Method and device for optimizing timeliness of data stream

Abstract

The present application relates to the field of communication technology, and in particular to a method and device for optimizing the timeliness of data streams. The method comprises: for any data type to be transmitted, determining the link weight corresponding to the data type to be transmitted on each link according to the transmission rate of each link, the link comprising an adjacent first network node and a second network node; determining the target data type from each data type to be transmitted according to the link weight corresponding to each data type to be transmitted on each link; determining the target scheduling link from each link according to the link weight corresponding to the target data type on each link, and the transmission rate of each link, and transmitting the data stream corresponding to the target data type on the target scheduling link. The use of this method can improve the timeliness of the data stream, which is conducive to maintaining the stability of the network and the freshness of information.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method and device for optimizing the timeliness of a data stream. Background Art

[0002] In the current fifth-generation (5G) mobile communications, the connectivity and reliability of Internet of Things (IoT) devices have been significantly improved, and IoT technology has begun to be widely used in various fields, such as healthcare, intelligent transportation and logistics systems, smart cities, energy industry, etc. The rapid growth in the number of IoT devices and the amount of data generated by these devices pose a huge challenge to the transmission of information in the IoT. Therefore, it is necessary to design an effective routing mechanism to adaptively control network congestion and increase network throughput.

[0003] For example, the commonly used backpressure routing is an algorithm that uses queue backlog differences in multi-hop networks to control congestion and optimize network throughput. Backpressure routing algorithms are mainly used to solve problems such as long delays and high queue complexity. However, in applications such as the Internet of Vehicles, there are high requirements for the timeliness of information, and traditional backpressure routing cannot meet the requirements of high timeliness.

[0004] It can be seen that the current backpressure routing algorithm has the problem of low timeliness of data flow. Summary of the invention

[0005] Based on this, it is necessary to provide an invention that proposes a method, device, computer equipment, computer-readable storage medium and computer program product for optimizing the timeliness of data flow in response to the above-mentioned technical problems, so as to realize the design of high-efficiency routing mechanism in information transmission of the Internet of Things and improve the timeliness of data flow.

[0006] In a first aspect, the present application provides a method for optimizing the timeliness of a data stream, the method comprising:

[0007] For any data type to be transmitted, determining a link weight corresponding to the data type to be transmitted on each link according to a transmission rate of each link, wherein the link includes an adjacent first network node and a second network node;

[0008] Determining a target data type from each of the data types to be transmitted according to the link weights corresponding to each of the data types to be transmitted on each of the links;

[0009] According to the link weight corresponding to the target data type on each of the links and the transmission rate of each of the links, a target scheduling link is determined from each of the links, and a data stream corresponding to the target data type is transmitted on the target scheduling link.

[0010] In one embodiment, determining the link weight corresponding to the data type to be transmitted on each link according to the transmission rate of each link includes:

[0011] Get the transmission rate of each link;

[0012] For any of the links, respectively obtain the queue backlog lengths corresponding to the data types to be transmitted in the first network node and the second network node in the current time slot;

[0013] Determine, according to the queue backlog lengths corresponding to the data type to be transmitted in the first network node and the second network node in each of the links in the current time slot, a queue backlog length difference corresponding to the data type to be transmitted on each of the links;

[0014] According to the queue backlog length difference corresponding to the data type to be transmitted on each link, and the transmission rate of each link, the link weight corresponding to the data type to be transmitted on each link is determined, wherein the link weight is positively correlated with the queue backlog length difference and the transmission rate.

[0015] In one embodiment, determining the target scheduling link from each of the links according to the link weight corresponding to the target data type on each of the links and the transmission rate of each of the links includes:

[0016] Obtain a set of schedulable link sets;

[0017] For any schedulable link set in the set of schedulable link sets, according to the link weights corresponding to the target data type on each of the links in the schedulable link set and the transmission rate of each of the links, obtain a sum of weights corresponding to the schedulable link set;

[0018] According to the sum of weights corresponding to each of the schedulable link sets, a target schedulable link set is determined from the set of the schedulable link sets, and each of the links in the target schedulable link set is used as a target scheduling link.

[0019] In one embodiment, obtaining a set of schedulable link sets includes:

[0020] Get the link set;

[0021] According to the restriction condition of link scheduling, a set of schedulable link sets is determined from the link set.

[0022] In one of the embodiments, determining a set of schedulable link sets from the link set according to the restriction condition of link scheduling includes:

[0023] For any link in the link set, determining an activation state of the link;

[0024] Determining, according to the activation state of each of the links, a transmission state of two adjacent network nodes in each of the links;

[0025] According to the link scheduling constraints and the transmission status of two adjacent network nodes in each link, each schedulable link set is determined from the link set, and each of the schedulable link sets constitutes a set of schedulable link sets. The link scheduling constraints include that a network node cannot be in the transmission state of sending data and receiving data at the same time.

[0026] In one of the embodiments, for any data type to be transmitted, determining the link weight corresponding to the data type to be transmitted on each link according to the transmission rate of each link includes:

[0027] According to the queue backlog length corresponding to each data type to be transmitted in two network nodes on each link, a Lyapunov drift function is established;

[0028] Determine a penalty item according to the timeliness of each data type to be transmitted, and for any data type to be transmitted, the timeliness of the data type to be transmitted is determined according to the transmission rate of each link where the data type to be transmitted is located;

[0029] According to the Lyapunov drift function and the penalty term, a link weight corresponding to each data type to be transmitted on each link is determined.

[0030] In a second aspect, the present application further provides a device for optimizing the timeliness of a data stream, the device comprising:

[0031] A link weight determination module, configured to determine, for any data type to be transmitted, a link weight corresponding to the data type to be transmitted on each of the links according to a transmission rate of each link, wherein the link includes an adjacent first network node and a second network node;

[0032] A target data type determination module, configured to determine a target data type from each of the data types to be transmitted according to the link weights corresponding to each of the data types to be transmitted on each of the links;

[0033] A target scheduling link determination module is used to determine a target scheduling link from each of the links according to the link weight corresponding to the target data type on each of the links and the transmission rate of each of the links, and transmit the data stream corresponding to the target data type on the target scheduling link.

[0034] In one of the embodiments, the link weight determination module is also used to obtain the transmission rate of each link; for any of the links, respectively obtain the queue backlog length corresponding to the data type to be transmitted in the first network node and the second network node in the current time slot; determine the queue backlog length difference corresponding to the data type to be transmitted on each of the links based on the queue backlog length corresponding to the data type to be transmitted in the first network node and the second network node in each of the links in the current time slot; determine the link weight corresponding to the data type to be transmitted on each of the links based on the queue backlog length difference corresponding to the data type to be transmitted on each of the links and the transmission rate of each of the links, wherein the link weight is positively correlated with the queue backlog length difference and the transmission rate.

[0035] In one of the embodiments, the target scheduling link determination module is also used to obtain a set of schedulable link sets; for any schedulable link set in the set of schedulable link sets, the sum of the weights corresponding to the schedulable link sets is obtained according to the link weights corresponding to each link in the schedulable link set according to the target data type, and the transmission rate of each link; based on the sum of the weights corresponding to each schedulable link set, a target schedulable link set is determined from the set of schedulable link sets, and each link in the target schedulable link set is used as a target scheduling link.

[0036] In one of the embodiments, the target scheduling link determination module is further used to obtain a link set; and determine a set of schedulable link sets from the link set according to the restriction conditions of link scheduling.

[0037] In one of the embodiments, the target scheduling link determination module is further used to determine the activation state of any link in the link set; determine the transmission state of two adjacent network nodes in each link based on the activation state of each link; determine each schedulable link set from the link set based on the link scheduling constraints and the transmission state of two adjacent network nodes in each link, and each schedulable link set constitutes a set of schedulable link sets, and the link scheduling constraints include that a network node cannot be in the transmission state of sending data and receiving data at the same time.

[0038] In one of the embodiments, the link weight determination module is also used to establish a Lyapunov drift function based on the queue backlog length corresponding to each data type to be transmitted in the two network nodes on each link; determine a penalty item based on the timeliness of each data type to be transmitted, and for any data type to be transmitted, the timeliness of the data type to be transmitted is determined based on the transmission rate of each link where the data type to be transmitted is located; and determine the link weight corresponding to each data type to be transmitted on each link based on the Lyapunov drift function and the penalty item.

[0039] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0040] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0041] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0042] The above-mentioned data flow timeliness optimization method, device, computer equipment, computer-readable storage medium and computer program product, for any data type to be transmitted, determine the link weight corresponding to the data type to be transmitted on each link according to the transmission rate of each link, and the link includes an adjacent first network node and a second network node; determine the target data type from each data type to be transmitted according to the link weight corresponding to each data type to be transmitted on each link; determine the target scheduling link from each link according to the link weight corresponding to the target data type on each link and the transmission rate of each link, and transmit the data flow corresponding to the target data type on the target scheduling link. Compared with the backpressure routing algorithm that uses queue backlog difference to control congestion in traditional technology, the data flow timeliness optimization method, device, computer equipment, computer-readable storage medium and computer program product provided by the present application introduces the transmission rate of the link to determine the link weight, and then determines the most reasonable transmission link and transmission data type in the current time slot, so that routing and link scheduling strategies can be adaptively made in each time slot, improving the timeliness of the data flow, which is conducive to maintaining the stability of the network and the freshness of information. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1The figure is a flowchart of a method for optimizing the timeliness of a data stream in one embodiment.

[0044] Figure 2 A network model diagram in one embodiment.

[0045] Figure 3 FIG. 1 is a flow chart of step 102 in an embodiment.

[0046] Figure 4 FIG. 1 is a flow chart of step 106 in one embodiment.

[0047] Figure 5 FIG. 4 is a flow chart of step 402 in one embodiment.

[0048] Figure 6 FIG. 5 is a flow chart of step 504 in one embodiment.

[0049] Figure 7 FIG. 1 is a flow chart of step 102 in an embodiment.

[0050] Figure 8 FIG. 4 is an AoI curve diagram between adjacent nodes in one embodiment.

[0051] Fig. 9 FIG. 4 is a curve diagram showing a change in AoI at node b in an embodiment.

[0052] Fig.10 The figure is a flowchart of a method for optimizing the timeliness of a data stream in one embodiment.

[0053] Fig.11 It is a structural block diagram of a device for optimizing the timeliness of data streams in one embodiment.

[0054] Fig.12 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0056] By using IoT technology, tens of thousands of devices can communicate with each other and exchange information. In 2022, the number of IoT devices is expected to exceed 40 billion, and by 2025, it is expected to reach 75.4 billion. The rapid growth in the number of IoT devices and the amount of data generated by these devices pose a huge challenge to the transmission of information in the IoT. Therefore, it is necessary to design an effective routing mechanism to adaptively control network congestion and increase network throughput.

[0057] Backpressure routing is an algorithm that uses queue backlog differences to control congestion in multi-hop networks and optimizes network throughput. In recent years, research on backpressure routing algorithms has mainly focused on solving problems such as long delays and high queue complexity. However, no research has considered optimizing the timeliness of information at the destination node, which has high requirements for information timeliness in applications such as the Internet of Vehicles.

[0058] The backpressure routing algorithm routes data packets through the queue congestion gradient at each node in the network. The routing phase of the data packet is based on time slots. In each time slot, the node forwards the data packet to the neighboring node with the largest queue backlog. This feature is also in line with the laws of nature. It can be associated with the fact that in a pipe, water tends to flow in the direction of the largest pressure gradient, thus reaching the final stable state. The backpressure routing algorithm can be used not only in wireless communication networks, but also in mobile ad hoc networks and wired networks.

[0059] Age of Information (AoI) is an indicator used to characterize the timeliness of information. AoI refers to the time elapsed after the latest data packet is generated from the source node from the perspective of the destination node. The smaller the AoI, the fresher the received data. In control applications, the freshness of information must be maintained. If the received information is outdated, it will lead to incorrect control and decision-making, and even cause more serious disasters. Today's research mainly focuses on two indicators, namely the Time Average Age of Information and the Peak Age of Information. The current backpressure routing algorithm has the problem of low timeliness of data flow.

[0060] Based on this, an embodiment of the present application provides a method for optimizing the timeliness of data flow, which is a backpressure routing algorithm based on information age. It comprehensively considers the routing at the nodes in the network and the link scheduling strategy to optimize the timeliness of data flow, and is used to realize the design of high-timeliness routing mechanism in information transmission in the Internet of Things.

[0061] In one embodiment, Figure 1 As shown, a method for optimizing the timeliness of a data stream is provided, wherein the method comprises:

[0062] Step 102: for any data type to be transmitted, determine the link weight corresponding to the data type to be transmitted on each link according to the transmission rate of each link, where the link includes an adjacent first network node and a second network node.

[0063] In the embodiment of the present application, the model of the multi-hop Internet of Things can be described by a directed graph G=(N, L), such as Figure 2 As shown. N represents the set of all nodes in this multi-hop IoT system G, including source nodes, relay nodes s and destination nodes d. Therefore, the nodes n∈{0,1,…N} in the network, and the Nth node is the destination node of the network. There can be multiple relay nodes between the source node and the destination node. L is used to represent the set of all links in the IoT G, each link includes two adjacent nodes. Time is divided into discrete time slots, t∈{0,1,2…}.

[0064] The data type c to be transmitted is the type of data packet transmitted in the network. In each node n, different types of data packets are stored in corresponding queues. The transmission rate u of the link ij is the number of data packets transmitted from one node of the link to another node per unit time. The transmission rate of a link is equal in different time slots and different types of data to be transmitted. For any type of data to be transmitted, the weight of each link can be determined based on the transmission rate of each link and the queue backlog difference between the first and second adjacent network nodes in the link. The link weight can be used to characterize the possibility of the link being called for transmission in the current time slot. If the weight of the link (i, j) is negative, then the link (i, j) will not transmit data in the current time slot t; on the contrary, if the link weight is positive, data will be transmitted on the link (i, j), and the corresponding number of transmitted data packets is u. ij .

[0065] Step 104: Determine the target data type from the data types to be transmitted according to the link weights corresponding to the data types to be transmitted on the links.

[0066] For example, in the embodiment of the present application, the positive link weights can be selected from the link weights first, and then the transmission data type corresponding to the maximum value of the positive link weights is used as the target data type. The target data type can be defined as c * , satisfying the following formula (I).

[0067]

[0068] Where C is the set of data types to be transmitted, (i, j) is any link in the network, the link includes two adjacent nodes i and j, and node j is the neighbor node of node i. ij Represents the transmission rate of link (i, j). V is a non-negative value, which represents the trade-off between network stability and information timeliness. The larger the V value, the greater the weight of the information age (AOI), that is, the greater the proportion of the link transmission rate. represents the queue backlog length of data type c to be transmitted in network node i, It represents the queue backlog length of the data type c to be transmitted in the network node j. The queue backlog length is the number of data packets corresponding to the data type to be transmitted in the network node.

[0069] Step 106, determining a target scheduling link from each link according to the link weight corresponding to the target data type on each link and the transmission rate of each link, and transmitting the data stream corresponding to the target data type on the target scheduling link.

[0070] In an embodiment of the present application, the target scheduling link is a link in the current time slot network that is scheduled to transmit a data stream corresponding to the target data type. Multiple data packets corresponding to the target data type constitute the data stream corresponding to the target data type. After determining the target data type, the link weight corresponding to the target data type on each link can be obtained. In any schedulable link set, the product of the link weight corresponding to each link and the transmission rate is summed, and the maximum value is taken, wherein all links in the schedulable link set corresponding to the maximum value are target scheduling links.

[0071] The above-mentioned method for optimizing the timeliness of data streams, for any data type to be transmitted, determines the link weight corresponding to the data type to be transmitted on each link according to the transmission rate of each link, and the link includes an adjacent first network node and a second network node; determines the target data type from each data type to be transmitted according to the link weight corresponding to each data type to be transmitted on each link; determines the target scheduling link from each link according to the link weight corresponding to the target data type on each link, and the transmission rate of each link, and transmits the data stream corresponding to the target data type on the target scheduling link. Compared with the backpressure routing algorithm that uses queue backlog difference to control congestion in traditional technology, the method for optimizing the timeliness of data streams provided by the present application introduces the transmission rate of the link to determine the link weight, and then determines the most reasonable transmission link and transmission data type in the current time slot, so that there is no need to master the state information of the channel in advance, and the routing and link scheduling strategies can be adaptively made in each time slot, thereby improving the timeliness of the data stream, which is conducive to maintaining the stability of the network and the freshness of the information.

[0072] In one embodiment, Figure 3 As shown, in step 102, determining the link weight corresponding to the data type to be transmitted on each link according to the transmission rate of each link may include:

[0073] Step 302: Obtain the transmission rate of each link.

[0074] Wherein, any link (i, j) includes adjacent first network node i and second network node j. The transmission rate of the link is the number of data packets transmitted from the first network node i to the second network node j in the current time slot link (i, j).

[0075] Step 304: for any link, respectively obtain the queue backlog lengths corresponding to the data types to be transmitted in the first network node and the second network node in the current time slot.

[0076] For any link (i, j), the number of data packets corresponding to the data type to be transmitted in the first network node i in the current time slot can be obtained as the corresponding queue backlog length Get the number of data packets corresponding to the data type to be transmitted in the first network node j in the current time slot as the corresponding queue backlog length

[0077] Step 306: Determine the difference in queue backlog lengths corresponding to the data type to be transmitted on each link according to the queue backlog lengths corresponding to the data type to be transmitted in the first network node and the second network node in each link in the current time slot.

[0078] For any link (i, j), the queue backlog length in the first network node i can be Subtract the queue backlog length in the second network node j The queue backlog length difference corresponding to the data type to be transmitted is obtained. It should be noted that link (i, j) and link (j, i) are two different links.

[0079] Step 308, based on the queue backlog length difference corresponding to the data type to be transmitted on each link and the transmission rate of each link, determine the link weight corresponding to the transmission data type on each link, wherein the link weight is positively correlated with the queue backlog length difference and the transmission rate.

[0080] The link weight corresponding to each type to be transmitted on each link may satisfy the following formula (II).

[0081]

[0082] In the disclosed embodiment, the link weight not only takes into account the difference in queue backlog lengths between adjacent nodes, but also takes into account the link transmission rate. When the link transmission rate between node i and node j is greater, it is more likely to schedule this link for transmission, so that the information age performance can be optimized while ensuring network stability.

[0083] In one embodiment, Figure 4 As shown, in step 106, determining the target scheduling link from each link according to the link weight corresponding to the target data type on each link and the transmission rate of each link may include:

[0084] Step 402: Obtain a set of schedulable link sets.

[0085] The set of schedulable link sets includes multiple schedulable link sets, and each schedulable link set includes multiple links that can be scheduled simultaneously without conflict.

[0086] Step 404, for any schedulable link set in the set of schedulable link sets, obtain the sum of weights corresponding to the schedulable link set according to the link weights corresponding to the target data type on each link in the schedulable link set and the transmission rate of each link.

[0087] The schedulable link set includes multiple links that can be scheduled simultaneously. After determining the target data type, in any schedulable link set, the link weight corresponding to the target data type on the link can be multiplied by the transmission rate, and then the products corresponding to each link can be summed to obtain the weight sum corresponding to the schedulable link set.

[0088] The link weight corresponding to the target data type on each link can satisfy the following formula (III).

[0089]

[0090] in, is the link weight corresponding to the target data type on each link, is the queue backlog length corresponding to the target data type in the first network node i in the current time slot, is the queue backlog length corresponding to the target data type in the first network node j in the current time slot.

[0091] Step 406: determine a target schedulable link set from the set of schedulable link sets according to the sum of weights corresponding to each schedulable link set, and use each link in the target schedulable link set as a target scheduling link.

[0092] Among them, the current time slot target schedulable link set can satisfy the following formula (IV).

[0093]

[0094] Wherein, π(t) is the target schedulable link set of the current time slot. Γ represents the set of schedulable link sets, and π∈Γ is a schedulable link set arbitrarily selected from the set of schedulable link sets. After determining the sum of weights corresponding to each schedulable link set, the schedulable link set corresponding to the maximum weight sum among the weight sums is taken as the target schedulable link set.

[0095] The disclosed embodiment obtains the target scheduling link based on maximizing the product of the link weight and the transmission rate, so as to optimize the information age performance under the premise of ensuring network stability.

[0096] In one embodiment, Figure 5 As shown, in step 402, obtaining a set of schedulable link sets may include:

[0097] Step 502: Obtain a link set.

[0098] The collection of all links in the network is the link collection L. The links include the adjacent first network node i and second network node j. Link (i, j) and link (j, i) are two different links. Link (i, j) indicates that the data packet is transmitted from node i to node j, and link (j, i) indicates that the data packet is transmitted from node j to node i.

[0099] Step 504: determine a set of schedulable link sets from the link set according to the link scheduling restriction condition.

[0100] Among them, the restriction condition of link scheduling can satisfy formula (V).

[0101]

[0102] Among them, the binary variable y ij (t) represents the activation state of link (i, j) in time slot t. ij When (t) is 1, it means that the link (i, j) is activated and can transmit data. ij When (t) is 0, it indicates that the activation state of link (i, j) is that data cannot be transmitted. Represents the set of neighbor nodes of node i in the network. T is the total time required for link scheduling in the network.

[0103] Formula (5) limits y ij (t) and y ji (t) cannot be 1 at the same time, that is, a network node cannot receive data and send data at the same time. All links that satisfy formula (V) at the same time can form a schedulable link set. It can be understood that the solution of formula (V) is not unique, that is, the schedulable link set is not unique. All schedulable link sets form a set of schedulable link sets.

[0104] In the disclosed embodiment, a set of schedulable link sets is determined based on the restriction conditions of link scheduling to maintain the stability of the network.

[0105] In one embodiment, Figure 6 As shown, in step 504, according to the restriction condition of link scheduling, determining a set of schedulable link sets from the link set may include:

[0106] Step 602: For any link in the link set, determine the activation state of the link.

[0107] Among them, the binary variable yij (t) represents the activation state of link (i, j) in time slot t. ij When (t) is 1, it means that the link (i, j) is activated and can transmit data. ij When (t) is 0, it indicates that the activation state of link (i, j) is that data cannot be transmitted.

[0108] Step 604: Determine the transmission status of two adjacent network nodes in each link according to the activation status of each link.

[0109] The transmission status may include receiving data, sending data, and neither receiving nor sending data. ij When (t) is 1, the activation state of the link (i, j) is that data can be transmitted. At this time, the transmission state of the first network node i is sending data, and the transmission state of the second network node j is receiving data.

[0110] Step 606, according to the link scheduling constraints and the transmission status of two adjacent network nodes in each link, each schedulable link set is determined from the link set, and each schedulable link set constitutes a set of schedulable link sets. The link scheduling constraints include that a network node cannot be in the transmission state of sending data and receiving data at the same time.

[0111] After obtaining the transmission status of two adjacent network nodes in each link, we can obtain all other neighboring nodes that meet the restriction conditions at this time for any node according to formula (V). By analogy, we can obtain the nodes that meet y ij (t) and y ji (t) cannot be 1 at the same time, that is, multiple matrices that satisfy the transmission state that a network node cannot be in the state of sending and receiving data at the same time. The matrix is ​​the schedulable link set, and each parameter in the matrix is ​​the activation state of the link in the schedulable link set. Multiple matrices constitute a set of schedulable link sets.

[0112] In the disclosed embodiment, each schedulable link set is determined from a link set based on the transmission status of a network node, thereby maintaining the stability of the network.

[0113] In one embodiment, Figure 7 As shown, in step 102, for any data type to be transmitted, determining the link weight corresponding to the data type to be transmitted on each link according to the transmission rate of each link may include:

[0114] Step 702: Establish a Lyapunov drift function according to the queue backlog length corresponding to each data type to be transmitted in two network nodes on each link.

[0115] Among them, network stability needs to meet the restriction condition formula (six), and E is the mean function.

[0116]

[0117] In order to satisfy formula (VI), it is necessary to adopt Lyapunov optimization theory to maintain network stability.

[0118] The Lyapunov function is defined as the following formula (VII).

[0119]

[0120] Among them, L(t) is the Lyapunov function, and c_sum represents the number of all data types to be transmitted in the network. The queue backlog length of the data packets corresponding to the data type c to be transmitted at the node n in the time slot t satisfies the following formula (VIII).

[0121]

[0122] in, It represents the number of data packets corresponding to the data type c to be transmitted transmitted from node n to its neighbor node j. En(t) is the number of data packets corresponding to the data type c to be transmitted injected into the network from the external node n in this time slot t. The network system adopts a first-come-first-served queue model.

[0123] The Lyapunov drift function Ω(t) is defined as the change of the Lyapunov function from one time slot to the next time slot. In order to keep the system stable, this difference needs to be minimized, that is, to satisfy formula (IX).

[0124]

[0125] Step 704: determine a penalty item according to the timeliness of each data type to be transmitted. For any data type to be transmitted, the timeliness of the data type to be transmitted is determined according to the transmission rate of each link where the data type to be transmitted is located.

[0126] Among them, the timeliness of the data type to be transmitted is the average information age of the data type to be transmitted, and the penalty term can be determined based on the average information age. A new objective function can be determined based on the penalty term, and the objective function satisfies the following formula (10). The objective function determined by the penalty term can make the average information age at the destination node smaller, that is, ensure the timeliness of the information.

[0127]

[0128] Among them, A sumIt is the sum of the average information age of all packets corresponding to the data type to be transmitted at the destination node. V is a non-negative value, which represents the trade-off between network stability and information timeliness. The larger the V value, the greater the weight of information age. sum It is the penalty term. The average information age can be obtained based on the instantaneous information age.

[0129] Figure 8 is the AoI curve between adjacent nodes, such as Figure 8 As shown, the relationship between the curve of the instantaneous information age of node i and node j and the area enclosed by the horizontal axis can be obtained, which satisfies the following formula (XI).

[0130]

[0131] Wherein, k is the kth data packet. express Figure 8 The area enclosed by the solid line of node j and the horizontal axis, express Figure 8 The area enclosed by the solid line of node i and the horizontal axis. The shaded area G k The following formula (XII) can be satisfied.

[0132]

[0133] Where t(k) is the time when the kth data packet is sent from the source node, t(k-1) is the time when the k-1th data packet is sent from the source node, is the time when the kth data packet arrives at node j, is the time when the kth data packet arrives at node i.

[0134] According to the above formula, the relationship between the average information age at node i and node j can be obtained, which satisfies the following formula (thirteen).

[0135]

[0136] in, represents the average information age of the data packets corresponding to the data type c to be transmitted at node i, Represents the average information age of the data packets corresponding to the data type c to be transmitted at node j.

[0137] Let b be the next hop node of the source node s, then the AoI relationship between b and the destination node d can be expressed as formula (fourteen).

[0138]

[0139] Among them, A c is the average information age of the data packets corresponding to the data type c to be transmitted at the destination node, is the average information age of the data packets corresponding to the data type c to be transmitted at node b, which can be calculated based on Fig. 9 The shaded area P in k The area is calculated. Fig. 9 is a graph of the instantaneous information age at node b. The shaded area P k The surface satisfies the following formula (XV).

[0140]

[0141] Thus, the instantaneous AoI curve at node b and the area enclosed by the horizontal axis can be obtained. Satisfies the following formula (XVI).

[0142]

[0143] Among them, λ is the rate at which data packets are generated, u sb is the transmission rate of link (s, b).

[0144] The average AoI of the data packets corresponding to the data type c to be transmitted at the node b satisfies the following formula (XVII).

[0145]

[0146] Therefore, the average information age function of the data packets corresponding to the data type c to be transmitted at the destination node satisfies the following formula (XVIII).

[0147]

[0148] Among them, A c is the average information age of the data packets corresponding to the data type c to be transmitted at the destination node, that is, the timeliness of the data type c to be transmitted, λ is the generation rate of the data packets corresponding to the transmission data type c, u ij is the transmission rate of the link (i, j) where the data type c to be transmitted is located. Therefore, for any data type c to be transmitted, the timeliness of the data type c to be transmitted can be determined based on the transmission rate u of each link (i, j) where the data type c to be transmitted is located. ij Definitely got it.

[0149] Step 706: Determine the link weight corresponding to each data type to be transmitted on each link according to the Lyapunov drift function and the penalty term.

[0150] Among them, due to A c is the average information age of the data packets corresponding to the data type c to be transmitted at the destination node, A sumis the sum of the average information age of the data packets corresponding to all the data types to be transmitted at the destination node. Formula (18) can be substituted into formula (10), that is, A is calculated for any data type c to be transmitted. c Sum the offspring into A sum , simplifying to get the following formula (XIX).

[0151]

[0152] Among them, B is a constant, which is related to the transmission rate of the link and the arrival rate of the network flow. Formula (19) can be further simplified to be equivalent to maximizing the above formula (2).

[0153] Therefore, the link weight corresponding to each data type to be transmitted on each link can be determined according to formula (II).

[0154] In the disclosed embodiment, the function expression of the average information age at the destination node is first obtained, and then the Lyapunov drift plus penalty framework is introduced for analysis, that is, the average information age function is used as a penalty term, thereby deriving a new expression of the link weight. Routing and link scheduling strategies can be adaptively made in each time slot without the need to know the channel status information in advance, which is conducive to maintaining the stability of the network and the freshness of the information.

[0155] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0156] For further understanding of the embodiments of the present application, see Fig.10, the present application provides a most complete embodiment. In the current time slot t, the link weights of the data packets corresponding to the different types to be transmitted in all links are calculated, and the target data type is determined from the data types to be transmitted according to the link weights corresponding to the data types to be transmitted on each link, that is, the type of data packets to be transmitted on each link. According to formula (IV), the target scheduling link, that is, the optimal scheduling link, is selected from each link. By transmitting the data stream corresponding to the target data type on the target scheduling link, the timeliness of the data stream can be improved, which is conducive to maintaining the stability of the network and the freshness of the information. At this time, the c of each link is * The target schedulable link set π(t) of the current time slot has been determined. If the weight of link (i, j) is negative, then in the current time slot t, link (i, j) will not transmit data; on the contrary, if the link weight is positive, commodity c will be transmitted on link (i, j). * , the corresponding number of transmitted data packets is u ij When t is less than the total duration T, the above steps can be repeated to select the target scheduling link and target data type for the next time slot. When t is greater than or equal to the total duration T, the timeliness optimization method for the data stream can be terminated.

[0157] Based on the same inventive concept, the embodiment of the present application also provides a device for optimizing the timeliness of data streams for implementing the above-mentioned method for optimizing the timeliness of data streams. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of the device for optimizing the timeliness of one or more data streams provided below can be referred to the limitations of the method for optimizing the timeliness of data streams above, and will not be repeated here.

[0158] In one embodiment, see Fig.11 , a device 1100 for optimizing the timeliness of a data stream is provided. The device 1100 for optimizing the timeliness of a data stream includes:

[0159] The link weight determination module 1102 is used to determine, for any data type to be transmitted, the link weight corresponding to the data type to be transmitted on each link according to the transmission rate of each link, where the link includes an adjacent first network node and a second network node;

[0160] The target data type determination module 1104 is used to determine the target data type from each data type to be transmitted according to the link weight corresponding to each data type to be transmitted on each link;

[0161] The target scheduling link determination module 1106 is used to determine the target scheduling link from each link according to the link weight corresponding to the target data type on each link and the transmission rate of each link, and transmit the data flow corresponding to the target data type on the target scheduling link.

[0162] The above-mentioned data flow timeliness optimization device, for any data type to be transmitted, determines the link weight corresponding to the data type to be transmitted on each link according to the transmission rate of each link, and the link includes an adjacent first network node and a second network node; determines the target data type from each data type to be transmitted according to the link weight corresponding to each data type to be transmitted on each link; determines the target scheduling link from each link according to the link weight corresponding to the target data type on each link and the transmission rate of each link, and transmits the data flow corresponding to the target data type on the target scheduling link. Compared with the backpressure routing algorithm that uses queue backlog difference to control congestion in traditional technology, the data flow timeliness optimization device provided by the present application introduces the transmission rate of the link to determine the link weight, and then determines the most reasonable transmission link and transmission data type in the current time slot, so that there is no need to master the channel status information in advance, and the routing and link scheduling strategies can be adaptively made in each time slot, thereby improving the timeliness of the data flow, which is conducive to maintaining the stability of the network and the freshness of the information.

[0163] In one embodiment, the link weight determination module 1102 is also used to obtain the transmission rate of each link; for any link, respectively obtain the queue backlog length corresponding to the data type to be transmitted in the first network node and the second network node in the current time slot; determine the queue backlog length difference corresponding to the data type to be transmitted on each link according to the queue backlog length corresponding to the data type to be transmitted in the first network node and the second network node in each link in the current time slot; determine the link weight corresponding to the data type to be transmitted on each link according to the queue backlog length difference corresponding to the data type to be transmitted on each link and the transmission rate of each link, wherein the link weight is positively correlated with the queue backlog length difference and the transmission rate.

[0164] In one embodiment, the target scheduling link determination module 1106 is also used to obtain a set of schedulable link sets; for any schedulable link set in the set of schedulable link sets, the sum of weights corresponding to the schedulable link sets is obtained according to the link weights corresponding to the target data type on each link in the schedulable link set, and the transmission rate of each link; according to the sum of weights corresponding to each schedulable link set, the target schedulable link set is determined from the set of schedulable link sets, and each link in the target schedulable link set is used as a target scheduling link.

[0165] In one embodiment, the target scheduling link determination module 1106 is further configured to obtain a link set; and determine a set of schedulable link sets from the link set according to the restriction conditions of link scheduling.

[0166] In one embodiment, the target scheduling link determination module is further used to determine the activation state of any link in the link set; determine the transmission state of two adjacent network nodes in each link according to the activation state of each link; determine each schedulable link set from the link set according to the link scheduling restriction conditions and the transmission state of two adjacent network nodes in each link, and each schedulable link set constitutes a set of schedulable link sets, and the link scheduling restriction conditions include that a network node cannot be in the transmission state of sending data and receiving data at the same time.

[0167] In one embodiment, the link weight determination module 1102 is also used to establish a Lyapunov drift function based on the queue backlog length corresponding to each data type to be transmitted in the two network nodes on each link; determine the penalty item based on the timeliness of each data type to be transmitted, and for any data type to be transmitted, the timeliness of the data type to be transmitted is determined based on the transmission rate of each link where the data type to be transmitted is located; and determine the link weight corresponding to each data type to be transmitted on each link based on the Lyapunov drift function and the penalty item.

[0168] Each module in the above-mentioned data stream timeliness optimization device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0169] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig.12 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing the timeliness of a data stream is implemented.

[0170] Those skilled in the art will understand that Fig.12 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0171] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0172] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0173] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0174] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0175] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0176] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0177] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for optimizing the timeliness of data streams. It is characterized in that The method comprises: For any data type to be transmitted, determining a link weight corresponding to the data type to be transmitted on each link according to a transmission rate of each link, wherein the link includes an adjacent first network node and a second network node; Determining a target data type from each of the data types to be transmitted according to the link weights corresponding to each of the data types to be transmitted on each of the links; According to the link weight corresponding to the target data type on each of the links and the transmission rate of each of the links, a target scheduling link is determined from each of the links, and a data stream corresponding to the target data type is transmitted on the target scheduling link.

2. The method according to claim 1, It is characterized in that The determining, according to the transmission rate of each link, the link weight corresponding to the data type to be transmitted on each link includes: Get the transmission rate of each link; For any of the links, respectively obtain the queue backlog lengths corresponding to the data types to be transmitted in the first network node and the second network node in the current time slot; Determine, according to the queue backlog lengths corresponding to the data type to be transmitted in the first network node and the second network node in each of the links in the current time slot, a queue backlog length difference corresponding to the data type to be transmitted on each of the links; According to the queue backlog length difference corresponding to the data type to be transmitted on each link, and the transmission rate of each link, the link weight corresponding to the data type to be transmitted on each link is determined, wherein the link weight is positively correlated with the queue backlog length difference and the transmission rate.

3. The method according to claim 1, It is characterized in that The determining a target scheduling link from each of the links according to the link weight corresponding to the target data type on each of the links and the transmission rate of each of the links comprises: Obtain a set of schedulable link sets; For any schedulable link set in the set of schedulable link sets, according to the link weights corresponding to the target data type on each of the links in the schedulable link set and the transmission rate of each of the links, obtain a sum of weights corresponding to the schedulable link set; According to the sum of weights corresponding to each of the schedulable link sets, a target schedulable link set is determined from the set of the schedulable link sets, and each of the links in the target schedulable link set is used as a target scheduling link.

4. The method according to claim 3, It is characterized in that The obtaining of a set of schedulable link sets includes: Get the link set; According to the restriction condition of link scheduling, a set of schedulable link sets is determined from the link set.

5. The method according to claim 4, It is characterized in that The step of determining a set of schedulable link sets from the link set according to the restriction condition of link scheduling includes: For any link in the link set, determining an activation state of the link; Determining, according to the activation state of each of the links, a transmission state of two adjacent network nodes in each of the links; According to the link scheduling constraints and the transmission status of two adjacent network nodes in each link, each schedulable link set is determined from the link set, and each of the schedulable link sets constitutes a set of schedulable link sets. The link scheduling constraints include that a network node cannot be in the transmission state of sending data and receiving data at the same time.

6. The method according to claim 1, It is characterized in that The step of determining, for any data type to be transmitted, a link weight corresponding to the data type to be transmitted on each of the links according to the transmission rate of each link, includes: According to the queue backlog length corresponding to each data type to be transmitted in two network nodes on each link, a Lyapunov drift function is established; Determine a penalty item according to the timeliness of each data type to be transmitted, and for any data type to be transmitted, the timeliness of the data type to be transmitted is determined according to the transmission rate of each link where the data type to be transmitted is located; According to the Lyapunov drift function and the penalty term, a link weight corresponding to each data type to be transmitted on each link is determined.

7. A device for optimizing the timeliness of data streams, It is characterized in that The device comprises: a link weight determination module, configured to determine, for any data type to be transmitted, a link weight corresponding to the data type to be transmitted on each of the links according to a transmission rate of each link, wherein the link comprises an adjacent first network node and a second network node; A target data type determination module, configured to determine a target data type from each of the data types to be transmitted according to the link weights corresponding to each of the data types to be transmitted on each of the links; A target scheduling link determination module is used to determine a target scheduling link from each of the links according to the link weight corresponding to the target data type on each of the links and the transmission rate of each of the links, and transmit the data stream corresponding to the target data type on the target scheduling link.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program. It is characterized in that When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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