A routing method for underwater acoustic sensor networks based on fuzzy logic decision making
Through the water acoustic sensing network routing method based on fuzzy logic decision-making, the best forwarding node is selected using HELLO, PING, PONG packet interaction and fuzzy control systems, which solves the problems of unstable link quality and premature node failure in the underwater network, improves the packet delivery rate and network reliability, and extends the network life.
Patent Information
- Application Number
- CN202411443961.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-16
- Publication Date
- 2025-09-02
- Estimated Expiration
- 2044-10-16
AI Technical Summary
The underwater environment is complex and changeable, and the time-variability and asymmetry of link quality make it difficult to transplant wireless communication routing methods, premature node failure, short network life, insufficient coverage, frequent network topology changes, low packet delivery rate and poor reliability.
The water acoustic sensing network routing method based on fuzzy logic decisions is adopted to obtain node information through interactive interaction between HELLO, PING, and PONG packets, and the best forwarding node is selected using the fuzzy control system to deal with uncertainty and routing hole problems, and to make routing decisions based on link quality, residual energy and hops from Sink nodes.
It improves network reliability, extends network life, simplifies computing, solves uncertainty and hollow problems in underwater networks, and improves data packet delivery rate and reliability.
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Figure CN119449696B_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of underwater acoustic communication network routing layer, and relates to an underwater acoustic sensor network routing method algorithm based on fuzzy logic decision-making. Background Art
[0002] Underwater routing methods are crucial for ensuring efficient multi-hop data transmission in underwater acoustic sensor networks (UASNs). While network-layer routing methods are proliferating in wireless networks, the complex and volatile underwater environment and network characteristics, coupled with limited energy, make direct transfer of these methods from wireless communications difficult. Therefore, designing a high-performance routing method for underwater acoustic sensor networks is extremely challenging.
[0003] The rapid changes in the acoustic channel in the underwater environment may lead to time-varying and asymmetric link quality. In addition, overuse of certain nodes can cause premature node failure, shortening the network life. In addition to link quality issues, UASNs face other challenges. Due to the high cost of underwater nodes, they are usually sparsely deployed, which may lead to an insufficient number of nodes covering the target area, resulting in void areas, thereby reducing the packet delivery rate. The irregular movement of nodes causes the network topology to change continuously, which may cause the routing path to fail due to link disconnection, further reducing the reliability of data transmission. In addition, the geographical location information of underwater nodes is difficult to obtain, which increases the complexity of network management and routing selection. Summary of the Invention
[0004] In response to the above problems, the present invention proposes a method for underwater acoustic sensor network routing based on fuzzy logic decision-making, which can be applied to underwater acoustic sensor network scenarios. The present invention uses a fuzzy control system (FCS) to select the best forwarding node (BFN). The relative residual energy, link quality and relative distance from the sink node of the candidate node are comprehensively considered as input parameters of the fuzzy system. Fuzzy control can handle the interaction of uncertainties of different parameters in the process of data packet forwarding. The application of fuzzy control in routing protocols has significant advantages, such as improving network reliability, extending network life, handling uncertainty and simplifying calculations. The technical solution of the present invention is as follows:
[0005] A fuzzy logic-based underwater acoustic sensor network routing method uses data packets including HELLO packets, PING packets and PONG packets, where:
[0006] The PING packet contains: the sending node ID, the next hop node ID and the number of hops from the sink node;
[0007] The PONG packet contains: sending node ID, next hop node ID, remaining energy of candidate node, link quality and number of hops from sink node;
[0008] The underwater acoustic sensor network routing method includes the following steps:
[0009] (1) The Sink node periodically broadcasts HELLO packets to sense topology changes. Each node dynamically maintains a parameter Hcur representing the hop count of the node locally, which is used to represent the hop count from the node to the Sink node. All nodes in the entire network are time synchronized. If a node hears a HELLO packet within the HELLO broadcast time, it extracts the sending node ID and the hop count Hcount from the Sink node. If the Hcount in the packet is less than the Hcur-1 of this node, the hop count of the node is updated to Hcount+1, the Hcount in the HELLO packet is updated to Hcount+1, and the HELLO packet is forwarded. Otherwise, the HELLO packet is directly discarded.
[0010] (2) During the relay selection process, the sending node broadcasts a PING packet;
[0011] (3) After the neighbor node receives the PING packet, the neighbor node whose hop count from the Sink node is less than or equal to the sending node becomes the candidate node. The candidate node calculates the link quality between the sending node and the node, and replies with a PONG packet point-to-point.
[0012] (4) During the waiting time, the sending node receives the PONG packet replied from the candidate node and builds the candidate node routing table;
[0013] (5) After the waiting time ends, the best forwarding node is selected based on the candidate node routing table and the fuzzy system;
[0014] (6) The sending node sets the next-hop node ID in the data packet as the output optimal forwarding node ID of the fuzzy system, and then forwards the data packet. The data packet structure contains three parts: header, routing information, and data payload; among them, the routing information includes the next-hop node ID, forwarding times, and routing void_flag.
[0015] Furthermore, the routing hole problem is handled in the following ways:
[0016] Use HELLO broadcast to mark hollow nodes: the void_flag of all nodes is initialized to 0; during the most recent HELLO packet broadcast, if a node does not receive a HELLO packet, it considers itself a hollow node and sets the void_flag to 1; during data packet forwarding, when a neighboring node hears a PING packet, it first checks the void_flag of the node; if the void_flag is 0, the PONG packet can be sent; if the void_flag is 1, the PONG packet cannot be sent;
[0017] Hollow nodes are marked by information interaction between nodes: when the source node or the current node has data to send, it broadcasts a PING packet; if it does not receive a PONG packet from the neighboring node within the waiting time, it considers itself a hollow node, sets the void_flag to 1, and re-forwards the data packet to the previous hop node; after receiving the data packet, the previous hop node identifies and extracts the void_flag; if the void_flag is 1, it selects the second-priority candidate node to re-forward the data packet.
[0018] Furthermore, a packet delivery probability model is used to measure the link quality between nodes. The method is as follows: the underwater packet delivery probability p is modeled as a function of the transmission distance d, the number of bits m, and the signal frequency f, and is expressed as: p(d,m,f)=(1-p b (d,f)) m ; Among them, p b (d,f) is the bit error rate; f SNR (d, f, X) represents the probability density at a given transmission distance d, signal frequency f, and average signal-to-noise ratio X. By considering path loss gain and loss, the average signal-to-noise ratio (SNR) at the receiver is modeled as: Among them, E b represents the energy per bit transmitted, A(d,f) represents the path loss for the transmission distance d and signal frequency f, and N0 represents the noise power density in the additive white Gaussian noise (AWGN) channel. The path loss of the acoustic link is expressed as: A(d,f) = d k α(f) d ; where k∈[1, 2] represents the propagation loss factor related to the propagation geometry, and α(f) represents the absorption coefficient.
[0019] Furthermore, the fuzzy system includes three steps: variable fuzzification, fuzzy reasoning based on fuzzy rules, and defuzzification; in variable fuzzification, the residual energy, link quality, and relative hop count of the candidate node are fuzzified according to the input membership function.
[0020] The present invention proposes a fuzzy logic-based underwater acoustic sensor network routing method. The sending node obtains information such as real-time link quality, remaining energy, and the number of hops from the sink node by exchanging information with neighboring nodes, and uses a fuzzy control system (FCS) to select the best next-hop node (BFN). When the source node or current node has data to send, it first broadcasts a PING packet. After receiving it, the neighboring node will reply with a PONG packet containing node information. After receiving this information, the sending node selects the optimal next-hop node based on this information. In addition, the method effectively solves the routing void problem by introducing the void_flag flag. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] Figure 1 Routing table, HELLO packet, PING packet and PONG packet format
[0022] Figure 2 Is the data packet structure
[0023] Figure 3 Flowchart of underwater acoustic sensor network routing method based on fuzzy logic decision making
[0024] Figure 4 This is a flowchart of the routing hole recovery mechanism.
[0025] Figure 5 It is a fuzzy system
[0026] Figure 6 is the membership function, (a) is the membership function diagram of relative residual energy, (b) is the membership function diagram of link quality, (c) is the membership function diagram of relative Sink node hop count, and (d) is the membership function diagram of output priority
[0027] Figure 7 Diagram of the relay selection process DETAILED DESCRIPTION
[0028] The present invention will be described below with reference to the accompanying drawings.
[0029] The present invention provides a method for routing underwater acoustic sensor networks based on fuzzy logic decision making. The core idea of this method is that the sending node can exchange information with neighboring nodes to obtain information such as real-time link quality, remaining energy, and the number of hops from the sink node. When the source node or the current node has data to send, it first broadcasts an echo request (PING) packet with the following structure: Figure 1 After the neighbor node listens to the PING packet, the neighbor node with a distance from the Sink node less than or equal to the sending node becomes a candidate node. It will first calculate the link quality between it and the sending node, and then reply with a point-to-point echo response (PONG) packet. The packet structure is as follows: Figure 1 As shown in Figure 1, the PONG packet contains information such as the remaining energy of the candidate node, the link quality, and the number of hops from the Sink node. The sending node receives the PONG packet during the waiting time and establishes a candidate node routing table. The routing table structure is as follows: Figure 1 After the waiting time expires, a fuzzy control system (FCS) is used to select the best forwarding node (BFN) using the candidate node's relative residual energy, link quality, and relative distance to the sink node as input variables. Furthermore, the method introduces a flag bit, void_flag, to effectively address routing holes.
[0030] A fuzzy logic-based underwater acoustic sensor network routing method includes the following steps:
[0031] (1) The Sink node periodically broadcasts HELLO packets to detect topology changes. The packet structure is as follows: Figure 1 As shown. Each node dynamically maintains a parameter Hcur locally, which is used to indicate the number of hops from this node to the Sink node. The Hcur of the Sink node is set to 0, and the Hcur of other nodes is initialized to 0xFF. All nodes in the entire network are time synchronized. In order to avoid collisions and excessive energy consumption, nodes will only update and forward HELLO packets during the broadcast time. If a node hears a HELLO packet outside the HELLO broadcast time, it will discard the packet directly. If a node hears a HELLO packet during the HELLO broadcast time, it will extract the ID and hop count (Hcount) from it. If the Hcount in the packet is less than the node's Hcur-1, the node's hop count is updated to Hcount+1, the Hcount in the HELLO packet is updated to Hcount+1, and the HELLO packet is forwarded; otherwise, the HELLO packet is discarded directly. (Hcur represents the hop count of the current node, and Hcount represents the hop count of the previous hop node)
[0032] (2) During the relay selection process, the sending node broadcasts a PING packet.
[0033] (3) After the neighbor node receives the PING packet, the neighbor node whose hop count from the Sink node is less than or equal to the sending node becomes the candidate node, calculates the link quality between the sending node and the node, and replies to the PONG packet point-to-point.
[0034] (4) Within the waiting time δ, the sending node receives the PONG packet replied from the candidate node and builds the candidate node routing table.
[0035] (5) After the waiting time is up, the best forwarding node is selected based on the candidate node routing table and the fuzzy system. This process mainly consists of three steps: variable fuzzification, fuzzy reasoning based on fuzzy rules, and defuzzification.
[0036] (6) The sending node sets the next hop node ID in the data packet to the output optimal forwarding node ID of the fuzzy system, and then forwards the data packet. The packet structure is as follows: Figure 2 As shown, it contains three parts: header, routing information and payload.
[0037] (7) This method handles the routing hole problem in two ways:
[0038] Hole nodes are marked using HELLO broadcasts: All nodes' void_flags are initialized to 0. If a node does not receive a HELLO packet during the most recent HELLO broadcast, it considers itself a hole node and sets its void_flag to 1. When a neighboring node receives a PING packet during data forwarding, it first checks its own void_flag. If void_flag is 0, it can send a PONG packet; if void_flag is 1, it cannot.
[0039] Hole nodes are marked using inter-node information exchange: When a source node or current node has data to send, it broadcasts a PING packet. If it does not receive a PONG packet in reply from a neighboring node within the waiting time, it considers itself a hole node, sets the void_flag to 1, and reforwards the packet to the previous hop. Upon receiving the packet, the previous hop identifies and extracts the void_flag. If the void_flag is 1, it selects the second-priority candidate node to reforward the packet.
[0040] by Figure 7 Taking a system composed of underwater wireless sensors as an example, this routing algorithm is explained in detail. The broadcast period is defined as the time period from the sending of a HELLO packet to the sending of the next HELLO packet. The broadcast time refers to the actual time used to forward the HELLO packet in each broadcast period. During this period, the node forwards the HELLO packet to notify other nodes of its existence and obtain topology information. During the broadcast time of this HELLO broadcast period, node 5 did not hear any HELLO packets, and the void_flag was set to 1, marking it as a void node. During the packet forwarding period of this period, the network topology changed with the underwater environment. Nodes 1, 2, 3, and the void node were all within the broadcast range of the source node and were its neighbor nodes.
[0041] The specific operation process of the present invention is as follows:
[0042] 1. The sink node periodically broadcasts HELLO packets to detect topology changes. Nodes 1, 2, 3, and 4 hear the HELLO packets during this period and update their hop counts from the sink node. Node 5, which does not hear the HELLO packets, considers itself a hole node and sets its viod_flag to 1.
[0043] 2. During the relay selection process, if the source node has a data packet to send, it will first broadcast a PING packet.
[0044] 3. Node 1, node 2, node 3 and the hole node are all within the broadcast range of the source node and are neighbor nodes of the source node. After the neighbor node listens to the PING packet, the hole node does not reply to the PONG packet. The neighbor nodes whose hop count from the Sink node is less than or equal to the sending node become candidate nodes. Node 2 and node 3 meet the requirements. They will calculate the link quality between the source node and the local node, and reply to the PONG packet point-to-point. Among them, we use the data packet delivery probability model to measure the link quality between nodes. The underwater data packet delivery probability p is modeled as a function of the transmission distance d, the number of bits m and the signal frequency f, and is expressed as: p(d,m,f)=(1-p b (d,f)) m Where pb(d,f) is the bit error rate, expressed as: Here, P e (X) represents the probability of an error occurring when the average signal-to-noise ratio (SNR) is equal to X under the selected modulation mode. SNR (d, f, X) represents the probability density under given d, f, and X, and varies with different fading models. By considering path loss gain and loss, the average SNR at the receiver is modeled as: Among them, E b represents the energy per bit transmitted, A(d, f) represents the path loss at distance d and frequency f, and N0 represents the noise power density in the additive white Gaussian noise (AWGN) channel. The path loss of the acoustic link can be expressed as: A(d, f) = d k α(f) d Where k∈[1,2] represents the propagation loss factor related to the propagation geometry, and α(f) represents the absorption coefficient, which can be calculated using the Thorpe formula:
[0045] 4. During the waiting time δ, (R is the broadcast range and v is the speed of sound) The source node receives the PONG packets replied by nodes 2 and 3 and builds a candidate node routing table.
[0046] 5. After the waiting time expires, the optimal forwarding node is selected based on the candidate node routing table and the fuzzy system. This process mainly consists of three steps: variable fuzzification, fuzzy reasoning based on fuzzy rules, and defuzzification.
[0047] Variable fuzzification: In the algorithm, we input membership functions, such as Figure 6 As shown in Figure 1, the relative residual energy, link quality, and relative hop count of candidate nodes are fuzzified. Natural experience and expert experience are primarily used to establish the fuzzy subsets and membership functions. The specific parameters and their corresponding feature sets are shown in Table 1.
[0048] Fuzzy Reasoning: Based on the fuzzy if-then classification rules, fuzzy reasoning is performed on the candidate node's fuzzified input parameters to determine the membership degrees of different priorities. For example, if a candidate node with high residual energy has a membership degree of 0.8, a node with good link quality has a membership degree of 0.7, and a node with a small number of hops has a membership degree of 0.6, then the node with high priority will have a membership degree of 0.8. The MLFC algorithm contains 17 fuzzy rules, as shown in Table 2.
[0049] Defuzzification: Defuzzification is performed using the centroid method, where the x-axis value of the center point of the area enclosed by the membership function curve and the x-axis is the defuzzification output value, which is also the final output value of the fuzzy algorithm. Its algebraic expression is
[0050] Table 1 Linguistic values of input and output parameters
[0051]
[0052] Table 2 Total fuzzy rules used in fuzzy control
[0053]
[0054]
[0055] 6. Sort the output priorities of the candidate node parameters after the fuzzy system. Candidate node 3 has the highest priority. The source node sets the next hop node ID in the data packet to 3 and then forwards the data packet.
[0056] 7. Node 3 listens to the data packet, discovers it is the next-hop node, receives the packet, and broadcasts a PING packet. However, if it does not receive a PONG packet from the candidate node within the waiting time, it considers itself a void node and sets its local void_flag to 1. It then updates the void_flag in the data packet. It then forwards the packet to the previous hop, the source node. After receiving the packet, the source node sets its void_flag to 0 and forwards it to the next-best forwarding node, Node 2.
[0057] 8. Repeat the above process to forward the data packet to the Sink node on the water surface.
Claims
1. A fuzzy logic-based underwater acoustic sensor network routing method, the data packets used include HELLO packets, PING packets and PONG packets, wherein, The PING packet contains: the sending node ID, the next hop node ID and the number of hops from the sink node; The PONG packet contains: sending node ID, next hop node ID, remaining energy of candidate node, link quality and number of hops from sink node; The underwater acoustic sensor network routing method includes the following steps: (1) The Sink node periodically broadcasts HELLO packets to sense topology changes. Each node dynamically maintains a parameter Hcur representing the hop count of the node locally, which is used to represent the hop count from the node to the Sink node. All nodes in the entire network are time synchronized. If a node hears a HELLO packet within the HELLO broadcast time, it extracts the sending node ID and the hop count Hcount from the Sink node. If the Hcount in the packet is less than the Hcur-1 of this node, the hop count of the node is updated to Hcount+1, the Hcount in the HELLO packet is updated to Hcount+1, and the HELLO packet is forwarded. Otherwise, the HELLO packet is directly discarded. (2) During the relay selection process, the sending node broadcasts a PING packet; (3) After the neighbor node receives the PING packet, the neighbor node whose hop count from the Sink node is less than or equal to the sending node becomes the candidate node. The candidate node calculates the link quality between the sending node and the node, and replies with a PONG packet point-to-point. (4) During the waiting time, the sending node receives the PONG packet replied from the candidate node and builds the candidate node routing table; (5) After the waiting time ends, the best forwarding node is selected based on the candidate node routing table and the fuzzy system; (6) The sending node sets the next-hop node ID in the data packet as the output optimal forwarding node ID of the fuzzy system, and then forwards the data packet. The data packet structure contains three parts: header, routing information, and data payload; among them, the routing information includes the next-hop node ID, forwarding times, and routing void_flag.
2. The underwater acoustic sensor network routing method based on fuzzy logic according to claim 1 is characterized in that: The following methods are used to solve the routing hole problem: Use HELLO broadcast to mark hollow nodes: the void_flag of all nodes is initialized to 0; during the most recent HELLO packet broadcast, if a node does not receive a HELLO packet, it considers itself a hollow node and sets the void_flag to 1; during data packet forwarding, when a neighboring node hears a PING packet, it first checks the void_flag of the node; if the void_flag is 0, the PONG packet can be sent; if the void_flag is 1, the PONG packet cannot be sent; Hollow nodes are marked by information interaction between nodes: when the source node or the current node has data to send, it broadcasts a PING packet; if it does not receive a PONG packet from the neighboring node within the waiting time, it considers itself a hollow node, sets the void_flag to 1, and re-forwards the data packet to the previous hop node; after receiving the data packet, the previous hop node identifies and extracts the void_flag; if the void_flag is 1, it selects the second-priority candidate node to re-forward the data packet.
3. The underwater acoustic sensor network routing method based on fuzzy logic according to claim 1 is characterized in that: The packet delivery probability model is used to measure the link quality between nodes.
4. The underwater acoustic sensor network routing method based on fuzzy logic according to claim 3 is characterized in that: The method of using the packet delivery probability model to measure the link quality between nodes is as follows: the underwater packet delivery probability p is modeled as a function of the transmission distance d, the number of bits m and the signal frequency f, and is expressed as: p(d,m,f)=(1-p b (d,f)) m ; Among them, p b (d,f) is the bit error rate; f SNR (d, f, X) represents the probability density at a given transmission distance d, signal frequency f, and average signal-to-noise ratio X. By considering path loss gain and loss, the average signal-to-noise ratio (SNR) at the receiver is modeled as: Among them, E b represents the energy per bit transmitted, A(d,f) represents the path loss for the transmission distance d and signal frequency f, and N0 represents the noise power density in the additive white Gaussian noise (AWGN) channel. The path loss of the acoustic link is expressed as: A(d,f) = d k α(f) d ; where k∈[1, 2] represents the propagation loss factor related to the propagation geometry, and α(f) represents the absorption coefficient.
5. The underwater acoustic sensor network routing method based on fuzzy logic according to claim 1 is characterized in that: The fuzzy system includes three steps: variable fuzzification, fuzzy reasoning based on fuzzy rules, and defuzzification; In variable fuzzification, the residual energy, link quality and relative hop count of candidate nodes are fuzzified according to the input membership function.