CN118450464A - A data routing decision method based on comprehensive adaptive function - Google Patents

Abstract

The present invention relates to a data routing decision method based on a comprehensive adaptive function, which is used to process data routing decisions for a vehicle edge computing network, including: initializing the network in the routing discovery phase, and obtaining network topology information and a feasible path set based on a broadcast discovery mechanism; constructing a routing information table based on network topology information and a feasible path set in the routing decision phase, determining the priority based on a comprehensive adaptive function, selecting a next-hop terminal to obtain a primary path and an auxiliary path, and forming a full-network path set; and updating routing table information through a dual-stage routing maintenance mechanism in the routing maintenance phase. Compared with the prior art, the present invention has the advantages of achieving stable, efficient and reliable network data routing, and a wide range of applications.

Description

A data routing decision method based on comprehensive adaptive function

Technical Field

The present invention relates to the technical field of vehicle edge computing network routing, and in particular to a data routing decision method based on a comprehensive adaptive function.

Background technique

The self-organizing nature of V2X (vehicle-to-everything) networks enables wireless communication between vehicles, facilitating interaction between vehicles at different locations and seamless access to critical information. This capability provides a promising way to ensure road safety and improve traffic efficiency. In V2X networks, applications will generate a large number of data-intensive and computation-intensive tasks, such as assisted driving systems, video entertainment, vehicle status monitoring, and path planning calculations. However, resource-constrained vehicle and wireless mobility characteristics not only lead to network congestion, but also affect the stability and reliability of task routing, making it impossible for the executed tasks to meet the requirements of low-latency communication service quality, thus limiting the application and development of V2X networks. The emergence of the edge computing paradigm is widely regarded as a key enabler to meet the low-latency requirements. The basic idea of this paradigm is to reduce transmission delays by extending cloud services using edge servers deployed at the edge of the network. As external resource providers, edge servers play a key role in extending the computing power of resource-limited V2X systems. As intermediaries and coordinators, they also facilitate seamless communication across terminal cloud layers. As shown in Figure 2, computationally intensive tasks generated by vehicles can be offloaded to these edge servers to reduce task processing delays and response times. More importantly, offloading can more flexibly place tasks with low computational burdens on embedded terminal devices on vehicles for processing, with real-time and short-cycle tasks being calculated on edge servers and non-real-time long-cycle tasks being calculated on cloud servers. Obviously, how to find efficient and reliable transmission paths is the key to V2X task routing and offloading.

Although task transmission can be achieved through routing schemes, it cannot be guaranteed that each vehicle may always reach the edge server in one hop, especially when link failure in single-path routing schemes may lead to task termination. Compared with single-path routing schemes, multi-path routing schemes can store multiple paths. With its data forwarding capability, vehicles can distribute tasks to different paths, thereby balancing load distribution and improving network utilization. When one path fails, vehicles can choose other paths to continue forwarding tasks to ensure the reliability of transmission. However, the terminal devices on the vehicle are usually energy-constrained, and the energy exhaustion of the vehicle will cause the communication link to be interrupted, and the interruption will lead to the termination of task transmission; secondly, vehicle mobility may cause changes in the topology of the V2X network, resulting in communication interruption; then, not all places are covered by good wireless signals, and unstable wireless links will lead to the loss of transmission tasks; finally, the tasks forwarded by the vehicle are easily affected by environmental factors such as electromagnetic interference and external noise, resulting in the loss of transmission tasks and the degradation of quality. Therefore, a routing method that can identify the external environment and the stability of the link is needed to ensure the reliability and sustainability of data routing.

How to achieve stable, efficient and reliable V2X network data routing has become a technical problem that needs to be solved.

Summary of the invention

The purpose of the present invention is to provide a data routing decision method based on a comprehensive adaptive function in order to overcome the defects of the above-mentioned prior art.

The purpose of the present invention can be achieved by the following technical solutions:

According to one aspect of the present invention, a data routing decision method based on a comprehensive adaptive function is provided, the method is used to process data routing decisions for a vehicle edge computing network, and includes:

In the route discovery phase, the network is initialized and network topology information and feasible path sets are obtained based on the broadcast discovery mechanism;

In the routing decision stage, a routing information table is constructed based on network topology information and feasible path sets, priorities are determined based on comprehensive adaptive functions, next-hop terminals are selected to obtain primary and secondary paths, and a network-wide path set is formed;

And in the routing maintenance phase, the routing table information is updated through a two-phase routing maintenance mechanism.

Preferably, in the routing decision stage, the routing information table is constructed based on the network topology information and the feasible path set, the next hop terminal is selected based on the comprehensive adaptive function to obtain the main path and the auxiliary path, and the whole network path set is formed, including:

Based on the topology information table and the feasible path set, the terminals obtain each other's current state information through information exchange to build a routing information table;

With the help of the routing information table, the terminal will determine the priority according to the comprehensive adaptive function value, guide the selection of the next-hop terminal, and determine all the terminals on the main transmission path in this way. The auxiliary path selects the terminal with a large comprehensive adaptive function that is not occupied by the main path;

For the source terminal and the target server, a disjoint multi-path is constructed by selecting a primary path and multiple auxiliary paths to form a full network path set.

More preferably, the comprehensive adaptive function is specifically:

A comprehensive adaptive function is constructed based on the edge center function, residual energy function and risk environment function. The calculation expression is:

In the formula, is the comprehensive adaptive function, ω _EC is the weight coefficient of the edge-center function, ω _RE is the weight coefficient of the residual energy function, ω _R is the weight coefficient of the risk environment function, is the edge-center function, is the residual energy function, is the risk environment function;

The calculation expression of the edge center function is:

h _i =min{h _i→j |i∈V _T ,j∈V _E }

In the formula, is the edge-centric function, hi is the number of hops from terminal i to the nearest edge server j, V _T represents the set of terminals, and V _E represents the set of edge servers;

The calculation expression of the residual energy function is:

In the formula, E _i (t) represents the residual energy of terminal i at time t, E ₀ is the initial energy of the terminal, is the residual energy function;

The calculation expression of the risk environment function is:

r _max = max{r _j |j∈V _T }

In the formula, is the risk environment function of terminal i, r _max is the highest risk level in the network, V _T represents the set of terminals, and _ri is the risk level of terminal i.

Preferably, updating the routing table information by a two-stage routing maintenance mechanism during the routing maintenance phase includes:

During data transmission using the primary and secondary paths, the terminal monitors the link status with other terminals by sending Hello messages in real time, makes status judgments based on whether Resp messages are replied, and obtains a real-time updated network topology information table;

When the terminal status changes, the change is promptly fed back to the neighbor in the form of a message, and the routing information table is updated.

More preferably, the method further comprises re-determining the route if the contents of the topology information table and the routing information table are updated.

More preferably, when the terminal status changes, the change is fed back to the neighbor in a timely manner in the form of a message, and the routing information table is updated including: calculating the data transmission rate of different data transmission modes, calculating the data transmission delay and predicting the link life between terminals.

More preferably, the data transmission mode includes two modes: terminal to terminal and terminal to edge server;

The calculation of the data transmission rates of different data transmission modes is specifically as follows:

In the formula, represents the data transmission rate from terminal i to terminal j, represents the data transmission rate from terminal i to edge server m, _wi represents the bandwidth resources owned by terminal i, pi _,j represents the transmission power of terminal i, represents the channel gain from terminal i to terminal j, N ₀ represents the noise power, qi _,j (t) represents the dynamic proportional coefficient, represents the channel gain from terminal i to edge server m.

More preferably, the data transmission delay includes an execution delay of the terminal completing the computing task, a communication delay of the terminal transmitting the computing task to the edge server, and a computational delay of the edge server completing the computing task;

The data transmission delay is calculated as:

In the formula, represents the execution delay of terminal i to complete computing task _Ti , represents the communication delay of terminal i transmitting computing task _Ti to edge server m; represents the computational delay of edge server m to complete computational task _Ti , where the computational delay Depends on the computing power required to complete the computing task _Ti and the computing power of the edge server m itself; represents the computing power of edge server m, hi _{→m is} the number of relay hops from terminal i to edge server m, is the minimum transmission rate between different terminals on the transmission path, is the maximum transmission rate between different terminals on the transmission path, d _i is the size of the computing task, and ci _is the computing resources required to complete the computing task.

More preferably, the predicted inter-terminal link lifetime is specifically:

The link lifetime is calculated as follows:

In the formula, is the Euclidean distance between terminals; r is the communication radius; R _v = 0 indicates that the initial position of the vehicle is within the communication range of the roadside unit, and R _v = 1 indicates that the initial position of the vehicle is outside the communication range of the roadside unit; θ indicates the angle between the link vectors of the two terminals and the relative speed vector.

Preferably, the network initialization includes deploying network equipment in a designated area, setting IDs, classifying terminal types, presetting the forward direction and speed of dynamic vehicle terminals, and classifying risk areas of different levels, wherein the network equipment includes terminals and edge servers;

The method of obtaining network topology information and feasible path set based on the broadcast discovery mechanism is as follows: the terminal requests to obtain a neighbor list by broadcasting a Hello message, and then obtains the coordinate position, energy and environmental conditions of the neighbor based on the Resp message in reply to the request. The edge server synchronizes the neighbor lists of all terminals to obtain network topology information, and obtains a feasible path set based on an offline calculation method.

Compared with the prior art, the present invention has the following beneficial effects:

1) The present invention designs a comprehensive adaptive function for data routing decision-making, which mitigates the impact of link interruption on data transmission by identifying the stability of the wireless link, and mitigates the impact of external interference on data routing by sensing the environmental conditions around the vehicle and adjusting the path in real time to bypass the "dangerous environment area"; with the help of the comprehensive adaptive function, the vehicle is helped to make routing decisions to achieve stable, efficient and reliable network data routing, thereby ensuring the reliability, real-time and sustainability of data transmission.

2) The present invention proposes a two-stage routing maintenance mechanism, which updates the status information in real time through status evaluation and message feedback to assist in subsequent routing updates, thereby further improving various performances of data routing.

3) The comprehensive adaptive function of the present invention meets the application requirements of different scenarios by setting the weight ratios of different functions, which is convenient, flexible and has a wide range of applications.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of a flow chart of a data routing decision method based on a comprehensive adaptive function in the present invention;

FIG2 is a schematic diagram of a three-layer architecture of a vehicle edge computing network in the prior art;

FIG3 is a schematic diagram of a network initialization process in the present invention;

FIG4 is a schematic diagram of a routing discovery process in the present invention;

FIG5 is a schematic diagram of the routing decision process in the present invention;

FIG6 is a schematic diagram of the routing maintenance process in the present invention;

FIG7 is a schematic diagram showing the effect of the adaptive function weight on the data packet receiving rate in the present invention;

FIG8 is a schematic diagram showing the effect of the adaptive function weight on the packet loss rate in the present invention;

FIG9 is a schematic diagram showing the effect of the adaptive function weight on the energy balance index in the present invention;

FIG10 is a schematic diagram showing the effect of the adaptive function weight on the end-to-end delay in the present invention;

In the figure, PRR: packet reception rate, PLR: packet loss rate, EBI: energy balance index, EED: end-to-end delay.

Detailed ways

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

This embodiment relates to a data routing decision method based on a comprehensive adaptive function, as shown in FIG1 , including:

In the routing discovery phase, the network is initialized and the topology information and feasible path set are obtained based on the broadcast discovery mechanism.

In the routing decision stage, a routing information table is constructed through information interaction, and a comprehensive adaptive function is used to guide the selection of the next-hop terminal to obtain the main path and auxiliary path, and obtain the path set of the entire network.

In the routing maintenance phase, the routing table information is updated through a two-stage routing maintenance mechanism, which includes real-time updating of the topology information table based on status evaluation and updating of the routing table information based on message feedback. If the contents of the topology information table and the routing information table are updated, the routing decision needs to be made again, otherwise, the topology and routing updates will continue.

The data routing decision method based on the comprehensive adaptive function specifically includes the following steps:

Step S1: Initialize the network by deploying devices (i.e., terminals and edge servers) in a specified area and setting IDs, classifying terminal types (including dynamic vehicle terminals and static terminals), presetting the forward direction and speed of dynamic vehicle terminals, and dividing risk areas of different levels, as shown in Figure 3;

Step S2: After the network is initialized, the network device broadcasts a Hello message to request a neighbor list, and then obtains the coordinate position, energy and environmental conditions of the neighbor based on the Resp message in reply to the request. The edge server synchronizes the neighbor lists of all terminals to obtain network topology information, and obtains a feasible path set based on an offline calculation method, as shown in Figure 4;

Step S3: Based on the topology information table and the feasible path set, the network terminals obtain each other's current state information through information exchange to construct a routing information table;

Step S4: With the help of the routing information table, the network terminal will determine the priority and guide the selection of the next hop terminal according to the comprehensive adaptive function value, and in this way, all the terminals on the main transmission path can be determined, and the auxiliary path will select the terminal with a large comprehensive adaptive function that is not occupied by the main path;

Step S5: For the source terminal and the target server, a main path and multiple auxiliary paths may be selected to construct a disjoint multi-path, and a full network path set may be obtained, as shown in FIG5 ;

Step S6: During data transmission using the primary path and the auxiliary path, the terminal monitors the link status with other terminals by sending Hello messages in real time, makes status judgment based on whether a Resp message is replied, and obtains real-time updated network topology information;

Step S7: When the terminal status changes, the change will be fed back to the neighbors in a timely manner in the form of messages (including warning messages and reminder information), and the routing information table (including neighbors and status) will be updated.

In step S4, a comprehensive adaptive function is constructed based on the edge center function, the residual energy function and the risk environment function, and the calculation expression is:

In the formula, is the comprehensive adaptive function, ω _EC is the weight coefficient of the edge-center function, ω _RE is the weight coefficient of the residual energy function, ω _R is the weight coefficient of the risk environment function, is the edge-center function, is the residual energy function, is a function of the risk environment.

Among them, the edge center function The calculation expression is:

h _i =min{h _i→j |i∈V _T ,j∈V _E }

In the formula, is the edge-centric function, _hi is the number of hops from terminal i to the nearest edge server j, _VT represents the set of terminals, and _VE represents the set of edge servers.

The calculation expression of the residual energy function is:

In the formula, E _i (t) represents the residual energy of terminal i at time t, E ₀ is the initial energy of the terminal, is the residual energy function.

The calculation expression of the risk environment function is:

r _max = max{r _j |j∈V _T }

In the formula, is the risk environment function of terminal i, r _max is the highest risk level in the network, V _T represents the set of terminals, and _ri is the risk level of terminal i.

In step S7, updating the routing information table further includes calculating data transmission rates of different data transmission modes, calculating data transmission delays, and predicting the link life between terminals.

There are two transmission modes: terminal to terminal (T2T) and terminal to edge server (T2E). The data transmission rate calculation method under different data transmission modes is:

In the formula, represents the data transmission rate from terminal i to terminal j, represents the data transmission rate from terminal i to edge server m, _wi represents the bandwidth resources owned by terminal i, pi _,j represents the transmission power of terminal i, represents the channel gain from terminal i to terminal j, N ₀ represents the noise power, qi _,j (t) represents the dynamic proportional coefficient, represents the channel gain from terminal i to edge server m.

The data transmission delay is calculated as:

In the formula, represents the execution delay of terminal i to complete computing task _Ti , represents the communication delay of terminal i transmitting computing task _Ti to edge server m; represents the computational delay of edge server m to complete computational task _Ti , where the computational delay Depends on the computing power required to complete the computing task _Ti and the computing power of the edge server m itself; represents the computing power of edge server m, hi _{→m is} the number of relay hops from terminal i to edge server m, is the minimum transmission rate between different terminals on the transmission path, is the maximum transmission rate between different terminals on the transmission path, d _i is the size of the computing task, and ci _is the computing resources required to complete the computing task.

Before making a routing decision, the link lifetime is predicted. If the task transmission time is longer than the link lifetime, this transmission path will not be selected because the task transmission will be interrupted due to insufficient link lifetime during the transmission process. For example, the short-lived dotted path in Figure 6 will not be selected as the transmission path.

The link lifetime is calculated as follows:

In the formula, is the Euclidean distance between terminals; r is the communication radius; R _v =0 indicates that the initial position of the vehicle is within the communication range of the road-side unit (RSU), and R _v =1 indicates that the initial position of the vehicle is outside the communication range of the road-side unit; θ indicates the angle between the link vectors of the two terminals and the relative speed vector.

We will give priority to the path with a longer link lifetime, more remaining energy, and fewer relay times.

The data routing decision method includes three stages: routing discovery, routing decision and routing maintenance, and can be implemented based on the communication, processing and storage modules of the device itself without adding or modifying the components of the device.

The present invention also relates to an experimental study of a routing decision method based on a comprehensive adaptive function, and the results are as follows:

As shown in Figure 7, the packet reception rate (PRR) under different weight settings of the comprehensive adaptive function shows that as the weight of the edge-center function increases, the packet reception rate gradually increases.

As shown in Figure 8, when the residual energy is considered in the routing decision, the packet loss is mainly caused by the risk environment. When the residual energy is not considered, the data loss is mainly caused by the energy consumption of the terminal. In addition, as the weights of the residual energy function and the risk environment function increase, the packet loss rate (PLR) will gradually decrease.

The energy balance index (EBI) under different weight settings of the comprehensive adaptive function is shown in Figure 9. It can be observed that increasing the weight of the residual energy function can significantly reduce the EBI.

The cumulative distribution of end-to-end delay (EED) under different weight settings of the comprehensive adaptive function is shown in Figure 10. It can be observed that EED decreases with the increase of the edge center function weight.

The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope disclosed by the present invention, and these modifications or substitutions should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims (10)

1. A data routing decision method based on a comprehensive adaptive function, characterized in that the method is used to process data routing decisions for a vehicle edge computing network, comprising: In the route discovery phase, the network is initialized and network topology information and feasible path sets are obtained based on the broadcast discovery mechanism; In the routing decision stage, a routing information table is constructed based on network topology information and feasible path sets, priorities are determined based on comprehensive adaptive functions, next-hop terminals are selected to obtain primary and secondary paths, and a network-wide path set is formed; And in the routing maintenance phase, the routing table information is updated through a two-phase routing maintenance mechanism. 2. According to the data routing decision method based on comprehensive adaptive function in claim 1, it is characterized in that in the routing decision stage, a routing information table is constructed based on network topology information and a feasible path set, a next hop terminal is selected based on the comprehensive adaptive function to obtain a primary path and an auxiliary path, and a full network path set is formed, which includes: Based on the topology information table and the feasible path set, the terminals obtain each other's current state information through information exchange to build a routing information table; With the help of the routing information table, the terminal will determine the priority according to the comprehensive adaptive function value, guide the selection of the next-hop terminal, and determine all the terminals on the main transmission path in this way. The auxiliary path selects the terminal with a large comprehensive adaptive function that is not occupied by the main path; For the source terminal and the target server, a disjoint multi-path is constructed by selecting a primary path and multiple auxiliary paths to form a full network path set. 3. A data routing decision method based on a comprehensive adaptive function according to claim 2, characterized in that the comprehensive adaptive function is specifically: A comprehensive adaptive function is constructed based on the edge center function, residual energy function and risk environment function. The calculation expression is: Wherein, _FiIA is _{the comprehensive adaptive function, ωEC} ^is the weight coefficient of the edge-center function, _ωRE is the weight coefficient of the residual energy function, _ωR is the weight coefficient of the risk environment function, is the edge-center function ^, _FiRE is the residual energy function, ^and _FiR is the risk environment function; The calculation expression of the edge center function is: h _i =min{h _i→j |i∈V _T ,j∈V _E } Where _FiEC is ^the edge center function, _hi is the number of hops from terminal i to the nearest edge server j, _VT represents the set of terminals, and _VE represents the set of edge servers; The calculation expression of the residual energy function is: Where E _i (t) represents the residual energy of terminal i at time t, E ₀ is the initial energy of the terminal, and F _i ^RE is the residual energy function; The calculation expression of the risk environment function is: r _max = max{r _j |j∈V _T } Where _FiR is the risk environment function of terminal i, _rmax is the highest risk level in the network, _VT represents the set of terminals, ^and _ri is the risk level of terminal i. 4. The data routing decision method based on comprehensive adaptive function according to claim 1, characterized in that the updating of routing table information by a two-stage routing maintenance mechanism in the routing maintenance phase comprises: During data transmission using the primary and secondary paths, the terminal monitors the link status with other terminals by sending Hello messages in real time, makes status judgments based on whether Resp messages are replied, and obtains a real-time updated network topology information table; When the terminal status changes, the change is promptly fed back to the neighbor in the form of a message, and the routing information table is updated. 5. The data routing decision method based on comprehensive adaptive function according to claim 4 is characterized in that the method further comprises re-deciding the routing if the contents of the topology information table and the routing information table are updated. 6. According to claim 4, a data routing decision method based on a comprehensive adaptive function is characterized in that when the terminal state changes, the change is promptly fed back to the neighbor in the form of a message, and the routing information table is updated, including: calculating the data transmission rate of different data transmission modes, calculating the data transmission delay, and predicting the link life between terminals. 7. The data routing decision method based on comprehensive adaptive function according to claim 6, characterized in that the data transmission mode includes two modes: terminal to terminal and terminal to edge server; The calculation of the data transmission rates of different data transmission modes is specifically as follows: In the formula, represents the data transmission rate from terminal i to terminal j, represents the data transmission rate from terminal i to edge server m, _wi represents the bandwidth resources owned by terminal i, pi _,j represents the transmission power of terminal i, represents the channel gain from terminal i to terminal j, N ₀ represents the noise power, qi _,j (t) represents the dynamic proportional coefficient, represents the channel gain from terminal i to edge server m. 8. A data routing decision method based on a comprehensive adaptive function according to claim 6, characterized in that the data transmission delay includes the execution delay of the terminal completing the computing task, the communication delay of the terminal transmitting the computing task to the edge server, and the calculation delay of the edge server completing the computing task; The data transmission delay is calculated as: In the formula, represents the execution delay of terminal i to complete computing task _Ti , represents the communication delay of terminal i transmitting computing task _Ti to edge server m; represents the computational delay of edge server m to complete computational task _Ti , where the computational delay Depends on the computing power required to complete the computing task _Ti and the computing power of the edge server m itself; represents the computing power of edge server m, hi _{→m is} the number of relay hops from terminal i to edge server m, is the minimum transmission rate between different terminals on the transmission path, is the maximum transmission rate between different terminals on the transmission path, d _i is the size of the computing task, and ci _is the computing resources required to complete the computing task. 9. The data routing decision method based on comprehensive adaptive function according to claim 6, characterized in that the predicted link life between terminals is specifically: The link lifetime is calculated as follows: In the formula, is the Euclidean distance between terminals; r is the communication radius; R _v = 0 indicates that the initial position of the vehicle is within the communication range of the roadside unit, and R _v = 1 indicates that the initial position of the vehicle is outside the communication range of the roadside unit; θ indicates the angle between the link vectors of the two terminals and the relative speed vector. 10. A data routing decision method based on a comprehensive adaptive function according to claim 1, characterized in that the network initialization includes deploying network devices in a specified area, setting IDs, dividing terminal types, presetting the forward direction and speed of dynamic vehicle terminals, and dividing risk areas of different levels, wherein the network devices include terminals and edge servers; The method of obtaining network topology information and feasible path set based on the broadcast discovery mechanism is as follows: the terminal requests to obtain a neighbor list by broadcasting a Hello message, and then obtains the coordinate position, energy and environmental conditions of the neighbor based on the Resp message replied to the request. The edge server synchronizes the neighbor lists of all terminals to obtain network topology information, and obtains a feasible path set based on an offline calculation method.