Unmanned aerial vehicle ad hoc network routing method and system based on energy balance under QoS guarantee

Abstract

This invention relates to routing control technology in wireless communication, specifically providing a routing method and system for UAV ad hoc networks based on energy balance under QoS assurance. When a source node needs to send data packets, it sends a routing request and waits for the estimated maximum propagation hop count. After the estimated time, based on the routing responses received during the waiting period, a path table for the destination node is established. A fitness function is constructed using four constraint parameters: link hold-up time, remaining energy, congestion level, and hop count. The fitness value of each path is then quickly and accurately obtained, and the path with the highest fitness value is selected as the optimal path for data transmission. This invention solves the problems of low throughput, premature death of some nodes due to high energy consumption, and poor packet delivery rate in existing UAV ad hoc network routing algorithms, ensuring continuous, effective, and timely data transmission for UAVs.

Description

Technical Field

[0001] This invention relates to routing control technology in wireless communication, and more particularly to a routing method and system for UAV ad hoc networks based on energy balance under QoS guarantee. Background Technology

[0002] A Flying Ad Hoc Network (FANET) is a special type of mobile ad hoc network. When unmanned aerial vehicles (UAVs) are used as network nodes, it is also called a UAV ad hoc network. Nodes in a UAV ad hoc network can freely form a network and exchange data. Compared to traditional mobile ad hoc networks, vehicular ad hoc networks, and wireless sensor networks, UAV ad hoc networks, in addition to being multi-hop, self-organizing, and decentralized, also have characteristics such as limited bandwidth, high-speed mobility, and rapid topology changes.

[0003] While existing FANET routing algorithms demonstrate good performance in terms of energy efficiency and stability, the challenges remain. In addition to adapting to high movement speeds and frequently changing network topologies, drone nodes in a swarm network must also balance the load and energy consumption of nodes along the path. Currently, research on FANET routing technologies that can support this practical application is relatively limited. Therefore, designing an efficient routing protocol for drone ad hoc networks that is tailored to the characteristics of drones is a pressing issue that needs to be addressed. Summary of the Invention

[0004] In view of this, this invention first addresses the characteristics of high node density, rapid topology changes, and high real-time requirements of UAVs by proposing a UAV ad hoc network routing method based on energy balance under QoS guarantee. This method solves the problems of poor adaptability, unreliability, and premature death of some nodes in existing FANETs routing protocols, while ensuring energy balance and service quality of UAV nodes.

[0005] To achieve the above objectives, the specific technical solution adopted by the present invention is as follows:

[0006] A routing method for UAV ad hoc networks based on energy balancing under QoS guarantee includes the following steps:

[0007] S1, The source node sends a routing request;

[0008] S2: The source node receives the route reply packet within the maximum hop count propagation time;

[0009] S3: The source node extracts the preset index parameters corresponding to each path from the received routing reply packet and calculates the fitness value;

[0010] S4: The source node selects the path with the highest fitness value as the primary path for data transmission;

[0011] The preset index parameters include the minimum remaining energy, minimum link hold time, maximum congestion, and number of hops for each path.

[0012] Optionally, step S3 is performed according to:

[0013]

[0014] Calculate path p i fitness value F(p) i ), where: E(p i ) represents path p i Minimum residual energy after normalization, HT(p) i ) represents path p i The minimum link hold time, C(p) i ) represents path p i Maximum congestion, H(p) i ) represents path p i The number of jumps, ω1, ω2, ω3 and ω4 are the weight coefficients of the corresponding indicators, and ω1+ω2+ω3+ω4=1.

[0015] Optionally, after receiving the routing request from the source node, the destination node sets its current node coordinates, velocity vector, minimum link hold-up time, minimum remaining energy, and maximum congestion level in the routing response packet it sends back.

[0016] Optionally, after receiving the routing reply packet, the intermediate node calculates the link hold-up time based on the coordinates and velocity vectors of the current node and the sending node, updates the routing reply packet with the minimum link hold-up time of the sending node and the current node, updates the routing reply packet with the minimum remaining energy of the sending node and the current node, and updates the routing reply packet with the maximum congestion of the sending node and the current node.

[0017] Furthermore, the intermediate nodes are configured as follows:

[0018]

[0019] Calculate the link hold time, where r represents the communication range of the node, the coordinates of node M are (x1, y1, z1), and the magnitudes of the velocities on each axis are (v... 1x ,v 1y ,v 1z The coordinates of node N are (x2, y2, z2), and the magnitudes of the velocities along each axis are (v...). 2x ,v 2y ,v 2z Intermediate variables a = x2 - x1, b = v 2x -v 1x c = y2 - y1, d = v 2y -v1y e = z2 - z1, f = v 2z -v 1z .

[0020] Optionally, each node is configured according to E. current =E inital -E0 calculates the remaining energy E after time T0. current E inital The initial energy of the node. This represents the energy consumed within the time range T0, where i = 1 to 6. Statusi corresponds to one of the following states: Idle, Channel Clearance Assessment (CCA BUSY), Transmit (Tx), Receive (Rx), Channel Switching, or Sleep. Statusi and I Statusi These represent the power and operating current corresponding to the Statusi state, respectively, where U is the rated operating voltage of the node; T Statusi Working time for Status, total working time

[0021] Optionally, according to For path p i The minimum remaining energy min(energy(k)) is normalized, where energy(k) represents the path p. i The energy of any node, E inital The initial energy of the node.

[0022] Optionally, the ratio of the number of packets cached in the MAC layer interface queue to the maximum length of the interface queue can be used to measure the current node congestion.

[0023] Based on the above routing method, the present invention also provides a UAV ad hoc network communication system, the key of which is that: the system is equipped with multiple UAVs, any two UAVs can transmit wireless data through an ad hoc network, and the data transmission path is selected by the UAV ad hoc network routing method based on energy balance under QoS guarantee as described above during data transmission.

[0024] The significant effects of this invention are:

[0025] This invention provides a routing method and system for UAV ad hoc networks based on energy balance under QoS guarantee. By improving the routing reply packet and routing table in the existing classic AOMDV routing algorithm, a fitness function is constructed using four constraint parameters: link hold time, remaining energy, congestion degree, and hop count. The fitness value of each path is then obtained quickly and accurately. Finally, the path with the largest fitness value is selected as the optimal path for data transmission. This method has fast convergence speed, high accuracy, and reliable multipath routing, ensuring the quality of service and energy balance of UAV routing communication. Attached Figure Description

[0026] Figure 1 This is a three-dimensional deployment diagram used to simulate drone nodes in a specific embodiment of the present invention;

[0027] Figure 2 This is a comparison diagram of the EB-AOMD protocol and the classic AOMDV protocol in a specific embodiment;

[0028] Figure 3 This is a simulation comparison chart of the packet delivery rate of this invention and other classic protocols;

[0029] Figure 4 This is a simulation comparison diagram of the end-to-end delay of the present invention and other classic protocols;

[0030] Figure 5 This is a simulation comparison chart of the network lifetime of this invention and other classic protocols. Detailed Implementation

[0031] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. The embodiments are given for illustrative purposes only and should not be construed as limiting the present invention. The accompanying drawings are for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0032] In this embodiment, a UAV ad hoc network application scenario is set up: the number of UAV nodes is 10×10, evenly distributed within a space of 2000m×2000m×300m, and the height of the UAVs varies from 100m to 300m; the UAV transmission power is 1W, the initial energy is 1000J, the physical layer protocol uses IEEE 802.11, and the transmission radius of the nodes is 250m. This can be achieved through the NS3 simulation environment. Figure 1 The network node layout shown uses a Markov Gaussian movement model.

[0033] based on Figure 1 In the network scenario shown, this embodiment provides a routing method for UAV ad hoc networks based on energy balancing under QoS guarantees, such as... Figure 2As shown, this example utilizes this method to construct an energy-balanced multipath routing protocol (EB-AOMDV protocol), which includes the following steps:

[0034] S1. The source node sends a routing request. When this node needs to send a data packet, it searches for a valid route to the destination node through the routing table entry. If a valid route is found, it sends the packet directly. Otherwise, it sends a routing request and waits for the maximum hop propagation time.

[0035] S2: The source node receives the route reply packet within the maximum hop count propagation time; when the destination node receives the route request, it adds nine variable fields to the RREP packet, including the current node's X-axis coordinate and velocity, Y-axis coordinate and velocity, Z-axis coordinate and velocity, minimum link hold-up time, minimum remaining energy, and maximum congestion level; after receiving the route reply packet, the intermediate node calculates the link hold-up time based on the coordinates and velocity vectors of the current node and the sending node, updates the route reply packet with the minimum link hold-up time of the sending node and the current node, updates the route reply packet with the minimum remaining energy of the sending node and the current node, and updates the route reply packet with the maximum congestion level of the sending node and the current node.

[0036] S3: The source node extracts the preset index parameters corresponding to each path from the received routing reply packet and calculates the fitness value;

[0037] S4: The source node selects the path with the highest fitness value as the primary path for data transmission;

[0038] The preset index parameters include the minimum remaining energy, minimum link hold time, maximum congestion, and number of hops for each path.

[0039] The EB-AOMDV protocol provided in this embodiment improves upon the classic AOMMDV protocol by adding route reply packets and metric parameters in the routing table. When the source node needs to send a data packet, it sends a route request and waits for the estimated maximum propagation route hop count. After the deadline is reached, it establishes the path table of the destination node based on the route replies received during the waiting period. Based on the metric parameters of all saved paths, it calculates and selects the path with the maximum fitness value as the optimal path for data transmission according to the fitness function.

[0040] Taking IEEE 802.11, the most commonly used physical layer and MAC layer interaction protocol in networking, as an example, the wireless communication power consumption of network nodes is basically determined by the six states defined by the 802.11 protocol. Different device states correspond to different operating currents. Therefore, in specific implementations, each node should operate according to E...current =E inital -E0 calculates the remaining energy E after time T0. current E inital The initial energy of the node. This represents the energy consumed within the time range T0, where i = 1 to 6. Statusi corresponds to one of the following states: Idle, Channel Clearance Assessment (CCA BUSY), Transmit (Tx), Receive (Rx), Channel Switching, or Sleep. Statusi and I Statusi These represent the power and operating current corresponding to the Statusi state, respectively, where U is the rated operating voltage of the node; T Statusi Working time for Status, total working time

[0041] For congestion calculation, the ratio of the number of packets buffered in the MAC layer interface queue to the maximum length of the interface queue is used as the standard to measure the current load of a node. The current load C of node j on the i-th path is shown below. payload The calculation formula is as follows: C payload =Q c / Q l ;

[0042] Q c Q represents the number of packets cached in the MAC layer interface queue of node j. l This indicates the length of the MAC layer interface queue of node j, which is also the maximum number of packets it can hold. The length of the interface queue is the maximum data packet length set for each node's buffer, and its value is fixed. As data packets enter or leave the network, the number of packets buffered in the node's MAC layer interface queue changes continuously. Therefore, the ratio of the number of packets buffered in the MAC layer interface queue to the length of the interface queue differs at different times, indicating varying levels of congestion for the node.

[0043] In addition, intermediate nodes are also configured as follows:

[0044]

[0045] Calculate the link hold time, where r represents the communication range of the node, the coordinates of node M are (x1, y1, z1), and the magnitudes of the velocities on each axis are (v... 1x ,v 1y ,v 1z The coordinates of node N are (x2, y2, z2), and the magnitudes of the velocities along each axis are (v...). 2x ,v 2y ,v 2zIntermediate variables a = x2 - x1, b = v 2x -v 1x c = y2 - y1, d = v 2y -v 1y e = z2 - z1, f = v 2z -v 1z .

[0046] The fitness function is a multi-objective function with multiple constraints as independent variables. It is a standard for measuring the quality of a path. Assume there are m disjoint paths between the source and destination nodes, i.e., P = (p1, p2, ..., p...). m The maximum congestion metric of the nodes in the path is taken as the path's congestion metric. The path's congestion metric is: C(p i = max(payload(k));

[0047] Where payload(k) represents path p i The congestion level of any node;

[0048] For a path with n links, the minimum remaining energy among the nodes on the path is taken as the path's energy. The normalized energy of the path is:

[0049] Where energy(k) represents path p i The energy of any node; E inital The initial energy of the node.

[0050] A path has n links, and the minimum hold time of each link in the path can be expressed as:

[0051] HT(p i = min{holdtime(k)}

[0052] Where holdtime(k) represents path p i The hold time of any link in the previous process;

[0053] The fitness function in a multi-objective constrained energy-balanced routing protocol comprehensively considers minimum link hold time, hop count, normalized remaining energy, and congestion level, ultimately determining the path p. i The fitness function can be expressed as:

[0054]

[0055] Where: E(p) i ) represents path p i Minimum residual energy after normalization, HT(p) i ) represents path p iThe minimum link hold time, C(p) i ) represents path p i Maximum congestion, H(p) i ) represents path p i The number of jumps, ω1, ω2, ω3 and ω4 are the weight coefficients of the corresponding indicators, and ω1+ω2+ω3+ω4=1. The specific values ​​of each weight coefficient can be adjusted according to different application scenarios and experience data.

[0056] In addition, this embodiment also provides a drone ad hoc network communication system. The system is equipped with multiple drones, and any two drones can transmit wireless data through an ad hoc network. During data transmission, the drone ad hoc network routing method based on energy balance under QoS guarantee described above is used to select the data transmission path.

[0057] To verify the performance of the present invention, the NS3 simulation tool was used to compare and analyze the performance of the EB-AOMDV protocol in this embodiment with the traditional classic AOMDV and AODV algorithms for UAV data services. The simulation parameters are shown in Table 1.

[0058] Table 1 Simulation Parameter Design Table

[0059] parameter definition Emulator NS3 Number of nodes 10×10 Simulation Space Range 2000m×2000m×300m MAC layer protocol IEEE 802.11n bandwidth 20MHz CBR rate 2Mbps Transmission Protocol UDP Moving Model Markov Gaussian moving model Initial energy of drone nodes 1000J Simulation time 900s Comparison Protocol AOMDV and AODV

[0060] First, the packet delivery rate of the EB-AOMDV protocol of this invention was compared and analyzed with that of the AOMDV and AODV protocols. Figure 3 As shown, as the node mobility rate increases, the packet delivery rate of the three routing protocols also decreases. Because EB-AOMDV takes into account the stability of the link, the congestion level of intermediate nodes and the minimum number of hops, the decreasing trend of EB-AOMDV is significantly smoother than that of the other two protocols.

[0061] Then, the end-to-end delay of the EB-AOMDV protocol of this invention was compared and analyzed with that of the AOMDV and AODV protocols, such as... Figure 4 As shown, as the node movement rate increases, the delay of EB-AOMDV and AODV fluctuates around 0.5s, while the delay of AODV continues to increase. This is because multipath routing protocols always wait for a fixed time after issuing a routing request.

[0062] Finally, the network lifetime of the EB-AOMDV protocol of this invention was compared and analyzed with that of the AOMDV and AODV protocols. Figure 5As shown, with the increase of node movement speed, rapid movement is beneficial to the average consumption of node energy. The lifetime of the three protocols gradually increases. Since EB-AOMDV takes into account the problem of node remaining energy and performs protection routing for low-energy nodes in the later stages of network transmission, it is far better than the other two protocols in terms of differences in network lifetime.

[0063] In summary, the EB-AOMDV protocol proposed in this embodiment is an AOMDV protocol based on a multi-objective function. It improves the routing reply packet and routing table in AOMDV, and optimizes multi-objective parameters through linear weighting. The algorithm is not only simple and efficient, but also applicable to real-world scenarios. Furthermore, it combines the high reliability and fault tolerance of multipath routing, ensuring the quality of service and energy balance of UAV routing communication.

[0064] Finally, it should be noted that the above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention should be considered equivalent substitutions and are included within the protection scope of the present invention.

Claims

1. A routing method for UAV ad hoc networks based on energy balancing under QoS guarantee, characterized in that, Includes the following steps: S1, The source node sends a routing request; S2: The source node receives the route reply packet within the maximum hop count propagation time; S3: The source node extracts the preset index parameters corresponding to each path from the received routing reply packet and calculates the fitness value; S4: The source node selects the path with the highest fitness value as the primary path for data transmission; The preset index parameters include the minimum remaining energy, minimum link hold time, maximum congestion, and number of hops for each path; In step S3, follow these steps: Calculate path p i fitness value ,in: Represents path p i Minimum residual energy after normalization Represents path p i Minimum link hold time, Represents path p i Maximum congestion, Represents path p i The number of jumps, , , and These are the weighting coefficients for the corresponding indicators, and ; After receiving the routing request from the source node, the destination node sets its current coordinates, velocity vector, minimum link hold-up time, minimum remaining energy, and maximum congestion level in the routing response packet it sends back. After receiving the routing reply packet, the intermediate node calculates the link hold-up time based on the coordinates and velocity vectors of the current node and the sending node. It then updates the routing reply packet with the minimum link hold-up time between the sending node and the current node, the minimum remaining energy between the sending node and the current node, and the maximum congestion level between the sending node and the current node.

2. The UAV ad hoc network routing method based on energy balancing under QoS guarantee according to claim 1, characterized in that, Intermediate nodes are arranged as follows: Calculate the link hold time, where r represents the communication range of the node, and the node... The coordinates are The magnitudes of the velocities corresponding to each axis are respectively The coordinates of node N are The magnitudes of the velocities corresponding to each axis are respectively Intermediate variables , , , , , .

3. The UAV ad hoc network routing method based on energy balancing under QoS guarantee according to claim 1, characterized in that, Each node according to Calculate the remaining energy after time T0. , The initial energy of the node. This represents the energy consumed within the time range T0. =1~6, Statusi corresponds to one of the following states: Idle (IDLE), Channel Clearance Assessment (CCA BUSY), Transmit (Tx), Receive (Rx), Channel Switching (Switching), and Sleep. and These represent the power and operating current corresponding to the Statusi state, respectively, and U is the rated operating voltage of the node. Working time for Status, total working time .

4. The UAV ad hoc network routing method based on energy balancing under QoS guarantee according to claim 1 or 3, characterized in that, according to For path p i Minimum remaining energy Normalization is performed. Representing a path The energy of any node above, The initial energy of the node.

5. The UAV ad hoc network routing method based on energy balancing under QoS guarantee according to claim 1, characterized in that, The congestion level of the current node is measured by the ratio of the number of packets cached in the MAC layer interface queue to the maximum length of the interface queue.

6. A drone self-organizing network communication system, characterized in that: The system is equipped with multiple drones, and any two drones can transmit wireless data through an ad hoc network. During data transmission, the drone ad hoc network routing method based on energy balance under QoS guarantee as described in any one of claims 1-5 is used to select the data transmission path.

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