Relay node selection method of opportunity network, electronic equipment and medium

Abstract

The invention relates to a relay node selection method of an opportunity network, an electronic device and a medium, if a node i of the opportunity network has a plurality of neighbor nodes, each node pair is processed by the following steps: a coverage redundancy screening step: predicting a node movement track of the opportunity network, and selecting a relay node of the opportunity network based on a historical movement track and a predicted future movement track; analyzing the space-time coverage relation of the node pairs, identifying whether redundant nodes with approximately equivalent coverage functions exist or not, and if yes, entering a next-hop resource optimization step; a next hop resource optimization step: calculating the routing capability of each node, preferentially reserving the nodes with higher routing capability, and if the routing capabilities are the same, entering an energy continuation screening step; energy scores are calculated through energy continuation screening, and nodes with the high energy scores are preferentially reserved. Compared with the prior art, the method has the advantages of improving the data transmission efficiency of the opportunity network, guaranteeing the stability and sustainability of data transmission and the like.

Description

Technical Field

[0001] This invention relates to the field of opportunistic network technology, and in particular to a relay node selection method, electronic device, and medium for opportunistic networks. Background Technology

[0002] With the rapid development of mobile communication and IoT technologies, communication network coverage in daily life is already quite comprehensive. However, in environments lacking infrastructure or in extreme conditions (such as earthquake / flood disaster sites, remote mountainous areas, and sparsely populated IoT deployment areas), traditional cellular networks relying on fixed base stations or stable network infrastructure face fundamental failure. Opportunistic networks, as a special type of mobile ad hoc network (MANET), are attracting widespread attention from academia and industry due to their unique communication capabilities in environments with lacking or restricted infrastructure. The core feature of opportunistic networks lies in their use of intermittent encounter opportunities brought about by node movement to achieve asynchronous data transmission using a "store-carry-forward" model. This communication method enables opportunistic networks to demonstrate unique value in areas such as disaster emergency response (infrastructure damage after earthquakes and floods), remote areas (rural areas, wilderness, oceans), sparse urban environments (such as V2V vehicle-to-everything scenarios), wildlife monitoring, military tactical communications, and low-cost IoT deployment.

[0003] However, the effective operation of opportunistic networks faces a fundamental challenge: low data transmission efficiency. On one hand, due to the high mobility, random distribution, and sparse density of nodes, the network is prone to "coverage holes"—meaning that certain areas lack effective relay nodes for extended periods, preventing the effective transmission of information generated or carried by nodes within or passing through those areas. Furthermore, in opportunistic networks, the geographical location of destination nodes is often unknown, making targeted transmission methods unreliable and further exacerbating the difficulty of data transmission. On the other hand, opportunistic nodes (such as drones, handheld devices, and sensors) typically rely on battery power with limited energy reserves; when some nodes undertake heavy data caching and forwarding tasks, their battery power depletes rapidly. Adopting multi-replica strategies (such as Epidemic routing) to improve data transmission efficiency significantly increases overall network energy consumption; nodes remain in a wake-up state listening to channels for extended periods, but actual communication opportunities are scarce, resulting in wasted battery resources.

[0004] Traditional methods for optimizing coverage in static or high-density mobile networks (such as cellular base station deployment and MANET routing protocols) are difficult to apply directly to opportunistic networks. The fundamental reason is that the data transmission quality in opportunistic networks is not only limited by the physical spatial distribution of nodes but also affected by dynamic factors such as node mobility patterns, encounter frequency, buffer management strategies, and data forwarding mechanisms. Nodes are not continuously connected; connection opportunities are fleeting and unpredictable. To address these issues, a more rational relay node selection scheme is urgently needed to achieve more efficient data transmission rates and lower overall network power consumption.

[0005] A search revealed Chinese invention patent application publication number CN107333312A, which discloses a routing method in an opportunistic network. The steps are as follows: before sending, determine all neighboring nodes N of the message carried by the current message-carrying node C; determine the current moving speeds of the message-carrying node C, all neighboring nodes N, and the destination node D; and obtain the angles θ between the current moving speeds of the message-carrying node C and neighboring nodes N and the destination node D. C and θ N Select relay node R, obtain the position of destination node D at the next time step from the transition probability matrix P in the node position prediction model, determine the distance from message-carrying node C and relay node R to destination node D at the next time step, and obtain the forwarding priority P of different messages in the node buffer. m For the message carried by node C to relay node R i The decision to forward messages is made. This existing patent application suffers from redundant nodes and low transmission efficiency.

[0006] Improving the data transmission efficiency of opportunistic networks has become a technical problem that needs to be solved. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a relay node selection method for improving the data transmission efficiency of opportunistic networks.

[0008] The objective of this invention can be achieved through the following technical solutions: According to one aspect of the present invention, a relay node selection method for improving the data transmission efficiency of an opportunistic network is provided. If a node i in the opportunistic network has multiple neighboring nodes, the following processing is performed on each node pair consisting of node i and its neighboring nodes: Coverage redundancy screening step: Predict the movement trajectory of node i and its neighboring nodes in the opportunistic network. Based on the historical movement trajectory and the predicted future movement trajectory, analyze the spatiotemporal coverage relationship of the node pair to identify whether there are redundant nodes with approximately equivalent coverage functions. If so, proceed to the next hop resource optimization step. Next-hop resource optimization step: Calculate the routing capability of each node, prioritize the retention of nodes with higher routing capabilities, and if the routing capabilities of two nodes in a node pair are the same, proceed to the energy survival screening step. Energy survival screening steps: A two-dimensional energy scoring model is used to calculate the energy score of each node, and nodes with high energy scores are retained first. After traversing all node pairs, the retained node is used as the final relay node.

[0009] Preferably, the process of identifying whether there are redundant nodes with approximately equivalent coverage functions includes: calculating the coverage redundancy based on the coverage overlap and trajectory similarity of the node pair; if the coverage redundancy is less than a set redundancy threshold, both nodes of the node pair are retained; otherwise, proceed to the next hop resource optimization step.

[0010] More preferably, the calculation process of the trajectory similarity includes: The complete trajectory sequence is obtained by splicing the historical trajectory sequence and the future trajectory sequence; where the historical trajectory sequence is the actual trajectory within the sliding window before the current moment, and the future trajectory sequence is the predicted trajectory at a certain time step after the current moment; Based on the complete trajectory sequence, dynamic programming is used to calculate the discrete Fréchet distance of node pairs, obtain the Fréchet distance of node pairs, and normalize it to obtain the trajectory similarity.

[0011] More preferably, the coverage overlap is calculated based on the intersection-union ratio between node trajectory bounding boxes and the weight of time, specifically as follows: in, Let represent the overlap between node i and node j. Let be the intersection-union ratio between the bounding boxes of the trajectories of node i and node j at time τ; The weight is determined by time; the closer time τ is to the current time t, the higher the weight. The larger it is, the smaller it is.

[0012] Preferably, in the next-hop resource optimization, the neighbor discovery technology is used to determine the number of neighbor nodes of a node at the current time, and the neighbor node list is updated in real time; Routing capacity is defined as the normalized number of neighboring nodes, specifically: in, This represents the routing capability of node i at time t; This represents the number of neighboring nodes of node i at time t; This represents the maximum number of neighbors in the entire opportunity network.

[0013] Preferably, the process of calculating the energy score using a two-dimensional energy scoring model includes: in, The absolute remaining energy at the current time t; For capacity adaptive coefficients, C represents capacity; This refers to the relative energy ratio. , S(t) represents the estimated maximum energy of all nodes in the network; S(t) represents the energy score at the current time t.

[0014] Preferably, a Kalman filter is used to predict the trajectory sequence at a certain future time step.

[0015] Preferably, the method is triggered periodically or by events, wherein the events include events such as node energy falling below a threshold or voids appearing in the coverage.

[0016] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0017] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1) This invention improves the data transmission efficiency of opportunistic networks and ensures the stability and sustainability of data transmission by using coverage redundancy screening, next-hop resource optimization, and energy sustainability screening, retaining only key nodes that are independent and necessary in terms of coverage, nodes with strong routing capabilities and sufficient energy.

[0019] 2) Based on the spatiotemporal characteristics of the historical and future movement trajectories of nodes, this invention uses the coverage overlap and trajectory similarity of node pairs to calculate the coverage redundancy, and filters out redundant nodes with approximately equivalent coverage functions, thereby fundamentally reducing the resource waste caused by redundant coverage.

[0020] 3) Based on the redundancy removal results, this invention further evaluates the next-hop available resources of candidate nodes, including the number of reachable neighbors, routing potential, and topology scalability; by prioritizing nodes with higher forwarding diversity and stronger routing resilience, the stability and robustness of data transmission paths are improved.

[0021] 4) After completing the coverage redundancy screening and next-hop resource optimization, this invention adopts a two-dimensional energy score (relative remaining energy and absolute energy capacity) and combines it with a logarithmic adjustment factor for correction, so as to achieve a refined quantification of the node energy level; prioritize the retention of nodes with sufficient energy to ensure the long-term sustainability of network operation. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the relay node selection method in this invention. Figure 2 This is a detailed flowchart illustrating the trajectory prediction and consistency judgment of motion trajectory in this invention. Figure 3 This is a detailed flowchart illustrating the process of obtaining the number of valid neighbor nodes using neighbor discovery technology in this invention. Figure 4 This is a schematic diagram illustrating the effect of opportunistic network coverage expansion after optimizing node selection in this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] Example 1 This embodiment relates to a relay node selection method for improving the data transmission efficiency of opportunistic networks. The method aims to address the problems of low data transmission efficiency, uneven energy distribution, and energy waste caused by the lack of a reasonable data relay node selection mechanism in opportunistic networks, where communication nodes themselves have characteristics such as high mobility, random distribution, and sparse density.

[0025] The core of this method lies in constructing a progressive optimization framework, focusing sequentially on three key dimensions: coverage redundancy, next-hop available resources, and energy sustainability, to achieve ordered optimization of node selection and network resource scheduling. Specifically, if node i in the opportunistic network has multiple neighboring nodes, then for each node pair consisting of node i and its neighbors, the following processing is performed: Figure 1 The method includes the following steps: Redundancy screening step: A motion trajectory prediction and consistency judgment mechanism is introduced to analyze the spatiotemporal coverage relationship of node pairs, identifying and filtering out redundant nodes with approximately equivalent coverage functions, thereby reducing redundant consumption while ensuring coverage continuity. Specifically: the motion trajectories of node i and its neighboring nodes in the opportunistic network are predicted. Based on historical motion trajectories and predicted future motion trajectories, the spatiotemporal coverage relationship of node pairs is analyzed to identify whether there are redundant nodes with approximately equivalent coverage functions. If so, the process proceeds to the next-hop resource optimization step; otherwise, the nodes that have not been filtered out are retained.

[0026] Next-hop resource optimization step: Further examine the richness of "next-hop resources" for each node in the node pair, and prioritize nodes with greater routing potential and topology expansion capabilities to enhance the flexibility and robustness of network forwarding. Specifically: calculate the routing capability of each node, prioritize retaining nodes with higher routing capabilities, if two nodes in a node pair have the same routing capability, proceed to the energy survival screening step; otherwise, retain the nodes that have not been screened.

[0027] Energy survival screening steps: Based on the results of the first two screening layers, energy supply level is introduced as a decision factor to prioritize the retention of nodes with sufficient energy, thereby improving the overall lifespan and continuous operation capability of the network. Specifically, a two-dimensional energy scoring model is used to calculate the energy score of each node, and nodes with higher energy scores are prioritized for retention; if the energy scores of all nodes are the same, all nodes are retained.

[0028] Continue traversing other node pairs, and finally select the node to keep as the final relay node.

[0029] In this optimization framework, the three dimensions are not evaluated independently, but rather determined through a phased intelligent decision-making mechanism. First, a greedy algorithm is used for selection, followed by optimization based on a weighted scoring function. This ensures that while improving coverage continuity, the number of nodes and energy consumption are minimized, thereby improving data transmission efficiency. The specific logic of the three dimensions is as follows: Motion trajectory prediction and consistency judgment: In dynamic network environments, the spatial location and motion path of a node directly determine its coverage capability and the evolution trend of its service area. Therefore, motion trajectory prediction is introduced: First, a Kalman filter is used to smooth and predict the historical location sequence of nodes, thereby obtaining their future motion state in advance over time. Then, spatial similarity is calculated based on the Fréchet distance between predicted trajectories, identifying node clusters with highly similar motion trajectories within a certain time window. For these redundant nodes that are approximately equivalent in coverage function, only one needs to be retained to maintain overall coverage performance, while the remaining nodes can actively exit or enter a low-power mode to reduce unnecessary resource consumption.

[0030] Next-hop resources: In multi-hop routing scenarios, a node's routing potential depends not only on its instantaneous connectivity but also on its available next-hop forwarding space. Neighbor discovery technology allows for real-time evaluation of a node's local topology, identifying core nodes with a greater number of next-hop candidate nodes and more diverse forwarding paths within the same coverage area. These nodes with strong routing capabilities are prioritized. Conversely, nodes with limited next-hop resources and insufficient topology scalability are eliminated to prevent them from becoming bottlenecks in global routing. This mechanism effectively improves the network's robustness and flexibility in complex scenarios, making forwarding path construction more redundant and fault-tolerant, ensuring strong recoverability of the overall communication link in the face of node failure or link interruption.

[0031] Energy Sustainability: In wireless ad hoc networks, the energy supply level of nodes is a core factor determining long-term stable operation. Therefore, this invention quantifies the remaining energy of nodes as a key indicator and, when coverage areas overlap, prioritizes nodes with more abundant energy reserves to participate in service, effectively mitigating coverage gaps caused by the rapid depletion of energy by individual nodes.

[0032] Unlike traditional single-remaining energy comparison mechanisms, this invention introduces a two-dimensional energy scoring model: it considers both the percentage of remaining energy of a node and its absolute energy capacity for weighted evaluation. To avoid bias in the evaluation process between nodes of different capacities, the model further incorporates a logarithmic adjustment coefficient, enabling an adaptive balance between the energy consumption sensitivity of small-capacity nodes and the durability advantage of large-capacity nodes. Through this energy-aware node selection mechanism, the network can effectively avoid over-reliance on low-energy nodes while maintaining coverage performance, thereby preventing premature failure in local areas.

[0033] In summary, this invention logically grasps the progressive relationship of "coverage redundancy screening—routing resource optimization—energy guarantee," possessing inherent rationality. At the technical implementation level, it forms a systematic solution through phased screening and optimization strategies. This method can effectively maintain network coverage continuity while significantly reducing redundancy overhead, improving routing robustness, and extending network lifetime, thereby effectively improving overall data transmission efficiency. It provides an efficient and sustainable intelligent relay node selection mechanism for dynamic opportunistic networks.

[0034] Example 2 This embodiment also relates to a relay node selection method for improving the data transmission efficiency of opportunistic networks, considering three key factors in sequence: coverage redundancy, next-hop resources, and energy sustainability. Through layer-by-layer screening and optimization, precise selection of relay nodes and network regulation are achieved. Its core evaluation parameters have clear operability and measurability, thus providing strong support for the practical implementation and feasibility of the solution. Specifically, the core indicators of this method are implemented as follows: 1) Covering redundant screening steps: such as Figure 2 The system utilizes trajectory consistency for coverage redundancy filtering. Trajectory consistency includes motion trajectory prediction and motion trajectory consistency judgment, including: 11) Obtaining the trajectory: Nodes periodically report two-dimensional coordinates (longitude, latitude) and timestamps to form the original trajectory sequence: in, for The two-dimensional coordinates of time node i, where m is the reporting period. This is the historical trajectory sequence of node i within the previous reporting period m from the current time t.

[0035] To prevent the trajectory data from increasing indefinitely, a distributed sliding window collector is deployed to continuously capture trajectory segments in 5-minute windows.

[0036] 12) Trajectory Prediction: Predict the trajectory sequence for the next H time steps using a Kalman filter. : in, express To predict the future trajectory sequence H time steps starting from the current time t.

[0037] 13) Using Fréchet distance to determine trajectory consistency: Fréchet distance is often used to compare the overall shape similarity of two curves (trajectories). It considers the order of points and the shape of the path, making it more suitable for trajectory comparison than simple Euclidean distance or Hausdorff distance. The complete trajectory sequence is obtained by splicing historical trajectory sequences and future trajectory sequences. Dynamic programming is used to calculate the discrete Fréchet distance, and the final Fréchet distance can be obtained. Normalization yields trajectory similarity. .

[0038] in The scale parameter is the current time t. It takes the median or 75th percentile of the Fréchet distance between all the trajectory pairs to be compared, which has robust adaptability.

[0039] 14) Introduce time-related weights Calculate coverage overlap .

[0040] in, The weight is the intersection-union ratio (IUU) between the bounding boxes of the trajectories of node i and node j at time τ; the closer time τ is to the current time t, the higher the weight. The larger it is, the smaller it is.

[0041] 15) The two indicators are weighted and merged to determine the coverage redundancy. This is an adjustable parameter.

[0042] Redundant nodes with approximately equivalent coverage function are filtered out based on coverage redundancy. Specifically, if the coverage redundancy is less than the set redundancy threshold, it is determined that there is no coverage redundancy, and node i and node j are retained; otherwise, the filtering continues based on subsequent routing capabilities and energy.

[0043] 2) Next-hop resource optimization steps: A node's next-hop resource is determined by its routing capability, which in turn depends primarily on the node's degree. ,in It is a node i The set of neighboring nodes of node i. However, in opportunistic networks, the neighbors of a node often change dynamically as it moves. This invention uses neighbor discovery technology to determine the number of neighboring nodes of node i at the current time t. It also updates the list of neighboring nodes in real time.

[0044] Reference Figure 3 The steps by which the neighbor discovered the technology are as follows: 21) Node broadcast message: Nodes broadcast lightweight BEACON messages at fixed intervals (default 2 seconds). The messages carry the node identifier, location information and communication capability parameters, and are used for initial probing of neighboring nodes and link quality assessment.

[0045] 22) Neighbor nodes receive and respond to messages: After receiving a BEACON message, a neighbor node determines its validity based on signal strength (RSSI) and link stability. If the communication quality threshold is met (e.g., RSSI ≥ -80dBm and packet loss rate <10%), it replies with an ACK response message to confirm the bidirectional link reachability.

[0046] 23) Update the number of neighboring nodes: After receiving the response message from the neighboring node, the node records the neighbor information, updates the local neighbor table and topology table, and calculates the number of effective neighbors (i.e. the routing capacity base of the node) in real time, which serves as a key parameter for subsequent routing decisions.

[0047] 24) Define the routing capability of a node at time t. in, This represents the number of neighboring nodes (i.e., degree) of node i at time t. This represents the maximum number of neighbors in the entire opportunity network, used for normalization to ensure that the score ranges between [0,1]. This represents the routing capability of node i at time t; The next-hop resource is determined based on routing capabilities, retaining nodes with higher routing capabilities and filtering out nodes with limited next-hop resources or insufficient topology scalability. If all nodes have the same routing capability, the energy survival screening step is initiated.

[0048] 3) Energy survival screening This invention employs a two-dimensional energy scoring model: this model introduces a logarithmic adjustment coefficient. This achieves adaptive evaluation of nodes with different energy capacities: for large energy capacity nodes, the contribution of the relative energy ratio is appropriately amplified; for small energy capacity nodes, the score is mainly determined by the absolute remaining energy value. The energy score S(t) is defined as follows: in, The absolute remaining energy at the current time represents the physical energy reserve at the current time (in J). For capacity adaptive coefficients, C represents capacity; This refers to the relative energy ratio. , This is the estimated maximum energy of all nodes in the network.

[0049] Nodes with higher energy scores are prioritized for retention; a higher energy score S(t) indicates more abundant energy. If all nodes in a pair have the same energy score, both are retained.

[0050] Reference Figure 4 The specific operation process of this method in practice is as follows: Data acquisition and information exchange include: 11. Each node needs to periodically report its own location information (two-dimensional coordinates: latitude and longitude, timestamp) for trajectory prediction.

[0051] 12. Nodes simultaneously broadcast their own state information, including absolute remaining energy, relative remaining energy, and the number of available nodes for the next hop (through neighbor discovery technology), to optimize node selection.

[0052] 13. Information can be exchanged between neighboring nodes using lightweight control messages to maintain low overhead.

[0053] Indicator calculation includes: 21. Coverage Redundancy: Combining historical and predicted trajectories, the redundancy between nodes in terms of coverage and trajectory evolution is evaluated using Fréchet distance and IoU overlap. 22. Next-hop available resources: After eliminating coverage redundancy, the number of candidate nodes' available neighbors and their resource levels in the current topology are counted to measure the flexibility and scalability of the routing topology. 23. Energy Sustainability: Based on the results of the first two screening layers, a two-dimensional energy scoring model (weighted by the ratio of remaining energy and absolute capacity, supplemented by a logarithmic adjustment coefficient to correct for capacity differences) is used to evaluate the energy sustainability of nodes.

[0054] The optimization process follows a hierarchical relationship of "coverage redundancy priority → next-hop resource priority → energy priority," gradually shrinking the candidate node set, including: 31. Coverage redundancy is the primary constraint to ensure that redundant nodes are effectively screened out; 32. In the redundancy elimination results, nodes with higher topological potential are selected based on the next-hop resource index. 33. Finally, in the above candidate set, with energy sustainability as the key decision factor, nodes with sufficient energy are prioritized for retention to ensure the network's operational lifespan.

[0055] Node selection includes: 41. Each node compares its own performance with that of its neighboring nodes based on the hierarchical optimization results; 42. The system automatically selects nodes that meet the progressive constraints and are ultimately selected as reserved nodes to participate in forwarding, while the remaining nodes can enter sleep or low-power mode to avoid energy waste. 43. The decision-making process can be triggered periodically (e.g., updated every 30 seconds) or based on events (e.g., when node energy is below a threshold or when a hole appears in the coverage).

[0056] The effect of optimized node selection on the coverage expansion of the opportunistic network is as follows: Figure 4As shown, in opportunistic networks, a source node needs to send a message to a destination node. However, since the destination node's location is unknown, message propagation mainly relies on expanding the communication coverage area to improve delivery success rate. However, while traditional flooding protocols (such as Epidemic routing) can maximize the diffusion range, they cause serious energy consumption problems. To resolve this performance-cost trade-off, this invention proposes a resource-efficient selective forwarding strategy that intelligently filters out nodes that contribute redundancy to the coverage area (e.g., ...) while ensuring maximum communication coverage. Figure 4 Node 1) has achieved the goal of obtaining maximum coverage with as few nodes as possible.

[0057] The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0058] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0059] The processing unit performs the various methods and processes described above. For example, in some embodiments, the methods may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute the methods by any other suitable means (e.g., by means of firmware).

[0060] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0061] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0062] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0063] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for selecting relay nodes in an opportunistic network, characterized in that, If node i in an opportunistic network has multiple neighboring nodes, then for each node pair consisting of node i and its neighboring nodes, the following processing is performed, including: Coverage redundancy screening step: Predict the movement trajectory of node i and its neighboring nodes in the opportunistic network. Based on the historical movement trajectory and the predicted future movement trajectory, analyze the spatiotemporal coverage relationship of the node pair to identify whether there are redundant nodes with approximately equivalent coverage functions. If so, proceed to the next hop resource optimization step. Next-hop resource optimization step: Calculate the routing capability of each node, prioritize the retention of nodes with higher routing capabilities, and if the routing capabilities of two nodes in a node pair are the same, proceed to the energy survival screening step. Energy survival screening steps: A two-dimensional energy scoring model is used to calculate the energy score of each node, and nodes with high energy scores are retained first. After traversing all node pairs, the retained node is used as the final relay node.

2. The relay node selection method for an opportunistic network according to claim 1, characterized in that, The process of identifying whether there are redundant nodes with approximately equivalent coverage functions includes: calculating the coverage redundancy based on the coverage overlap and trajectory similarity of the node pair; if the coverage redundancy is less than a set redundancy threshold, both nodes of the node pair are retained; otherwise, proceed to the next hop resource optimization step.

3. The relay node selection method for an opportunistic network according to claim 2, characterized in that, The calculation process for the trajectory similarity includes: The complete trajectory sequence is obtained by splicing the historical trajectory sequence and the future trajectory sequence; where the historical trajectory sequence is the actual trajectory within the sliding window before the current moment, and the future trajectory sequence is the predicted trajectory at a certain time step after the current moment; Based on the complete trajectory sequence, dynamic programming is used to calculate the discrete Fréchet distance of node pairs, obtain the Fréchet distance of node pairs, and normalize it to obtain the trajectory similarity.

4. The relay node selection method for an opportunistic network according to claim 2, characterized in that, The coverage overlap is calculated based on the intersection-union ratio (CIU) between node trajectory bounding boxes and the time weight, specifically as follows: in, Let represent the overlap between node i and node j. Let be the intersection-union ratio between the bounding boxes of the trajectories of node i and node j at time τ; The weight is determined by time; the closer time τ is to the current time t, the higher the weight. The larger it is, the smaller it is.

5. The relay node selection method for an opportunistic network according to claim 1, characterized in that, In the next-hop resource optimization, the neighbor discovery technology is used to determine the number of neighbor nodes of a node at the current time, and the neighbor node list is updated in real time. Routing capacity is defined as the normalized number of neighboring nodes, specifically: in, This represents the routing capability of node i at time t; This represents the number of neighboring nodes of node i at time t; This represents the maximum number of neighbors in the entire opportunity network.

6. The relay node selection method for an opportunistic network according to claim 1, characterized in that, The process of calculating the energy score using the two-dimensional energy scoring model includes: in, The absolute remaining energy at the current time t; For capacity adaptive coefficients, C represents capacity; This refers to the relative energy ratio. , S(t) represents the estimated maximum energy of all nodes in the network; S(t) represents the energy score at the current time t.

7. The relay node selection method for an opportunistic network according to claim 1, characterized in that, Predict trajectory sequences at a certain future time step using a Kalman filter.

8. The relay node selection method for an opportunistic network according to claim 1, characterized in that, The method is triggered periodically or by events, wherein the events include events such as node energy falling below a threshold or voids appearing in the coverage.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 8.

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