. gave a distributed null formation algorithm that allows the transmitting node to steer the null in the desired direction simply by adjusting the carrier phase provided by feedback.

[0005] However, not all sensors will update their status according to a given algorithm. Due to internal anomalies in the system or external cyber attacks, sensors may show certain anomalies, which is very common in reality. Therefore, it is very meaningful to enable all normal sensors to achieve distributed beamforming in a system with abnormal sensors. In fact, network systems for abnormal sensors have been studied by many scholars, such as Pajic et al.'s work in cyberphysical systems, Abbasi et al.'s work in smart grids, and Zholbaryssov et al.'s work in wireless sensor networks. In fault-tolerant beamforming, Miao et al. used agnostic diagnostic technology to detect abnormal sensors, and Fuchs et al. proposed an antenna array diagnostic method using a small amount of far-field measurement information. However, there is still a lack of systematic research work on the application of distributed fault-tolerant beamforming problems to improve signal transmission power. [Summary of the invention]

[0006] In view of the deficiencies in the prior art, the technical problem to be solved by the present invention is to provide a distributed fault-tolerant beamforming method based on random grouping, so as to improve the signal transmission power, ensure the low energy consumption of the entire network, and solve the problem that some sensors may become abnormal due to cyber attacks inside and outside the system.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] A distributed fault-tolerant beamforming method for wireless sensor networks based on random grouping includes the following steps:

[0009] A sensor subset I including several sensor combinations is selected by probability according to the credibility value;

[0010] All sensor nodes in the set are randomly grouped, and the phase is adjusted and iterated according to the feedback information from the receiving end, where the feedback information is the signal power;

[0011] Update the credibility value C of each subset of sensors I And the selection probability p I , through multiple rounds of iteration until the optimal subset I * The selection probability is 1, and the signal power received by the receiving end reaches the optimal value.

[0012] Preferably, the method for randomly grouping all sensor nodes in the set is: determining the grouping probability q of the subset I I , and randomly divide all sensors in I into two groups G1 and G2 according to the grouping probability;

[0013] The probability of each sensor in the wireless sensor network in subset I being assigned to group G1 is q I , the probability of being assigned to group G2 is 1-q I .

[0014] Preferably, the method for adjusting the phase for iteration is: each iteration includes four time slots; the sensors in the G1 group send signals to the receiving end in each time slot, and adjust the phase offset according to the received feedback information; the sensors in the G2 group send signals to the receiving end in each time slot, and the phase offset in each time slot is zero.

[0015] Preferably, performing phase offset adjustment according to the received feedback information includes: after receiving feedback in the first time slot, the phase offset is π;

[0016] After receiving the feedback in the second time slot, the phase shift is π / 2;

[0017] After receiving the feedback in the third time slot, the phase shift is in, P(4n), P(4n+1), and P(4n+2) represent the signal powers received by the receiving end in the first, second, and third time slots in the nth iteration, respectively. 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, the phase offset is 0; otherwise, the phase offset is π.

[0018] Preferably, update the credibility value C of subset I I , for the subset I selected in this round, the updated credibility value where [P(4n+4)-P(4n)] - It means that if the value in the brackets is less than 0, its true value is returned, otherwise it returns 0. For other subsets J, its credibility value is consistent with the previous round.

[0019] Preferably, update the selection probability p of subset I I , the selection probability of each subset I is the ratio of its credibility value to the sum of the credibility values ​​of all subsets, that is, p I (4n+4)=C I (4n+4) / ∑ J C J (4n+4).

[0020] Preferably, the sensor node sends a signal As(t) to the receiving end, where A represents the amplitude of the transmission signal, s(t)=e iωt , i is the imaginary unit, ω is the carrier frequency.

[0021] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a distributed fault-tolerant beamforming method for a wireless sensor network based on random grouping is implemented.

[0022] The present invention also provides a medium, wherein the medium stores a computer program, and when the computer program is executed by a processor, a distributed fault-tolerant beamforming method for a wireless sensor network based on random grouping can be implemented.

[0023] The distributed fault-tolerant beamforming method for wireless sensor networks based on random grouping of the present invention randomly groups all sensors in the subset through probability selection of subsets, adjusts the corresponding phase offset four times, and uses multiple subset selections and iterations to ultimately ensure that the probability of selecting the optimal subset approaches 1, and that the phases of all normal signals are perfectly coupled at the receiving end, thereby maximizing the signal power received by the receiving end.

[0024] This method can be applied to wireless sensor networks. Compared with existing technologies, it can improve signal transmission power and be robust to cyber attacks inside and outside the system, thereby ensuring low energy consumption of the entire network.

[0025] These features and advantages of the present invention will be disclosed in detail in the following specific embodiments and drawings.

Brief Description of the Drawings

[0026] The invention will be further described below in conjunction with the accompanying drawings:

[0027] Figure 1 It is a flow chart of a distributed fault-tolerant beamforming method for a wireless sensor network based on random grouping provided by an embodiment of the present invention;

[0028] Figure 2 is a schematic diagram of operations in each time slot during an iteration after random grouping provided by an embodiment of the present invention;

[0029] Figure 3 A graph showing a probability change of selecting an optimal subset provided by an embodiment of the present invention;

[0030] Figure 4 A diagram of a change in the receiving power of a receiving end provided by an embodiment of the present invention;

[0031] Figure 5 A graph showing how the convergence time varies with the number of sensors and the number of abnormal sensors provided in an embodiment of the present invention. [Specific implementation method]

[0032] The technical solutions of the embodiments of the present invention are explained and described below in conjunction with the drawings of the embodiments of the present invention, but the following embodiments are only preferred embodiments of the present invention, not all. Based on the embodiments in the implementation mode, other embodiments obtained by those skilled in the art without creative work are all within the protection scope of the present invention.

[0033] Those skilled in the art will appreciate that, in the absence of conflict, the features in the following embodiments and implementations may be combined with each other.

[0034] The present invention provides a distributed fault-tolerant beamforming method based on random grouping in a wireless sensor network, aiming to solve the problem that the power of sensor transmitted signals is weakened at the receiving end due to the limitation of sensor hardware conditions and the interference of the surrounding environment in the wireless sensor network, and at the same time, some sensors are susceptible to cyber attacks inside and outside the system, resulting in abnormalities.

[0035] The present invention constructs multiple subsets according to different sensor combinations, randomly groups the sensors in the subsets and adjusts the phases according to the feedback from the receiving end, so that the probability of selecting the optimal subset approaches 1 and the signal power received by the receiving end is maximized.

[0036] In view of the application characteristics of this type of sensor network, the present invention establishes the following network model: In the wireless sensor network, there are m sensors (some of which are abnormal sensors), each of which can send a signal As(t) to the receiving end, where A represents the amplitude of the transmission signal, s(t) = e iωt , i is the imaginary unit, ω is the carrier frequency.

[0037] Each sensor can also adjust its own phase offset according to the feedback information from the receiving end, so that the transmitted signal spreads evenly in all directions.

[0038] Assume that each sensor in the system has its own local oscillator that can be synchronized to the carrier frequency ω. During the sensor state update process of the subset, the time is divided into time periods with an iteration period of T, T = T x +T r T x Indicates the time period during which the sensor sends a signal, T r Indicates the time period during which the node receives feedback information. Each round consists of four time slots, that is, the nth iteration occurs during the time interval [4nT, 4nT+4T]. Figure 2 As shown in the figure, the signal will have a certain delay during the propagation process and will have a certain attenuation with the propagation distance.

[0039] like Figure 1 As shown, the detailed steps of the method of the embodiment of the present invention are described as follows:

[0040] Step A: Network initialization: Initialize the phase of each sensor in the network and the credibility of each subset.

[0041] Step B: Select the subset I of the current round according to the probability p of each subset.

[0042] Step C: Determine the grouping probability q of subset I I (0 I <1), and divide all sensors in subset I into two groups G1 and G2 according to the probability.

[0043] The probability of each sensor in the wireless sensor network in subset I being assigned to group G1 is q I , the probability of being assigned to group G2 is 1-q I .

[0044] ​Step D: The sensors in group G1 send signals to the receiving end in each time slot, and perform phase shift according to the received feedback information.

[0045] The phase shift strategy is: after receiving feedback in the first time slot, the phase shift is π; after receiving feedback in the second time slot, the phase shift is π / 2; after receiving feedback in the third time slot, the phase shift is

[0046] in, P(4n), P(4n+1), and P(4n+2) represent the signal powers received by the receiving end in the first, second, and third time slots in the nth iteration, respectively. 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, the phase offset is 0; otherwise, the phase offset is π.

[0047] Step E: The sensors in group G2 send signals to the receiving end, and no phase update is performed in the four time slots.

[0048] Step F: Update the credibility value of subset I And the selection probability p I (4n+4)=C I (4n+4) / ∑ J C J (4n+4).

[0049] Step G, repeat the above steps BCDEF. After multiple cycles, the optimal subset I * The selection probability is 1, and the signal power received by the receiving end reaches the optimal value.

[0050] The method of the embodiment of the present invention is used in a specific case as follows:

[0051] 1. There are 8 sensors with random initial phases in the network system, two of which are abnormal sensors, one is randomly changing, and one is a constant. subsets, of which only one is the optimal subset I * Does not contain any unusual sensors.

[0052] 2. Signal attenuation parameter v of each sensor i A=0.5, and its grouping probability q=0.5.

[0053] 3. Initialize the credibility value of each subset to 1.

[0054] 4. Probabilistically select subset I based on the credibility value of each subset, and divide all sensors in the subset into two groups, with each sensor being assigned to group G1 or G2 with a probability of 0.5.

[0055] 5. The sensors in group G1 send signals to the receiver in each time slot and perform phase shift according to the received feedback information. The phase shift strategy is: after receiving feedback in the first time slot, the phase shift is π; after receiving feedback in the second time slot, the phase shift is π / 2; after receiving feedback in the third time slot, the phase shift is For the fourth phase shift, if the power value received by the receiver in the fourth time slot is greater than the power value received at the initial moment of the iteration phase, the phase should be adjusted to 0, otherwise it should be adjusted to π.

[0056] 6. The sensors in group G2 send signals to the receiver without phase update in four time slots.

[0057] 7. Update the credibility value of the subset I selected in this round For other subsets J, the credibility values ​​are consistent with the previous round.

[0058] Repeat the above steps 4-7 for a certain number of iterations.

[0059] According to the iteration of the system, the optimal subset I * The probability of selecting Figure 3 As shown, it gradually approaches 1, while the selection probability of other subsets gradually approaches 0. The signal power received by the receiving end gradually converges to the maximum value of 9dB, as shown in Figure 4 shown.

[0060] When the number of sensors m increases from 6 to 12, the number of abnormal sensors increases from 3 to m-3, and the signal attenuation parameter and grouping probability remain at 0.5, the convergence time of the system in each case is Figure 5 As shown in Figure 2, the convergence time of this method increases with the number of sensors, while when the number of abnormal sensors increases, the convergence time first increases and then decreases.

[0061] The fault-tolerant beamforming method of the present invention adopts multiple iterations. In each iteration, a subset is selected according to the credibility value, and the sensors in the subset are randomly grouped and the sensor phase offset is adjusted, so that the optimal subset I that does not contain any abnormal sensors is finally obtained. * The selection probability of is close to 1, and the phases of all signals are perfectly coupled at the receiving end, thereby maximizing the signal power sent by the sensor. This method can be used in wireless sensor network systems in power systems to improve signal transmission power and identify abnormal sensors with abnormal behavior.

[0062] Embodiment 2

[0063] An electronic device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the distributed fault-tolerant beamforming method based on random grouping as described in Embodiment 1 is implemented.

[0064] The electronic devices in the embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, PDAs (personal digital assistants), PADs (tablet computers), etc., and fixed terminals such as desktop computers, etc.

[0065] Electronic devices may include processing means (e.g., central processing unit) that may perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) or programs loaded from a storage device into a random access memory (RAM). In RAM, various programs and data required for the operation of the electronic device are also stored. The processing means, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0066] The computer program carried on a computer-readable medium includes a program code for executing an algorithm. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, the above functions defined in the method of the embodiment of the present disclosure are executed.

[0067] It should be noted that the computer-readable medium disclosed in the present invention may be a computer-readable signal medium or a computer-readable medium or any combination of the two.

[0068] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0069] The above is only a specific embodiment of the invention, but the protection scope of the invention is not limited thereto. Those skilled in the art should understand that the invention includes but is not limited to the contents described in the drawings and the above specific embodiments. Any modification that does not deviate from the functional and structural principles of the invention will be included in the scope of the claims.

Claims

1. A distributed fault-tolerant beamforming method for wireless sensor networks based on random grouping, characterized in that: The steps include: A sensor subset I including several sensor combinations is selected by probability according to the credibility value; All sensor nodes in the set are randomly grouped, and the phase is adjusted and iterated according to the feedback information from the receiving end, where the feedback information is the signal power; Update the credibility value C of each subset of sensors I And the selection probability p I , through multiple rounds of iteration until the optimal subset I * The selection probability is 1, and the signal power received by the receiving end reaches the optimal value. The method of randomly grouping all sensor nodes in the set is: determine the grouping probability q of the subset I I , and randomly divide all sensors in I into two groups G1 and G2 according to the grouping probability; The probability of each sensor in the wireless sensor network in subset I being assigned to group G1 is q I , the probability of being assigned to group G2 is 1-q I ; The method for adjusting the phase for iteration is as follows: each iteration includes four time slots; the sensors in the G1 group send signals to the receiving end in each time slot, and adjust the phase offset according to the received feedback information; the sensors in the G2 group send signals to the receiving end in each time slot, and the phase offset in each time slot is zero; Update the credibility value C of subset I I , for the subset I selected in this round, the updated credibility value C I (4n+4)=C I (4n)*e [P(4n+4)-P(4n)]- , where [P(4n+4)-P(4n)] - Indicates that if the value in the brackets is less than 0, its true value is returned, otherwise it returns 0. For other subsets J, its credibility value is consistent with the previous round; update the selection probability p of subset I I , the selection probability of each subset I is the ratio of its credibility value to the sum of the credibility values ​​of all subsets, That is, p I (4n + 4)= C I (4n + 4) / ∑ J C J (4n + 4).

2. The distributed fault-tolerant beamforming method for wireless sensor networks based on random grouping according to claim 1 is characterized in that: The phase offset is adjusted according to the received feedback information, including: after receiving the feedback in the first time slot, the phase offset is π; After receiving the feedback in the second time slot, the phase shift is π / 2; After receiving the feedback in the third time slot, the phase shift is in, P(4n), P(4n+1), and P(4n+2) represent the signal powers received by the receiving end in the first, second, and third time slots in the nth iteration, respectively. 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, the phase offset is 0; otherwise, the phase offset is π.

3. The distributed fault-tolerant beamforming method for wireless sensor networks based on random grouping according to claim 1, characterized in that: The sensor node sends a signal As(t) to the receiving end, where A represents the amplitude of the transmission signal, s(t) = e iωt , i is the imaginary unit, ω is the carrier frequency.

4. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the distributed fault-tolerant beamforming method for wireless sensor networks based on random grouping is implemented as claimed in any one of claims 1 to 3.

5. A medium storing a computer program, characterized in that: When the computer program is executed by a processor, it is capable of implementing the random grouping-based distributed fault-tolerant beamforming method for wireless sensor networks as described in any one of claims 1 to 3.

Images