阅读说明：本技术 一种基于速度与能量感知的无人机自组网改进aodv路由方法 (Unmanned aerial vehicle ad hoc network improved AODV routing method based on speed and energy perception ) 是由 刘庆华 肖菁颖 叶金才 蒋俊正 谢跃雷 于 2021-07-26 设计创作，主要内容包括：本发明公开了一种基于速度与能量感知的无人机自组网改进AODV路由方法,结合跨层思想,提出链路代价概念,把能量剩余率、相对移动度、拥塞度作为度量指标定义链路代价,修改路由判据,依据最小链路代价进行择路,设计具有速度能量意识的改进AODV路由协议,此外,提出基于邻居节点数的中继广播策略,引入概率因子和随机因子实现中继概率分级广播功能来降低RREQ广播冗余。相较于传统AODV路由协议,改进后的协议缩短了节点间端到端的延时,提高了分组投递率,降低了路由发现频率,还减少了能耗增多了节点平均剩余能量,有效控制路由开销,提高了网络的整体性能,更加适应无人机自组网的拓扑环境。(The invention discloses an unmanned aerial vehicle ad hoc network improved AODV routing method based on speed and energy perception, which combines a cross-layer thought to provide a link cost concept, defines link cost by taking energy surplus rate, relative mobility and congestion degree as measurement indexes, modifies routing criteria, carries out route selection according to minimum link cost, designs an improved AODV routing protocol with speed energy awareness, and provides a relay broadcasting strategy based on neighbor node number, introduces probability factors and random factors to realize a relay probability grading broadcasting function to reduce RREQ broadcasting redundancy. Compared with the traditional AODV routing protocol, the improved protocol shortens the end-to-end delay between nodes, improves the packet delivery rate, reduces the route discovery frequency, reduces the energy consumption, increases the average residual energy of the nodes, effectively controls the route overhead, improves the overall performance of the network, and is more suitable for the topological environment of the unmanned aerial vehicle ad hoc network.)

1. An unmanned aerial vehicle ad hoc network improved AODV routing method based on speed and energy perception is characterized by comprising the following steps:

introducing link cost to establish a routing criterion algorithm;

expanding the newly added data domain;

adding an energy model;

acquiring the length of a current queue in a cross-layer manner;

updating the routing judgment data to select the route with the minimum link cost;

and the intermediate node adds a relay probability grading broadcast strategy.

2. The unmanned aerial vehicle ad hoc network improved AODV routing method based on speed and energy perception according to claim 1, wherein the link cost is determined by an energy surplus rate, a relative mobility degree and a congestion degree of a previous hop node and the node.

3. The unmanned aerial vehicle ad hoc network improved AODV routing method based on speed and energy perception according to claim 1, wherein in the process of expanding the newly added data field, the newly added data field is used for storing a horizontal component, a vertical component, an absolute value of speed and link cost of node speed in message formats of a routing request and a routing reply, and a link cost data field is newly added in a routing table entry.

4. The unmanned aerial vehicle ad hoc network improved AODV routing method based on speed and energy perception of claim 1, wherein a routing layer learns the queue length of a link layer through a MAC layer.

5. The unmanned aerial vehicle ad hoc network improved AODV routing method based on speed and energy perception according to claim 1, wherein in the process of routing with the minimum link cost in the updated routing data, the minimum hop count in the routing updating condition in the original RREQ and RREP receiving function is modified into the link cost, and the updating of the routing criterion is completed. And calculating the current link cost before the node updates the route, comparing and updating the current accumulated link cost with the link cost cached in the route table entry, and finally selecting the path with the minimum link cost by the destination node for route reply.

6. The unmanned aerial vehicle ad hoc network improved AODV routing method based on speed and energy perception according to claim 1, wherein the step of performing link cost calculation comprises the steps of:

extracting the speed information of the previous hop node carried in the routing request message;

obtaining the speed and energy information of the node by using a GPS positioning and energy model to calculate the energy residual rate and the relative mobility;

obtaining the current queue length through a cross-layer to calculate the node congestion rate;

comprehensively calculating the link cost of the previous hop node to the node;

and accumulating the link cost of the previous hop node reaching the node and the link cost carried in the routing request message to obtain the current link cost.

7. The method as claimed in claim 1, wherein the intermediate node adds a relay probability hierarchical broadcast strategy, introduces a probability factor and a random factor, the probability factor determines its probability level according to the number of neighboring nodes and the number of nodes of the unmanned aerial vehicle, the random factor is obtained according to a random function, and finally compares the probability factor with the value of the random factor to determine whether to forward the packet.

Technical Field

The invention relates to the technical field of unmanned aerial vehicle ad hoc networks, in particular to an unmanned aerial vehicle ad hoc network improved AODV routing method based on speed and energy perception.

Background

A ad hoc network (FANET) of unmanned planes is a special wireless ad hoc network (MANET), and its mobile nodes are small unmanned planes or small aircrafts, equipped with communication and professional equipment for other different fields. Although FANET and MANET have many similarities, due to their unique features, routing protocols designed for MANET are not applicable to FANET, and several key points in FANET design are as follows: all system units (such as a transmitter, a receiver device, a control unit, an information processing unit and a payload) of the unmanned aerial vehicle in flight are powered by an internal power supply, the flight time of the unmanned aerial vehicle with limited airborne energy is limited, the overall performance of the unmanned aerial vehicle network is seriously influenced, and the optimization of energy consumption among nodes becomes one of important research challenges; disaster monitoring, searching and destruction operations require minimal delay because information needs to be transmitted at very high rates, and although it is almost impossible to have a network without delay, how to minimize and control delay in the network to a certain extent is also a concern for FANET; the mobility of the FANET node is much higher than that of the MANET, all unmanned aerial vehicle nodes are highly mobile, the speed is from 30 to 460 km/h, the fluctuation of a wireless link is caused, the efficiency of the routing technology can change along with the speed of the unmanned aerial vehicle, and therefore, how to develop a routing protocol for stable and reliable communication in the unmanned aerial vehicle networking with high-speed dynamic topological structure and uneven node distribution becomes a difficult task.

The AODV (Ad Hoc On-Demand Distance Vector) routing protocol is a classical reactive routing protocol in MANET, integrates the advantages of DSDV (destination sequence Distance Vector) and DSR (dynamic resource routing) protocols, is very suitable for the environment with scarce network resources such as bandwidth and power, only needs to store necessary path information by nodes, does not need to maintain all nodes of the whole network all the time, reduces a large amount of memory requirements and routing overhead, has the inherent defects of reactive routing, needs relatively long time delay to establish a link, only uses the minimum hop count as routing basis in a routing exploration stage, does not consider the influence of any physical factors of real environment, is ideal, and is difficult to be practically applied in unmanned aerial vehicle networking.

Disclosure of Invention

The invention aims to provide an improved AODV routing method for unmanned aerial vehicle ad hoc network based on speed and energy perception, and aims to improve the overall performance of the network and enable AODV to be more adaptive to the topological environment of the unmanned aerial vehicle ad hoc network.

The method specifically comprises the following steps:

introducing link cost to establish a routing criterion;

expanding the newly added data domain;

adding an energy model;

acquiring the length of a current queue in a cross-layer manner;

updating the routing judgment data to select the route with the minimum link cost;

and the intermediate node adds a relay probability grading broadcast strategy.

And determining the link cost by the energy surplus rate, the relative mobility and the congestion degree of the previous hop node and the node.

In the process of expanding the newly added data domain, the newly added data domain is used for storing the horizontal component, the vertical component, the absolute value of the speed and the link cost of the node speed in the message format of the routing request and the routing reply, and the link cost data domain is newly added in the routing table entry.

Wherein, the routing layer learns the queue length of the link layer through the MAC layer.

In the process of routing by using the minimum link cost in the updated routing judgment data, the minimum hop number in the routing updating condition in the original RREQ and RREP receiving function is modified into the link cost, and the updating of the routing judgment is completed. And calculating the current link cost before the node updates the route, comparing and updating the current accumulated link cost with the link cost cached in the route table entry, and finally selecting the path with the minimum link cost by the destination node for route reply.

The step of calculating the link cost comprises the following steps:

extracting the speed information of the previous hop node carried in the routing request message;

obtaining the speed and energy information of the node by using a GPS positioning and energy model to calculate the energy residual rate and the relative mobility;

obtaining the current queue length through a cross-layer to calculate the node congestion rate;

comprehensively calculating the link cost of the previous hop node to the node;

and accumulating the link cost of the previous hop node reaching the node and the link cost carried in the routing request message to obtain the current link cost.

The intermediate node is added with a relay probability grading broadcast strategy, a probability factor and a random factor are introduced, the probability factor determines the probability grade according to the number of the neighbor nodes and the number of the unmanned aerial vehicle nodes, the random factor is obtained according to a random function, and finally the probability factor is compared with the random factor value to determine whether to forward the package.

The invention discloses an unmanned aerial vehicle ad hoc network improved AODV routing method based on speed and energy perception, which combines a cross-layer thought to put forward a link cost concept, defines link cost by taking energy surplus rate, relative mobility and congestion degree as measurement indexes, modifies routing criteria, carries out routing selection according to the minimum link cost, designs an improved AODV routing protocol with speed and energy awareness, and introduces probability factors and random factors to realize a relay probability hierarchical broadcasting function to reduce RREQ broadcasting redundancy. Compared with the traditional AODV routing protocol, the improved protocol shortens the end-to-end delay between nodes, improves the packet delivery rate, reduces the route discovery frequency, reduces the energy consumption, increases the average residual energy of the nodes, effectively controls the route overhead, improves the overall performance of the network, and enables the AODV to be more adaptive to the topological environment of the unmanned aerial vehicle ad hoc network.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flow chart of an unmanned aerial vehicle ad hoc network improved AODV routing method based on speed and energy perception according to the present invention.

Fig. 2 is a RREQ message frame format diagram of the unmanned aerial vehicle ad hoc network improved AODV routing method based on speed and energy perception.

Fig. 3 is a routing table entry format diagram of an improved AODV routing method for unmanned aerial vehicle ad hoc network based on speed and energy perception.

Fig. 4 is a flow chart of the route request work of the present invention.

Fig. 5 is a graph comparing average end-to-end delay at different mobility rates according to an embodiment of the present invention.

Fig. 6 is a comparison graph of the routing initiation frequency at different mobility rates according to an embodiment of the present invention.

Fig. 7 is a graph comparing packet delivery rates at different mobility rates according to an embodiment of the present invention.

Fig. 8 is a comparison of routing overhead at different mobility rates according to an embodiment of the present invention.

Fig. 9 is a graph of average residual energy of nodes at different mobility rates according to an embodiment of the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.

In this application, the route request and the route reply may also be referred to as other names, for example, the route request is RREQ and the route reply is RREP.

Referring to fig. 1, the present invention provides a speed and energy perception-based method for improving AODV routing in an unmanned aerial vehicle ad hoc network, including the following steps:

s1: introducing link cost to establish a routing criterion algorithm;

s2: expanding the newly added data domain;

s3: adding an energy model;

s4: acquiring the length of a current queue in a cross-layer manner;

s5: updating the routing judgment data to select the route with the minimum link cost;

s6: and the intermediate node adds a relay probability grading broadcast strategy.

The link cost is determined by the energy surplus rate, the relative mobility and the congestion degree of the previous hop node and the node.

In the process of expanding the newly added data domain, the newly added data domain is used for storing the horizontal component, the vertical component, the absolute value of the speed and the link cost of the node speed in the message format of the routing request and the routing reply, and the link cost data domain is newly added in the routing table entry.

The routing layer learns the queue length of the link layer through the MAC layer.

And in the process of selecting the route by the minimum link cost according to the updated route judgment data, modifying the minimum hop number in the route updating condition in the original RREQ and RREP receiving function into the link cost to finish the updating of the route judgment. And calculating the current link cost before the node updates the route, comparing and updating the current accumulated link cost with the link cost cached in the route table entry, and finally selecting the path with the minimum link cost by the destination node for route reply.

Calculating the link cost, comprising the following steps:

extracting the speed information of the previous hop node carried in the routing request message;

obtaining the speed and energy information of the node by using a GPS positioning and energy model to calculate the energy residual rate and the relative mobility;

obtaining the current queue length through a cross-layer to calculate the node congestion rate;

comprehensively calculating the link cost of the previous hop node to the node;

and accumulating the link cost of the previous hop node reaching the node and the link cost carried in the routing request message to obtain the current link cost.

The intermediate node is added with a relay probability grading broadcast strategy, a probability factor and a random factor are introduced, the probability factor determines the probability grade according to the number of the neighbor nodes and the number of the unmanned aerial vehicle nodes, the random factor is obtained according to a random function, and finally the probability factor is compared with the random factor value to determine whether to forward the package.

In the invention, the routing criterion algorithm is as follows:

λ_s＝αR_s+βE_s＝α(σ_ij+CM)+β(1-η_i) (2)

η_i＝E_i/E₀ (3)

V_ijmax＝|v_i|+|v_j| (6)

wherein eta is_iThe energy surplus rate; sigma_ijIs the relative movement of node i with respect to node jDegree, V_ijIs the relative velocity of movement of node i with respect to node j, v_ixAnd v_jxRepresenting the velocity components, v, of nodes i and j in the horizontal direction_iyAnd v_jyRepresenting the vertical velocity component, V, of nodes i and j_ijmaxIs the maximum value of the relative speeds of two nodes, v_iAnd v_jRepresents an absolute value of the node velocity; CM is degree of congestion, L_sIs the queue length of the link for a period of time, L_tCaching the maximum number of data packets for the nodes in the link; lambda [ alpha ]_sIs the cost function of node i relative to node j, N is the total number of nodes in the link, E_sIs an energy factor representing the energy loss value of the node, R_sThe stability factor is a stability factor, which represents the stability of a node, and α and β are weight coefficients, which satisfy that α + β is 1 (which can be adjusted according to different network environments, and is better than 0.5 in the unmanned aerial vehicle cluster environment studied by us through simulation). Lambda [ alpha ]_sThe cost between two nodes is continuously accumulated, and the link cost lambda of the whole path can be obtained.

The established routing criterion is as follows:

PathSelect＜source,dest＞＝min[λ(k)] k∈[1,M]

where M represents the number of routes available for selection. And if a plurality of communication paths exist from the source node to the destination node, selecting the link with the minimum link cost for communication.

Extended Route Request (RREQ) and Route Reply (RREP) message formats. As shown in fig. 2, the new data field is used to store the horizontal component, the vertical component, the absolute value of the velocity and the link cost of the velocity of the node in the message format; referring next to fig. 3, the link cost data field is extended in the routing table entry. In order to extract the last hop node and the relevant information buffered in the routing table entry from the received message packet when the node calculates the comparative link cost.

The traditional AODV routing protocol has no node energy model, and the energy model is added to calculate the node energy loss. The loss calculation in the energy model is defined as:

E_i＝E₀-E_c (8)

E_c＝(P_r+P_t)×Time (9)

wherein E is₀Is the initial energy of the node, E_iTo surplus energy, E_cTo consume energy; p_tAnd P_rTransmit power and receive power, respectively; PacketSize is the size of the transmitted packet, and Bandwidth is the channel Bandwidth.

In the process of acquiring the current queue length in a cross-layer manner, a routing layer needs to acquire the queue length of a Link Layer (LL), firstly, the routing layer is ensured to acquire the information of an MAC layer, and after the information is successfully acquired from the routing layer to the MAC layer in the cross-layer manner, the queue length is acquired from the MAC layer to the LL layer in the cross-layer manner. Accessing an MAC layer from AODV, adding a MAC file to an AODV header file to create an interface object, assigning an initial value to the MAC object, and initializing node information in a configuration file; accessing the LL layer from the MAC layer requires defining common member variables and inline functions in the 802_11 class, modifying the interface function and then initializing, so that the MAC can obtain the queue length. At this time, the RREQ function in the routing layer AODV may obtain the queue length information in the corresponding interface queue LL by using the MAC member variable function after obtaining the channel number Iface.

The intermediate node is added with a relay broadcasting strategy based on the number of the neighbor nodes, and probability factors and random factors are introduced to realize probability classification. Referring to fig. 4, before forwarding an RREQ message, each intermediate node calculates a random factor and a relay probability factor to determine whether to continue broadcasting the RREQ message or to discard the RREQ message. The random factor is a random number from 0 to 1 generated by the node, the relay probability factor is determined according to the number of the neighbor nodes, the first probability p1 is taken when the number of the neighbor nodes is smaller than a threshold value, and the second probability p2 is taken when the number of the neighbor nodes exceeds the threshold value. If the random factor is larger than the relay probability factor, the RREQ message is forwarded; otherwise, the RREQ message will be discarded.

The relay probability broadcast strategy is:

step 1, calculating a neighbor node threshold value NB, and calculating the number nn of the unmanned aerial vehicle nodes as a formula (11);

step 2, acquiring the number nbnum of neighbor nodes, and calculating a probability factor p according to the formula (12);

step 3, generating a random factor RN within the range of 0 to 1 by a random function;

RN＝Random::uniform(0,1) (13)

and Step 4, if the random factor value is larger than or equal to the relay probability factor value, broadcasting the RREQ, otherwise, discarding.

The invention also provides a specific embodiment:

the specific communication process is simulated by using an NS2.35 network protocol simulator, and simulation parameters thereof are shown in table 1:

TABLE 1 simulation parameters

Compared with the AODV routing protocols before and after improvement, the AODV routing protocols before and after improvement are simulated, as can be seen from the graph from figure 5 to figure 9, the improved routing protocols are improved in performance to different degrees, and the simulation results show that the average end-to-end time delay of the network is greatly reduced, although the reduction range in the route discovery frequency is small, the packet delivery rate is improved obviously, the routing overhead is effectively controlled, the energy consumption is reduced, and the average residual energy of the nodes is increased. Although the complexity of the routing algorithm and the length of the request message are increased, the method improves the link stability and the bandwidth utilization rate on the whole, comprehensively improves the network performance, proves that the method achieves the purpose of optimization, and creates a greater possibility for the realization and development of the unmanned aerial vehicle ad hoc network.

While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.