CN116709341B - Practical Bayesian-busy fault-tolerant consensus algorithm optimization method and system for unmanned bee colony - Google Patents
Practical Bayesian-busy fault-tolerant consensus algorithm optimization method and system for unmanned bee colony Download PDFInfo
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Abstract
The invention provides a practical Bayesian fault-tolerant consensus algorithm optimization method and system for an unmanned bee colony, and belongs to the technical field of consensus optimization for the unmanned bee colony. According to the invention, unmanned bee colonies are grouped according to the service domain or the functional characteristic of the unmanned aerial vehicle, and then a reward and punishment mechanism and a voting mechanism are introduced, wherein the reward and punishment mechanism is used for mobilizing enthusiasm of each node for participating in consensus, identifying and eliminating malicious or wrong nodes, the voting mechanism is used for selecting nodes with high trust values as main nodes, and preventing the main nodes from being used as centralization risks caused by too high trust values for a long time. The invention simplifies the consensus process of the unmanned bee colony according to the grouping condition of the unmanned bee colony, reduces the communication times of the consensus nodes of the unmanned bee colony by first performing intra-group consensus and then performing external group consensus, and greatly improves the consensus efficiency.
Description
Technical Field
The invention belongs to the technical field of consensus optimization of unmanned bee colonies, and particularly relates to a practical Bayesian fault-tolerant consensus algorithm optimization method and system for the unmanned bee colonies.
Background
With the rapid development of unmanned aerial vehicle embedded systems, networking communication and other technologies, unmanned bee colony has become an important research field of current unmanned aerial vehicle application. In general, unmanned swarms are intelligent systems composed of a plurality of unmanned aerial vehicles with autonomous decision-making and networking interaction functions, and are widely applied to the tasks of performing wide-area reconnaissance monitoring, saturated military strike, continuous flight maintenance, collaborative search coverage, joint cluster countermeasure and the like which are difficult to be realized by single unmanned aerial vehicle. However, under a high dynamic complex task environment, because unmanned aerial vehicle calculation and storage resources are limited, the traditional safety protection means cannot be carried, unmanned bee colony based on wireless link communication is easy to suffer from link attacks such as passive interception, data tampering and the like, and even attacks such as denial of service, data injection and the like are carried out through identity hijacking, so that a great threat is generated to the safety of the whole unmanned bee colony system. The block chain technology provides a safety solution for the unmanned bee colony by the characteristics of decentralization, tamper resistance, traceability and the like, realizes the node error tolerance of the unmanned bee colony through distributed consensus, improves the overall toughness and reliability of the system, realizes that data is difficult to tamper and the whole life cycle is traced through a chain data structure, and becomes the main direction of the current unmanned bee colony network application.
In the unmanned bee colony networking mode, due to the high dynamic property of unmanned aerial vehicle nodes, the network topology is frequently changed, the link stability is poor, the wireless link bandwidth is limited, the unmanned aerial vehicle nodes are insufficient in calculation and storage resources, and great challenges are generated for the direct application of the blockchain technology, and the unmanned bee colony networking mode is required to be modified in light weight and high delay tolerance, so that the practical application requirements of the unmanned bee colony are met.
In terms of consensus decisions, the consensus algorithms of the blockchain mainstream have a proof of work (PoW), proof of equity (PoS), practical bayer fault tolerance (PBFT, PRACTICAL BYZANTINE FAULT TOLERANCE), and the like. The PoW and PoS algorithm needs to use economic means to stimulate miners to dig ores, complete the work of block packing and release and the like, and needs a large amount of computing resources. However, in the unmanned swarm system, each unmanned plane node has definite task division, no need of economic means for excitation and lack of large-scale data calculation conditions. Therefore, PBFT algorithm is one of the most effective consensus algorithms in the current block-chain based unmanned swarm networks, can reduce the algorithm complexity to polynomial level, and can tolerate no more than one third of the bad nodes. However, in the unmanned bee colony application scene, the problems of high communication overhead, high complexity, dislike of the master node and the like still exist.
At present, the main optimization and improvement thought of the algorithm is to reduce two ideas of participating in consensus nodes and reducing consensus flows, wherein the reduction of the number of the consensus nodes generally reduces the number of the consensus nodes through mechanisms such as node partition or credit level setting; the reduction of the consensus flow generally simplifies or develops layered consensus through the consensus flow, thereby greatly reducing communication overhead and improving consensus efficiency. Although the improvement thought can improve the consensus efficiency to a certain extent, the problem that the safety and the high efficiency are not compatible often exists in the unmanned bee colony networking mode.
Disclosure of Invention
Aiming at the technical problems, a practical Bayesian fault-tolerant consensus algorithm optimization scheme aiming at unmanned bee colony is provided.
The first aspect of the invention provides a practical Bayesian fault-tolerant consensus algorithm optimization method aiming at unmanned bee colonies. The method comprises the following steps: step S1, grouping a plurality of unmanned aerial vehicles in the unmanned bee colony, and respectively calculating respective reputation values of each unmanned aerial vehicle in the grouped unmanned bee colony based on the historical behaviors of each unmanned aerial vehicle in the consensus process; s2, respectively judging each unmanned aerial vehicle in the grouped unmanned bee colony as a disqualified node, a consensus node or a high reputation value node according to the reputation value, and determining a main node in the group of the grouped unmanned bee colony from the high reputation value node; and S3, receiving the consensus proposal sent by the client by the main node in the group of each unmanned bee colony group, executing the intra-group consensus and the external-group consensus of the consensus proposal, and returning the preliminary consensus result of the intra-group consensus and the global consensus result of the external-group consensus to the client.
According to the method of the first aspect of the invention, in said step S1:
when grouping a plurality of unmanned aerial vehicles in the unmanned bee colony, the adopted grouping method comprises automatic grouping, average grouping and optimal algorithm grouping;
Acquiring historical behaviors of each unmanned aerial vehicle in the grouped unmanned bee colony in the consensus process, wherein the historical behaviors comprise participation consensus and node disfigurement, the participation consensus comprises message forwarding and consensus voting in the consensus process, and the node disfigurement comprises node response delay and feedback error information;
The calculation model of the reputation value R is as follows:
Wherein R represents the reputation value, the reputation value is updated after each time slot for nodes participating in consensus, and exponential decay is carried out according to time t for nodes participating in dislike, 0 < < 1, S 1 and S 2 are introduced nonlinear functions, the reputation value rising and reputation value falling are respectively and adaptively adjusted, gamma is the value of the variation range of S 1 and S 2, and gamma=0.5,/>For the value step of S 1 and S 2,/>=1, N represents the number of times a node participates in a consensus in one slot, N represents the number of times all consensus is reached in one slot.
According to the method of the first aspect of the present invention, in the step S2, when the reputation value is within the interval (0, a), the unmanned aerial vehicle corresponding to the reputation value is the disqualified node; when the reputation value is in the interval [ a, b), the unmanned aerial vehicle corresponding to the reputation value is the consensus node; when the reputation value is in the interval [ b, c), the unmanned aerial vehicle corresponding to the reputation value is the node with high reputation value; wherein a is less than b and less than c, a is a first reputation value threshold, b is a second reputation value threshold, and c is a third reputation value threshold.
According to the method of the first aspect of the present invention, in the step S2, other nodes in the unmanned swarm group except for the high-reputation node elect an intra-group master node from a plurality of the high-reputation nodes in the unmanned swarm group by voting, the intra-group master node obtains the largest number of votes or the number of votes is not lower than m/2, and m represents the number of nodes contained in the unmanned swarm group; and when the main node in the group fails, updating the reputation value of the main node, and selecting the node with the maximum reputation value from other nodes with high reputation values as a new main node in the group.
According to the method of the first aspect of the present invention, in the step S2, the intra-group master node broadcasts history data of participation of the wrongly generated nodes to the commonly generated nodes in the group, the commonly generated nodes vote whether to reject the wrongly generated nodes, and when the proportion of the endorsement votes to the total votes exceeds 50%, the intra-group master node rejects the wrongly generated nodes.
According to the method of the first aspect of the present invention, in said step S3, a preliminary consensus result of said intra-group consensus is obtained by: after receiving the consensus proposal sent by the client, the master node in the group of each unmanned swarm group broadcasts a pre-preparation message to other nodes in the group in a pre-preparation stage of the consensus in the group; each of the other nodes in the group, after verifying the pre-preparation message, respectively broadcasting a preparation message to the master node in the group and the other nodes in the group in a preparation phase of the intra-group consensus, and entering a confirmation phase of the intra-group consensus after receiving 2f+1 broadcasted preparation messages; the main node in the group and other nodes in the group broadcast confirmation messages to other nodes except the main node in the group in the confirmation stage of the intra-group consensus, and the main node in the group completes the intra-group consensus and acquires the preliminary consensus result after receiving 2f+1 broadcast confirmation messages; wherein f is the number of wrongly-used nodes which can be tolerated by the unmanned bee colony group.
According to the method of the first aspect of the present invention, in said step S3, a global consensus result of said out-of-group consensus is obtained by: during a preparation phase of the out-of-group consensus, each in-group master node broadcasts the preparation message to other in-group master nodes and enters a confirmation phase of the out-of-group consensus after receiving 2f+1 of the broadcasted preparation messages; during the confirmation phase of the out-of-group consensus, each in-group master node broadcasts the confirmation message to the other in-group master nodes, and after 2f+1 of the broadcasted confirmation messages are received, the out-of-group consensus is completed and the global consensus result is obtained.
The second aspect of the invention provides a practical Bayesian fault-tolerant consensus algorithm optimization system aiming at unmanned bee colonies. The system comprises: a first processing unit configured to: grouping a plurality of unmanned aerial vehicles in the unmanned aerial vehicle swarm, and respectively calculating respective reputation values of each unmanned aerial vehicle in the grouped unmanned aerial vehicle swarm based on the historical behaviors of each unmanned aerial vehicle in the consensus process; a second processing unit configured to: respectively judging each unmanned aerial vehicle in the grouped unmanned bee colony as a disqualified node, a consensus node or a high-reputation node according to the reputation value, and determining a main node in the group of the grouped unmanned bee colony from the high-reputation node; a third processing unit configured to: and responding to the intra-group main node of each unmanned bee colony group to receive a consensus proposal sent by a client, executing intra-group consensus and out-group consensus of the consensus proposal, and returning a preliminary consensus result of the intra-group consensus and a global consensus result of the out-group consensus to the client.
According to the system of the second aspect of the present invention, in said step S1:
when grouping a plurality of unmanned aerial vehicles in the unmanned bee colony, the adopted grouping method comprises automatic grouping, average grouping and optimal algorithm grouping;
Acquiring historical behaviors of each unmanned aerial vehicle in the grouped unmanned bee colony in the consensus process, wherein the historical behaviors comprise participation consensus and node disfigurement, the participation consensus comprises message forwarding and consensus voting in the consensus process, and the node disfigurement comprises node response delay and feedback error information;
The calculation model of the reputation value R is as follows:
Wherein R represents the reputation value, the reputation value is updated after each time slot for nodes participating in consensus, and exponential decay is carried out according to time t for nodes participating in dislike, 0 < < 1, S 1 and S 2 are introduced nonlinear functions, the reputation value rising and reputation value falling are respectively and adaptively adjusted, gamma is the value of the variation range of S 1 and S 2, and gamma=0.5,/>For the value step of S 1 and S 2,/>=1, N represents the number of times a node participates in a consensus in one slot, N represents the number of times all consensus is reached in one slot.
According to the system of the second aspect of the present invention, in the step S2, when the reputation value is within the interval (0, a), the unmanned aerial vehicle corresponding to the reputation value is the disqualified node; when the reputation value is in the interval [ a, b), the unmanned aerial vehicle corresponding to the reputation value is the consensus node; when the reputation value is in the interval [ b, c), the unmanned aerial vehicle corresponding to the reputation value is the node with high reputation value; wherein a is less than b and less than c, a is a first reputation value threshold, b is a second reputation value threshold, and c is a third reputation value threshold.
According to the system of the second aspect of the present invention, in the step S2, other nodes in the unmanned swarm group except for the high-reputation value node elect an intra-group master node from a plurality of the high-reputation value nodes in the unmanned swarm group by voting, the intra-group master node obtains the largest number of votes or the number of votes is not lower than m/2, and m represents the number of nodes contained in the unmanned swarm group; and when the main node in the group fails, updating the reputation value of the main node, and selecting the node with the maximum reputation value from other nodes with high reputation values as a new main node in the group.
According to the system of the second aspect of the present invention, in the step S2, the intra-group master node broadcasts history data of participation of the wrongly generated nodes to the commonly generated nodes in the group, the commonly generated nodes vote on whether to reject the wrongly generated nodes, and when the proportion of the endorsement votes to the total votes exceeds 50%, the intra-group master node rejects the wrongly generated nodes.
According to the system of the second aspect of the present invention, in said step S3, a preliminary consensus result of said intra-group consensus is obtained by: after receiving the consensus proposal sent by the client, the master node in the group of each unmanned swarm group broadcasts a pre-preparation message to other nodes in the group in a pre-preparation stage of the consensus in the group; each of the other nodes in the group, after verifying the pre-preparation message, respectively broadcasting a preparation message to the master node in the group and the other nodes in the group in a preparation phase of the intra-group consensus, and entering a confirmation phase of the intra-group consensus after receiving 2f+1 broadcasted preparation messages; the main node in the group and other nodes in the group broadcast confirmation messages to other nodes except the main node in the group in the confirmation stage of the intra-group consensus, and the main node in the group completes the intra-group consensus and acquires the preliminary consensus result after receiving 2f+1 broadcast confirmation messages; wherein f is the number of wrongly-used nodes which can be tolerated by the unmanned bee colony group.
According to the system of the second aspect of the present invention, in said step S3, a global consensus result of said out-of-group consensus is obtained by: during a preparation phase of the out-of-group consensus, each in-group master node broadcasts the preparation message to other in-group master nodes and enters a confirmation phase of the out-of-group consensus after receiving 2f+1 of the broadcasted preparation messages; during the confirmation phase of the out-of-group consensus, each in-group master node broadcasts the confirmation message to the other in-group master nodes, and after 2f+1 of the broadcasted confirmation messages are received, the out-of-group consensus is completed and the global consensus result is obtained.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps in the method for optimizing the practical Bayesian fault-tolerant consensus algorithm for the unmanned bee colony according to the first aspect of the disclosure when executing the computer program.
A fourth aspect of the invention discloses a computer-readable storage medium. The computer readable storage medium stores a computer program which, when executed by a processor, implements the steps in a method for optimizing a practical bayer pattern fault-tolerant consensus algorithm for an unmanned bee colony according to the first aspect of the disclosure.
In summary, the invention groups unmanned bee colony according to unmanned aerial vehicle service domain or functional characteristic, and introduces a reward and punishment mechanism and a voting mechanism; the reward and punishment mechanism is used for mobilizing enthusiasm of each node to participate in consensus, identifying and eliminating malicious or wrong nodes, the voting mechanism is used for selecting a node with a high trust value as a main node, and preventing the centralization risk of the main node caused by the fact that the trust value is too high for a long time. The invention simplifies the consensus process of the unmanned bee colony according to the grouping condition of the unmanned bee colony, reduces the communication times of the consensus nodes of the unmanned bee colony by first performing intra-group consensus and then performing external group consensus, and greatly improves the consensus efficiency.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are some embodiments of the invention and that other drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a practical bayer fault-tolerant consensus algorithm optimization method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of an iterative process of the introduced nonlinear functions S 1 and S 2 according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of rejecting offending nodes according to an embodiment of the present invention.
Fig. 4 is a schematic flow diagram of packet consensus according to an embodiment of the present invention.
Fig. 5 is a schematic flow chart of implementing consensus of the unmanned bee colony network according to the embodiment of the invention.
Fig. 6 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The first aspect of the invention provides a practical Bayesian fault-tolerant consensus algorithm optimization method aiming at unmanned bee colonies. The method comprises the following steps: step S1, grouping a plurality of unmanned aerial vehicles in the unmanned bee colony, and respectively calculating respective reputation values of each unmanned aerial vehicle in the grouped unmanned bee colony based on the historical behaviors of each unmanned aerial vehicle in the consensus process; s2, respectively judging each unmanned aerial vehicle in the grouped unmanned bee colony as a disqualified node, a consensus node or a high reputation value node according to the reputation value, and determining a main node in the group of the grouped unmanned bee colony from the high reputation value node; and S3, receiving the consensus proposal sent by the client by the main node in the group of each unmanned bee colony group, executing the intra-group consensus and the external-group consensus of the consensus proposal, and returning the preliminary consensus result of the intra-group consensus and the global consensus result of the external-group consensus to the client.
Specifically, as shown in fig. 1, the main optimization content can be divided into three parts, the first part is reputation evaluation, reputation scoring is carried out on unmanned swarm nodes which finish grouping by establishing a reputation model, and a high reputation value node has a chance to become a master node; the second part is voting confirmation, the nodes with high reputation value participating in consensus vote to select the master node, and vote rejection is carried out on the nodes with low reputation value; the third part is grouping consensus, firstly, performing intra-group consensus to form a preliminary consensus result, then entering an out-group consensus stage, and performing out-group consensus by the main nodes of each group to form global decision consistency.
In some embodiments, in said step S1: when grouping a plurality of unmanned aerial vehicles in the unmanned bee colony, the adopted grouping method comprises automatic grouping, average grouping and optimal algorithm grouping; acquiring historical behaviors of each unmanned aerial vehicle in the grouped unmanned bee colony in the consensus process, wherein the historical behaviors comprise participation consensus and node disfigurement, the participation consensus comprises message forwarding and consensus voting in the consensus process, and the node disfigurement comprises node response delay and feedback error information; the calculation model of the reputation value R is as follows:
Wherein R represents the reputation value, the reputation value is updated after each time slot for nodes participating in consensus, and exponential decay is carried out according to time t for nodes participating in dislike, 0 < < 1, S 1 and S 2 are introduced nonlinear functions, the reputation value rising and reputation value falling are respectively and adaptively adjusted, gamma is the value of the variation range of S 1 and S 2, and gamma=0.5,/>For the value step of S 1 and S 2,/>=1, N represents the number of times a node participates in a consensus in one slot, N represents the number of times all consensus is reached in one slot.
Specifically, the master node randomly selects and switches in a time slot or when the master node is down and unresponsive, which causes the master node to be vulnerable or a rogue node, affecting the overall consensus process. By designing a reputation mechanism, the same reputation initial value is set for all unmanned aerial vehicle nodes in the same group, the reputation value of the unmanned aerial vehicle node is updated in each time slot according to the historical behavior of each node in the consensus process, and the reputation value is stored in a block and is used as the basis for selecting the next consensus node.
And (3) performing reputation adjustment based on the performance of the nodes in the consensus process, if the nodes actively perform message forwarding confirmation and complete the processes of consensus voting and the like, the reputation value rises, and if the nodes do not respond or feed back error information in time or even do dislike in the group, the reputation value falls. The reputation evaluation model is as follows:
wherein R is the reputation value of the node, for the nodes participating in consensus, reputation value adjustment is carried out according to the consensus situation after one time slot, for malicious nodes, exponential decay is directly carried out according to time t, The value is less than 1.S 1 and S 2 are introduced nonlinear functions, and respectively perform self-adaptive adjustment on the rising of the reputation value of the positive node and the falling of the reputation value of the negative node, and the specific calculation formula is as follows:
wherein, gamma is the value of the variation range of S 1 and S 2, and the value is generally 0.5; the step length of the values of S 1 and S 2 is generally 1; n is the number of times the node participates in the consensus in one time slot, N is the number of times all the nodes participate in the consensus in one time slot, and when the value of N is 50, the iterative processes of S 1 and S 2 are shown in fig. 2.
In some embodiments, in the step S2, when the reputation value is within the interval (0, a), the unmanned aerial vehicle corresponding to the reputation value is the disqualified node; when the reputation value is in the interval [ a, b), the unmanned aerial vehicle corresponding to the reputation value is the consensus node; when the reputation value is in the interval [ b, c), the unmanned aerial vehicle corresponding to the reputation value is the node with high reputation value; wherein a is less than b and less than c, a is a first reputation value threshold, b is a second reputation value threshold, and c is a third reputation value threshold.
Specifically, according to the difference of the node reputation values, the nodes of the unmanned bee colony can be divided into disfiguring nodes, consensus nodes and high reputation value nodes, and the nodes are specifically shown in table 1. Wherein the high reputation node has an opportunity to vote on the master node. In order to prevent the centralization risk caused by the too high reputation value of the main node, resetting is carried out when the reputation value of the node reaches a threshold value c, and the reputation value is reassigned to a.
In some embodiments, in the step S2, other nodes except for the high reputation value node in the unmanned bee colony group elect an intra-group master node from a plurality of high reputation value nodes in the unmanned bee colony group by voting, and the number of votes obtained by the intra-group master node is the largest or not less than m/2, where m represents the number of nodes contained in the unmanned bee colony group; and when the main node in the group fails, updating the reputation value of the main node, and selecting the node with the maximum reputation value from other nodes with high reputation values as a new main node in the group.
Specifically, voting is performed on the basis of reputation value evaluation. Firstly, high-reputation nodes in a selected group form a selection list to be voted, the number of the nodes in the list is m, a master node is initiated in the group before the start of consensus, a certain high-reputation node P_ { i } in the list is set, the number of votes which are not less than m/2 is obtained or the number of votes obtained after one round of voting is highest, and the process of the consensus in the group is developed by the master node.
In a certain view, due to node failure or external interference, the master node P_ { i } is down or does not respond to feedback information of other consensus nodes, view switching is started, the node P_ { i } is removed from the selection list to be voted, the reputation value is reset, and the node P_ { j } with the highest reputation value in the current selection list to be voted is selected to serve as the master node, so that the follow-up consensus process is completed.
In some embodiments, in the step S2, the intra-group master node broadcasts history data of participation of the wrongly generated nodes to the commonly generated nodes in the group, the commonly generated nodes vote whether to reject the wrongly generated nodes, and when the proportion of the number of the commonly generated votes exceeds 50%, the intra-group master node rejects the wrongly generated nodes.
Specifically, voting culling is performed for bad nodes, as shown in FIG. 3. According to the reputation value evaluation model, the reputation value of the node is approaching 0 in one time slot, the master node collects the disfiguring evidence and broadcasts the disfiguring evidence to all the consensus nodes to perform rejection voting, the consensus nodes verify that the message is correct and vote to the master node, and if the master node receives more than half of votes, the disfiguring node is rejected.
In some embodiments, in said step S3, a preliminary consensus result of said intra-group consensus is obtained by: after receiving the consensus proposal sent by the client, the master node in the group of each unmanned swarm group broadcasts a pre-preparation message to other nodes in the group in a pre-preparation stage of the consensus in the group; each of the other nodes in the group, after verifying the pre-preparation message, respectively broadcasting a preparation message to the master node in the group and the other nodes in the group in a preparation phase of the intra-group consensus, and entering a confirmation phase of the intra-group consensus after receiving 2f+1 broadcasted preparation messages; the main node in the group and other nodes in the group broadcast confirmation messages to other nodes except the main node in the group in the confirmation stage of the intra-group consensus, and the main node in the group completes the intra-group consensus and acquires the preliminary consensus result after receiving 2f+1 broadcast confirmation messages; wherein f is the number of wrongly-used nodes which can be tolerated by the unmanned bee colony group.
In some embodiments, in the step S3, a global consensus result of the out-of-group consensus is obtained by: during a preparation phase of the out-of-group consensus, each in-group master node broadcasts the preparation message to other in-group master nodes and enters a confirmation phase of the out-of-group consensus after receiving 2f+1 of the broadcasted preparation messages; during the confirmation phase of the out-of-group consensus, each in-group master node broadcasts the confirmation message to the other in-group master nodes, and after 2f+1 of the broadcasted confirmation messages are received, the out-of-group consensus is completed and the global consensus result is obtained.
Specifically, to reduce the number of communications, packet consensus is developed according to the packet situation of the unmanned bee colony. The high reputation value nodes P_ { i } selected based on voting in each group are the main nodes of each group and are responsible for completing the intra-group consensus and participating in the external group consensus. After the client side C initiates a consensus proposal to the main nodes P_ { i } of each group, grouping consensus begins, the intra-group consensus is performed first to obtain a preliminary consensus result, and then the out-group consensus is performed to obtain a global consensus result, wherein the overall consensus flow is shown in figure 4.
In the intra-group consensus phase, each consensus node in the group follows PBFT consensus algorithm, first enters a PRE-preparation phase, the master node P_ { i } receives a client request, broadcasts a PRE-PREPARE PRE-preparation message, broadcasts the PREPARE preparation message after the received message passes verification, and enters a confirmation phase after receiving 2f+1 PREPARE messages, broadcasts a COMMIT confirmation message. After receiving 2f+1 COMMIT acknowledgement messages, the master node P_ { i } completes the intra-group consensus and starts the out-group consensus. In the out-of-group consensus phase, each group of master nodes sends the initial consensus result in the group to other groups of master nodes, and the preparation phase and the COMMIT confirmation phase are completed according to the same algorithm flow, so as to obtain the global consensus result.
As a consensus optimization algorithm for the unmanned bee colony, the core idea is to group the unmanned bee colony, screen high-reliability active unmanned aerial vehicle nodes by using a reputation mechanism, ensure the safety and stability of the main nodes in the group by combining a voting mechanism, and finally reduce the number of consensus messages by using a group consensus algorithm, improve the consensus efficiency and meet the consensus requirement of the unmanned bee colony, as shown in fig. 5: grouping the unmanned bee colony according to the function or service characteristics and other optimal algorithms; each unmanned plane node after grouping establishes a reputation value model according to the consensus participation condition, and a reputation value is dynamically given; before the start of consensus, nodes with high reputation value in the group vote to establish master nodes in the group; the master node responds to the client request to develop intra-group consensus to obtain an initial consensus result; and each group of master nodes performs out-of-group consensus on the initial consensus result to obtain a global consensus result.
The second aspect of the invention provides a practical Bayesian fault-tolerant consensus algorithm optimization system aiming at unmanned bee colonies. The system comprises: a first processing unit configured to: grouping a plurality of unmanned aerial vehicles in the unmanned aerial vehicle swarm, and respectively calculating respective reputation values of each unmanned aerial vehicle in the grouped unmanned aerial vehicle swarm based on the historical behaviors of each unmanned aerial vehicle in the consensus process; a second processing unit configured to: respectively judging each unmanned aerial vehicle in the grouped unmanned bee colony as a disqualified node, a consensus node or a high-reputation node according to the reputation value, and determining a main node in the group of the grouped unmanned bee colony from the high-reputation node; a third processing unit configured to: and responding to the intra-group main node of each unmanned bee colony group to receive a consensus proposal sent by a client, executing intra-group consensus and out-group consensus of the consensus proposal, and returning a preliminary consensus result of the intra-group consensus and a global consensus result of the out-group consensus to the client.
According to the system of the second aspect of the present invention, in said step S1:
when grouping a plurality of unmanned aerial vehicles in the unmanned bee colony, the adopted grouping method comprises automatic grouping, average grouping and optimal algorithm grouping;
Acquiring historical behaviors of each unmanned aerial vehicle in the grouped unmanned bee colony in the consensus process, wherein the historical behaviors comprise participation consensus and node disfigurement, the participation consensus comprises message forwarding and consensus voting in the consensus process, and the node disfigurement comprises node response delay and feedback error information;
The calculation model of the reputation value R is as follows:
Wherein R represents the reputation value, the reputation value is updated after each time slot for nodes participating in consensus, and exponential decay is carried out according to time t for nodes participating in dislike, 0 < < 1, S 1 and S 2 are introduced nonlinear functions, the reputation value rising and reputation value falling are respectively and adaptively adjusted, gamma is the value of the variation range of S 1 and S 2, and gamma=0.5,/>For the value step of S 1 and S 2,/>=1, N represents the number of times a node participates in a consensus in one slot, N represents the number of times all consensus is reached in one slot.
According to the system of the second aspect of the present invention, in the step S2, when the reputation value is within the interval (0, a), the unmanned aerial vehicle corresponding to the reputation value is the disqualified node; when the reputation value is in the interval [ a, b), the unmanned aerial vehicle corresponding to the reputation value is the consensus node; when the reputation value is in the interval [ b, c), the unmanned aerial vehicle corresponding to the reputation value is the node with high reputation value; wherein a is less than b and less than c, a is a first reputation value threshold, b is a second reputation value threshold, and c is a third reputation value threshold.
According to the system of the second aspect of the present invention, in the step S2, other nodes in the unmanned swarm group except for the high-reputation value node elect an intra-group master node from a plurality of the high-reputation value nodes in the unmanned swarm group by voting, the intra-group master node obtains the largest number of votes or the number of votes is not lower than m/2, and m represents the number of nodes contained in the unmanned swarm group; and when the main node in the group fails, updating the reputation value of the main node, and selecting the node with the maximum reputation value from other nodes with high reputation values as a new main node in the group.
According to the system of the second aspect of the present invention, in the step S2, the intra-group master node broadcasts history data of participation of the wrongly generated nodes to the commonly generated nodes in the group, the commonly generated nodes vote on whether to reject the wrongly generated nodes, and when the proportion of the endorsement votes to the total votes exceeds 50%, the intra-group master node rejects the wrongly generated nodes.
According to the system of the second aspect of the present invention, in said step S3, a preliminary consensus result of said intra-group consensus is obtained by: after receiving the consensus proposal sent by the client, the master node in the group of each unmanned swarm group broadcasts a pre-preparation message to other nodes in the group in a pre-preparation stage of the consensus in the group; each of the other nodes in the group, after verifying the pre-preparation message, respectively broadcasting a preparation message to the master node in the group and the other nodes in the group in a preparation phase of the intra-group consensus, and entering a confirmation phase of the intra-group consensus after receiving 2f+1 broadcasted preparation messages; the main node in the group and other nodes in the group broadcast confirmation messages to other nodes except the main node in the group in the confirmation stage of the intra-group consensus, and the main node in the group completes the intra-group consensus and acquires the preliminary consensus result after receiving 2f+1 broadcast confirmation messages; wherein f is the number of wrongly-used nodes which can be tolerated by the unmanned bee colony group.
According to the system of the second aspect of the present invention, in said step S3, a global consensus result of said out-of-group consensus is obtained by: during a preparation phase of the out-of-group consensus, each in-group master node broadcasts the preparation message to other in-group master nodes and enters a confirmation phase of the out-of-group consensus after receiving 2f+1 of the broadcasted preparation messages; during the confirmation phase of the out-of-group consensus, each in-group master node broadcasts the confirmation message to the other in-group master nodes, and after 2f+1 of the broadcasted confirmation messages are received, the out-of-group consensus is completed and the global consensus result is obtained.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps in the method for optimizing the practical Bayesian fault-tolerant consensus algorithm for the unmanned bee colony according to the first aspect of the disclosure when executing the computer program.
Fig. 6 is a block diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 6, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the electronic device is used for conducting wired or wireless communication with an external terminal, and the wireless communication can be achieved through WIFI, an operator network, near Field Communication (NFC) or other technologies. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the electronic equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 6 is merely a block diagram of a portion related to the technical solution of the present disclosure, and does not constitute a limitation of the electronic device to which the technical solution of the present disclosure is applied, and a specific electronic device may include more or less components than those shown in the drawings, or may combine some components, or have different component arrangements.
A fourth aspect of the invention discloses a computer-readable storage medium. The computer readable storage medium stores a computer program which, when executed by a processor, implements the steps in a method for optimizing a practical bayer pattern fault-tolerant consensus algorithm for an unmanned bee colony according to the first aspect of the disclosure.
In summary, the invention groups unmanned bee colony according to unmanned aerial vehicle service domain or functional characteristic, and introduces a reward and punishment mechanism and a voting mechanism; the reward and punishment mechanism is used for mobilizing enthusiasm of each node to participate in consensus, identifying and eliminating malicious or wrong nodes, the voting mechanism is used for selecting a node with a high trust value as a main node, and preventing the centralization risk of the main node caused by the fact that the trust value is too high for a long time. The invention simplifies the consensus process of the unmanned bee colony according to the grouping condition of the unmanned bee colony, reduces the communication times of the consensus nodes of the unmanned bee colony by first performing intra-group consensus and then performing external group consensus, and greatly improves the consensus efficiency.
Note that the technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be regarded as the scope of the description. The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (4)
1. A practical bayer pattern fault-tolerant consensus algorithm optimization method for an unmanned bee colony, the method comprising:
Step S1, grouping a plurality of unmanned aerial vehicles in the unmanned bee colony, and respectively calculating respective reputation values of each unmanned aerial vehicle in the grouped unmanned bee colony based on the historical behaviors of each unmanned aerial vehicle in the consensus process;
S2, respectively judging each unmanned aerial vehicle in the grouped unmanned bee colony as a disqualified node, a consensus node or a high reputation value node according to the reputation value, and determining a main node in the group of the grouped unmanned bee colony from the high reputation value node;
S3, receiving a consensus proposal sent by a client by each group of unmanned bee colony main nodes, executing intra-group consensus and out-group consensus of the consensus proposal, and returning a preliminary consensus result of the intra-group consensus and a global consensus result of the out-group consensus to the client;
wherein the intra-group consensus is performed first, and then the out-group consensus is performed;
In the step S1:
when grouping a plurality of unmanned aerial vehicles in the unmanned bee colony, the adopted grouping method comprises automatic grouping, average grouping and optimal algorithm grouping;
Acquiring historical behaviors of each unmanned aerial vehicle in the grouped unmanned bee colony in the consensus process, wherein the historical behaviors comprise participation consensus and node disfigurement, the participation consensus comprises message forwarding and consensus voting in the consensus process, and the node disfigurement comprises node response delay and feedback error information;
the calculation model of the reputation value is as follows: ，
Wherein R represents the reputation value, the reputation value is updated after each time slot for nodes participating in consensus, and exponential decay is carried out according to time t for nodes participating in dislike, 0 < < 1, S 1 and S 2 are introduced nonlinear functions, the reputation value rising and reputation value falling are respectively and adaptively adjusted, gamma is the value of the variation range of S 1 and S 2, and gamma=0.5,/>For the value step of S 1 and S 2,/>=1, N represents the number of times a node participates in consensus in one slot, N represents the number of times all consensus is reached in one slot; in the step S2, when the reputation value is within the interval (0, a), the unmanned aerial vehicle corresponding to the reputation value is the disqualified node; when the reputation value is in the interval [ a, b), the unmanned aerial vehicle corresponding to the reputation value is the consensus node; when the reputation value is in the interval [ b, c), the unmanned aerial vehicle corresponding to the reputation value is the node with high reputation value; wherein a is less than b and less than c, a is a first reputation value threshold, b is a second reputation value threshold, and c is a third reputation value threshold;
In the step S2, other nodes except for the high-reputation node in the unmanned bee colony group elect a master node in the group from a plurality of high-reputation nodes in the unmanned bee colony group by voting, the number of votes obtained by the master node in the group is the largest or not lower than m/2, and m represents the number of nodes contained in the unmanned bee colony group; when the main node in the group fails, updating the reputation value of the main node, and selecting the node with the maximum reputation value from other nodes with high reputation values as a new main node in the group;
In the step S2, the master node in the group broadcasts the history data of the wrongly generated nodes to the commonly generated nodes in the group, the commonly generated nodes vote whether to reject the wrongly generated nodes, and when the proportion of the commonly generated votes to the total votes exceeds 50%, the master node in the group rejects the wrongly generated nodes.
2. A practical bayer pattern fault-tolerant consensus algorithm optimizing method for unmanned swarms according to claim 1, wherein in step S3, a preliminary consensus result of the intra-group consensus is obtained by:
After receiving the consensus proposal sent by the client, the master node in the group of each unmanned swarm group broadcasts a pre-preparation message to other nodes in the group in a pre-preparation stage of the consensus in the group;
each of the other nodes in the group, after verifying the pre-preparation message, respectively broadcasting a preparation message to the master node in the group and the other nodes in the group in a preparation phase of the intra-group consensus, and entering a confirmation phase of the intra-group consensus after receiving 2f+1 broadcasted preparation messages;
The main node in the group and other nodes in the group broadcast confirmation messages to other nodes except the main node in the group in the confirmation stage of the intra-group consensus, and the main node in the group completes the intra-group consensus and acquires the preliminary consensus result after receiving 2f+1 broadcast confirmation messages;
Wherein f is the number of wrongly-used nodes which can be tolerated by the unmanned bee colony group.
3. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps in a practical bayer pattern fault-tolerant consensus algorithm optimizing method for an unmanned bee colony according to any of claims 1-2 when the computer program is executed.
4. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, which computer program, when being executed by a processor, implements the steps of a practical bayer tolerant consensus algorithm optimizing method for unmanned bee colonies according to any one of claims 1-2.
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