Heterogeneous ad hoc network OLSR protocol implementation method based on topology control and MPR selection optimization

Abstract

The invention discloses a heterogeneous ad hoc network OLSR protocol implementation method based on topology control and MPR selection optimization, and the method comprises the steps: carrying out the clustering through calculating a link quality factor LF, thereby forming a stable cluster structure; afterwards, an AOW clustering algorithm is used, an appropriate node is selected to serve as a cluster head according to multiple factors of the node, and the frequency of cluster head replacement and related overhead are reduced. Besides, in order to ensure connectivity between clusters and form a complete network topology, a multi-frequency clustering inter-cluster connection algorithm based on a TDMA protocol is introduced based on a design concept of cross-layer routing. And continuously maintaining the topology routing. A link stability factor based on a scanning angle of a laser transceiver is introduced to optimize an MPR selection mechanism. The method has been implemented in an EXata network simulation environment and is verified by experiments. The optimized protocol is more excellent in performance in the environment with high transmission rate requirement, high mobility and atmosphere attenuation, and the performance and reliability of the network are effectively improved.

Description

Technical Field

[0001] The invention relates to a method for realizing an OLSR protocol of a heterogeneous self-organizing network based on topology control and MPR selection optimization, and belongs to the technical field of computer communication. Background Art

[0002] In the face of mobile ad hoc network scenarios where network topology changes frequently and real-time requirements are high, immediate routing availability is critical. Unlike passive routing protocols such as AODV, the OLSR protocol does not need to start looking for or establishing routing paths when data transmission needs arise. Therefore, for real-time applications that require fast and reliable responses, such as drone ad hoc networks or emergency response networks, the OLSR protocol is widely used. However, the OLSR protocol only uses the hop count as the basis for route selection. The selected indicator is too single and difficult to adapt to the highly dynamic and high-load mobile ad hoc network environment.

[0003] Currently, RF communications are susceptible to interference from environmental magnetic fields such as high-voltage lines and base stations in today's battlefields and commercial applications. Its broadcast nature may also cause co-channel interference and security issues, limiting concealment and communication efficiency, especially in military communications that require high immediacy, high speed, large bandwidth and high security.

[0004] To address these challenges, RF / FSO hybrid technology provides an effective solution. Under this framework, network nodes can flexibly choose RF or FSO communication according to actual network needs, which enhances network adaptability and reliability. However, when using the existing OLSR routing protocol, node movement and changes in atmospheric conditions in the FSO network will cause signal strength fluctuations, affecting reception quality. In addition, OLSR requires frequent broadcast signaling to update the network topology and maintain routing tables. This is inefficient in FSO networks with point-to-point communication and requires a large number of laser transceivers to be installed on each node. Summary of the invention

[0005] The purpose of the present invention is to address the defects and shortcomings of the prior art and propose a method for implementing the OLSR protocol in a heterogeneous self-organizing network based on topology control and MPR selection optimization. The method uses a dynamic clustering topology control method and is based on the AOW algorithm to dynamically cluster the network according to the movement of nodes and atmospheric attenuation to adapt to the rapidly changing network environment. In addition, considering the particularity of using laser as a carrier in FSO communication, in order to reduce the broadcast demand of the entire network. The present invention introduces a link stability factor based on the scanning angle of the laser transceiver to optimize the MPR selection mechanism. It has been implemented in the EXata network simulation environment and verified by experiments. The experimental results show that the optimized protocol performs better in an environment with high transmission rate requirements, high mobility and atmospheric attenuation, effectively improving the performance and reliability of the network.

[0006] The technical solution adopted by the present invention to solve the technical problem is: a method for implementing the OLSR protocol in a heterogeneous ad hoc network based on topology control and MPR selection optimization, the method comprising the following contents:

[0007] First, clustering is performed by calculating the link quality factor LF to form a stable cluster structure. Subsequently, the AOW clustering algorithm is used to select appropriate nodes as cluster heads based on various factors of the nodes, aiming to reduce the frequency of cluster head replacement and related overhead. In addition, in order to ensure the connectivity between clusters and form a complete network topology, the present invention introduces a multi-frequency clustering inter-cluster connection algorithm based on the TDMA protocol based on the design concept of cross-layer routing. By continuously maintaining the topology routing, the present invention can adjust the strategy in a timely manner according to the dynamic changes of the network environment and effectively implement the topology control of heterogeneous networks.

[0008] (1) Cluster structure formation

[0009] In the process of cluster structure formation, considering the motion characteristics of the nodes, the formula is used The link quality factor LF calculated in the protocol is used as the decision basis for cluster allocation. The protocol will set a global cluster ID to identify which cluster the node is in. The specific algorithm steps and flow chart are as follows: Figure 2 shown.

[0010] (a) Initialization phase: The initial cluster ID of the node is set to the node ID, and the link quality factor threshold LF is set according to the specific network conditions. th , set LF th A higher value of means a stricter requirement on link quality, which results in a reduction in the number of nodes in the cluster structure.

[0011] (b) The current receiving node u receives the one-hop neighbor node N iWhen a HELLO packet is received, it will obtain the neighbor node ID, cluster ID and neighbor location information contained in the packet.

[0012] (c) Node u will compare its own cluster ID with that of its neighbor node N i If the two are equal, it means that the two nodes are already in the same cluster structure, so the subsequent process is skipped.

[0013] (d) According to the formula Calculate the link quality factor between the two nodes and the set quality threshold LF th When the value is less than the threshold, it is considered that the link quality between the two nodes is low and it is difficult for the two nodes to form a stable cluster structure. When the value is greater than the threshold, it is considered that the two nodes meet the clustering conditions. d Indicates the maximum communication distance of the node. In the RWP model, if the maximum speed of the node v max Lower and residence time t p If the distance is longer, the relative moving distance per unit time will be reduced, and the link quality between the two nodes will be improved accordingly.

[0014] (e) The cluster IDs of the two nodes and their cluster members are uniformly modified, and the cluster ID value with the smaller cluster ID is selected as the unified cluster ID. If the node also receives HELLO packets from other neighbors, it will return to stage (b) and repeat the entire process until the entire network forms a stable cluster structure.

[0015] (2) Cluster head selection

[0016] After the clustering algorithm is completed, multiple independent cluster structures will be formed. Each cluster needs a cluster head to effectively manage its internal nodes. Selecting a suitable cluster head is the key. For this purpose, the AOW algorithm that adapts to the characteristics of RF / FSO networks is used to select cluster heads.

[0017] When selecting a cluster head, the following three evaluation indicators are given priority, taking node u in the cluster as an example:

[0018] (a) Since each node has limited transceiver equipment, its node degree becomes a key factor. Only when a node has sufficient residual node degree can it be considered as a candidate for cluster head.

[0019] Node u counts the number of its neighboring nodes N, which is defined as the degree d of node u. u , the residual node degree Du of node u is the difference between its access degree and physical degree K, and the calculation formula is:

[0020] D u =|Kd u | (4.4)

[0021] The physical degree K refers to the maximum number of concurrent links that a node can support. For FSO nodes, it refers to the number of independent point-to-point optical links that the node can maintain simultaneously. This value is determined by the number of laser transceiver units equipped on the node. For example, if a node is equipped with 5 transmitting units and 5 receiving units, its physical degree K can be considered to be 5.

[0022] (b) In order to ensure the stability of the cluster head, nodes with lower mobility are selected as cluster heads. The relative moving speed of node u relative to node v is V(u,v):

[0023] V(u,v)=|V u -V v |(4.5)

[0024] Where V u and V v are the speeds of node u and node v respectively, then the average relative mobility of node u in the cluster is M u for:

[0025]

[0026] (c) In the design of RF / FSO networks, the transmit power of a node should be determined based on the actual distance it needs to cover and the atmospheric attenuation factor. The ideal transmit power setting should ensure that the node can maintain effective communication with its one-hop neighbors and should also take into account the impact of atmospheric attenuation. Therefore, it is an effective method to estimate the ideal transmit power by calculating the sum of the path losses from node u to all its one-hop neighbors. Therefore, the ideal transmit power P of node u is u for:

[0027]

[0028] Among them, d u,v is the distance between nodes u and v, N 1 represents the set of one-hop neighbor nodes of node u, and ε is the attenuation coefficient of the atmosphere.

[0029] According to the node mobility, remaining node degree and ideal transmission power calculated above, the combined weight W of node u is u for:

[0030]

[0031] where ω d ,ω m ,ω p are the weight factors of mobility, remaining node degree and transmit power, respectively, satisfying ω d +ω m +ω p= 1. Finally, based on the comprehensive weight W of the nodes, the node with the smallest weight will be selected as the cluster head. If there is an equal weight, the node with the smaller ID will be selected as the cluster head.

[0032] (3) Inter-cluster connections

[0033] In order to reduce channel conflicts and improve channel utilization in relatively dense mobile ad hoc networks, an inter-cluster connection mechanism based on the cross-layer routing design concept is proposed. This mechanism adopts multi-frequency cluster communication at the MAC layer and uses the TDMA protocol to allocate exclusive communication frequency bands for different clusters. For each cluster, the communication frequency will be configured for the node according to the cluster ID of the current node, and the gateway node uses the inter-cluster time-hopping access method to solve the communication access problem between different working frequency bands of the gateway. The schematic diagram of the connection between two adjacent clusters is shown in the figure. Figure 3 shown.

[0034] Specifically, before the protocol runs, several available communication frequencies are configured according to the scenario requirements. During the protocol operation, the communication frequency band of the cluster where the node is located is updated in real time through the cluster ID of the current node. This process can be triggered periodically by configuring a timer. For the gateway node at the junction of two adjacent clusters, in order to ensure stable data exchange between clusters, it is necessary to have a certain frequency hopping capability. The following scheme is adopted: switch to the frequency band of the neighboring cluster with a smaller ID in the odd time frame period, and switch to the frequency band of the neighboring cluster with a larger ID in the even time frame period. The gateway node alternately switches the working frequency band between the two neighboring clusters to ensure the continuity and efficiency of data transmission.

[0035] 1.1.1 MPR selection optimization based on link stability

[0036] In RF / FSO communication networks, various factors in the process of laser sending, transmission and reception significantly affect the stability of the link. In order to accurately evaluate and optimize network performance, it is crucial to consider the quantification of these factors. A link stability calculation method based on laser transceiver parameters and the distance between nodes is introduced. The calculation formula of LS is as follows:

[0037]

[0038] Where α is the scanning angle of the laser transceiver The influence on link stability is greater. Systems with larger scanning angles are more tolerant of node movement and alignment errors, thereby enhancing link stability. Distance d represents the actual distance between two nodes, while D max Refers to the farthest effective communication distance between two transceivers. ρ describes the ratio of the farthest effective communication distance to the optimal communication distance, which can be adjusted according to the performance of different transceivers.

[0039] The value range of link stability LS is [0,1], which is used to reflect the stability of each node in the network in real time. The higher the LS value, the stronger the stability of the node, so the probability of the node being selected as MPR is also higher. This indicator helps optimize the routing protocol and ensure more stable and efficient network communication.

[0040] Figure 4 This is the optimized MPR selection process. Similar to the standard MPR node set selection algorithm, it is assumed that the MPR set selected by the central node is set M; the one-hop neighbor set of the node is N 1 ; The node’s two-hop neighbor set is N 2 , which does not include the central node and N 1 Nodes in the. Specifically include:

[0041] Step 1) Add a neighbor node with a willingness value of ALWAYS to the set M first;

[0042] Step 2) Get N from the neighbor table 1 The geographical location of the nodes, calculate the distance between nodes, and use the formula Calculate link stability LS and depth D(y);

[0043] Step 3) If the two-hop neighbor can only be connected to the point of the present invention through one one-hop neighbor, add this one-hop neighbor to the set M and remove the two-hop neighbors it covers;

[0044] Step 4) If N 1 If there is a node that is already the MPR of other nodes, then select the node with the largest LS among these nodes as the MPR of the central node. If there is no such node, proceed to the next step;

[0045] Step 5) Calculate the reachability of node N1 and execute in sequence:

[0046] (a) Add the node with the highest willingness to the set M;

[0047] (b) Among multiple candidates, select the node with the largest LS to join M;

[0048] (c) Among multiple candidates, select the node with the highest reachability to join M;

[0049] Step 6: Check whether the set M covers all two-hop neighbors N 2 If it is covered, M is the final MPR set; otherwise, return to step 4).

[0050] Beneficial effects:

[0051] 1. Experimental results show that the optimized protocol of the present invention performs better in environments with high transmission rate requirements, high mobility and atmospheric attenuation, effectively improving the performance and reliability of the network.

[0052] 2. The optimized MPR selection algorithm of the present invention maximizes the utility of each MPR node by reusing the nodes that have been used as MPRs, thereby reducing the number of nodes that need to be broadcast in the network. At the same time, nodes with higher link stability are selected as MPRs, which reduces the risk of link breakage and enhances the stability of the network.

[0053] 3. The present invention first analyzes the limitations of traditional RF communications and the challenges encountered by the existing OLSR protocol in RF / FSO networks; secondly, it introduces the channel model of the FSO network, especially the impact of atmospheric attenuation on signal transmission, and introduces the node model of the RF / FSO network and the adaptive on-demand weighted clustering algorithm. The present invention describes the design ideas of the protocol, proposes a clustering topology control method based on link quality and an MPR optimization strategy based on link stability; finally, the experimental results of the EXata simulation software show that the optimization protocol exhibits better performance under high mobility and atmospheric attenuation conditions, especially in environments with high transmission rate requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a framework diagram of the clustering topology control process of the present invention.

[0055] Figure 2 This is a flow chart of the clustering algorithm based on link quality of the present invention.

[0056] Figure 3 It is a schematic diagram of connecting two adjacent clusters according to the present invention.

[0057] Figure 4 This is a flow chart of MPR selection based on link stability of the present invention.

[0058] Figure 5 It is a schematic diagram of the overall flow of the TC-M-OLSR protocol of the present invention.

[0059] Figure 6 It is a schematic diagram of the simulation scenario of the air-ground information self-organizing network of the present invention.

[0060] Figure 7 This is a schematic diagram of the variation of delay with node speed under different weather conditions in the present invention.

[0061] Figure 8 This is a schematic diagram of the change of packet delivery rate with node speed under different weather conditions in the present invention.

[0062] Fig. 9 This is a schematic diagram of the change of broadcast control overhead with the node movement speed of the present invention.

[0063] Fig.10This is a schematic diagram of the change of the broadcast control overhead with the number of nodes in the present invention. DETAILED DESCRIPTION

[0064] The invention will be further described in detail below in conjunction with the accompanying drawings.

[0065] The RF / FSO communication network node model specifically includes:

[0066] Although RF and FSO communication networks have essential differences in transmission media and methods, it is necessary to integrate the advantages of both in designing network nodes to meet a wider range of application requirements. Therefore, the design of nodes in RF / FSO communication networks should meet the following conditions:

[0067] (1) RF / FSO nodes allow communication in multiple directions

[0068] Similar to the omnidirectional antenna technology used in RF communications, RF / FSO nodes need to have multi-directional communication capabilities. This design feature is critical because it enables the routing protocol to effectively implement neighbor discovery functions by broadcasting control messages. However, the transmission method of lasers is inherently highly directional. At present, in order to enable RF / FSO nodes to achieve an effect similar to that of omnidirectional antennas when communicating, the node's transceiver can adopt a three-dimensional symmetrical antenna structure (such as a regular dodecahedron, a regular icosahedron, etc.), and each face is equipped with multiple transmitting and receiving units. Such a design optimizes the distribution of transmission and reception, realizes signal transmission in multiple directions, and can achieve long-distance laser communication while keeping the beam divergence angle as small as possible.

[0069] (2) RF / FSO nodes are equipped with precise signal alignment technology

[0070] APT technology is crucial for FSO systems because FSO communications are highly dependent on the precise alignment of light beams. The core function of APT technology is to ensure that the laser path between communication devices is stable and precise, thereby achieving efficient data transmission. At present, accurate APT technology mainly relies on high-speed sensors, beam stabilization technology, and efficient PID control algorithms.

[0071] (3) RF / FSO nodes support multiple network access protocols

[0072] In terms of working mechanism and function, the multi-directional communication capability of RF / FSO network nodes is similar to diversity reception and MIMO technology in RF communication, and also supports different network access protocols and routing protocols. It has the ability to forward information in multiple hops, thereby supporting extensive data distribution and powerful link control in the network. At the same time, the node supports multi-transmission and multi-reception information processing capabilities, and can efficiently transmit information within the specified communication range.

[0073] The FSO communication network channel loss model specifically includes:

[0074] The FSO communication channel loss model mainly includes two aspects: atmospheric attenuation effect and atmospheric turbulence effect. Since the atmospheric turbulence model requires not only a large number of real-time environmental parameters, but also high-frequency calculation updates, this will lead to a significant increase in the computational load and routing decision complexity. Therefore, in order to simplify the channel loss model and improve computational efficiency, the present invention chooses to consider only the atmospheric attenuation effect. The atmospheric attenuation effect is mainly caused by weather factors (such as haze, precipitation, etc.), and its model is relatively simple, with low computational overhead, and the impact on channel loss can be evaluated through more direct parameter measurements.

[0075] Considering the influence of atmospheric attenuation on the network channel, in the FSO communication network, the path loss between nodes u and v is defined as:

[0076] PL FSO =εd u,v (4.1)

[0077] where d u,v is the distance between nodes u and v, and ε is the atmospheric attenuation coefficient. The calculation method of this coefficient is shown in formula (2.1). This invention mainly studies and simulates the network performance under two weather conditions: sunny day and light foggy day. Take the average value of the corresponding weather attenuation coefficient. When the weather is sunny, ε 1 =0.137; when the weather is light foggy, ε 2 =5.865.

[0078] The FSO communication network control overhead model of the present invention specifically includes:

[0079] In wireless communication networks, the control overhead of routing protocols is one of the important factors affecting network performance. In RF networks and FSO networks, the performance of control overhead is also significantly different due to the differences in transmission media and communication characteristics. In FSO networks, due to their high directivity and environmental sensitivity, the control overhead of the OLSR protocol may increase significantly, thus affecting network efficiency. Therefore, in order to better understand the control overhead when using the OLSR protocol in FSO networks, it is necessary to establish a quantitative evaluation model to compare the differences in control signaling overhead between FSO networks and RF networks.

[0080] For each node, the broadcast control message needs to be transmitted repeatedly to ensure that all neighboring nodes receive the message. Here, the success rate P of the single-hop transmission of the data packet is introduced. For FSO networks, due to high directivity and environmental influences, the success rate of a single transmission P FSO At the same time, the signal will also be attenuated due to atmospheric conditions during transmission. Taking all the above factors into consideration, the control overhead of the FSO network is CFSO It can be expressed as:

[0081]

[0082] Where N is the total number of nodes in the network, F is the frequency of control signaling, and its value is the reciprocal of the transmission interval. S is the size of a single control signaling message, P FSO Indicates the success rate of single-hop signaling single transmission, PL FSO Used to characterize the attenuation of the channel in the atmosphere. The calculation method is shown in formula (4.1).

[0083] The present invention can significantly improve the transmission rate and anti-interference ability of the network when applying the RF / FSO hybrid technology to the mobile ad hoc network. However, when the node model, signal loss model and dynamic topology model of the FSO network are comprehensively considered, the direct application of the existing OLSR protocol to the RF / FSO network mainly faces the following two problems:

[0084] (1) Node dynamics and atmospheric factors affect network stability

[0085] In RF / FSO heterogeneous networks, the characteristics of FSO communication include high directivity and environmental sensitivity, which makes signal transmission extremely susceptible to influence in highly dynamic environments. The rapid movement of nodes can lead to the breakage of transmission links; atmospheric factors such as fog and haze can cause laser blocking or signal attenuation on the transmission path. Referring to formula (4.1), the atmospheric channel loss on foggy days is significantly higher than that on sunny days. The existing OLSR protocol does not fully consider the impact of dynamics and environment on network performance when designing.

[0086] (2) The broadcast characteristics of the OLSR protocol introduce a large amount of control overhead in FSO networks

[0087] The OLSR protocol relies on periodic control message broadcasts to maintain the network's routing table and topology information. This broadcast mechanism can be effectively propagated to the entire network in traditional RF networks, but it is inefficient in FSO networks that use point-to-point communication. Referring to formula (4.2), since the FSO network requires precise alignment, the transmission success rate P FSO In addition, the signal attenuation PL of the FSO network FSO It also fluctuates significantly due to changes in the atmospheric environment. Therefore, when using the OLSR protocol, FSO networks will introduce greater control signaling overhead compared to RF networks. Although it is difficult to modify the broadcast mechanism of the OLSR protocol, it is feasible to reduce the broadcast control overhead in the network by improving the protocol.

[0088] The heterogeneous ad hoc network OLSR protocol based on topology control and MPR selection optimization of the present invention specifically includes:

[0089] The TC-M-OLSR protocol of the present invention improves the OLSR protocol, mainly including two aspects:

[0090] (1) A clustering topology control method based on the characteristics of RF / FSO heterogeneous networks and considering node mobility is introduced. This topology control method is based on an adaptive on-demand weighted clustering algorithm, which effectively adapts to the dynamic changes of the network and reduces network overhead.

[0091] (2) Aiming at the link stability of RF / FSO heterogeneous networks, the MPR selection strategy is optimized within the cluster. The link stability comprehensively considers the distance between nodes and the scanning angle of the laser transceiver, thereby further reducing the broadcast control overhead.

[0092] 1.1.2 Adaptive on-demand weighted clustering algorithm

[0093] In mobile ad hoc networks, clustering strategies are usually used to build hierarchical communication topologies. Stable clustering algorithms can significantly reduce the computational and communication overhead of the network. The Adaptive On-demand weighted clustering algorithm (AOW) is known for its high flexibility. Unlike clustering algorithms based on a single factor, the AOW algorithm selects cluster heads by comprehensively considering multiple factors that affect network performance through weight factors. The weight coefficient can be adaptively adjusted according to network needs.

[0094] When selecting the cluster head node, the AOW algorithm comprehensively considers the four core factors of node mobility M, node degree D, transmission power P and residual energy E, and calculates the final weight W of the node through the weight formula. The calculation process conforms to formula (4.3). In actual application, the node will select the node with the smallest total weight from its neighbors as the cluster head. If there is a situation where the weights are the same, the node with a smaller ID will be selected as the cluster head.

[0095] W=ω m M+ω d D+ω p P+ω e E(4.3)

[0096] Introducing the AOW clustering model into RF / FSO heterogeneous ad hoc networks brings significant advantages. First, by optimizing the selection of cluster heads, the model effectively reduces the control overhead of the network, thereby improving the operating efficiency of the system. Second, the AOW model enhances the network's adaptive capabilities, enabling it to respond more flexibly to changing environmental conditions. This approach not only optimizes the allocation and utilization of resources, but also improves the stability and reliability of the network under different operating environments.

[64] .

[0097] 1.1.3 Cluster topology control method

[0098] In RF / FSO networks, node movement may lead to instability in link quality. To address this issue, clustering is first performed by calculating the link quality factor LF to form a stable cluster structure. Subsequently, the AOW clustering algorithm is used to select appropriate nodes as cluster heads based on various factors of the nodes, aiming to reduce the frequency of cluster head replacement and related overhead. In addition, in order to ensure the connectivity between clusters and form a complete network topology, the present invention introduces a multi-frequency clustering inter-cluster connection algorithm based on the TDMA protocol based on the design concept of cross-layer routing. By continuously maintaining the topology routing, the present invention can adjust the strategy in a timely manner according to the dynamic changes of the network environment and effectively implement the topology control of heterogeneous networks. The framework diagram of the entire process is shown below: (1) Clustering structure formation

[0099] In the process of cluster structure formation, considering the motion characteristics of the nodes, the formula is used The link quality factor LF calculated in is used as the decision basis for cluster allocation, where R d Indicates the maximum communication distance of the node. In the RWP model, if the maximum speed of the node v max Lower and residence time t p If the distance per unit time is longer, the relative moving distance per unit time will be reduced, and the link quality between the two nodes will be improved accordingly. The protocol will set a global cluster ID to identify which cluster the node is in. The specific algorithm steps and flow chart are as follows: Figure 2 As shown:

[0100] (a) Initialization phase: The initial cluster ID of the node is set to the node ID, and the link quality factor threshold LF is set according to the specific network conditions. th , set LF th A higher value of means a stricter requirement on link quality, which results in a reduction in the number of nodes in the cluster structure.

[0101] (b) The current receiving node u receives the one-hop neighbor node N i When a HELLO packet is received, it will obtain the neighbor node ID, cluster ID and neighbor location information contained in the packet.

[0102] (c) Node u will compare its own cluster ID with that of its neighbor node N i If the two are equal, it means that the two nodes are already in the same cluster structure, so the subsequent process is skipped.

[0103] (d) Calculate the link quality factor between the two nodes according to formula (3.3) and compare it with the set quality threshold LF thWhen the comparison is less than the threshold, it is considered that the link quality between the two points is low and it is difficult for the two nodes to form a stable cluster structure. When it is greater than the threshold, it is considered that the two nodes meet the clustering conditions.

[0104] (e) The cluster IDs of the two nodes and their cluster members are uniformly modified, and the cluster ID value with the smaller cluster ID is selected as the unified cluster ID. If the node also receives HELLO packets from other neighbors, it will return to stage (b) and repeat the entire process until the entire network forms a stable cluster structure.

[0105] (2) Cluster head selection

[0106] After the clustering algorithm is completed, multiple independent cluster structures will be formed. Each cluster needs a cluster head to effectively manage its internal nodes. Selecting a suitable cluster head is the key. For this purpose, the AOW algorithm that adapts to the characteristics of RF / FSO networks is used to select cluster heads.

[0107] When selecting a cluster head, the following three evaluation indicators are given priority, taking node u in the cluster as an example:

[0108] (a) Since each node has limited transceiver equipment, its node degree becomes a key factor. Only when a node has sufficient residual node degree can it be considered as a candidate for cluster head.

[0109] Node u counts the number of its neighboring nodes N, which is defined as the degree d of node u. u , the residual node degree Du of node u is the difference between its access degree and physical degree K, and the calculation formula is:

[0110] D u =|Kd u | (4.4)

[0111] The physical degree K refers to the maximum number of concurrent links that a node can support. For FSO nodes, it refers to the number of independent point-to-point optical links that the node can maintain simultaneously. This value is determined by the number of laser transceiver units equipped on the node. For example, if a node is equipped with 5 transmitting units and 5 receiving units, its physical degree K can be considered to be 5.

[0112] (b) In order to ensure the stability of the cluster head, nodes with lower mobility are selected as cluster heads. The relative moving speed of node u relative to node v is V(u,v):

[0113] V(u,v)=|V u -V v |(4.5)

[0114] Where V u and V vare the speeds of node u and node v respectively, then the average relative mobility of node u in the cluster is M u for:

[0115]

[0116] (c) In the design of RF / FSO networks, the transmit power of a node should be determined based on the actual distance it needs to cover and the atmospheric attenuation factor. The ideal transmit power setting should ensure that the node can maintain effective communication with its one-hop neighbors and should also take into account the impact of atmospheric attenuation. Therefore, it is an effective method to estimate the ideal transmit power by calculating the sum of the path losses from node u to all its one-hop neighbors. Therefore, the ideal transmit power P of node u is u for:

[0117]

[0118] Among them, d u,v is the distance between nodes u and v, N 1 represents the set of one-hop neighbor nodes of node u, and ε is the attenuation coefficient of the atmosphere.

[0119] According to the node mobility, remaining node degree and ideal transmission power calculated above, the combined weight W of node u is u for:

[0120]

[0121] where ω d ,ω m ,ω p are the weight factors of mobility, remaining node degree and transmit power, respectively, satisfying ω d +ω m +ω p = 1. Finally, based on the comprehensive weight W of the nodes, the node with the smallest weight will be selected as the cluster head. If there is an equal weight, the node with the smaller ID will be selected as the cluster head.

[0122] (3) Inter-cluster connections

[0123] In order to reduce channel conflicts and improve channel utilization in relatively dense mobile ad hoc networks, the present invention proposes an inter-cluster connection mechanism based on the cross-layer routing design concept. The mechanism adopts a multi-frequency cluster communication method at the MAC layer and uses the TDMA protocol to allocate exclusive communication frequency bands for different clusters. For each cluster, the communication frequency will be configured for the node according to the cluster ID of the current node, and the gateway node adopts the inter-cluster time-hopping access method to solve the communication access problem of the gateway between different working frequency bands. The schematic diagram of the connection between two adjacent clusters is shown in the figure. Figure 3 shown.

[0124] Specifically, before the protocol runs, several available communication frequencies are configured according to the scenario requirements. During the protocol operation, the communication frequency band of the cluster where the node is located is updated in real time through the cluster ID of the current node. This process can be triggered periodically by configuring a timer. For the gateway node at the junction of two adjacent clusters, in order to ensure stable data exchange between clusters, it is necessary to have a certain frequency hopping capability. The following scheme is adopted: switch to the frequency band of the neighboring cluster with a smaller ID in the odd time frame period, and switch to the frequency band of the neighboring cluster with a larger ID in the even time frame period. The gateway node alternately switches the working frequency band between the two neighboring clusters to ensure the continuity and efficiency of data transmission.

[0125] 1.1.4 MPR selection optimization based on link stability

[0126] In RF / FSO communication networks, various factors in the process of laser sending, transmission and reception significantly affect the stability of the link. In order to accurately evaluate and optimize network performance, it is crucial to consider the quantification of these factors. A link stability calculation method based on laser transceiver parameters and the distance between nodes is introduced. The calculation formula of LS is as follows:

[0127]

[0128] Where α is the scanning angle θ of the laser transceiver scan The influence on link stability is greater. Systems with larger scanning angles are more tolerant of node movement and alignment errors, thereby enhancing link stability. Distance d represents the actual distance between two nodes, while D max Refers to the farthest effective communication distance between two transceivers. ρ describes the ratio of the farthest effective communication distance to the optimal communication distance, which can be adjusted according to the performance of different transceivers.

[0129] The value range of link stability LS is [0,1], which is used to reflect the stability of each node in the network in real time. The higher the LS value, the stronger the stability of the node, so the probability of the node being selected as MPR is also higher. This indicator helps optimize the routing protocol and ensure more stable and efficient network communication.

[0130] Figure 4 This is the optimized MPR selection process. Similar to the standard MPR node set selection algorithm, it is assumed that the MPR set selected by the central node is set M; the one-hop neighbor set of the node is N 1 ; The node’s two-hop neighbor set is N 2 , which does not include the central node and N 1 Nodes in .

[0131] (1) Add a neighbor node with the willingness value ALWAYS to the set M first;

[0132] (2) Get N from the neighbor table 1 The geographical location of the nodes, calculate the distance between nodes, and calculate the link stability LS and depth D(y) according to formula (4.9);

[0133] (3) If a two-hop neighbor can only be connected to the point of the present invention through a one-hop neighbor, add this one-hop neighbor to the set M and remove the two-hop neighbors it covers;

[0134] (4) If N 1 If there is a node that is already the MPR of other nodes, then select the node with the largest LS among these nodes as the MPR of the central node. If there is no such node, proceed to the next step;

[0135] (5) Calculate the reachability of node N1 and execute the following steps in sequence:

[0136] (a) Add the node with the highest willingness to the set M;

[0137] (b) Among multiple candidates, select the node with the largest LS to join M;

[0138] (c) Among multiple candidates, select the node with the highest reachability to join M;

[0139] (6) Check whether the set M covers all two-hop neighbors N 2 If it is covered, M is the final MPR set; otherwise, return to step (4).

[0140] The optimized MPR selection algorithm maximizes the utility of each MPR node by reusing nodes that have been used as MPRs, reducing the number of nodes that need to be broadcast in the network. At the same time, nodes with higher link stability are selected as MPRs, reducing the risk of link breakage and enhancing the reliability and stability of the network.

[0141] 1.1.1 TC-M-OLSR protocol overall process

[0142] Figure 5 The overall process diagram of the TC-M-OLSR protocol is shown. Similar to the protocol in Chapter 3, this protocol also inherits the key processes of the OLSR protocol and optimizes two key processes: First, in the link perception phase, nodes share local measurement information with neighbors and form a cluster structure based on this information; second, the MPR selection process within the cluster is optimized by estimating the link stability of the RF / FSO network. For details of the relevant processes, see Figure 4 The optimized parts of the TC-M-OLSR protocol compared to the OLSR protocol are marked in gray in the figure. These improvements improve the adaptability of the protocol to RF / FSO networks and enhance its responsiveness and efficiency in the face of dynamic changes in the network environment.

[0143] The simulation result analysis of the present invention specifically includes the following:

[0144] 1.1.2 Simulation model design

[0145] (1) Routing Protocol Model

[0146] The routing protocol model is consistent with the routing model in Section 3.4.1 and will not be repeated here.

[0147] (2) Channel Model

[0148] Consistent with the description in Section 3.4.1, the channel model mainly includes two parts: shadow fading and path loss.

[0149] (a) Shadow fading

[0150] In FSO networks, although there are models such as double gamma or exponential Weber that are more suitable for describing the effects of atmospheric turbulence, in order to simplify the calculation as much as possible, the log-normal model can also approximately describe the signal fading when the atmospheric conditions are relatively stable.

[66] Therefore, this chapter continues to use the log-normal model from the previous chapter as the shadow fading model.

[0151] (b) Path loss

[0152] In the FSO communication network, the calculation method of path loss has been described in detail in Section 4.2.2. The path loss model based on the FSO communication network needs to be designed as a third-party model added by the user. In order to integrate the model into the EXata simulation platform, the calculation of path loss and related calling mechanisms need to be added to the source code file propagation.cpp. Specifically, it includes:

[0153] Define the path loss function: Write a new function in propagation.cpp to calculate the path loss based on the FSO communication model. This function transmits relevant parameters such as distance and atmospheric attenuation coefficient, and returns the calculated path loss value.

[0154] Modify the calling mechanism: In the core code of the EXata simulation platform, find the calling point of the path loss calculation and modify it to call the new path loss function. Ensure that the newly defined path loss calculation method can be correctly called and applied during the simulation process.

[0155] The simulation scenarios and parameters of the present invention specifically include the following:

[0156] The simulator uses the EXata simulation platform. In order to simulate the actual RF / FSO communication network scenario, the network architecture adopts a structure similar to the air-ground information network. The ground network and the communication between high-altitude drones use RF networking, using an RF-based two-path fading model; the communication between airships and air-ground in the high altitude uses FSO networking, using a custom FSO atmospheric fading model. The routing protocol uses the TC-M-OLSR protocol proposed in this chapter. In order to demonstrate the performance of the protocol, it is compared with the standard version of the OLSR protocol in the network simulation, and the following simulation experiments are designed:

[0157] (1) Verify the impact of node mobility on network latency and packet delivery rate under different atmospheric conditions;

[0158] (2) Verify the impact of the average node movement speed and the number of network nodes on the broadcast routing overhead under the same atmospheric conditions.

[0159] The scale of the simulation scene is set to 10km×10km, in which the ratio of airships, drones, and ground units is 1:4.5:4.5. Figure 6 For example, the total number of nodes is 80, and the number of airships, drones, and ground units is 8, 36, and 36 respectively. Compared with drones, airships in the sky have a larger scanning angle on the laser transceiver. In the simulation, the scanning angle of the airship is set to θ_scan_a = πrad, and the scanning angle of the drone is set to θ_scan_u = π / 3rad. The specific simulation scenario and other simulation parameters are as follows Figure 6 As shown in Table 4.1:

[0160] Table 4.1 Simulation parameter configuration

[0161]

[0162] The simulation performance indicators include the following:

[0163] The present invention also uses the average end-to-end delay and packet delivery rate of CBR services as evaluation indicators of network performance. The calculation method is referred to Section 3.4.2. In addition, in order to more comprehensively measure network efficiency, the present invention introduces the concept of Broadcast Control Overhead (BCO). The BCO of a node is defined as the ratio of the average number of bytes of control information sent in the network to the average number of bytes of total data (including control data and service data) sent per unit time. The calculation formula is as follows:

[0164]

[0165] Among them: B c Indicates the average number of bytes of control information sent in the network, Bd Indicates the average number of bytes of any data sent on the network.

[0166] The simulation results analysis of the present invention includes the following:

[0167] In order to comprehensively evaluate the impact of network topology changes on protocol performance under different weather conditions and ensure adaptability to network scenarios and diversity of performance evaluation, this study designed multiple CBR service flows including ground-to-ground, air-to-ground, and air-to-air, and performed multiple rounds of simulation experiments. Statistical data was obtained by averaging the performance results of multiple service flows.

[0168] The end-to-end delay and packet delivery rate changes when the maximum node moving speed is 0-25m / s are as follows: Figure 7 and Figure 8 As shown. Under the condition of consistent node movement speed, light fog has greater path loss than sunny days. Correspondingly, when the maximum node speed is 5m / s, the average delay on light fog is 27.3% more than that on sunny days. Since the TC-M-OLSR protocol uses atmospheric fading factors as the evaluation factor for cluster head selection in the cluster topology control process, its delay is reduced by 2.6% compared with the OLSR protocol in light fog, and the packet delivery rate is increased by 2.8%. On the other hand, since the node movement factor is considered in the process of cluster structure formation and MPR node selection, when the maximum node speed is 15m / s, the average delay is reduced by 4.2%, and the packet delivery rate is increased by 14.0%.

[0169] The variation of broadcast control overhead with node movement speed and number of nodes is as follows: Fig. 9 and Fig.10 As shown, Fig. 9 shows the situation when the number of nodes is fixed at 60, while Fig.10 The maximum moving speed of the node is set to 5m / s.

[0170] As can be observed from the figure, the TC-M-OLSR protocol significantly reduces the broadcast control overhead compared to the OLSR protocol by introducing a clustering topology control method based on the AOW algorithm and a link stability calculation method for RF / FSO networks, and optimizing the MPR selection mechanism based on this stability. When the speed and number of node movements increase, although the control overhead of both protocols increases, the broadcast overhead of the TC-M-OLSR protocol in the topology movement scenario is reduced by an average of 67.7% compared to the OLSR protocol. When the number of nodes reaches 80, its overhead is reduced by 65.2%. These data show that the optimized protocol exhibits higher efficiency and stability in dynamic and large-scale network environments.

[0171] In summary, the present invention first analyzes the limitations of traditional RF communications and the challenges encountered by the existing OLSR protocol in RF / FSO networks; secondly, it introduces the channel model of the FSO network, especially the impact of atmospheric attenuation on signal transmission, and introduces the node model of the RF / FSO network and the adaptive on-demand weighted clustering algorithm. The present invention describes the design ideas of the protocol, proposes a clustering topology control method based on link quality and an MPR optimization strategy based on link stability; finally, the experimental results of the EXata simulation software show that the optimized protocol exhibits better performance under high mobility and atmospheric attenuation conditions, especially in environments with high transmission rate requirements.

Claims

1. A method for implementing the OLSR protocol in a heterogeneous ad hoc network based on topology control and MPR selection optimization, characterized in that: The method comprises the following steps: Step 1: Cluster structure formation; In the process of cluster structure formation, considering the motion characteristics of the nodes, the formula is used The link quality factor LF calculated in is used as the decision basis for cluster allocation. The protocol will set a global cluster ID to identify which cluster the node is located in. (a) Initialization phase: The initial cluster ID of the node is set to the node ID, and the link quality factor threshold LF is set according to the specific network conditions. th , set LF th The value of is higher; (b) The current receiving node u receives the one-hop neighbor node N i When receiving a HELLO packet, it will obtain the neighbor node ID, cluster ID and neighbor location information contained in the packet; (c) Node u will compare its cluster ID with that of its neighbor node N i If the two nodes are equal, it means that the two nodes are already in the same cluster structure, and the subsequent process is skipped; (d) According to the formula Calculate the link quality factor between the two nodes and the set quality threshold LF th When the value is less than the threshold, it is considered that the link quality between the two nodes is low and it is difficult for the two nodes to form a stable cluster structure. When the value is greater than the threshold, it is considered that the two nodes meet the clustering conditions. d Indicates the maximum communication distance of the node. In the RWP model, if the maximum speed of the node v max Lower and residence time t p If the distance is longer, the relative moving distance per unit time will be reduced, and the link quality between the two nodes will be improved accordingly; (e) The cluster IDs of the two nodes and their cluster members are uniformly modified, and the cluster ID value with the smaller cluster ID is selected as the unified cluster ID. If the node also receives HELLO packets from other neighbors, it will return to stage (b) and repeat the whole process until the entire network forms a stable cluster structure. Step 2: Cluster head selection; After the clustering algorithm is completed, multiple independent cluster structures will be formed. Each cluster needs a cluster head to effectively manage its internal nodes. Selecting a suitable cluster head is the key. The AOW algorithm that adapts to the characteristics of RF / FSO networks is used to select the cluster head, including: (a) Since each node has limited transceiver equipment, its node degree becomes a key factor. Only when a node has sufficient residual node degree can it be considered as a candidate for cluster head; Node u counts the number of its neighboring nodes N, which is defined as the degree d of node u. u , the residual node degree Du of node u is the difference between its access degree and physical degree K, and the calculation formula is: D u =|K-d u |(4.4) The physical degree K refers to the maximum number of concurrent links that a node can support. For FSO nodes, it refers to the number of independent point-to-point optical links that the node can maintain simultaneously. This value is determined by the number of laser transceiver units equipped on the node. A node is equipped with 5 transmitting units and 5 receiving units, and its physical degree K can be considered to be 5. (b) In order to ensure the stability of the cluster head, nodes with lower mobility are selected as cluster heads. The relative moving speed of node u relative to node v is V(u,v): V(u,v)=|V u -V v |(4.5) Where V u and V v are the speeds of node u and node v respectively, then the average relative mobility of node u in the cluster is M u for: (c) In the design of RF / FSO networks, the transmit power of a node should be determined based on the actual distance it needs to cover and the atmospheric attenuation factor. The ideal transmit power setting should ensure that the node can maintain effective communication with its one-hop neighbors. The influence of atmospheric attenuation should also be considered. It is an effective method to estimate the ideal transmit power by calculating the sum of the path losses from node u to all its one-hop neighbors. Therefore, the ideal transmit power P of node u is u for: Among them, d u,v is the distance between nodes u and v, N1 represents the set of one-hop neighbor nodes of node u, and ε is the attenuation coefficient of the atmosphere; According to the node mobility, remaining node degree and ideal transmission power calculated above, the combined weight W of node u is u for: where ω d ,ω m ,ω p are the weight factors of mobility, remaining node degree and transmit power, respectively, satisfying ω d +ω m +ω p =1, finally, based on the comprehensive weight W of the nodes, the node with the smallest weight will be selected as the cluster head. If there is an equal weight, the node with the smaller ID will be selected as the cluster head; Step 3: Inter-cluster connection; In order to reduce channel conflicts and improve channel utilization in relatively dense mobile ad hoc networks, an inter-cluster connection mechanism based on the cross-layer routing design concept is proposed. The mechanism adopts multi-frequency cluster communication at the MAC layer and uses the TDMA protocol to allocate exclusive communication frequency bands for different clusters. For each cluster, the communication frequency is configured for the node according to the cluster ID of the current node, and the gateway node adopts the inter-cluster time-hopping frequency access method. Before the protocol runs, several available communication frequencies are configured according to the scenario requirements. During the protocol operation, the communication frequency band of the cluster where the node is located is updated in real time through the cluster ID of the current node. This process is periodically triggered by configuring a timer. For the gateway node at the junction of two adjacent clusters, in order to ensure stable data exchange between clusters, it is necessary to have a certain frequency hopping capability. In odd-numbered time frame periods, it switches to the frequency band of the neighboring cluster with a smaller ID, and in even-numbered time frame periods, it switches to the frequency band of the neighboring cluster with a larger ID. The gateway node alternately switches the working frequency band between the two neighboring clusters to ensure the continuity and efficiency of data transmission.

2. The method for implementing the OLSR protocol in a heterogeneous ad hoc network based on topology control and MPR selection optimization according to claim 1, characterized in that: The method includes: in the RF / FSO communication network, various factors in the process of laser sending, transmission and reception significantly affect the stability of the link. In order to accurately evaluate and optimize the network performance, it is very important to consider the quantification of these factors. A link stability calculation method based on laser transceiver parameters and the distance between nodes is introduced. The calculation formula of LS is as follows: Where α is the scanning angle θ of the laser transceiver scan The influence ratio on link stability is that the system with a larger scanning angle can tolerate node movement and alignment errors more, thus enhancing the stability of the link. The distance d represents the actual distance between the two nodes, while D max Refers to the farthest effective communication distance between two transceivers. ρ describes the ratio of the farthest effective communication distance to the optimal communication distance. This ratio is adjusted according to the performance of different transceivers. The value range of link stability LS is [0,1], which is used to reflect the stability of each node in the network in real time. The higher the LS value, the stronger the stability of the node, so the probability of the node being selected as MPR is also correspondingly higher. This indicator helps optimize the routing protocol and ensure more stable and efficient network communication.

3. The method for implementing the OLSR protocol in a heterogeneous ad hoc network based on topology control and MPR selection optimization according to claim 2, characterized in that: The optimized MPR selection process includes: if the MPR set selected by the central node is a set M; The one-hop neighbor set of a node is N1; the two-hop neighbor set of a node is N2, which does not include the central node and the nodes in N1, and includes: Step 1) Add a neighbor node with a willingness value of ALWAYS to the set M first; Step 2) Get the geographical location of node N1 from the neighbor table, calculate the distance between nodes, and use the formula Calculate link stability LS and depth D(y); Step 3) If the two-hop neighbor can only be connected to the point of the present invention through one one-hop neighbor, add this one-hop neighbor to the set M and remove the two-hop neighbors it covers; Step 4) If there is a node in N1 that is already the MPR of other nodes, then select the node with the largest LS among these nodes as the MPR of the central node. If there is no such node, proceed to the next step; Step 5) Calculate the reachability of node N1 and execute in sequence: (a) Add the node with the highest willingness to the set M; (b) Among multiple candidates, select the node with the largest LS to join M; (c) Among multiple candidates, select the node with the highest reachability to join M; Step 6: Check whether the set M covers all two-hop neighbors N2. If so, M is the final MPR set; otherwise, return to step 4). The optimized MPR selection algorithm maximizes the utility of each MPR node by reusing the nodes that have been used as MPRs, reducing the number of nodes that need to be broadcast in the network. At the same time, it selects nodes with higher link stability as MPRs, reducing the risk of link breakage and enhancing the reliability and stability of the network.

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