CN114422086B - Unmanned aerial vehicle self-networking self-adaptive MAC protocol method based on flow prediction and consensus algorithm - Google Patents
Unmanned aerial vehicle self-networking self-adaptive MAC protocol method based on flow prediction and consensus algorithm Download PDFInfo
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Abstract
The invention discloses a method for self-adapting MAC protocol of unmanned aerial vehicle self-organizing network based on flow prediction and consensus algorithm, firstly, MAC preselection operation is carried out based on flow prediction, and secondly, unification of communication networking MAC protocol is achieved through consensus algorithm. And performing rewarding calculation according to the switching of the related consensus space behaviors, and acting on the preselection of the MAC protocol to calculate the ratio of TDMA and CSMA/CA in the process of unifying the MAC protocol in a period of time. Experiments prove that compared with other self-adaptive protocols, the method has the advantages that the delay is reduced, the throughput of the distributed unmanned aerial vehicle self-organizing network is greatly improved, the PRR performance is optimized, and different QoS requirements are met.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle ad hoc network communication protocols, and particularly relates to a method for an unmanned aerial vehicle ad hoc network self-adaptive MAC protocol based on a flow prediction and consensus algorithm.
Background
In recent years, the demand for mobile communication network systems in the land, sea, air and space fields has increased. With the continuous development of artificial intelligence technology and intelligent control theory research, research related to novel mobile ad hoc networks is increasingly underway. Mobile communication networks have become an important subject of social networking research, and the medium access control (Medium Access Control, MAC) protocol problem therein is certainly one of the core research contents not coming from the development of organization networks.
With the development of the mobile internet, more and more devices will be connected into the mobile network, and new services and applications are endless, so that mobile data traffic will come into the surge of blowout, which will bring serious challenges and challenges to future mobile communication. Therefore, in order to meet the increasing mobile traffic demand, development of sixth generation mobile communication technologies (6 th-Generation Mobile Networks, 6G) is required. Various air platforms represented by unmanned aerial vehicles play an important role in aviation 6G, and the research on the unmanned aerial vehicle ad hoc network self-adaptive MAC communication protocol is significant.
The unmanned aerial vehicle has the advantages of small size, low cost, difficult discovery, convenient deployment and the like, plays a great role in the present or even future, is not only used for real-time monitoring, automatic tracking, searching, rescuing and the like in the civil field, but also plays a role in the military field, is used for in-situ reconnaissance and accurate striking, and can even ensure the safety of fighters.
In communication of a distributed unmanned aerial vehicle ad hoc network, transmitted messages include two types: security control class information and user traffic class information. The safety control information comprises information such as node speed, position, node collision faults and the like, and is usually transmitted to all neighbor nodes or all nodes in the network in a broadcast mode, and the information has strict requirements on delay performance and reliability of the network. The user service information mainly relates to multimedia service information such as images, videos and the like, and is usually transmitted point to point in a unicast mode. Compared with the safety control information, the user service information generally requires higher network throughput, has certain requirements on the channel utilization rate, and has certain tolerance capability on time delay and reliability. How to ensure different QoS requirements of two kinds of information brings serious tests to the design of the self-adaptive MAC protocol, and is also the key of whether the unmanned aerial vehicle ad hoc network can be successfully and efficiently applied.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for self-adapting MAC protocol of unmanned aerial vehicle self-organizing network based on flow prediction and consensus algorithm, which firstly performs MAC preselection operation based on flow prediction and secondly achieves unification of communication networking MAC protocol through consensus algorithm. And performing rewarding calculation according to the switching of the related consensus space behaviors, and acting on the preselection of the MAC protocol to calculate the ratio of TDMA and CSMA/CA in the process of unifying the MAC protocol in a period of time.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
The unmanned aerial vehicle self-networking self-adaptive MAC protocol method based on the flow prediction and consensus algorithm is characterized in that: firstly, preselecting an MAC protocol based on flow prediction, then, based on the preselecting MAC protocol, unifying the communication networking MAC protocols through a consensus algorithm, performing rewarding calculation according to switching of consensus behaviors, and then acting on preselecting the MAC protocol.
In order to optimize the technical scheme, the specific measures adopted further comprise:
Further, the pre-selection of the MAC protocol is performed based on the flow prediction, namely the arrival quantity of the transmission data packets is subjected to the flow prediction, substituted into the future state of the unmanned aerial vehicle and compared with the transmission performance of the unmanned aerial vehicle, and the pre-selected MAC protocol of the unmanned aerial vehicle in the next time slot is judged; if the future channel is predicted to be busy, the transmission performance of the unmanned aerial vehicle cannot meet the transmission requirement, and a TDMA mechanism is preselected to transmit the data packet; otherwise, adopting a CSMA/CA competition mechanism to transmit the data packet.
Further, the pre-selection of the MAC protocol is performed based on the flow prediction, and the specific process is as follows:
Setting each unmanned aerial vehicle to be provided with a transmission buffer zone with the same length, wherein overflow of queue backlog is lost; transmission data packet representing a discrete time queuing sequence of all neighbor unmanned aerial vehicles of the current unmanned aerial vehicle defined on a time slot t epsilon {0,1,2 }, initial state Is a non-negative real-valued random variable; the future state of the unmanned aerial vehicle is calculated by the number of arrival of transmission data packets according to the following kinetic equationAnd transmission performanceAnd (3) driving:
Wherein, The value of (c) represents the number of data packets that a neighbor drone queue can handle over time slot t,The value of (2) indicates the number of new packets arriving at time slot t;
predicting the arrival quantity of data packets based on wavelet neural network, wherein Affected by packet arrival rate; setting the input of the wavelet neural network in a period of time as a PAR sequence x t＝{x1,x2,x3,...,xm}T, wherein x represents a packet arrival rate sequence of input traffic, m represents the number of nodes of an input layer, and the expression of a Morlet wavelet basis function is as follows:
the predicted update of the packet arrival number based on the wavelet neural network is as follows:
Wherein ζ (j) represents the output of the j-th node of the hidden layer, w ij represents the connection weight between the i-th input node and the j-th node of the hidden layer; n and h represent the node numbers of the hidden layer and the output layer respectively; a i and b j represent scale factors and shift factors of the Morlet wavelet basis function; v ih denotes the connection weight between the jth hidden node of the hidden layer and the h node of the output layer; in the training process of the wavelet neural network, v jh is adjusted according to the prediction error;
Then substituting the predicted arrival quantity of the data packets into the future state of the unmanned aerial vehicle, comparing the future state with the transmission performance of the unmanned aerial vehicle, and judging the pre-selected MAC protocol of the unmanned aerial vehicle in the next time slot; if the future channel is predicted to be busy, the transmission performance of the unmanned aerial vehicle cannot meet the transmission requirement, and a TDMA mechanism is preselected to transmit the data packet; otherwise, a CSMA/CA competition mechanism is adopted for transmission.
Further, the communication networking is a distributed unmanned aerial vehicle ad hoc network, and the consensus algorithm adoptsConsensus mechanism.
Further, the unification of the communication networking MAC protocol is achieved through a consensus algorithm, which is specifically as follows:
based on pre-selected MAC protocol of unmanned aerial vehicle communication networking Consensus mechanism, willThe algorithm is combined with PBFT, all indiscriminate consensus nodes in original PBFT are partitioned, clustered and layered by using a clustering algorithm, clustered cluster center nodes are used as master nodes of all nodes in the cluster, and non-clustered center nodes in each cluster are used as slave nodes in the cluster;
After the unmanned aerial vehicles of the master node reach consensus, the unmanned aerial vehicles in the slave node cluster are subjected to consensus; the unmanned aerial vehicle of the main node sends a data packet of voting reply, and any unmanned aerial vehicle receives the data packet of any voting reply, so that the unmanned aerial vehicle considers that consensus is achieved; meanwhile, the unmanned aerial vehicle switches the MAC protocol at a preset switching time; when one unmanned aerial vehicle receives the data packet replied by the voting, all the unmanned aerial vehicles without faults can receive the data packet replied by the voting, and further synchronous switching of the unmanned aerial vehicle ad hoc network MAC protocol is realized.
Further, the reward calculation is performed according to the switching of the consensus behavior, and then the pre-selection of the MAC protocol is performed according to the switching of the consensus behavior, so as to judge the duty ratio of the TDMA and the CSMA/CA in the process of unifying the MAC protocol in a period of time, wherein the duty ratio acts on the pre-selection of the MAC protocol.
Further, the reward calculation is performed according to the switching of the consensus behavior, specifically as follows:
After the unmanned aerial vehicle i takes action and requests to switch consensus, the rewarding value is not obtained immediately before the consensus operation is completed; for drone i at time slot t, its reward function The method comprises the following steps:
wherein p i(t)、di(t)、li (t) represents the successful transmission data amount, delay and data packet retransmission rate of the unmanned plane i in the time slot t, m i (t) represents the MAC protocol of the unmanned plane i in the current state, ψ, β, γ and ω represent the reward coefficients, β+γ+ω=1.
The beneficial effects of the invention are as follows: unlike conventional unmanned aerial vehicle ad hoc network MAC protocol, the method provided by the invention can ensure that the network can switch and unify the MAC protocol in different states through the algorithm for predicting traffic and achieving consensus by clustering for large-scale unmanned aerial vehicle ad hoc network, unlike the traditional single MAC protocol which can not change along with the change of the network state, the requirement and the scene. The invention combines the flow prediction algorithm and the consensus algorithm to be applied to the MAC protocol of the unmanned aerial vehicle ad hoc network for the first time, and has the unique advantages. Experiments prove that compared with other unmanned aerial vehicle ad hoc network MAC protocols, the method provided by the invention has the advantages that the delay is reduced, the throughput of the distributed unmanned aerial vehicle ad hoc network is greatly improved, the PRR performance is optimized, and different QoS requirements are met.
Drawings
Fig. 1 is a schematic diagram of an adaptive frame structure of an unmanned aerial vehicle ad hoc network distributed adaptive MAC according to an embodiment of the present invention.
Fig. 2 is an adaptive MAC protocol flow diagram.
Fig. 3 is a schematic view of an ad hoc network structure of an unmanned aerial vehicle.
FIG. 4 is a diagram ofSchematic diagram of consensus process.
Fig. 5 is a graph of time delay as a function of data node.
Fig. 6 is a graph of packet retransmission rate as a function of data node.
Fig. 7 is a graph of throughput as a function of data nodes.
Fig. 8 is a graph of CSMA/CA mode and TDMA mode usage frequency as a function of data nodes.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings.
The invention provides a method for self-adapting MAC protocol of unmanned aerial vehicle self-organizing network based on flow prediction and consensus algorithm, firstly, MAC preselection operation is carried out based on flow prediction, and secondly, unification of communication networking MAC protocol is achieved through consensus algorithm.
The unmanned aerial vehicle self-networking self-adaptive MAC protocol method based on the flow prediction and consensus algorithm comprises the following steps:
Firstly, predicting the arrival quantity of the data packet queues, substituting the arrival quantity into the future state of the unmanned aerial vehicle, comparing the future state with the transmission performance of the unmanned aerial vehicle, and judging the pre-selected MAC protocol of the unmanned aerial vehicle in the next time slot. If the future channel is predicted to be busy, the transmission performance of the unmanned aerial vehicle cannot meet the transmission requirement, and a TDMA mechanism is preselected to transmit the data packet; otherwise, a CSMA/CA competition mechanism is adopted for transmission.
Distributed unmanned aerial vehicle ad hoc network passing(Practical bayer fault tolerance) consensus mechanism to achieve unification of distributed communication networking MAC protocols.
Finally, the reward calculation is carried out according to the switching of the related consensus space behaviors, and the reward calculation is acted on the preselection of the MAC protocol so as to judge the ratio of the TDMA and the CSMA/CA in the process that the MAC protocol is unified in a period of time.
The self-adaptive unmanned aerial vehicle self-networking MAC protocol comprises the following steps:
s1: it is assumed that each drone is equipped with a transmission buffer of the same length, where overflow of the queue backlog will be lost. Is provided with Transmission data packets representing all NUs (neighbor drones) discrete time queuing sequences of the current drone defined on time slot t e {0,1, 2..Is assumed to be a non-negative real-valued random variable. Future states are reached from random data packets according to the following kinetic equationAnd transmission performanceAnd (3) driving:
Wherein, The value of (c) represents the number of packets that the NU queue can handle over slot t,The value of (c) represents the number of new packets arriving at time slot t and is assumed to be non-negative. Since the unmanned aerial vehicles share the same spatial environment, it is assumed that one slot is inIs constant. Thus, the first and second substrates are bonded together,Is predictive of backlog workIs very important.
S2: packet arrival prediction based on wavelet neural network (Modified Wavelet Neural Network, MWNN) as an important variable affecting queue backlog length, wherein random packet arrival processCan be affected by packet arrival rate (PACKET ARRIVAL RATE, PAR). Setting the input of the improved MWNN in a period of time to be PAR sequence x t＝{x1,x2,x3,...,xm}T, wherein x represents a packet arrival rate sequence of input traffic, m represents the number of nodes of an input layer, and the expression of a Morlet wavelet basis function is as follows:
the packet arrival prediction y (h) update based on MWNN is as follows:
Wherein ζ (j) represents the output of the j-th node of the hidden layer, w ij represents the connection weight between the i-th input node and the j-th node of the hidden layer; n and h represent the node numbers of the hidden layer and the output layer respectively; a i and b j represent scale factors and shift factors of the Morlet wavelet basis function; v jh denotes the connection weight between the hidden layer jth hidden node and the output layer h node. MWNN during training, v jh is adjusted according to the prediction error.
And substituting the predicted arrival quantity of the data packets into the future state of the unmanned aerial vehicle, and comparing the future state with the transmission performance of the unmanned aerial vehicle to judge the pre-selected MAC protocol of the unmanned aerial vehicle in the next time slot. If the future channel is predicted to be busy, the transmission performance of the unmanned aerial vehicle cannot meet the transmission requirement, and a TDMA mechanism is preselected to transmit the data packet; otherwise, a CSMA/CA competition mechanism is adopted for transmission.
S3: based on the pre-selection of the obtained unmanned aerial vehicle communication networking MAC protocolConsensus mechanism, willAnd combining the algorithm with PBFT, carrying out partition clustering and layering on all indiscriminate consensus nodes in the original PBFT by using a clustering algorithm, taking clustered cluster center nodes as master nodes of all nodes in the cluster, and taking non-clustered center nodes in each cluster as slave nodes in the cluster.
Some failed unmanned aerial vehicles may exist in the unmanned aerial vehicle ad hoc network, wherein some unmanned aerial vehicles are controlled by opponents (enemies) to interfere with the operation of the unmanned aerial vehicle ad hoc network, and other unmanned aerial vehicles fail due to self factors (such as collision). For the former, encryption techniques may be used to prevent spoofing and to detect corrupted data packets from these failed drones. For the latter, these faulty drones can be easily detected, since they cannot send or receive data packets. Thus, it can be directly assumed that the malfunctioning drone cannot participate in the agreed decision process.
Finally, if any one of the unmanned aerial vehicles receives any data packet replied to the vote, the unmanned aerial vehicle considers that consensus has been reached. At the same time, the drone will switch MAC protocols at a predetermined switching time. Due toRegardless of broadcast failure, when one drone receives a voting response packet, all non-faulty drones may receive the voting response packet. Therefore, synchronous switching of the unmanned aerial vehicle ad hoc network MAC protocol can be realized.
S4: after the drone i takes action and requests to switch consensus, it does not immediately obtain the prize value until the consensus operation is completed, the preselected operation may be different from the consensus selection. For the drone i at time slot t, its reward function is:
Wherein, p i(t)、di(t)、li (t) respectively represents the successful transmission data quantity (Successful Transferred Amount of Data, STAD), delay and data packet retransmission rate (Packet Retransmission Ratio, PRR) of the unmanned aerial vehicle i in t time slots, m i (t) represents the MAC protocol of the unmanned aerial vehicle i in the current state, and ψ, beta, gamma and omega represent reward coefficients, and the variation range is 0-1. Beta + gamma + omega = 1. Furthermore, throughput and latency are common key indicators of the current state of the drone, but PRR performance in TDMA is always better than CSMA/CA. Thus, ω and m i (t) are used to adjust l i (t).
Fig. 1 is a schematic diagram of an unmanned aerial vehicle ad hoc network distributed adaptive MAC adaptive framework structure including a traffic prediction-based MAC preselection operation and a traffic prediction-based MAC preselection operationTo generate MAC switching decisions as inputs to the underlying MAC adaptation layer. When the top MAC adaptation layer receives a packet (packet) from the network layer, the top MAC adaptation layer selects an appropriate MAC protocol according to the output of the MAC switching scheme. The top MAC adaptation layer then passes the packet to the selected MAC protocol for encapsulation. To distinguish between different MAC protocols, the adaptive MAC framework adds a1 byte Global MAC Identifier (GMI) in the header of each frame.
Fig. 2 is a flow chart of an adaptive MAC protocol for an unmanned aerial vehicle ad hoc network. Firstly, performing MAC protocol pre-selection operation through flow prediction, and determining which MAC protocol (TDMA or CSMA/CA) is more suitable for the current state of the unmanned aerial vehicle; the drone will then check its handoff timer, which is used to avoid frequent MAC protocol handoff negotiation requests. If the switching timer expires, the drone will reset the switching timer and require other drones to operate based onAnd (3) the common decision algorithm of the unmanned aerial vehicle avoids performance degradation caused by protocol asynchronism among all unmanned aerial vehicles. Meanwhile, the MAC switching scheme can be based onObtains a prize value after completion of the consistency decision process.
Fig. 3 is a schematic view of an ad hoc network structure of an unmanned aerial vehicle.And combining the algorithm with PBFT, carrying out partition clustering and layering on all indiscriminate consensus nodes in the original PBFT by using a clustering algorithm, taking clustered cluster center nodes as master nodes of all nodes in the cluster, and taking non-clustered center nodes in each cluster as slave nodes in the cluster.
FIG. 4 is a diagram ofSchematic diagram of consensus process. Each drone sends MAC SWITCHING a request to the master node of the cluster to which it belongs (cluster center node). After receiving a request for a period of time, the master node packages a plurality of requests into a block, and then broadcasts the block to the sub-consensus cluster to which the master node belongs for one-time PBFT consensus. After the block passes the consensus verification process of the sub-consensus cluster, the secondary PBFT consensus verification is performed on the backbone consensus cluster.
Fig. 5 to 8 are experimental results of the present invention, which respectively show time delay, data packet retransmission rate, throughput, and graphs of CSMA/CA mode and TDMA mode usage frequency changing with data nodes, and it can be seen that compared with other adaptive protocols, the time delay is reduced, throughput of the distributed unmanned aerial vehicle ad hoc network is greatly improved, PRR performance is optimized, and different QoS requirements are satisfied.
The unmanned aerial vehicle ad hoc network self-adaptive MAC protocol method based on the flow prediction and consensus algorithm provided by the invention has the following two main advantages of ensuring different QoS services of the unmanned aerial vehicle ad hoc network: firstly, performing MAC preselection operation based on flow prediction, and secondly, achieving unification of communication networking MAC protocols through a consensus algorithm. And performing rewarding calculation according to the switching of the related consensus space behaviors, and acting on the preselection of the MAC protocol to calculate the ratio of TDMA and CSMA/CA in the process of unifying the MAC protocol in a period of time. Experiments prove that compared with other self-adaptive protocols, the method has the advantages that the delay is reduced, the throughput of the distributed unmanned aerial vehicle self-organizing network is greatly improved, the PRR performance is optimized, and different QoS requirements are met.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.
Claims (3)
1. The unmanned aerial vehicle self-networking self-adaptive MAC protocol method based on the flow prediction and consensus algorithm is characterized in that: firstly, preselecting an MAC protocol based on flow prediction, then, based on the preselect MAC protocol, unifying the communication networking MAC protocol through a consensus algorithm, performing rewarding calculation according to switching of consensus behaviors, and then acting on preselecting the MAC protocol;
The pre-selection of the MAC protocol is carried out based on the flow prediction, namely the arrival quantity of the transmission data packets is subjected to the flow prediction, substituted into the future state of the unmanned aerial vehicle and compared with the transmission performance of the unmanned aerial vehicle, and the pre-selected MAC protocol of the unmanned aerial vehicle in the next time slot is judged; if the future channel is predicted to be busy, the transmission performance of the unmanned aerial vehicle cannot meet the transmission requirement, and a TDMA mechanism is preselected to transmit the data packet; otherwise, adopting a CSMA/CA competition mechanism to transmit the data packet;
the pre-selection of the MAC protocol is carried out based on the flow prediction, and the specific process is as follows:
Setting each unmanned aerial vehicle to be provided with a transmission buffer zone with the same length, wherein overflow of queue backlog is lost; Transmission data packet representing discrete time queuing sequence of all neighbor unmanned aerial vehicles of current unmanned aerial vehicle defined on time slot t epsilon {0,1,2, … }, initial state Is a non-negative real-valued random variable; the future state of the unmanned aerial vehicle is calculated by the number of arrival of transmission data packets according to the following kinetic equationAnd transmission performanceAnd (3) driving:
Wherein, The value of (c) represents the number of data packets that a neighbor drone queue can handle over time slot t,The value of (2) indicates the number of new packets arriving at time slot t;
predicting the arrival quantity of data packets based on wavelet neural network, wherein Affected by packet arrival rate; setting inputs of a wavelet neural network to PAR sequences over a period of time The packet arrival rate sequence representing the input traffic, m represents the number of nodes of the input layer, and the expression of the Morlet wavelet basis function is as follows:
the predicted update of the packet arrival number based on the wavelet neural network is as follows:
Wherein, Representing the first hidden layerThe output of the individual nodes is provided with,Representing the first hidden layerInput node and the firstThe connection weight between the individual nodes; And Respectively representing the node numbers of the hidden layer and the output layer; And Scale factors and shift factors representing Morlet wavelet basis functions; Indicating hidden layer number Hidden node and output layerThe connection weight between the individual nodes; the wavelet neural network is used for the training process,Is adjusted according to the prediction error;
Then substituting the predicted arrival quantity of the data packets into the future state of the unmanned aerial vehicle, comparing the future state with the transmission performance of the unmanned aerial vehicle, and judging the pre-selected MAC protocol of the unmanned aerial vehicle in the next time slot; if the future channel is predicted to be busy, the transmission performance of the unmanned aerial vehicle cannot meet the transmission requirement, and a TDMA mechanism is preselected to transmit the data packet; otherwise, adopting a CSMA/CA competition mechanism to transmit;
The switching according to the consensus behavior performs rewarding calculation, and then acts on the preselection of the MAC protocol, namely performs rewarding calculation according to the switching of the consensus behavior so as to judge the ratio of TDMA and CSMA/CA in the process of unifying the MAC protocol in a period of time, wherein the ratio acts on the preselection of the MAC protocol;
the reward calculation is carried out according to the switching of the consensus behavior, and the method specifically comprises the following steps:
After the unmanned aerial vehicle i takes action and requests to switch consensus, the rewarding value is not obtained immediately before the consensus operation is completed; for drone i at time slot t, its reward function The method comprises the following steps:
Wherein, D i(t)、li (t) represents the successful transmission data amount, delay and packet retransmission rate of the unmanned aerial vehicle i in the time slot t, m i (t) represents the MAC protocol of the unmanned aerial vehicle i in the current state, ψ, β, γ and ω represent the reward coefficient, β+γ+ω=1.
2. The method of unmanned aerial vehicle ad hoc network adaptive MAC protocol based on traffic prediction and consensus algorithm according to claim 1, wherein: the communication network is a distributed unmanned aerial vehicle ad hoc network, and the consensus algorithm adopts-PBFT consensus mechanism.
3. The method of unmanned aerial vehicle ad hoc network adaptive MAC protocol based on the traffic prediction and consensus algorithm according to claim 2, wherein: the unification of the communication networking MAC protocol is achieved through a consensus algorithm, and the unification is specifically as follows:
based on pre-selected MAC protocol of unmanned aerial vehicle communication networking -PBFT consensus mechanism, willCombining the medoids algorithm with PBFT, carrying out partition clustering and layering on all indiscriminate consensus nodes in the original PBFT by using a clustering algorithm, taking clustered cluster center nodes as master nodes of all nodes in the cluster, and taking non-clustered center nodes in each cluster as slave nodes in the cluster;
After the unmanned aerial vehicles of the master node reach consensus, the unmanned aerial vehicles in the slave node cluster are subjected to consensus; the unmanned aerial vehicle of the main node sends a data packet of voting reply, and any unmanned aerial vehicle receives the data packet of any voting reply, so that the unmanned aerial vehicle considers that consensus is achieved; meanwhile, the unmanned aerial vehicle switches the MAC protocol at a preset switching time; when one unmanned aerial vehicle receives the data packet replied by the voting, all the unmanned aerial vehicles without faults can receive the data packet replied by the voting, and further synchronous switching of the unmanned aerial vehicle ad hoc network MAC protocol is realized.
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