CN111918403B - Industrial wireless network deterministic transmission scheduling method based on improved Monte Carlo search tree - Google Patents
Industrial wireless network deterministic transmission scheduling method based on improved Monte Carlo search tree Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/12—Wireless traffic scheduling
- H04W72/1263—Mapping of traffic onto schedule, e.g. scheduled allocation or multiplexing of flows
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
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- H04W72/0446—Resources in time domain, e.g. slots or frames
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Abstract
The invention discloses an industrial wireless network deterministic transmission scheduling method based on improved MCTS. Establishing a probabilistic determination transmission scheduling model, constructing a scheduling mode on each node in an industrial wireless network, generating and verifying the scheduling mode by using a heuristic method based on a Monte Carlo search tree, iterating until the probability reliability corresponding to a time slot allocation scheme is greater than or equal to a target reliability, outputting the time slot allocation scheme, and using each scheduling mode S in the time slot allocation scheme S i And controlling each corresponding node of the industrial wireless network to carry out communication transmission. The invention adopts the probabilistic definite transmission scheduling model to ensure the deterministic communication of the reliability packet delivery deadline guarantee required by the industrial wireless network, reduces the calculation complexity, obviously reduces the difficulty of generating the deterministic transmission scheduling scheme of the industrial wireless network, and has stable output result.
Description
Technical Field
The invention relates to a processing method of industrial wireless network communication transmission scheduling, in particular to a method for generating and verifying industrial wireless network probabilistic determination transmission scheduling based on an improved Monte Carlo search tree (MCTS).
Background
Industrial wireless networks are an economical, efficient and scalable solution for connecting industrial field devices. Compared with the traditional wired network, the system is flexible to deploy and easy to maintain. However, industrial wireless networks often require deterministic communications with reliability and packet delivery deadline guarantees. This is difficult to achieve in a wireless network because of the natural unreliability of the wireless link. Since the optimal solution of the wireless network time slot allocation scheduling scheme is NP-hard, it is not feasible to directly find the optimal communication scheduling process for each node.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an industrial wireless network probabilistic determination transmission scheduling generation and detection method based on an improved Monte Carlo search tree.
The invention aims at realizing the following specific technical scheme, which comprises the following specific steps:
a1, establishing a probabilistic determination transmission scheduling model:
for each node N in the industrial wireless network i Building a scheduling pattern S i I represents the ordinal number of a node in an industrial wireless network, scheduling mode S i Represented by (N, T, W) i P), N represents the total number of nodes in the scheduling mode, T represents the total number of transmitted time slots, and N is the node i Deadline, W, of packet transmission i Representing a time slot allocated to an ith node for transmission, wherein P represents target reliability, and N, T and P parameters form scheduling model parameters; and establishes a scheduling pattern S i Reliability P':
wherein X is i Is the number of times the ith node successfully performs transmission.
A node refers to a communication node in an industrial wireless network. In each node, the scheduling model parameters are identical.
Successful transmission refers to a successful transmission of a data packet by one node to another node in the industrial wireless network and receipt by the other node.
A2, generating and verifying a scheduling mode by using a heuristic method based on a Monte Carlo search tree, iterating until the probability reliability corresponding to the obtained time slot allocation scheme is greater than or equal to a target reliability P, and outputting a time slot allocation scheme S;
a3, each scheduling mode S in the time slot allocation scheme S i And controlling each corresponding node of the industrial wireless network to carry out communication transmission.
In particular, the industrial wireless network refers to a network formed by wireless network among sensors, actuators, gateways and the like in an industrial environment.
The A2 specifically comprises the following steps:
a2-1, constructing a Monte Carlo search tree according to the scheduling model parameters in the A1, and constructing a root node R of the Monte Carlo search tree, wherein the root node R is empty;
a2-2, starting from the root node R as the current node, performing the following processing:
a2-3, expansion: performing child node expansion by taking the current node as a father node to complete the next-layer expansion of the current node of the Monte Carlo search tree, wherein each child node obtained by expansion comprises a scheduling mode set of all nodes of the industrial wireless network;
a2-4, simulation: according to preset boundary conditions, performing simulation of time slot allocation on the expanded sub-nodes of the next layer to obtain a scheduling mode set of each sub-node of the next layer as a time slot allocation scheme;
a2-5, reliability calculation:
converting the slot allocation scheme generated in A2-4 to a binary number, using logical bit operations to determine if it is possible: extracting a plurality of samples lambda from the space of each sub-node, wherein one sample is a scheduling mode, and the scheduling mode S of each sample i Conversion to binary number alpha i Then further calculating to obtain the successful transmission times zeta i And judging:
η i ＝α i &！(α 1 |α 2 |...|α i-1 |α i+1 |...|α n )
wherein eta i Representing the available space binary number;&representation logicEdit and operation, | represents logical or operation, |! Representing a logical non-operation;
then, the probability reliability P', which is the sub-node, of the reliability of all samples is calculated according to the following reliability formula:
wherein ζ i Is the number of times the i-th node corresponding to the sample successfully transmits, λ represents the amount of reliability check extracted from all communication node transmission cases;
after the simulation is completed, the simulation result is evaluated, and the calculation amount can be reduced by adopting a random sampling mode.
A2-6, counter-propagating:
the method comprises the steps of carrying out back propagation processing and updating the accessed times of a parent node of a child node, namely the accessed times of a current node, according to a time slot allocation scheme obtained by A2-4 simulation; the child node and its parent node are accessed during the back propagation.
A2-7, rollback mechanism:
the slot allocation scheme obtained in the A2-4 simulation after the reliability calculation of A2-5,
if at least one of the reliability of each child node exceeds the reliability of the parent node, the next step is carried out;
if the reliability of all the child nodes does not exceed the reliability of the parent node, triggering a rollback mechanism, and continuing to iterate again when the Monte Carlo search tree is reset to the state before the fixed round;
a2-8, calculating the confidence upper limit value (UCT) of each sub-node expanded by the current node in the Monte Carlo search tree, selecting the sub-node with the maximum confidence upper limit value, reserving the sub-node, and deleting other sub-nodes;
a2-9, comparing and judging the probability reliability P' of the reserved sub-nodes with the target reliability P: if the probability reliability P' is larger than or equal to the target reliability P, stopping iteration and outputting a time slot allocation scheme corresponding to the currently reserved sub-node; otherwise, the reserved sub-nodes are used as current nodes, and the step A2-3 is returned to the iterative processing.
The calculation of the confidence upper limit value of the sub-node in the Monte Carlo search tree is as follows:
wherein v is i Is the reliability of the ith sub-node, n i Is the number of times the ith child node has been accessed in the back propagation, M is the total number of times the current node, which is the parent node of the ith child node, C represents the weight coefficient, is a constant, and is usually taken
Because the problem of the generation of the transmission scheduling scheme determined by the probability of the industrial wireless network is NP-Hard, the invention provides a heuristic algorithm based on a Monte Carlo search tree to generate a scheduling mode so as to reduce the whole search space, and meanwhile, random sampling and logic bit operation are adopted in the method to reduce the calculation complexity of the algorithm.
The beneficial effects of the invention are as follows:
the invention adopts a probabilistic determined transmission scheduling model to ensure the deterministic communication of the reliability packet delivery deadline guarantee required by the industrial wireless network, adopts improved Monte Carlo search tree processing to obtain the approximate optimal scheduling result, and adopts random sampling and logic bit operation in the algorithm to reduce the calculation complexity of the algorithm. The invention obviously reduces the difficulty of generating deterministic transmission scheduling results in the industrial wireless network, and the algorithm output results are stable, and the generated results are superior to the random time slot allocation results.
Drawings
Fig. 1 is an iterative flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of a logic bit operation calculation method.
Fig. 3 is a graph comparing the average, maximum and minimum values of the results of the method and random slot allocation algorithm with improved monte carlo search tree processing of the present invention.
Fig. 4 is a graph of the standard deviation comparison of the results of the random slot allocation algorithm with the method of the present invention for improving the monte carlo search tree processing.
Detailed Description
The objects and effects of the present invention will become more apparent when the following description of the present invention is given with reference to the accompanying drawings and examples.
The specific embodiment of the invention and the implementation process are as follows:
a1, establishing a probabilistic determination transmission scheduling model and a node N i Scheduling pattern S on i Can be expressed as (N, T, W) i P), N represents the total number of nodes in the scheduling mode, T represents the total number of time slots in the scheduling mode, and also represents the deadline of data packet transmission, W i Representing the time slots allocated to the ith node for transmission in the scheduling pattern, and P represents the probabilistic deterministic transmission requirements that the scheduling scheme needs to achieve. Scheduling pattern S i Reliability of (2)P'. Gtoreq.P, wherein X i The number of times that the ith node successfully transmits;
a2, generating and verifying a scheduling mode by using a heuristic algorithm based on a Monte Carlo search tree according to the given requirement in the A1;
a3, iteratively performing the step in A2 until the reliability of the scheme S is greater than or equal to the P required in A1, and outputting a scheduling scheme S;
the A2, as shown in fig. 1, specifically includes:
a2-1, constructing a Monte Carlo search tree root node R according to the scheduling model parameters given in the A1;
a2-2, selecting: if the node is not expanded, sub-node expansion is carried out; otherwise, selecting the sub-node with the largest UCT value according to the UCT value obtained by calculation; the algorithm used is UCT, and the UCT calculation method comprises the following steps:
wherein v is i Is the reliability of the node, n i Is the number of times the node has been accessed, N is the total number of times its parent node has been accessed, C is a constant, taken
A2-3, expansion: expanding the next layer of the node after the node is selected;
a2-4, simulation: according to the set strategy, the expanded sub-nodes are simulated for the next time slot allocation;
a2-5, reliability calculation: after the simulation is completed, the results after the simulation need to be evaluated. A random sampling method is employed to reduce computation space. Extracting a sample lambda from the total computation space of each node, and rewriting the reliability computation formula in A1 to beWherein ζ i Is the number of times the i-th node in the sample successfully performs transmission. Logical bit operations are used to convert the slot allocation scheme generated in A2-4 to a binary number, and bit operations are used to determine if it is feasible. I.e. node N i Scheduling pattern S on i Can be converted into binary number alpha i Calculating to obtain beta i And then further calculating to obtain ζ i ：
η i ＝α i &！(α 1 |α 2 |...|α i-1 |α i+1 |...|α n )
As shown in FIG. 2, N 1 Can be represented by binary number 0b11000, let it be alpha 1 ，N 2 And N 3 Can be similarly represented as 0b10100,0b10010, let it be alpha 2 And alpha 3 . For the purpose ofCalculation zeta 1 Will be in addition to alpha 1 All numbers except for the one are logically or-operated to obtain omega 1 Then to omega 1 Performing logic non-operation to obtain beta 1 Finally, alpha is 1 And beta 1 Performing logical AND operation to obtain eta 1 . If eta 1 > 0, then it indicates that node 1 can successfully transmit, ζ, under this scheduling condition 1 An increase of 1; conversely, node 1 cannot successfully transmit ζ 1 Is unchanged.
A2-6, counter-propagating: updating parent node information according to the simulation result of the A2-4;
a2-6, rollback mechanism: since random sampling is adopted, a systematic error exists in the reliability calculation result, and a threshold value tau is defined. When all simulation results in A2-4 are subjected to reliability calculation of A2-5, and the reliability of the obtained nodes does not exceed the reliability of the father node, a rollback mechanism is triggered, and the search tree is reset to a state before a plurality of rounds for iterating again; conversely, this result is also considered acceptable by the search tree; a rollback wheel number xi is set i Representing the number of wheels needing to be retracted when the ith wheel is retracted; when the ith round of iteration triggers rollback, xi i Self-increment by 1 to eliminate cumulative improper allocation as much as possible;
to verify the performance of the present invention, the present invention is compared with the random slot allocation algorithm after implementation. Let n=10, t=95, λ=10000, τ=0.05%, p=99.0%, 100 tests were performed on both algorithms respectively. The scheme reliability average value, the maximum value, the minimum value and the standard deviation output by each round of algorithm are used as judging standards, and the larger the average value, the maximum value and the minimum value, the smaller the standard deviation, the better and more stable algorithm results are shown.
The scheme reliability average value, maximum value and minimum value results of the method for improving Monte Carlo search tree processing and the random time slot allocation algorithm are shown in figure 3, and the variance results are shown in figure 4. The two figures can be used for obtaining that the invention is superior to a random time slot allocation algorithm in four indexes, and the invention can be used for more accurately and stably calculating to obtain a scheduling scheme which accords with the probability determination transmission scheduling requirement of the industrial wireless network.
Claims (2)
1. An industrial wireless network deterministic transmission scheduling method based on improved MCTS is characterized in that: the method comprises the following steps:
a1, establishing a probabilistic determination transmission scheduling model:
for each node N in the industrial wireless network i Building a scheduling pattern S i I represents the ordinal number of a node in an industrial wireless network, scheduling mode S i Represented by (N, T, W) i P), N represents the total number of nodes in the scheduling mode, T represents the total number of transmitted time slots, and N is the node i Deadline, W, of packet transmission i Representing a time slot allocated to an ith node for transmission, wherein P represents target reliability, and N, T and P parameters form scheduling model parameters;
a2, generating and verifying a scheduling mode by using a heuristic method based on a Monte Carlo search tree, iterating until the probability reliability corresponding to the obtained time slot allocation scheme is greater than or equal to a target reliability P, and outputting a time slot allocation scheme S;
a3, each scheduling mode S in the time slot allocation scheme S i Controlling each corresponding node of the industrial wireless network to carry out communication transmission;
the A2 specifically comprises the following steps:
a2-1, constructing a root node R of a Monte Carlo search tree, wherein the root node R is empty;
a2-2, starting from the root node R as the current node, performing the following processing:
a2-3, expansion: performing child node expansion by taking the current node as a father node to complete the next-layer expansion of the current node of the Monte Carlo search tree, wherein each child node obtained by expansion comprises a scheduling mode set of all nodes of the industrial wireless network;
a2-4, simulation: according to preset boundary conditions, performing simulation of time slot allocation on the expanded sub-nodes of the next layer to obtain a scheduling mode set of each sub-node of the next layer as a time slot allocation scheme;
a2-5, reliability calculation:
converting the slot allocation scheme generated in A2-4 to a binary number, using logical bit operations to determine if it is possible: extracting samples lambda from each sub-node, taking one sample as a scheduling pattern, and taking the scheduling pattern S of each sample i Conversion to binary number alpha i Then further calculating to obtain the successful transmission times zeta i And judging:
η i ＝α i &！(α 1 |α 2 |...|α i-1 |α i+1 |...|α n )
wherein eta i Representing the available space binary number;&represents a logical AND operation, | represents a logical OR operation, | -! Representing a logical non-operation;
then, the probability reliability P', which is the sub-node, of the reliability of all samples is calculated according to the following reliability formula:
wherein ζ i Is the number of times the i-th node corresponding to the sample successfully transmits, λ represents the amount of reliability check extracted from all communication node transmission cases;
a2-6, counter-propagating:
the method comprises the steps of carrying out back propagation processing according to a time slot allocation scheme obtained by A2-4 simulation and updating the accessed times of a parent node of a child node;
a2-7, rollback mechanism:
if at least one of the reliability of each child node exceeds the reliability of the parent node, the next step is carried out;
if the reliability of all the child nodes does not exceed the reliability of the parent node, triggering a rollback mechanism, and continuing to iterate again when the Monte Carlo search tree is reset to the state before the fixed round;
a2-8, calculating the confidence upper limit value (UCT) of each sub-node expanded by the current node in the Monte Carlo search tree, selecting the sub-node with the maximum confidence upper limit value, reserving the sub-node, and deleting other sub-nodes;
a2-9, comparing and judging the probability reliability P' of the reserved sub-nodes with the target reliability P: if the probability reliability P' is more than or equal to the target reliability P, stopping iteration and outputting a time slot allocation scheme corresponding to the currently reserved sub-node; otherwise, the reserved sub-nodes are used as current nodes, and the step A2-3 is returned to the iterative processing.
2. The improved MCTS-based industrial wireless network deterministic transmission scheduling method as claimed in claim 1, wherein: the calculation of the confidence upper limit value of the sub-node in the Monte Carlo search tree is as follows:
wherein v is i Is the reliability of the ith sub-node, n i Is the number of times the i-th child node has been accessed in the back propagation, M is the total number of times the current node, which is the parent node of the i-th child node, has been accessed, and C represents the weight coefficient.
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