An underwater sensor network cross-layer mac protocol implementation method and system

Abstract

The application discloses an underwater sensor network cross-layer MAC protocol implementation method and system, and belongs to the technical field of underwater acoustic wireless sensor networks. The protocol constructs a clustering network structure, combines OFDM physical layer technology and TDMA medium access control, dynamically allocates transmission modes, subcarrier numbers, transmission powers and symbol numbers, so as to meet node differentiated rate requirements and adapt to underwater acoustic channel characteristics. The NSGA-OT algorithm is adopted to solve a multi-objective optimization problem, and the network throughput and total energy consumption are simultaneously optimized under the guarantee of the bit error rate constraint. The application overcomes the problems that the existing MAC protocol ignores node differentiated rate requirements and wastes resources, and improves data transmission reliability, network life cycle and real-time performance.

Description

Technical Field

[0001] This invention belongs to the field of underwater acoustic wireless sensor network technology, and specifically relates to a cross-layer MAC protocol design for underwater sensor networks that combines orthogonal frequency division multiplexing and time division multiple access technologies. Background Technology

[0002] Underwater Acoustic Sensor Networks (UASNs) are self-organizing communication and sensing networks composed of nodes carrying various sensors and submersibles deployed in the water. They are capable of collaboratively completing information collection and monitoring tasks within their deployment area. UASNs have broad application prospects in marine scientific research, environmental monitoring, military applications, and navigation assistance, and are an important means of acquiring marine information.

[0003] Medium Access Control (MAC) protocols are a key technology in UASNs. Time Division Multiple Access (TDMA)-based MAC protocols have received widespread attention due to their simplicity and practicality. However, most existing TDMA-based protocols assume: 1) all sensor nodes generate data at the same rate; 2) the underwater acoustic channel conditions are ideal. These assumptions limit the application of MAC protocols in real underwater environments. To address this issue, the rational allocation of physical layer resources is essential. Orthogonal Frequency Division Multiplexing (OFDM) is a mature physical layer technology widely used in UASNs. Summary of the Invention

[0004] To address the shortcomings of existing underwater MAC protocols, which ignore node rate differences and the physical characteristics of underwater acoustic channels, resulting in low resource utilization and high energy consumption, a service quality-aware underwater cross-layer multi-access control method and system is proposed. Based on a clustered topology underwater acoustic sensor network architecture, the method dynamically adjusts resource allocation through RTC / RTA / ANC packet interaction and the NSGA-OT algorithm in the protocol flow, realizing an underwater sensor network cross-layer MAC protocol method and system adapted to the multipath effect and low signal-to-noise ratio characteristics of the underwater environment.

[0005] This invention proposes a cross-layer MAC protocol (OT-MAC) that combines OFDM and TDMA technologies. OT-MAC comprehensively considers the time slot scheduling of the MAC layer and the resource allocation of the physical layer. Under the conditions of meeting node rate requirements and bit error rate constraints, it jointly allocates the transmission mode, number of subcarriers, transmission power and number of symbols to optimize the throughput and total energy consumption of UASNs.

[0006] To achieve the above-mentioned objectives, the present invention proposes the following technical solution:

[0007] Firstly, a method for implementing a cross-layer MAC protocol in an underwater sensor network includes the following steps:

[0008] Step S1: Construct a clustered topology underwater acoustic sensor network, which includes cluster head sensor nodes, cluster intra-cluster sensor nodes, autonomous underwater vehicles, gateway nodes, and a land control center. The cluster intra-cluster sensor nodes transmit the collected data to the cluster head sensor nodes. The cluster intra-cluster sensor nodes are responsible for underwater data acquisition, and the cluster head sensor nodes are responsible for intra-cluster communication coordination and resource allocation.

[0009] Step S2: Based on OFDM-TDMA technology, define the transmission mode, number of subcarriers, number of symbols, and transmission power as allocable resource parameters, and set a communication round structure including a resource allocation phase and a data transmission phase. The data transmission phase is divided into multiple frames, and each frame contains multiple time slots.

[0010] Step S3: Configure the decision model, that is, establish a multi-objective optimization problem with the goal of minimizing the total network energy consumption and maximizing the network throughput. Its constraints include node rate requirement constraints, bit error rate constraints and total resource constraints, and preset the optimization decision algorithm to solve the problem.

[0011] Step S4: Execute the protocol workflow: The cluster head sensor node broadcasts a resource request collection packet; after receiving the resource request collection packet, each of the cluster's sensor nodes sends a resource allocation request packet at a specified time. The resource allocation request packet carries the node's rate requirement and channel parameters.

[0012] After receiving resource allocation request packets from all nodes, the cluster head sensor node runs the optimization decision algorithm. Based on the multi-objective optimization problem and its constraints, it selects the Pareto optimal solution set from the candidate schemes and chooses the final resource allocation scheme from the Pareto optimal solution set. The resource allocation scheme determines a set of parameter combinations for each node, including transmission mode, number of subcarriers, number of symbols, and transmission power. The cluster head sensor node distributes the resource allocation scheme to each node via a broadcast notification packet. Each cluster sensor node transmits data to the cluster head sensor node according to the resource parameters and transmission time slots allocated in the notification packet. After each data transmission frame ends, the cluster head sensor node broadcasts an acknowledgment packet to confirm the data transmission status.

[0013] In some implementations, the transmission mode, number of subcarriers, number of symbols, and transmission power in step S2 are as follows:

[0014] The transmission mode balances bit error rate and transmission rate based on channel conditions and node rate requirements; the number of subcarriers balances parallel transmission capability and inter-carrier interference based on bandwidth and node rate requirements; the number of symbols serves as the smallest unit for OFDM data transmission, enabling symbol-level resource allocation; and the transmission power is dynamically adjusted based on the channel signal-to-noise ratio to reduce bit error rate and optimize energy consumption.

[0015] In some implementations, the optimization decision algorithm in step S3 adopts the NSGA-OT algorithm, which includes the following steps: initializing the population, randomly assigning the transmission mode, number of subcarriers, transmission power and number of symbols to nodes; generating a offspring population through selection, crossover and mutation; selecting solutions from the merged population based on fast non-dominated sorting and congestion distance to enter the next generation; and outputting the Pareto optimal solution set after the iteration terminates, wherein the non-dominated sorting defines the dominance relationship according to the rate constraint violation value (RCV) and bit error rate constraint violation value (BCV).

[0016] In some implementations, the specific process of the fast non-dominated sorting includes: calculating the number of solutions dominated by each solution and the set of solutions that dominate other solutions; placing the solution with the lowest non-dominated level into the first-level Pareto front; processing each front in turn until all solutions are classified; and for solutions in the same Pareto front, selecting the preferred solution by comparing congestion distance, which is calculated based on the reciprocal of network throughput and total energy consumption.

[0017] In some implementations, the constraints of the multi-objective optimization problem include a rate requirement constraint that the node transmission rate is not lower than its rate requirement, a bit error rate constraint that the node bit error rate is not higher than the maximum allowable bit error rate, and a total resource constraint that limits the transmission power, number of subcarriers, and number of symbols to a preset range.

[0018] In some implementations, the protocol workflow avoids RTA packet sending collisions by scheduling RTC packets with timestamps, specifies data transmission time by using ANC packets, and ensures data transmission reliability by using ACK packets, thereby achieving collision-free communication.

[0019] In some implementations, the transmission mode corresponds to BPSK, QPSK, 16QAM or 64QAM modulation, the number of bits carried by the symbol is calculated based on the number of subcarriers and the proportion of guard subcarriers, and the bit error rate is derived through the complementary error function and the signal-to-noise ratio model.

[0020] Secondly, this invention proposes a system for implementing a cross-layer MAC protocol in an underwater sensor network, comprising:

[0021] A network construction module is used to construct a clustered topology underwater acoustic sensor network. The network includes cluster head sensor nodes, cluster intra-cluster sensor nodes, autonomous underwater vehicles, gateway nodes, and a land control center. The cluster intra-cluster sensor nodes are used for underwater data acquisition, and the cluster head sensor nodes are used for intra-cluster communication coordination and resource allocation.

[0022] The resource definition module is used to define allocatable resource parameters based on OFDM-TDMA technology. The resource parameters include transmission mode, number of subcarriers, number of symbols and transmission power. It is also used to set the communication round structure that includes a resource allocation phase and a data transmission phase. The data transmission phase is divided into multiple frames, and each frame contains multiple time slots.

[0023] The optimization decision module, connected to the resource definition module, is used to establish a multi-objective optimization problem with the goal of minimizing the total network energy consumption and maximizing the network throughput. The constraints of the multi-objective optimization problem include node rate requirement constraints, bit error rate constraints, and total resource constraints. The optimization decision module is also used to pre-configure optimization decision algorithms for solving the multi-objective optimization problem.

[0024] The configuration module configures the decision model, which establishes a multi-objective optimization problem with the goal of minimizing the total network energy consumption and maximizing the network throughput. Its constraints include node rate requirement constraints, bit error rate constraints, and total resource constraints. The module also presets the optimization decision algorithm to solve the problem.

[0025] The protocol execution module is connected to the network construction module, the resource definition module and the optimization decision module respectively, and is used to execute the protocol workflow. Its resource request collection unit is used to broadcast resource request collection packets by the cluster head sensor node.

[0026] The resource request reporting unit is used to send a resource allocation request packet at a specified time after each of the cluster's sensing nodes receives the resource request collection packet. The resource allocation request packet carries the node's rate requirement and channel parameters.

[0027] The resource allocation and notification unit is used to run the optimization decision algorithm after the cluster head sensor node receives all the resource allocation request packets. Based on the multi-objective optimization problem and its constraints, it selects the Pareto optimal solution set from the candidate schemes and selects the final resource allocation scheme from the Pareto optimal solution set. The resource allocation scheme determines the parameter combination of transmission mode, number of subcarriers, number of symbols and transmission power for each of the cluster sensor nodes, and sends the resource allocation scheme to each node through a broadcast notification packet.

[0028] The data transmission unit is used to transmit data from each of the cluster-internal sensing nodes to the cluster head sensing node according to the resource parameters and transmission time slots allocated by the notification packet.

[0029] The transmission confirmation unit is used to broadcast an confirmation packet by the cluster head sensor node after each data transmission frame ends, in order to confirm the data transmission status.

[0030] Thirdly, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements any one of the underwater sensor network cross-layer MAC protocol implementation methods.

[0031] Fourthly, the present invention proposes a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements any one of the underwater sensor network cross-layer MAC protocol implementation methods.

[0032] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0033] 1. By introducing node rate parameters and symbol-level resource allocation, the transmission mode, number of subcarriers, number of symbols and transmission power are dynamically adjusted to ensure that the transmission rate of each node matches its data generation rate, avoid data loss or resource idleness, and improve resource utilization.

[0034] 2. Combining OFDM's multipath resistance and dynamic power adjustment, it reduces the bit error rate caused by multipath effects and noise interference. At the same time, it ensures complete data transmission through the ACK confirmation mechanism, adapting to the low signal-to-noise ratio characteristics of underwater acoustic channels.

[0035] 3. With the dual objectives of maximizing throughput and minimizing energy consumption, the NSGA-OT algorithm is used to achieve multi-objective optimization. While improving network data transmission efficiency, it significantly reduces node energy consumption, extends network lifetime, and achieves network performance optimization.

[0036] 4. Improve protocol real-time performance and reliability: Avoid control packet collisions through timestamp scheduling, reduce retransmissions and delays, ensure that the protocol process runs without collisions, and enhance network real-time performance and stability.

[0037] 5. The above-mentioned effects achieve collaboration between the physical layer and the MAC layer through cross-layer design, which solves the limitations caused by the idealized assumptions of traditional protocols and provides an efficient and energy-saving communication solution for underwater sensor networks; it improves the reliability, energy efficiency and real-time performance of underwater sensor networks and is suitable for scenarios such as marine monitoring and military reconnaissance. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the overall process of an underwater sensor network cross-layer MAC protocol implementation method according to the present invention;

[0039] Figure 2 This is a schematic diagram of the system structure for implementing a cross-layer MAC protocol in an underwater sensor network according to the present invention.

[0040] Figure 3 This is a diagram of the underwater acoustic sensor network architecture with a clustered topology constructed in this invention.

[0041] Figure 4 This is a schematic diagram of the cross-layer MAC protocol communication process of the underwater sensor network of the present invention.

[0042] Figure 5 A schematic diagram illustrating a pseudocode example for calculating constraint violation values.

[0043] Figure 6 This is a schematic diagram of the Knee point. Detailed Implementation

[0044] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0045] Example 1: As Figure 1 As shown, the present invention provides an underwater cross-layer multiple access control method driven by node rate demand. The specific process of this method includes the following steps:

[0046] Step S1: Construct a clustered topology underwater acoustic sensor network, which includes a cluster head sensor node, cluster intra-cluster sensor nodes, autonomous underwater vehicles, sink nodes, and a land control center. The cluster intra-cluster sensor nodes transmit the collected data to the cluster head sensor node. The cluster intra-cluster sensor nodes are responsible for underwater data acquisition, and the cluster head sensor node is responsible for intra-cluster communication coordination and resource allocation.

[0047] The network nodes consist of: each cluster contains one cluster head sensor node (CH) and multiple intra-cluster sensor nodes (SN), and the network also deploys autonomous underwater vehicles (AUVs), sink nodes, and a land control center.

[0048] The data transmission path is as follows: intra-cluster sensor nodes transmit the collected data to cluster head sensor nodes; AUVs periodically collect data from each cluster head sensor node and offload it to sink nodes; sink nodes forward the data to the land control center via radio communication. This invention focuses on optimizing the intra-cluster data collection process, with cluster head sensor nodes responsible for time slot scheduling and physical layer resource allocation for intra-cluster sensor nodes.

[0049] Step S2: Define resource allocation parameters based on OFDM-TDMA technology, including the transmission mode, number of subcarriers, number of symbols, and transmission power of each node as allocatable resource parameters, and set a communication round structure including a resource allocation phase and a data transmission phase. Each round includes a resource allocation phase and a data transmission phase. The data transmission phase is divided into multiple frames, each frame contains multiple time slots, and a different number of OFDM symbols are allocated in each time slot.

[0050] Based on the characteristics of communication systems combining OFDM and TDMA, the key parameters are defined as follows:

[0051] Transmission Modes: Most OFDM transceivers support multiple transmission modes, each offering different transmission rates and bit error rates. Considering BPSK, QPSK, 16QAM, and 64QAM modes, higher-order modes generally offer higher transmission rates but also higher bit error rates.

[0052] Subcarrier count: As a core resource of the OFDM physical layer, the number of subcarriers determines the system's parallel transmission capability. More subcarriers theoretically result in a higher transmission rate, but an excessive number can cause inter-carrier interference (ICI). Therefore, a balance needs to be struck between transmission rate and interference suppression.

[0053] Symbol count: In OFDM systems, the basic unit of data transmission is the OFDM symbol. Allocating different numbers of symbols according to the node rate requirements allows for more refined resource allocation than traditional slot-level allocation, thereby avoiding resource waste.

[0054] Transmission power: Transmission power affects the signal-to-noise ratio (SNR) and system energy consumption. Increasing transmission power can improve SNR and reduce the bit error rate (BER), but it also increases energy consumption. Therefore, it needs to be dynamically adjusted based on channel conditions to optimize the balance between signal quality and energy efficiency.

[0055] Time slot and frame structure: The OT-MAC protocol operates on a round cycle, with each round comprising a resource allocation phase and a data transmission phase. The data transmission phase is further divided into multiple frames, each containing several time slots. A different number of OFDM symbols can be allocated within each time slot to accommodate the rate requirements of different nodes.

[0056] Step S3: Configure the decision model, that is, establish a multi-objective optimization problem with the goal of minimizing the total network energy consumption and maximizing the network throughput. Its constraints include node rate requirement constraints, bit error rate constraints and total resource constraints, and preset the optimization decision algorithm to solve the problem.

[0057] Step S4: Execute the protocol workflow: The cluster head sensor node broadcasts a resource request collection packet; after receiving the resource request collection packet, each of the cluster's sensor nodes sends a resource allocation request packet at a specified time. The resource allocation request packet carries the node's rate requirement and channel parameters.

[0058] The protocol execution workflow is as follows: The cluster head sensor node broadcasts a Resource Request Collection (RTC) packet; sensor nodes send Resource Allocation Request (RTA) packets at specified times, carrying node rate requirements and channel parameters; after receiving all RTA packets, the cluster head sensor node runs the NSGA-OT algorithm to allocate resources, redetermining the transmission mode, number of subcarriers, number of symbols, and transmission power for each node based on the resource allocation, and broadcasts the allocation results via an ANC packet; sensor nodes transmit data according to the resources and time allocated by the ANC packet; after each frame transmission, the cluster head sensor node broadcasts an Acknowledgment (ACK) packet; within each round, the cluster head sensor node and other sensor nodes within the cluster complete resource allocation and data transmission through the following steps to ensure collision-free and efficient operation; this workflow specifically includes the following five steps executed sequentially:

[0059] RTC Packet Broadcast: The cluster head sensor node broadcasts a Resource Request to Collect (RTC) packet to all sensor nodes within the cluster. The RTC packet pre-allocates specific timestamps for each node to send subsequent Request to Allocate (RTA) packets, thereby avoiding collisions caused by multiple nodes sending simultaneously.

[0060] RTA packet transmission: After receiving the RTC packet, the sensor node sends an RTA packet to the cluster head sensor node at a specified time according to the timestamp assigned to it. The RTA packet carries key information such as the node's rate requirements and channel parameters for the cluster head to make resource decisions.

[0061] Resource Allocation and ANC Packet Broadcast: After collecting RTA packets from all nodes, the cluster head sensor node runs the NSGA-OT algorithm to optimize resource allocation, determining parameters such as transmission mode, number of subcarriers, number of symbols, and transmission power for each node. Subsequently, the cluster head broadcasts the allocation results and the specified data transmission time to all sensor nodes via a notification packet. The channel parameters mentioned are used to estimate the SNR.

[0062] Data transmission: After receiving the ANC packet, the intra-cluster sensor node sends data to the cluster head sensor node through the OFDM-TDMA channel according to the resources and transmission time allocated by the ANC packet.

[0063] ACK Confirmation: After the transmission of each frame is completed, the cluster head sensor node broadcasts an ACK packet to all sensor nodes in the cluster and informs each node of the data reception status to ensure the reliability of the data transmission process.

[0064] The problem is to minimize the total network energy consumption and maximize the network throughput. Constraints are established, including rate requirement constraints, bit error rate constraints, and total resource constraints. The NSGA-II algorithm is used to solve the Pareto optimal solution set. Finally, a key inflection point Knee point on the Pareto front (composed of all non-dominated solutions) is selected as the resource allocation scheme that achieves the best balance between total network throughput and total system energy consumption.

[0065] like Figure 2 As shown, this invention provides a cross-layer MAC protocol implementation system for underwater sensor networks, comprising:

[0066] The network construction module 100 is used to construct a clustered topology underwater acoustic sensor network. The network includes cluster head sensor nodes, intra-cluster sensor nodes, autonomous underwater vehicles, gateway nodes, and a land control center. The intra-cluster sensor nodes are used for underwater data acquisition, and the cluster head sensor nodes are used for intra-cluster communication coordination and resource allocation.

[0067] The resource definition module 200 defines resource allocation parameters based on OFDM-TDMA technology. The resource allocation parameters include transmission mode, number of subcarriers, number of symbols, and transmission power. It is used to set the communication round structure that includes a resource allocation phase and a data transmission phase. The data transmission phase is divided into multiple frames, each frame contains multiple time slots, and a different number of OFDM symbols are allocated in each time slot.

[0068] The optimization decision module 300, connected to the resource definition module, is used to establish a multi-objective optimization problem with the goal of minimizing the total network energy consumption and maximizing the network throughput. The constraints of the multi-objective optimization problem include node rate requirement constraints, bit error rate constraints, and total resource constraints. The optimization decision module is also used to pre-configure an optimization decision algorithm for solving the multi-objective optimization problem.

[0069] The protocol execution module 400 is connected to the network construction module, the resource definition module and the optimization decision module respectively, and is used to execute the protocol workflow. Its resource request collection unit is used to broadcast resource request collection packets by the cluster head sensor node.

[0070] Resource request collection unit 401 is used to broadcast resource request collection packets by the cluster head sensor node;

[0071] The resource request reporting unit 402 is used to send a resource allocation request packet at a specified time after each of the cluster's intra-cluster sensing nodes receives the resource request collection packet. The resource allocation request packet carries the node's rate requirement and channel parameters.

[0072] The resource allocation and notification unit 403 is used to run the optimization decision algorithm after the cluster head sensor node receives all the resource allocation request packets. Based on the multi-objective optimization problem and its constraints, it selects the Pareto optimal solution set from the candidate schemes and selects the final resource allocation scheme from the Pareto optimal solution set. The resource allocation scheme determines the parameter combination of transmission mode, number of subcarriers, number of symbols and transmission power for each of the cluster sensor nodes, and sends the resource allocation scheme to each node through a broadcast notification packet.

[0073] The data transmission unit 404 is used to transmit data from each of the cluster intra-cluster sensing nodes to the cluster head sensing node according to the resource parameters and transmission time slots allocated by the notification packet.

[0074] The transmission confirmation unit 405 is used to broadcast an confirmation packet by the cluster head sensor node after each data transmission frame ends to confirm the data transmission status.

[0075] Furthermore, the dynamic allocation of transmission mode, number of subcarriers, number of symbols, and transmission power in step S2 satisfies the following: the transmission mode balances bit error rate and transmission rate according to channel conditions and node rate requirements; the number of subcarriers balances parallel transmission capability and inter-carrier interference according to bandwidth and node rate requirements; the number of symbols serves as the smallest unit for OFDM data transmission, realizing symbol-level resource allocation; and the transmission power is dynamically adjusted according to the channel signal-to-noise ratio to reduce bit error rate and optimize energy consumption.

[0076] Furthermore, the NSGA-OT algorithm in step S3 includes: initializing the population, randomly assigning the transmission mode, number of subcarriers, transmission power and number of symbols to the nodes; generating a child population through selection, crossover and mutation; selecting solutions from the merged population to enter the next generation based on fast non-dominated sorting and crowding distance; and outputting the Pareto optimal solution set after the iteration terminates, wherein the non-dominated sorting defines the dominance relationship according to the rate constraint violation value RCV and the bit error rate constraint violation value BCV.

[0077] The specific process of the fast non-dominated sorting includes: calculating the number of solutions dominated by each solution and the set of solutions that dominate other solutions; placing the solution with the lowest non-dominated level into the first-level Pareto front; processing each level of front sequentially until all solutions are classified; for solutions in the same Pareto front, selecting the preferred solution by comparing congestion distance, where congestion distance is based on the reciprocal of network throughput. and total energy consumption calculate.

[0078] Among the constraints of the multi-objective optimization problem, the rate requirement constraint requires that the node transmission rate is not lower than its rate requirement. The bit error rate constraint requires that the node bit error rate not exceed the maximum allowable bit error rate. The total resource constraints limit the transmission power, number of subcarriers, and number of symbols to a preset range.

[0079] The protocol workflow avoids RTA packet collisions by scheduling RTC packets with timestamps, specifies data transmission time through ANC packets, and ensures data transmission reliability through ACK packets, thereby achieving collision-free communication.

[0080] The transmission mode corresponds to BPSK, QPSK, 16QAM or 64QAM modulation. The number of bits carried by the symbol is calculated based on the number of subcarriers and the proportion of guard subcarriers. The bit error rate is derived through the complementary error function and the signal-to-noise ratio model.

[0081] Furthermore, for underwater acoustic sensor networks employing a clustered topology, the resource allocation problem can be modeled as a multi-objective optimization model. This model takes the network within a single cluster as the research object and formalizes the resource allocation problem by defining explicit optimization objectives and constraints. The first optimization objective is to minimize the total network energy consumption, specifically defined as the sum of the energy transmitted by all nodes within the cluster. The expression for the total network energy consumption is:

[0082] ;

[0083] in, This represents the total number of sensor nodes within a single cluster. For the first Transmission power of sensor nodes within a cluster, For the first Data transmission time of sensor nodes within a cluster. Calculated by the following formula:

[0084] ;

[0085] in, To allocate to sensing nodes within the cluster The number of symbols, This indicates the guard interval between symbols, which is used to eliminate inter-symbol interference (ISI). The duration of the symbol for that node, and Data is modulated into discrete symbols for transmission, each symbol carrying a certain number of bits of information. The number of symbols, determined by the allocated resources, decides the duration and capacity of data transmission by each node. Subcarrier spacing. Calculated by the following formula:

[0086] ;

[0087] in, This represents the number of subcarriers for that node. For bandwidth.

[0088] The second optimization objective is to maximize network throughput. Network throughput is the sum of the transmission rates of all sensor nodes within a cluster. Calculated by the following formula:

[0089] ;

[0090] in, For cluster-intra-cluster sensing nodes The transmission rate is calculated by the following formula:

[0091] ;

[0092] in, The number of bits carried by an OFDM symbol of this node. It is the frame length, and Symbol duration Calculated by the following formula:

[0093] ;

[0094] in, This indicates the proportion of guard subcarriers among all subcarriers; guard subcarriers do not transmit data. It corresponds one-to-one with the four transmission modes: BPSK, QPSK, 16QAM, and 64QAM.

[0095] Furthermore, the performance of underwater acoustic sensor networks employing clustered topologies must meet the following two constraints:

[0096] (1) Rate constraints include:

[0097] Constraint 1: Node transmission rate It should not be lower than its specified demand rate. ,Right now ;

[0098] Constraint 1: Violation Judgment Criteria: When Record the rate constraint violation value (RCV).

[0099] (2) Bit error rate (BER) constraints include:

[0100] Constraint 2: Node Transmission error rate It should not exceed the maximum permissible bit error rate. ,Right now ;

[0101] Constraint 2: Violation Judgment Criteria: If If this occurs, a BER constraint violation value (BCV) is generated. This represents the bit error rate of that node. Calculated by the following formula:

[0102] ;

[0103] in, For SNR per bit, The complementary error function, the complementary error function Calculated by the following formula:

[0104] ;

[0105] SNR can be calculated using the following formula:

[0106] ;

[0107] in, , , For underwater acoustic signals, the propagation distance, signal frequency, and transmission power are considered. For path loss, Environmental noise. Path loss. Calculated by the following formula:

[0108] ;

[0109] in, The unit normalization constant, As a propagation factor, Let be the absorption coefficient. From Thorp's formula, 𝑎(𝑓) can be calculated using the following formula:

[0110] ;

[0111] Environmental noise Calculated by the following formula:

[0112] ;

[0113] in, , , and These are turbulent noise, ship noise, wind and wave noise, and thermal noise, respectively. It is a factor of ship activity. This refers to wind speed.

[0114] (3) Total resource constraints: Transmission power, number of subcarriers, and number of symbols must not exceed the specified range. The formulaic expression of the multi-objective optimization model is as follows:

[0115] ;

[0116] ;

[0117] ;

[0118] ;

[0119] ;

[0120] ;

[0121] The above model shows that the key to the resource allocation problem is to set the optimal transmission mode for each node under the constraints of bit error rate and rate. Number of subcarriers Transmission power and symbol number This is to increase network throughput and reduce energy consumption.

[0122] Furthermore, in the multi-objective optimization problem, maximizing network throughput and minimizing energy consumption are two conflicting objectives, and their optimal solutions cannot be achieved simultaneously. Therefore, solving the multi-objective optimization model problem requires a trade-off between the two objectives, aiming to find a set of non-dominated solutions (or Pareto optimal solutions). Specifically, for any two candidate solutions... and ,

[0123] If candidate solution Non-inferior to candidate solutions on all objectives And is strictly superior to at least one objective. Then it is called Dominate A non-dominated solution is a solution that is not dominated by any other solution; it is also called a Pareto optimal solution. Pareto optimality is a core concept in economics and multi-objective optimization, referring to an ideal state of resource allocation where, without compromising the interests of any optimization objective, it is impossible to improve the situation of at least one objective. The set of all non-dominated solutions constitutes the Pareto front. The core of multi-objective optimization problems lies in finding or approximating this Pareto front.

[0124] The Nondominated Sorting Genetic Algorithm II (NSGA-II) is one of the most commonly used multi-objective optimization algorithms due to its fast running speed and good convergence of solution sets. This invention employs the NSGA-II algorithm (NSGA-II for OT-MAC, NSGA-OT) to compare a large number of candidate solutions, ultimately generating a Pareto optimal solution set, from which the knee point is selected as the actual resource allocation scheme. This comparison mechanism ensures that the protocol can find the optimal balance between throughput and energy consumption, meeting the differentiated needs of underwater sensor networks. In this invention, the knee point specifically refers to the final resource allocation scheme selected from the Pareto optimal solution set generated by the NSGA-OT algorithm that achieves the optimal balance between total network throughput and total system energy consumption.

[0125] Furthermore, a knee point ultimately manifests as a specific set of executable protocol parameter configurations, including:

[0126] The transmission mode of each sensor node (e.g., QPSK, 16QAM).

[0127] The number of subcarriers and OFDM symbols allocated to each node.

[0128] Transmission power of each node.

[0129] In this invention, the Knee point is a critical decision point automatically determined through a multi-objective optimization algorithm. The specific resource configuration parameters corresponding to it are ultimately adopted as the protocol execution scheme, thereby ensuring that the underwater sensor network can achieve the optimal system-level balance between overall throughput and network energy efficiency while meeting differentiated rate requirements.

[0130] like Figure 3 As shown, a traditional underwater acoustic sensor network adopts a clustered topology, mainly including a coastal control system 1, a satellite system 2, a radio communication link 3, a gateway node (Sink node) 4, cluster head sensor nodes 5, cluster-internal sensor nodes 6, an autonomous underwater vehicle (AUV) 7, and an underwater acoustic communication link 8. Each cluster consists of multiple sensor nodes 6 within the cluster, with each cluster head sensor node responsible for monitoring different areas. The sensor nodes within the cluster transmit data to the cluster head sensor node 4 via the underwater acoustic communication link 8; the AUV 7 periodically collects data from each cluster head sensor node and offloads the data to the Sink node; finally, the Sink node forwards the data to the coastal control system on land via the radio communication link 3.

[0131] like Figure 4 The diagram shows the flow of the underwater sensor network cross-layer MAC protocol method of the present invention. Taking a complete, bidirectional communication interaction process as an example, it specifically includes the following steps:

[0132] 1. The cluster head sensor node 5 broadcasts a resource request collection packet (RTC) to all cluster sensor nodes 6. The RTC packet contains a timestamp for sending RTAs allocated to each sensor node, which is used for scheduling and avoiding control packet collisions.

[0133] 2. After receiving the RTC packet through the in-cluster sensor node 6, it extracts its own corresponding RTA transmission time and sends a resource allocation request packet (RTA) at the specified time. The RTA packet carries information necessary for resource allocation, such as the node's rate requirement and channel parameters (used to estimate the signal-to-noise ratio SNR).

[0134] 3. After receiving RTA packets from all cluster-internal sensor nodes 6 through cluster head sensor node 5, the NSGA-OT algorithm is run to make resource allocation decisions, determining the transmission mode, number of subcarriers, number of symbols and transmission power of each node; then the resource allocation results are broadcast to all cluster-internal sensor nodes 6 through allocation notification packets (ANC); the ANC packets also specify the data transmission time slots of each node.

[0135] 4. After receiving the ANC packet through the intra-cluster sensor node 6, data is sent to the cluster head sensor node 5 through the OFDM-TDMA channel according to the allocated resources and transmission time.

[0136] 5. After the transmission of each frame is completed, the cluster head sensor node 5 broadcasts an ACK packet to notify the sensor nodes in each cluster of the data reception status, ensuring the reliability of the data transmission process.

[0137] Furthermore, the specific process of the NSGA-OT algorithm described in step S3 is as follows:

[0138] 3.1 Initial scale is population ;

[0139] In the NSGA-OT algorithm, a solution is called an individual, and the solution takes the form of: The set of all solutions is called the population. Initialization is... Each individual is randomly assigned The transmission mode, number of subcarriers, transmission power, and number of symbols of each node are used to calculate the throughput, energy consumption, and constraint violation value for each individual node. For example... Figure 5 The image shows an example of calculating constraint violation values.

[0140] 3.2, in Selection, crossover, and mutation are performed to generate offspring populations. ,for For each individual, the objective function value and constraint violation value are calculated.

[0141] 3.3 Obtaining the merged population Based on the fast non-dominated sorting and crowding distance algorithm, from Select Each solution is passed to the next generation, generating a new population. .

[0142] 3.4. If the number of iterations reaches the maximum and the termination condition is met, the loop ends. Otherwise, continue to step 2.

[0143] 3.5 Output the Pareto optimal solution set.

[0144] Furthermore, the fast non-dominated sorting described in 3.3 involves dividing the population into multiple Pareto fronts at different levels. The specific process is as follows:

[0145] 3.3.1. Traverse all solutions in the population. For each solution... Calculate two values: a) , dominate The number of solutions, b) , The set of solutions to a dominating problem. The definition of domination is as follows:

[0146] (a) Solution of Less than solution of ;

[0147] (b) Solution of Equal to solution of And solve of Less than solution of ;

[0148] (c) Solution of and All and Solution They are equal and satisfy:

[0149] ;

[0150] If any of the above conditions are met, then the solution is... Dominant Solution In the population The solution is extracted and placed into the first Pareto front layer. , The non-dominant level of all members in the group. Set to 1.

[0151] 3.3.2, For Each solution in traversal Each member ,make If it occurs Then the solution will be found. Place in the second layer of Pareto front In the middle, the non-dominant hierarchy of 𝑞 Set to 2.

[0152] 3.3.3 Repeat 3.3.2 until all Pareto fronts are determined.

[0153] As can be seen from the above process, the lower the rank, the closer it is to the Pareto optimum. Therefore, the algorithm will prioritize keeping solutions with lower ranks in the next generation, that is, it will first retain... Then …until the next generation reaches a certain scale Assuming satisfy and ,but Only a portion of the solutions can be carried over to the next generation. At this point, it is necessary to compare the crowding distance to determine which solutions will be retained.

[0154] For solutions with the same non-dominated level (i.e., belonging to the same Pareto front), the solution with the larger crowding distance will be preferentially selected to enter the next generation. Pareto Front The solution in the target The method for calculating the congestion distance is as follows:

[0155] a) will According to the solution in Arranged in ascending order;

[0156] b) The first solution after sorting and the last solution The crowding distance is set to infinity;

[0157] c) For Solution Crowded distance equals Solution in the target The method of calculating crowded distance on and in Similarly, the sum of the congestion distances on the two targets is the final congestion distance.

[0158] After the NSGA-OT algorithm terminates its iterations, it outputs a Pareto optimal solution set. The "Utopia point" is defined as the theoretical optimal value for each optimization objective (maximum throughput, minimum energy consumption). The solution closest to the Utopia point in the Pareto optimal solution set is selected as the Knee point, which is the final resource allocation scheme. Figure 6 The image shows a schematic diagram of a knee point.

[0159] In summary, this approach meets the differentiated transmission rate requirements of different sensor nodes, ensuring that the node data transmission rate matches the data generation rate; reduces the bit error rate (BER) of data transmission, improves signal transmission quality, adapts to the multipath effect and low SNR characteristics of underwater acoustic channels; increases network throughput, reduces resource idleness, and simultaneously reduces node energy consumption, extending the network lifetime.

[0160] Thirdly, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the underwater sensor network cross-layer MAC protocol implementation method.

[0161] Fourthly, the present invention proposes a computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the underwater sensor network cross-layer MAC protocol implementation method.

[0162] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0163] This invention is not limited to the embodiments described above. The above description of specific embodiments is intended to illustrate and explain the technical solutions of this invention. The specific embodiments described above are merely illustrative and not restrictive. Without departing from the spirit and scope of the claims, those skilled in the art can make many specific modifications based on the teachings of this invention, and these modifications all fall within the scope of protection of this invention.

Claims

1. A method for implementing a cross-layer MAC protocol in an underwater sensor network, characterized in that, Includes the following steps: Step S1: Construct a clustered topology underwater acoustic sensor network, which includes cluster head sensor nodes, cluster intra-cluster sensor nodes, autonomous underwater vehicles, gateway nodes, and a land control center. The cluster intra-cluster sensor nodes transmit the collected data to the cluster head sensor nodes. The cluster intra-cluster sensor nodes are responsible for underwater data acquisition, and the cluster head sensor nodes are responsible for intra-cluster communication coordination and resource allocation. Step S2: Based on OFDM-TDMA technology, define the transmission mode, number of subcarriers, number of symbols, and transmission power as allocable resource parameters, and set a communication round structure including a resource allocation phase and a data transmission phase. The data transmission phase is divided into multiple frames, and each frame contains multiple time slots. Step S3: Configure the decision model, that is, establish a multi-objective optimization problem with the goal of minimizing the total network energy consumption and maximizing the network throughput. Its constraints include node rate requirement constraints, bit error rate constraints and total resource constraints, and preset the optimization decision algorithm to solve the problem. Step S4: Execute the protocol workflow: The cluster head sensor node broadcasts a resource request collection packet; after receiving the resource request collection packet, each of the cluster's sensor nodes sends a resource allocation request packet at a specified time. The resource allocation request packet carries the node's rate requirement and channel parameters. After receiving resource allocation request packets from all nodes, the cluster head sensor node runs the optimization decision algorithm. Based on the multi-objective optimization problem and its constraints, it selects the Pareto optimal solution set from the candidate schemes and chooses the final resource allocation scheme from the Pareto optimal solution set. The resource allocation scheme determines a set of parameter combinations for each node, including transmission mode, number of subcarriers, number of symbols, and transmission power. The cluster head sensor node distributes the resource allocation scheme to each node via a broadcast notification packet. Each cluster sensor node transmits data to the cluster head sensor node according to the resource parameters and transmission time slots allocated in the notification packet. After each data transmission frame ends, the cluster head sensor node broadcasts an acknowledgment packet to confirm the data transmission status.

2. The method for implementing cross-layer MAC protocol in underwater sensor networks according to claim 1, characterized in that, The transmission mode, number of subcarriers, number of symbols, and transmission power in step S2 are as follows: The transmission mode balances bit error rate and transmission rate based on channel conditions and node rate requirements; the number of subcarriers balances parallel transmission capability and inter-carrier interference based on bandwidth and node rate requirements; the number of symbols serves as the smallest unit for OFDM data transmission, enabling symbol-level resource allocation; and the transmission power is dynamically adjusted based on the channel signal-to-noise ratio to reduce bit error rate and optimize energy consumption.

3. The method for implementing cross-layer MAC protocol in underwater sensor networks according to claim 1, characterized in that, The optimization decision algorithm in step S3 adopts the NSGA-OT algorithm. The algorithm process includes: initializing the population, randomly assigning the transmission mode, number of subcarriers, transmission power and number of symbols of the nodes; generating the offspring population through selection, crossover and mutation; selecting solutions from the merged population based on fast non-dominated sorting and crowding distance to enter the next generation; and outputting the Pareto optimal solution set after the iteration terminates, wherein the non-dominated sorting defines the dominance relationship according to the rate constraint violation value RCV and the bit error rate constraint violation value BCV.

4. The method for implementing cross-layer MAC protocol in underwater sensor networks according to claim 3, characterized in that, The specific process of the fast non-dominated sorting includes: calculating the number of solutions dominated by each solution and the set of solutions that dominate other solutions; placing the solution with the lowest non-dominated level into the first-level Pareto front; processing each front layer in turn until all solutions are classified; for solutions in the same Pareto front, selecting the preferred solution by comparing the congestion distance, which is calculated based on the reciprocal of the network throughput and the total energy consumption.

5. The method for implementing cross-layer MAC protocol in underwater sensor networks according to claim 1, characterized in that, Among the constraints of the multi-objective optimization problem, the rate requirement constraint requires that the node transmission rate is not lower than its rate requirement, the bit error rate constraint requires that the node bit error rate is not higher than the maximum allowable bit error rate, and the total resource constraint limits the transmission power, the number of subcarriers, and the number of symbols to a preset range.

6. The method for implementing cross-layer MAC protocol in underwater sensor networks according to claim 1, characterized in that, The protocol workflow avoids RTA packet collisions by scheduling RTC packets with timestamps, specifies data transmission time through ANC packets, and ensures data transmission reliability through ACK packets, thereby achieving collision-free communication.

7. The method for implementing cross-layer MAC protocol in underwater sensor networks according to claim 1, characterized in that, The transmission mode corresponds to BPSK, QPSK, 16QAM or 64QAM modulation. The number of bits carried by the symbol is calculated based on the number of subcarriers and the proportion of guard subcarriers. The bit error rate is derived through the complementary error function and the signal-to-noise ratio model.

8. A system for implementing a cross-layer MAC protocol in an underwater sensor network according to any one of claims 1 to 7, characterized in that, include: A network construction module is used to construct a clustered topology underwater acoustic sensor network. The network includes cluster head sensor nodes, cluster intra-cluster sensor nodes, autonomous underwater vehicles, gateway nodes, and a land control center. The cluster intra-cluster sensor nodes are used for underwater data acquisition, and the cluster head sensor nodes are used for intra-cluster communication coordination and resource allocation. The resource definition module defines allocatable resource parameters based on OFDM-TDMA technology. These resource parameters include transmission mode, number of subcarriers, number of symbols, and transmission power. It is also used to set the communication round structure, which includes a resource allocation phase and a data transmission phase. The data transmission phase is divided into multiple frames, and each frame contains multiple time slots. The optimization decision module, connected to the resource definition module, is used to establish a multi-objective optimization problem with the goal of minimizing the total network energy consumption and maximizing the network throughput. The constraints of the multi-objective optimization problem include node rate requirement constraints, bit error rate constraints, and total resource constraints. The optimization decision module is also used to pre-configure optimization decision algorithms for solving the multi-objective optimization problem. The protocol execution module is connected to the network construction module, the resource definition module and the optimization decision module respectively, and is used to execute the protocol workflow. Its resource request collection unit is used to broadcast resource request collection packets by the cluster head sensor node. A resource request collection unit is used to broadcast a resource request collection packet by the cluster head sensor node; The resource request reporting unit is used to send a resource allocation request packet at a specified time after each of the cluster's sensing nodes receives the resource request collection packet. The resource allocation request packet carries the node's rate requirement and channel parameters. The resource allocation and notification unit is used to run the optimization decision algorithm after the cluster head sensor node receives all the resource allocation request packets. Based on the multi-objective optimization problem and its constraints, it selects the Pareto optimal solution set from the candidate schemes and selects the final resource allocation scheme from the Pareto optimal solution set. The resource allocation scheme determines the parameter combination of transmission mode, number of subcarriers, number of symbols and transmission power for each of the cluster sensor nodes, and sends the resource allocation scheme to each node through a broadcast notification packet. The data transmission unit is used to transmit data from each of the cluster-internal sensing nodes to the cluster head sensing node according to the resource parameters and transmission time slots allocated by the notification packet. The transmission confirmation unit is used to broadcast an confirmation packet by the cluster head sensor node after each data transmission frame ends, in order to confirm the data transmission status.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the underwater sensor network cross-layer MAC protocol implementation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the underwater sensor network cross-layer MAC protocol implementation method as described in any one of claims 1 to 7.

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