A service-driven composite routing agent for underwater wireless sensor networks

Abstract

The present invention relates to a service-driven composite routing agent for an underwater wireless sensor network, which comprises a routing function module and a plug-in platform. The routing function module includes a reinforcement learning routing component and multiple function components. When sending data, the routing component obtains the optimal transmission path within a limited range according to the optimization objective. The multiple function components are used to support the data transmission requirements of different services. The plug-in platform generates a service QoS field according to the service requirements, and the service QoS field has a mapping relationship with the multiple function components. The routing function module selects and configures at least one function component from the multiple function components according to the mapping relationship and based on the value of the service QoS field, so as to perform corresponding processing on data and / or the next-hop node set during data acquisition, data reception or data transmission. Configuring function components based on service requirements can dynamically output the forwarding ability according to services in the underwater wireless sensor network.

Description

Technical Field

[0001] The present invention relates to the technical field of network routing, and in particular to a service-driven composite routing agent for an underwater wireless sensor network. Background Art

[0002] Underwater wireless sensor networks are widely used in many fields such as underwater disaster warning, pollutant monitoring, hydrological data monitoring, marine resource exploration, and assisted navigation, and serve as important infrastructure for studying, building, and developing the ocean. An underwater wireless sensor network consists of sensor nodes, communication nodes, and sink nodes, etc. Data is collected by underwater sensor nodes and forwarded hop by hop through communication nodes at different depths to the surface sink node, and then forwarded by the sink node to the shore-based data center. The underwater transmission channel has problems such as large transmission delay, limited transmission bandwidth, many interference factors, and severe multipath phenomena. Affected by water flow, the positions and node relationships of underwater communication nodes change dynamically, with high communication energy consumption and difficult energy supply. The underwater transmission environment is open, and communication nodes are vulnerable to attacks by malicious nodes. However, there are many underwater monitoring applications with large differences in data transmission requirements. This makes it extremely difficult for underwater wireless sensor networks to achieve efficient, reliable, and secure data communication.

[0003] The routing algorithm is the key to achieving efficient, reliable, and secure communication for various services. The routing algorithm for an underwater wireless sensor network is used to construct a transmission route from underwater data collection nodes to surface data sink nodes, and its core is to select "good" relay nodes. The routing algorithm not only needs to pay attention to whether the channel bandwidth between nodes meets the service requirements, whether the transmission direction points to the target node, whether the energy of the relay node is sufficient, and whether there are more neighbor nodes to facilitate data forwarding, but also needs to consider the topological changes caused by node movement, the changes in the hydrological environment, and the impact of external noise on underwater wireless communication. At the same time, it is also necessary to meet the quality of service requirements of different types of service data communication as much as possible, ensure the security, robustness, and reliability of the routing, reduce the data transmission delay, and improve the data sending success rate.

[0004] To meet the communication requirements of different services, routing algorithms suitable for different application scenarios have been proposed.

[0005] To meet the requirements of high-throughput business data transmission, a variety of routing algorithms that can handle congestion, adapt to different priorities, and have edge prediction and compression have been proposed. RCAR proposed by Jin et al. can achieve a method for congestion avoidance in relay nodes under high traffic conditions. By designing a reinforcement learning routing algorithm, it optimizes the transmission delay and energy consumption distribution in underwater data communication. PB-ACR proposed by Chen et al. can distinguish different application priorities. High-priority business applications choose the shortest route, while low-priority business applications choose routes with more remaining energy. ACOR proposed by Singhal et al. supports adaptively adjusting the data sampling frequency based on data correlation to reduce transmission energy consumption. TBDP proposed by Wang et al. is a two-level bidirectional prediction model applicable to 3D underwater wireless sensor networks with AUVs, relying on AUVs to carry data for communication. For sequence data transmission requirements, EP-ADTA proposed by Wang et al. adaptively adjusts the transmission accuracy of time-series monitoring data according to the transmission environment while selecting the transmission route. When the transmission environment is good and the transmission route can provide sufficient transmission bandwidth, high-precision monitoring data is transmitted. When the transmission environment is poor and the transmission route cannot provide sufficient bandwidth, the transmission accuracy of the monitoring data is appropriately reduced to ensure that feature data is preferentially transmitted to the surface aggregation node. For picture data transmission requirements, VRC-ADTA proposed by Wang et al. can decompose the transmitted picture data into average value data and multi-level detail coefficient data, and achieve the transmission of picture data in a progressive transmission manner.

[0006] To meet the requirements of high-reliability business data transmission, routing algorithms based on deterministic rules, reinforcement learning, multi-agent reinforcement learning, and cooperative communication have been proposed. The VBF proposed by Xie et al. establishes a virtual pipeline on the vector between the source node and the destination node, restricts the candidate forwarding node set by controlling the virtual pipeline radius, and selects the best relay node according to the distance of the node from the forwarding vector. The QELAR proposed by Hu et al. is a routing algorithm based on Q-learning. When performing routing planning, it not only selects the shortest transmission path but also comprehensively considers the remaining energy of the receiving node to avoid the problem of premature exhaustion of node energy on the optimal path. The DMARL algorithm proposed by Li et al. is a distributed multi-agent reinforcement learning routing algorithm that comprehensively considers factors such as remaining energy, link stability, and data transmission quality to route for underwater wireless optical networks. The SA-FRL algorithm proposed by Zhang et al. realizes cooperative communication for underwater networks based on the Q-learning algorithm. SA-FRL defines the feedback channel transmission state and system mutual information as the state of the reinforcement learning algorithm, defines the system mutual information and access delay as the reward, and selects cooperative nodes with good link quality and low access delay to improve the cooperative communication efficiency. The SQMCR proposed by Wang et al. selects the next-hop node and cooperative communication node based on the methods of reinforcement learning and game theory, improving the reliability of data forwarding in multi-hop underwater wireless sensor networks.

[0007] To meet the requirements of high-security business data transmission, routing algorithms based on encryption, authentication, beam control, and trust evaluation have been proposed. The SEEORVA proposed by Varun et al. uses lightweight encryption technology to encrypt the transmitted data. The SecFUN proposed by Giuseppe et al. uses AES in the Galois counter mode and a short digital signature algorithm as the encryption block to ensure the confidentiality, integrity, authentication, and non-repudiation of the forwarded data. The SARP proposed by Manjula uses the direction of arrival of the signal to authenticate the security of neighbor nodes. The ST-CJ proposed by Su et al. defines the security coverage of the network and uses the long propagation delay of underwater acoustic signals to cause collisions of cooperative interference signals at eavesdroppers without affecting the reception of legitimate users. Yang et al. proposed a trusted routing based on blockchain and reinforcement learning, managing the credibility of nodes through blockchain, making the routing information traceable. The intrusion detection model DOIDS for opportunistic routing proposed by Zhang et al. selects the energy consumption, forwarding, and link quality information of candidate nodes as detection eigenvalues, and detects potential abnormal nodes through the DBSCAN clustering algorithm, reducing the false detection rate. The GTR proposed by Wang et al. realizes the trust evaluation of neighbor nodes based on GAN and performs routing selection based on reinforcement learning, improving the security of data forwarding in multi-hop underwater wireless sensor networks.

[0008] However, underwater wireless sensor networks often need to support multiple services to achieve the expected construction goals. Routing algorithms that simply meet the data transmission requirements of specific services cannot yet meet the application requirements of underwater wireless sensor networks. Therefore, it is necessary to design a composite routing algorithm that can meet the data transmission requirements of different services.

[0009] In existing routing algorithms for underwater wireless sensor networks, typically, multiple typical routing algorithm components are constructed to meet the data transmission requirements of specific services, which lays a foundation for constructing a composite routing algorithm.

[0010] The EP-ADTA routing algorithm, implemented based on edge prediction and reinforcement learning algorithms, mainly provides transmission services for high-throughput sequence data in underwater wireless sensor networks. It can automatically adjust the data transmission accuracy according to changes in the transmission channel conditions, support progressive transmission, and ensure the efficient transmission of monitored sequence data that meets the service requirements to the destination. The EP-ADTA routing algorithm includes an edge prediction function module, a virtual pipeline function module, and a reinforcement learning routing function module. The edge prediction function module is used to implement the prediction and compression of sequence data. Based on the Arma algorithm, it realizes the prediction of newly arrived data according to the previous data, prediction parameters, and correction data. Under certain transmission accuracy requirements, transmitting prediction parameters and correction data is more efficient than transmitting the original newly arrived data. The virtual pipeline function module is used to restrict the selection of the next-hop node, ensure the priority selection of shorter routes, and reduce the transmission delay. The virtual pipeline function module can also dynamically adjust the pipeline radius according to changes in the remaining energy to ensure the balance of node energy consumption. The reinforcement learning routing function module, based on the Q-learning algorithm, realizes the selection of the next-hop node and data accuracy. The optimization objectives include reducing the number of hops, balancing energy consumption, reducing delay, and improving transmission accuracy, etc.

[0011] The VRC-ADTA routing algorithm, implemented based on edge compression and reinforcement learning algorithms, mainly provides transmission services for high-throughput image data in underwater wireless sensor networks. It can customize the data transmission accuracy according to the transmission channel conditions, support progressive transmission, and ensure the efficient transmission of monitored image data that meets the service requirements to the destination. The VRC-ADTA routing algorithm includes an edge compression function module and a reinforcement learning routing function module. The edge compression function module is used to implement the compression of image data. Based on the Haar algorithm, the image data is decomposed into average value data and multi-level detail coefficient data. According to the transmission channel conditions and service requirements, only the average value data and some levels of detail coefficient data can be transmitted, or progressive transmission can be adopted. The reinforcement learning routing function module, based on the Q-learning algorithm, realizes the selection of the next-hop node. The optimization objectives include reducing the number of hops, balancing energy consumption, and reducing delay, etc.

[0012] The SQMCR routing algorithm is implemented based on cooperative communication and reinforcement learning algorithms. It mainly provides highly reliable data transmission services for underwater wireless sensor networks. It can select data transmission paths and cooperative communication nodes according to the changes in the transmission channel and the status of neighbor nodes, and can ensure the reliable transmission of data in a multi-hop network. The SQMCR algorithm includes a cooperative communication function module and a reinforcement learning routing function module. The reinforcement learning routing function module selects the next-hop node based on the Q-learning algorithm, and the optimization objectives include reducing the number of hops, balancing energy consumption, reducing latency, and enhancing the robustness of the network topology. The cooperative communication function module selects the nodes and timing for participating in cooperative communication based on the Stackelberg game theory and the Q-learning algorithm according to the selected transmission path.

[0013] The GTR routing algorithm is implemented based on trust evaluation and reinforcement learning algorithms. It mainly provides data transmission services for high-security services in underwater wireless sensor networks. It can identify malicious nodes, avoid receiving data sent by malicious nodes or transmitting data to malicious nodes, and can ensure the transmission of data between trusted nodes. The GTR routing algorithm includes a trust evaluation function module and a reinforcement learning routing function module. The trust evaluation function module is used to identify malicious nodes among neighbor nodes, based on the GAN algorithm and the trust feature profile of trusted nodes, to identify malicious nodes, and can dynamically adjust the trust feature profile according to the changes in the transmission channel to ensure the detection performance of malicious nodes. The reinforcement learning routing function module selects the next-hop node based on the Q-learning algorithm, and the optimization objectives include reducing the number of hops, balancing energy consumption, and enhancing the robustness of the network topology.

[0014] The above routing algorithms can achieve good performance in meeting the data transmission requirements of specific services in underwater wireless sensor networks, but cannot meet the requirements of comprehensive service transmission. The routing algorithms encapsulate function modules in a component manner, which can greatly improve their reusability. Driven by business requirements, the combined application of multiple components can meet the comprehensive service data transmission requirements of underwater wireless sensor networks.

[0015] The methods described in this section are not necessarily methods that have been previously envisioned or adopted. Unless otherwise specified, no method described in this section should be considered prior art solely because it is included in this section. Similarly, unless otherwise specified, the problems mentioned in this section should not be considered to have been recognized in any prior art. Summary of the Invention

[0016] In view of the technical problems existing in the prior art, the present invention provides a service-driven composite routing agent for underwater wireless sensor networks.

[0017] The technical solution of the present invention to solve the above technical problems is as follows:

[0018] A service-driven composite routing agent for underwater wireless sensor networks, comprising a routing function module and a plug-in platform;

[0019] The routing function module includes a routing component and multiple function components; the routing component is used to obtain the best transmission path within a limited range according to the optimization goal when data is sent; the multiple function components are used to support the data transmission requirements of different services;

[0020] The plug-in platform includes a database reading and writing module, a data packet processing module, an information sending and receiving module, a service requirement library, an environmental status library, a transmission strategy library, and a service database;

[0021] The database reading and writing module is used to read service requirements from the service requirement library and generate service QoS fields according to the service requirements. The service QoS fields have a mapping relationship with the multiple function components; the routing function module selects at least one function component from the multiple function components according to the mapping relationship and the value of the service QoS field for corresponding processing of data and / or the next-hop node set during data collection, data reception, or data transmission;

[0022] The data packet processing module is used to read and write the content of data packets during data collection, data reception, or data transmission; the data packets include control data packets and service data packets;

[0023] The information sending and receiving module is used to receive or send data packets based on underwater acoustic signals;

[0024] The service requirement library is used to store service types and service requirements related to the service types;

[0025] The environmental status library is used to store the status information of the current node, neighbor nodes, and transmission channels;

[0026] The transmission strategy library is used to store the relevant parameters of the routing component and multiple function components in the routing function module;

[0027] The service database is used to store the queue of service data that has been sent and the queue of service data with different priorities to be sent.

[0028] Further, the function components include: a virtual pipeline component, an edge prediction component, an edge compression component, a collaborative communication component, and a trust evaluation component;

[0029] The virtual pipeline component is used to limit the range of the next-hop node set, so that the transmission path is close to the best transmission path between the sending source node and the destination node;

[0030] The edge prediction component is used to compress the transmission volume required for sequence data;

[0031] The edge compression component is used to compress the transmission volume required for picture data;

[0032] The collaborative communication component is used to obtain the nodes and timing participating in collaborative communication according to the transmission path and optimization objectives;

[0033] The trust evaluation component is used to evaluate whether a neighbor node is a malicious node.

[0034] Further, the length of the service QoS field is 1 byte, including priority, delay, precision, throughput, reliability, security, and service type fields; among them,

[0035] The priority field occupies 2 bits and is used to represent 4 priorities. 00 is the lowest priority, 01 is the low priority, 10 is the high priority, and 11 is the highest priority;

[0036] The delay occupies 1 bit and is used to represent the requirement of the service for the data forwarding delay. 0 is the normal delay, and 1 is the low delay;

[0037] The precision occupies 1 bit and is used to represent the requirement of the service for the data forwarding precision. 0 is the normal precision, and 1 is the high precision;

[0038] The throughput occupies 1 bit and is used to represent the throughput requirement of the service for transmitting data. 0 is the normal throughput, and 1 is the high throughput;

[0039] The reliability occupies 1 bit and is used to represent the requirement of the service for the data forwarding reliability. 0 is the normal reliability, and 1 is the high reliability;

[0040] The security occupies 1 bit and is used to represent the requirement of the service for the data forwarding security. 0 is the normal security, and 1 is the high security;

[0041] The service type occupies 1 bit and is used to represent the classification of service data. 0 is sequence data, and 1 is picture data.

[0042] Further, the routing function module selects and configures at least one function component from multiple function components according to the mapping relationship and based on the value of the service QoS field, including:

[0043] When the delay is low, the virtual pipeline component is configured; when the delay is normal, the composite routing does not configure the virtual pipeline component;

[0044] When the service type is sequence data, when the throughput is high throughput and the accuracy is normal accuracy, the edge prediction component is configured to transmit the prediction parameters and calibration data that meet the accuracy requirements in the direct transmission mode. When the throughput is high throughput and the accuracy is high accuracy, the edge prediction component is configured to transmit the prediction parameters and calibration data that meet the accuracy requirements in the progressive transmission mode. When the throughput is normal throughput and the accuracy is normal accuracy, the edge prediction component is configured to transmit the prediction parameters and calibration data that meet the accuracy requirements in the direct transmission mode. When the throughput is normal throughput and the accuracy is high accuracy, the edge prediction component is not configured;

[0045] When the service type is image data, when the throughput is high throughput and the accuracy is normal accuracy, the edge compression component is configured to transmit the average value data and partial detail coefficient data that meet the accuracy requirements in the progressive transmission mode. When the throughput is high throughput and the accuracy is high accuracy, the edge compression component is configured to transmit the average value data and all detail coefficient data in the progressive transmission mode. When the throughput is normal throughput and the accuracy is normal accuracy, the edge compression component is configured to transmit the average value data and partial detail coefficient data in the progressive transmission mode. When the throughput is normal throughput and the accuracy is high accuracy, the edge compression component is not configured;

[0046] When the reliability is high reliability, the cooperative communication component is configured; when the reliability is normal reliability, the cooperative communication component is not configured;

[0047] When the security is normal security, the trust evaluation component is configured according to the security of the transmission environment.

[0048] Furthermore, during data collection: The database read-write module reads the service requirements from the service requirement library and generates a service QoS field according to the service requirements; The routing function module selects and matches the edge prediction component or the edge compression component according to the values of the throughput, accuracy, and service type fields in the service QoS field; The database read-write module reads the corresponding parameter data from the transmission policy library; The data packet processing module generates a service data packet by using the data output by the edge prediction component or the edge compression component or the original collected data and the parameter data; The database read-write module writes the service data packet into the corresponding service data queue in the service database according to the priority in the service QoS field.

[0049] Further, when data is sent: the data packet processing module reads the service data packets to be sent from the service database according to the priority order and the principle of first-in, first-out; the routing function module selects a trust evaluation component and a virtual pipeline component according to the security and latency fields in the service QoS field of the service data packet to be sent; the plug-in platform filters the next-hop nodes from the next-hop node set through the routing component, the trust evaluation component, and the virtual pipeline component; the data packet processing module modifies the receiving node in the service data packet to the next-hop node screened by the routing function module; the information transceiver module sends the service data packet modified by the data packet processing module according to the corresponding transmission mode.

[0050] Further, when data is received: the information transceiver module receives the service data packet and sends it to the data packet processing module; the data packet processing module reads the service data packet header to obtain the service QoS field; the routing function module selects function components according to the service QoS field; the plug-in platform uses the selected function components to perform corresponding processing on the service data packet, including discarding the data packet, saving the data, or writing the service data packet into the forwarding queue of the service database by using the database reading and writing module.

[0051] Further, the plug-in platform uses the selected function components to perform corresponding processing on the service data packet, and further includes: if a cooperative communication component is selected, the plug-in platform uses the cooperative communication component to determine whether the current composite routing agent participates in cooperative communication. If it participates in cooperative communication, the node ID of the current composite routing agent is written into the cooperative communication ID in the service data packet header, and the plug-in platform writes the service data packet into the highest priority queue of the service database through the database reading and writing module.

[0052] Further, the control data packet is forwarded in a broadcast manner to synchronize the status information and neighbor node information of underwater communication nodes, including: node ID, node location, remaining energy, and the number and IDs of in-degree nodes, out-degree nodes, and malicious nodes. When a trust evaluation component is configured, the in-degree nodes and out-degree nodes become trusted in-degree nodes and trusted out-degree nodes.

[0053] Further, the service data packet is forwarded in a point-to-point manner to forward service data, including: data transceiver related address information, data sending information related to the routing component, and information related to the service data. When an edge prediction component is configured, the content of the data content transmission also includes prediction parameters, corrected data sequence numbers and content, and the number of corrected data.

[0054] The beneficial effects of the present invention are: 1. Constructing a composite routing agent based on a component method can meet the comprehensive service data transmission requirements in an underwater wireless sensor network.

[0055] Underwater wireless sensor networks are widely used and usually have the need for integrated service data transmission. However, existing routing algorithms for underwater wireless sensor networks are often designed based on specific service requirements and are difficult to meet the data transmission requirements of integrated services. Based on the research of existing routing algorithms, the present invention decomposes typical routing algorithms applied to specific services in a component manner, then constructs a composite routing algorithm with components as units, designs the service processes between components, and forms a composite routing algorithm that can adapt to different service data transmissions at the same time. The composite routing agent built based on the composite routing algorithm can be compatible with the data transmissions of different services by selecting different functional components, enabling the underwater wireless sensor network to carry integrated services.

[0056] 2. Based on the service configuration of the composite routing algorithm components, the composite routing agent can dynamically output the forwarding ability according to the services in the underwater wireless sensor network.

[0057] Supporting the services to provide the required data forwarding ability is the goal of routing algorithm design. However, existing routing algorithms for underwater wireless sensor networks pay more attention to the changes in the transmission channels and lack the adaptation to the changes in the service data transmission requirements. Based on the research of the main services in the underwater wireless sensor network, the present invention forms the classification of the quality of service (QoS) required for typical service data transmissions, designs the QoS fields for characterizing the service data transmission requirements, and then defines the configurations of the composite routing algorithm components mapped by different QoS fields to drive the dynamic output of the data forwarding ability of the composite routing algorithm. Based on the configuration of the composite routing algorithm components driven by the service QoS fields, the underwater wireless sensor network can dynamically adjust the output of the data forwarding ability according to different service requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a structural diagram of a composite routing agent model for an underwater wireless sensor network provided by an embodiment of the present invention;

[0059] Figure 2 It is a schematic diagram of the service QoS field structure provided by an embodiment of the present invention;

[0060] Figure 3 It is a packet structure diagram provided by an embodiment of the present invention;

[0061] Figure 4 It is a schematic diagram of the data acquisition process of the composite routing agent as an underwater sensor node provided by an embodiment of the present invention;

[0062] Figure 5Schematic diagram of the data sending process of the composite routing agent serving as an underwater sensor node or communication node provided by an embodiment of the present invention;

[0063] Figure 6 Schematic diagram of the data receiving process of the composite routing agent serving as an underwater communication node provided by an embodiment of the present invention;

[0064] Figure 7 Schematic diagram of the structure of the simulation experiment system for the underwater wireless sensor network routing protocol provided by an embodiment of the present invention;

[0065] Figure 8 Packet delivery rate of sequence data under different test scenarios;

[0066] Figure 9 Packet delivery rate of picture data under different scenarios;

[0067] Figure 10 Average transmission delay of sequence data under different test scenarios;

[0068] Figure 11 Average transmission delay of picture data under different test scenarios. Detailed implementation manners

[0069] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0070] In the description of the present application, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality of" means two or more, unless otherwise specifically defined.

[0071] In the description of the present application, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present application is not necessarily construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without the use of these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but rather to be in line with the broadest scope consistent with the principles and features disclosed in the present application.

[0072] An embodiment of the present invention provides an underwater wireless sensor network composite routing agent, as Figure 1 shown, including a routing function module and a plug-in platform. The routing function module is used to implement routing selection and adapt to various service requirements. The plug-in platform is used to access various routing function components and provide functions such as communication, packet processing, and database for them.

[0073] The routing function module is obtained by decomposing and encapsulating based on the four underwater wireless sensor network routing algorithms of EP-ADTA, VRC-ADTA, SQMCR, and GTR, and includes multiple functional components such as a routing component, a virtual pipeline component, an edge prediction component, an edge compression component, a cooperative communication component, and a trust evaluation component, supporting data transmission requirements of sequence data monitoring services and picture data monitoring services with different latency requirements, reliability requirements, security requirements, and different throughputs.

[0074] Among them, the routing component is a reinforcement learning routing component obtained by decomposing, fusing, and encapsulating according to the reinforcement learning routing module functions of the EP-ADTA, VRC-ADTA, SQMCR, and GTR routing algorithms. The core algorithm of the reinforcement learning routing component is the Q-learning algorithm. The input is the set of candidate next-hop nodes, and the output is the selected next-hop node. The optimization objectives include reducing the number of hops, balancing energy consumption, reducing latency, and enhancing the robustness of the network topology, etc. The optimization strategy is obtained by iterating based on the Bellman equation. The Q-value initialization and dynamic exploration probability optimization method defined in the SQMCR algorithm are used to improve the convergence speed of the reinforcement learning routing component. The function of the reinforcement learning routing component is to obtain the best transmission path within a limited range according to the optimization objectives. The reinforcement learning routing component is an essential component of the composite routing algorithm.

[0075] It should be understood here that although the reinforcement learning routing component is adopted in this embodiment, those skilled in the art can also replace the core algorithm of the routing component with other routing algorithms of the same type according to actual needs.

[0076] The virtual pipeline component is obtained after decomposition and encapsulation according to the virtual pipeline function module of the EP-ADTA routing algorithm. The input of the virtual pipeline component is the neighbor node set of the current node where the data packet is located, and the output is the candidate next-hop node set. The virtual pipeline component is established based on the vector between the source node and the destination node, and the pipeline radius is set with an initial value based on the underwater network and service requirements, and expands as the average remaining energy of the neighbor nodes decreases. The function of the virtual pipeline component is to limit the range of the next-hop node set, making the transmission path closer to the optimal transmission path between the source node and the destination node, and reducing the transmission delay. The virtual pipeline component is an optional component of the composite routing algorithm and is selected according to the delay requirements of service data transmission.

[0077] The edge prediction component is obtained after decomposition and encapsulation according to the edge prediction function model of the EP-ADTA routing algorithm. The input of the edge prediction component is the sequence data and the accuracy requirement, and the output is the prediction parameter and the correction data. The edge prediction component is implemented based on the Arma algorithm and can be configured into two modes: direct transmission and progressive transmission. Direct transmission means that the prediction parameter and the correction data are packed together and transmitted simultaneously with the same priority. Progressive transmission means that the prediction parameter and the correction data with different accuracies are packed separately and transmitted in sequence with different priorities. The function of the edge prediction component is to compress the transmission amount required for the sequence data and improve the throughput of the underwater wireless sensor network carrying the sequence data. The edge prediction component is an optional component of the composite routing algorithm and is selected according to the throughput requirements of sequence data transmission in the service.

[0078] The edge compression component is obtained after decomposition and encapsulation according to the edge compression function model of the VRC-ADTA routing algorithm. The input of the edge compression component is the image data and the accuracy requirement, and the output is the average value data and the multi-level detail coefficient data. The edge compression component is implemented based on the Haar algorithm and adopts the progressive transmission mode. During data transmission, the average value data is transmitted first, and then the multi-level detail coefficient data is transmitted with different priorities. The function of the edge compression component is to compress the transmission amount required for the image data and improve the throughput of the underwater wireless sensor network carrying the atlas data. The edge compression component is an optional component of the composite routing algorithm and is selected according to the throughput requirements of image data transmission in the service.

[0079] The collaborative communication component is obtained by decomposing and encapsulating according to the collaborative communication function module of the SQMCR routing algorithm. The core algorithm of the collaborative communication component is the Stackelberg Q-learning algorithm. The input is the set of candidate collaborative communication nodes, and the output is the collaborative communication nodes. According to the transmission path selected by the sending node, the collaborative communication nodes are further determined. The optimization goal is to improve the overall data delivery rate and reduce communication energy consumption. The optimization strategy is obtained by iterating based on the Bellman equation. The function of the collaborative communication component is to obtain the nodes and timing participating in collaborative communication according to the transmission path and optimization goal. The collaborative communication component is an optional component of the composite routing algorithm and is selected according to the reliability requirements of service data.

[0080] The trust evaluation component is obtained by decomposing and encapsulating according to the trust evaluation function model of the GTR routing algorithm. The core algorithm of the trust evaluation component is the GAN algorithm. The input is the trust characteristic attributes of the neighbor nodes to be evaluated, and the output is the credibility of the neighbor nodes. The trust evaluation component generates a trust characteristic profile of the trusted nodes by regularly collecting the trust characteristic attributes of the transmission nodes, channels, and data, which is used to compare with the neighbor nodes to be evaluated to determine whether the neighbor nodes are malicious nodes. The trust evaluation component is an optional component of the composite routing algorithm and is selected according to the transmission environment and the security requirements of service data.

[0081] The plug-in platform includes a database read-write module, a data packet processing module, an information transceiver module, a service requirement library, an environment status library, a transmission policy library, and a service database. It is responsible for communicating with other communication nodes, sending and receiving data packets, and storing the control information and service information of the data packets into the corresponding databases to support various functional components to complete their functions.

[0082] The service requirement library stores the service types, as well as the requirements of the service for data transmission priority, delay, throughput, security, and reliability.

[0083] The environment status library stores the status information of the current node, neighbor nodes, and transmission channels, including the location information of the nodes, remaining energy, out-degree nodes and in-degree nodes, packet error rate of data forwarding, transmission delay of the channels, packet loss rate, etc.

[0084] The transmission policy library stores the Q table of the reinforcement learning routing component, the thresholds and hyperparameters of each functional component, including the initial radius of the virtual pipeline, the accuracy setting of the edge prediction and edge compression components, the revenue-cost adjustment parameter of the collaborative communication component, and the malicious node threshold and model degradation threshold of the trust evaluation component, etc.

[0085] The service database stores the queue of the already sent service data, as well as the queues of the service data with different priorities to be sent.

[0086] The database read and write module is used to implement the communication and management functions between external components or modules and various databases.

[0087] The data packet processing module is used to process service data packets and control data packets, and read and write the contents of the data packets.

[0088] The signal transceiver module receives and sends various data packets based on underwater acoustic signals to achieve communication with each communication node.

[0089] The database read and write module reads service requirements from the service requirement library and generates service QoS fields according to the service requirements; the routing function module selects at least one functional component from multiple functional components according to the value of the service QoS field, and performs corresponding processing on the data and / or the next-hop node set during data acquisition or data reception or data transmission.

[0090] The service QoS field is used to characterize the requirements of service data for data transmission. Its length is 1 byte and includes priority, delay, accuracy, throughput, reliability, security, and service type fields, as Figure 2 shown. Among them,

[0091] Priority (Pri 2bit): Used to characterize 4 priorities. Among them, 00 is the lowest priority, 01 is the low priority, 10 is the high priority, and 11 is the highest priority. The data to be forwarded with different priorities is queued separately, and the data in the high-priority queue is sent first. The data in the same-priority queue is sent according to the first-in, first-out principle.

[0092] Delay (D 1bit): Used to characterize the requirements of the service for data forwarding delay. Among them, 0 is the normal delay and 1 is the low delay. The delay requirements are only compared within services of the same priority.

[0093] Accuracy (P 1bit): Used to characterize the requirements of the service for data forwarding accuracy. Among them, 0 is the normal accuracy and 1 is the high accuracy. The accuracy requirements vary for different service types.

[0094] Throughput (T 1bit): Used to characterize the throughput requirements of the service for data transmission. Among them, 0 is the normal throughput and 1 is the high throughput.

[0095] Reliability (R 1bit): Used to characterize the requirements of the service for data forwarding reliability. Among them, 0 is the normal reliability and 1 is the high reliability.

[0096] Security (S 1bit): Used to characterize the requirements of the service for data forwarding security. Among them, 0 is the normal security and 1 is the high security.

[0097] Service type (Type 1bit): Used to characterize the classification of service data. Among them, 0 represents sequence data, and 1 represents picture data.

[0098] Service-driven means selecting functional components in the routing function module according to the service QoS fields generated by service requirements to achieve multi-hop forwarding in the underwater wireless sensor network.

[0099] The intelligent agent Agent, as a communication node in the underwater wireless sensor network, will select the functional components to be configured for the composite routing algorithm based on the service QoS fields in the forwarded data packet and according to the mapping relationship between service QoS and functional components. That is, at least one functional component is selected and configured from multiple functional components according to the value of the service QoS field. The mapping relationship between the service QoS field and the functional components is shown in Table 1.

[0100] Table 1 Mapping relationship table of service QoS and functional components

[0101]

[0102] Queues are set for the data to be forwarded according to different priorities to ensure the forwarding of high-priority data first. For service data with the same priority, functional components are configured according to other fields of the service QoS.

[0103] When the delay (D) is low delay (1), the virtual pipeline component is configured; when the delay (D) is normal delay (0), the virtual pipeline component is not configured.

[0104] When the service type (Type) is sequence data (0), when the throughput (T) is high throughput (1) and the accuracy (P) is normal accuracy (0), the edge prediction component is configured to transmit the prediction parameters and calibration data meeting the accuracy requirements in the direct transmission mode. When the throughput (T) is high throughput (1) and the accuracy (P) is high accuracy (1), the edge prediction component is configured to transmit the prediction parameters and calibration data meeting the accuracy requirements in the progressive transmission mode. When the throughput (T) is normal throughput (0) and the accuracy (P) is normal accuracy (0), the edge prediction component is configured to transmit the prediction parameters and calibration data meeting the accuracy requirements in the direct transmission mode. When the throughput (T) is normal throughput (0) and the accuracy (P) is high accuracy (1), the edge prediction component is not configured.

[0105] When the service type (Type) is picture data (1), if the throughput (T) is high throughput (1) and the accuracy (P) is normal accuracy (0), then configure the edge compression component to transmit the average value data and part of the detail coefficient data that meet the accuracy requirements in a progressive transmission mode. When the throughput (T) is high throughput (1) and the accuracy (P) is high accuracy (1), then configure the edge compression component to transmit the average value data and all the detail coefficient data in a progressive transmission mode. When the throughput (T) is normal throughput (0) and the accuracy (P) is normal accuracy (0), then configure the edge compression component to transmit the average value data and part of the detail coefficient data in a progressive transmission mode. When the throughput (T) is normal throughput (0) and the accuracy (P) is high accuracy (1), then do not configure the edge compression component.

[0106] When the reliability (R) is high reliability (1), then configure the cooperative communication component; when the reliability (R) is normal reliability (0), then do not configure the cooperative communication component.

[0107] When the security (S) is high security (1) and the transmission environment where it is located should also be of high security, then configure the trust evaluation component; when the security (S) is normal security (0), then configure the trust evaluation component according to the security of the transmission environment.

[0108] The composite routing agent synchronizes the status of underwater wireless sensor network nodes through data packets to achieve the forwarding of service data. Its data packet structure is as Figure 3 shown. Among them, Figure 3 -(a) represents the service data packet structure for forwarding service data, Figure 3 -(b) represents the data content in the service data packet after configuring the edge prediction component, Figure 3 -(c) represents the control data packet structure for node status synchronization. The control data packet and the service data packet are forwarded on the control channel and the service channel respectively in a time-division manner.

[0109] 1. Control data packet design

[0110] The control data packet is forwarded in a broadcast manner, mainly used to synchronize the status information and neighbor node information of underwater communication nodes, including: node ID, node location, remaining energy, and in-degree, out-degree, and the number of malicious nodes and their IDs. When the trust evaluation component is configured, the in-degree and out-degree nodes become trusted in-degree and out-degree nodes.

[0111] 2. Service data packet design

[0112] Service data packets are forwarded in a point-to-point manner, mainly for forwarding service data, including: address information related to data transmission and reception (sender node, receiver node, collaborative communication node, and target node ID), data transmission information related to the reinforcement learning routing component (V value, timestamp, and transmission count), and information related to service data (data ID, service QoS, and data content). When the edge prediction component is configured, the content of data content transmission includes prediction parameters, corrected data sequence number and content, and the number of corrected data.

[0113] In the underwater wireless sensor network composed of the above composite routing agents, the data forwarding process is implemented by each underwater communication node and underwater sensor node based on the composite routing agent Agent distributed in the network.

[0114] During data collection

[0115] In the initialization stage of the underwater wireless sensor network, each underwater communication node initializes the service requirements, environmental status, transmission strategy, and service data database of the underwater communication node based on Agent, as well as the component configuration of the composite routing algorithm according to the initial conditions of the transmission environment and the initial requirements of the transmission service.

[0116] The database read-write module reads the service requirements from the service requirements library and generates a service QoS field according to the service requirements; the routing function module selects and configures the edge prediction component or the edge compression component according to the values of the throughput, accuracy, and service type fields in the service QoS field. The database read-write module reads the corresponding parameter data from the transmission strategy library; the data packet processing module generates a service data packet by using the data output by the edge prediction component or the edge compression component or the original collected data and the parameter data. If the service type is picture data, the edge compression component and the corresponding transmission mode and the data of the transmission detail coefficient required are selected according to the throughput and accuracy. If the service type is sequence data, the edge prediction component and the corresponding transmission mode and the corrected data meeting the accuracy requirements are selected according to the throughput and accuracy. The database read-write module writes the service data packet into the corresponding service data queue in the service database according to the priority in the service QoS field. The data collection process of the underwater sensor node is as Figure 4 shown.

[0117] During data transmission

[0118] An underwater communication node in the data forwarding state, when the channel for sending service data packets arrives, periodically sends service data packets according to the priority order and the first-in-first-out principle. Before sending a data packet, it first checks the security and latency of the service QoS field of the data packet and confirms the configuration of the trust evaluation component and the virtual pipeline component. The reinforcement learning routing component first filters the neighbor nodes on the neighbor node to target node vector as the candidate next-hop node set. According to the security field, if the trust evaluation component is configured, the next-hop node set becomes the trusted candidate next-hop neighbor node set after being filtered by the trust evaluation component. According to the latency field, if the virtual pipeline component is configured, the next-hop node set is filtered by the virtual pipeline component as the candidate next-hop neighbor node set located within the virtual pipeline. The reinforcement learning routing component calculates the Q values of each candidate next-hop node based on the Q-learning algorithm and the historical Q table according to the finally determined candidate next-hop node set and its status, and selects the node with the maximum Q value as the next-hop node, that is, the receiving node. Modify the ID of the receiving node in the data packet to the next-hop node ID and send out the data packet. An underwater communication node in the data forwarding state, when the channel for sending control data packets arrives, periodically broadcasts control data packets externally according to the status information in the database to achieve status synchronization among neighbor nodes. The data sending process of the underwater communication node is as Figure 5 shown.

[0119] When receiving data

[0120] During the operation phase of the underwater wireless sensor network, an underwater communication node in the data forwarding state continuously listens on its transmission channel for the arrival of new data packets. If a new data packet arrives, it first detects whether it is a service data packet or a control data packet. If it is a control data packet, it reads the content of the data packet and updates the database content related to the neighbor sending node in the underwater communication node. If a trust evaluation module is configured, the credibility of the node needs to be evaluated and the status of the node is marked in the database. If it is a service data packet, it reads the data packet header. It views the service QoS field of the service data packet, checks the type, priority, accuracy, throughput, and security of the service data, and configures the required functional components for the composite routing algorithm according to the service QoS. If a trust evaluation module is configured, it detects whether the data packet comes from a malicious node based on the sending node ID in the data packet. If the data packet comes from a malicious node, it discards it. If the data packet comes from a trusted node, it updates the database content related to the node according to the data packet header information. It checks whether the receiving node ID in the header is the same as this node. If it is the same, it further checks whether the data is response data. If it is response data, it marks the forwarded data according to the response result. If it is not response data, it then detects whether the target node ID is the same as this node. If it is not the same, it extracts the content of the data packet, reads the priority in the service data QoS, and stores it in the forwarding queue with the corresponding priority according to the priority of the data. If it is the same, it extracts the content in the data packet and saves it according to the service type and service ID. After collecting all the complete data, it checks the service data type. If it is picture data, it checks whether an edge compression module is configured. If it is configured, it restores the transmitted picture data according to the Haar algorithm. If it is not configured, it directly restores the picture data. If it is sequence data, it checks whether an edge prediction module is configured. If it is configured, it restores the transmitted sequence data according to the Arma algorithm. If it is not configured, it directly restores the sequence data. It checks whether the receiving node ID in the header is the same as this node. If it is not the same, it views the service QoS field of the service data packet and checks the reliability field of the service data to confirm whether a cooperative communication component is configured. If a cooperative communication component is configured, it needs to select whether to participate in cooperative communication according to the cooperative communication component. If it participates in cooperative communication, it writes the ID of this node into the cooperative communication ID and puts the data packet into the data to be forwarded with the highest priority. If it does not participate in cooperative communication, it discards the data packet. If no cooperative communication component is configured, it discards the data packet. The data reception process of the underwater communication node is as Figure 6 shown.

[0121] Figure 7 It is an underwater wireless sensor network routing protocol simulation experiment system implemented by the underwater wireless sensor network composite routing agent based on the above embodiments of the present invention.

[0122] "Underwater Wireless Sensor Network Routing Protocol Simulation Experiment System" is a simulation experiment system for underwater wireless sensor networks implemented based on Python. It can simulate various transmission environments and various service applications of underwater wireless sensor networks, is applicable to different network scales, supports the collection, preservation, analysis, and management of information throughout the process of data packet forwarding, can access various routing algorithms, and realizes the testing and evaluation of the data forwarding performance of routing algorithms.

[0123] The business-driven composite routing algorithm for underwater wireless sensor networks proposed in the embodiments of the present invention is implemented using the Python programming language, and related functional components of routing algorithms such as EP-ADTA, VRC-ADTA, SQMCR, and GTR are deployed together in the "Underwater Wireless Sensor Network Routing Protocol Simulation Experiment System". The "Underwater Wireless Sensor Network Routing Protocol Simulation Experiment System" simulates and tests the data forwarding performance of communication nodes in underwater wireless sensor networks under different transmission channels, different network scales, and mixed transmission conditions of various service data. The business-driven composite routing algorithm, as the routing algorithm resident in underwater communication nodes, can effectively schedule various functional components according to changes in service requirements to ensure the achievement of QoS requirements for service applications.

[0124] In this embodiment, the packet delivery ratio and average transmission delay of transmitting 150 KBytes of data from the underwater sensor node to the surface aggregation node are respectively tested for sequence data and picture data under different network scales, service traffic, dynamic range of node positions, channel interruption probability, and different proportions of malicious nodes. The sequence data is based on the application of underwater temperature monitoring, and the data comes from the KEO station in the NOAA database. One temperature data occupies 4 bytes. The picture data is a single-channel picture of (300, 500) to reflect the situation of underwater fish. One pixel data occupies 1 byte. The size of the data content encapsulated in each data packet does not exceed 100 bytes. The three-dimensional underwater area is set to 500m × 500m × 500m. The sound speed is 1500m / s, and the bandwidth is 10kHz. The communication distance and sensing distance of communication nodes are set to 150m. The MAC layer protocol is implemented using S-FAMA. The parameters of each functional component are all based on the parameters pointed out in the corresponding papers. Among them, the accuracy after edge prediction of the sequence data is controlled within 0.1 °C, and the picture data is compressed at the edge using the method of "average value data + level 1 and 2 detail coefficients". The data forwarding performance test scenarios include 9, which are set according to Table 2.

[0125] Table 2 Data Performance Test Scenario Configuration Table

[0126]

[0127] The packet delivery ratio refers to the ratio of the number of packets received by the surface nodes to the number of packets sent by the underwater sensor nodes. A higher packet delivery ratio reflects that the routing algorithm can overcome the impacts of dynamic transmission channels and changing traffic flows, as well as the interference of malicious nodes, and complete the transmission of service data with fewer packets. Figure 8 and Figure 9 reflect the packet delivery ratios of sequence data and picture data in the underwater wireless sensor network configured with this composite routing algorithm under different test scenarios. By Figure 8 and Figure 9 it is not difficult to find that the routing algorithm proposed by this technology can adapt to different service requirements and transmission channels under different test scenarios, and achieve reliable, efficient and secure packet transmission.

[0128] The average transmission delay refers to the ratio of the time required to transmit all packets to the number of packets. A shorter average transmission delay reflects that the routing algorithm can overcome the impacts of dynamic transmission channels and changing traffic flows, as well as the interference of malicious nodes, and occupy less transmission time to complete the transmission of service data. Figure 10 and Figure 11 reflect the average transmission delays of sequence data and picture data in the underwater wireless sensor network configured with this composite routing algorithm under different test scenarios. By Figure 10 and Figure 11 it is not difficult to find that the routing algorithm proposed by this technology can adapt to different service requirements and transmission channels under different test scenarios, and achieve efficient, reliable and secure packet transmission.

[0129] It should be understood that various forms of the processes shown above can be used, with steps reordered, added or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitation is made herein.

[0130] Although the embodiments or examples of this disclosure have been described with reference to the accompanying drawings, it should be understood that the above methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only limited by the authorized claims and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, the steps can be executed in a different order from that described in this disclosure. Further, various elements in the embodiments or examples can be combined in various ways. Importantly, with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after this disclosure.​

Claims

1. A business-driven underwater wireless sensor network composite routing agent, characterized in that: Including routing function modules and plug-in platforms, The routing function module includes a routing component and multiple functional components; the routing component is used to obtain the best transmission path within a limited range according to the optimization target when sending data; the multiple functional components are used to support the data transmission requirements of different services; The plug-in platform includes a database reading and writing module, a data packet processing module, an information transceiver module, a business demand library, an environment status library, a transmission strategy library, and a business database; The database reading and writing module is used to read the business requirements from the business requirements library, and generate a business QoS field according to the business requirements, and the business QoS field has a mapping relationship with the multiple functional components; the routing function module selects at least one functional component from the multiple functional components according to the mapping relationship and the value of the business QoS field, and is used to perform corresponding processing on the data and / or the next hop node set when collecting data, receiving data, or sending data; The data packet processing module is used to read and write data packet content during data acquisition, data reception or data transmission; the data packets include control data packets and business data packets; The information transceiver module is used to receive or send data packets based on the hydroacoustic signal; The business requirement library is used to store business types and business requirements related to the business types; The environment status library is used to store status information of the current node, neighboring nodes and transmission channels; The transmission strategy library is used to store the routing components and related parameters of multiple functional components in the routing function module; The service database is used to store the service data queues that have been sent and the service data queues of different priorities to be sent; The functional components include: a virtual pipeline component, an edge prediction component, an edge compression component, a collaborative communication component, and a trust assessment component; The virtual pipe component is used to limit the range of the next hop node set so that the transmission path is close to the best transmission path between the sending source node and the destination node; The edge prediction component is used to compress the transmission amount required for sequence data; The edge compression component is used to compress the transmission amount required for the image data; The collaborative communication component is used to obtain nodes and timings participating in collaborative communication according to the transmission path and optimization target; The trust evaluation component is used to evaluate whether a neighbor node is a malicious node.

2. The composite routing agent according to claim 1, characterized in that: The length of the service QoS field is 1 byte, including priority, delay, accuracy, throughput, reliability, security and service type fields; The priority field occupies 2 bits and is used to represent 4 priorities, 00 is the lowest priority, 01 is low priority, 10 is high priority, and 11 is the highest priority; The latency occupies 1 bit and is used to represent the service's demand for data forwarding latency. 0 represents normal latency and 1 represents low latency. The precision occupies 1 bit and is used to represent the service's demand for data forwarding precision. 0 represents normal precision and 1 represents high precision. Throughput occupies 1 bit and is used to represent the throughput requirement of business transmission data. 0 represents normal throughput and 1 represents high throughput. Reliability occupies 1 bit and is used to characterize the service's demand for data forwarding reliability. 0 represents normal reliability and 1 represents high reliability. Security occupies 1 bit and is used to represent the business's demand for data forwarding security. 0 represents normal security and 1 represents high security. The service type occupies 1 bit and is used to characterize the classification of service data. 0 represents sequence data and 1 represents image data.

3. The composite routing agent according to claim 2, characterized in that: The routing function module selects at least one functional component from a plurality of functional components according to the mapping relationship and the value of the service QoS field, including: When the delay is low, the virtual pipeline component is configured; when the delay is normal, the composite routing is not configured with the virtual pipeline component; When the service type is sequence data, when the throughput is high throughput and the accuracy is normal accuracy, the edge prediction component is configured to transmit the prediction parameters and the correction data that meets the accuracy requirements in direct transmission mode; when the throughput is high throughput and the accuracy is high accuracy, the edge prediction component is configured to transmit the prediction parameters and the correction data that meets the accuracy requirements in progressive transmission mode; when the throughput is normal throughput and the accuracy is normal accuracy, the edge prediction component is configured to transmit the prediction parameters and the correction data that meets the accuracy requirements in direct transmission mode; when the throughput is normal throughput and the accuracy is high accuracy, the edge prediction component is not configured; When the service type is image data, when the throughput is high throughput and the precision is normal precision, the edge compression component is configured to transmit the average value data and some detail coefficient data that meet the precision requirements in progressive transmission mode; when the throughput is high throughput and the precision is high precision, the edge compression component is configured to transmit the average value data and all detail coefficient data in progressive transmission mode; when the throughput is normal throughput and the precision is normal precision, the edge compression component is configured to transmit the average value data and some detail coefficient data in progressive transmission mode; when the throughput is normal throughput and the precision is high precision, the edge compression component is not configured; When the reliability is high reliability, the collaborative communication component is configured; when the reliability is normal reliability, the collaborative communication component is not configured; When the security is normal, the trust evaluation component is configured according to the security of the transmission environment.

4. The composite routing agent according to claim 2, characterized in that: During data collection: the database read-write module reads the business requirements from the business requirements library and generates a business QoS field based on the business requirements; the routing function module selects the edge prediction component or the edge compression component according to the throughput, accuracy and business type field values ​​in the business QoS field; the database read-write module reads the corresponding parameter data from the transmission strategy library; the data packet processing module generates a business data packet using the data output by the edge prediction component or the edge compression component or the original collected data and the parameter data; the database read-write module writes the business data packet into the corresponding business data queue in the business database according to the priority in the business QoS field.

5. The composite routing agent according to claim 2, characterized in that: When data is sent: the data packet processing module reads the service data packet to be sent from the service database according to the priority order and the first-in-first-out principle; the routing function module selects the trust evaluation component and the virtual pipe component according to the security and delay fields in the service QoS field in the service data packet to be sent; The patching platform selects the next hop node from the next hop node set through the routing component, the trust evaluation component, and the virtual pipeline component; the data packet processing module modifies the receiving node in the service data packet to the next hop node selected by the routing function module; The information transceiver module sends out the data packet to be sent modified by the data packet processing module according to the corresponding transmission mode.

6. The composite routing agent according to claim 2, characterized in that: When receiving data: the information transceiver module receives the business data packet and sends it to the data packet processing module; the data packet processing module reads the business data packet header and obtains the business QoS field; the routing function module selects functional components according to the business QoS field; the plug-in platform uses the selected functional components to perform corresponding processing on the business data packet, including discarding the data packet, saving the data, or using the database read-write module to write the business data packet into the forwarding queue of the business database.

7. The composite routing agent according to claim 6, characterized in that: The plug-in platform utilizes the optional functional components to perform corresponding processing on the business data packet, and also includes: if a collaborative communication component is selected, the plug-in platform utilizes the collaborative communication component to determine whether the current composite routing intelligent entity participates in collaborative communication, and if so, writes the node ID of the current composite routing intelligent entity into the collaborative communication ID in the header of the business data packet, and the plug-in platform writes the business data packet into the highest priority queue of the business database through the database read and write module.

8. The composite routing agent according to claim 1, characterized in that: The control data packet is forwarded in a broadcast manner to synchronize the status information of the underwater communication node and the neighbor node information, including: node ID, node location, remaining energy, and the number of in-degree nodes, out-degree nodes and malicious nodes and their IDs. When a trust evaluation component is configured, the in-degree nodes and out-degree nodes become trusted in-degree nodes and trusted out-degree nodes.

9. The composite routing agent according to claim 1, characterized in that: The business data packet is forwarded in a point-to-point manner and is used to forward business data, including: address information related to data sending and receiving, data sending information related to the routing component, and information related to the business data. When the edge prediction component is configured, the data content transmitted also includes prediction parameters, correction data sequence number and content, and correction data quantity.

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