CN113347677A - Multi-node communication method based on particle swarm optimization - Google Patents
Multi-node communication method based on particle swarm optimization Download PDFInfo
- Publication number
- CN113347677A CN113347677A CN202110482222.1A CN202110482222A CN113347677A CN 113347677 A CN113347677 A CN 113347677A CN 202110482222 A CN202110482222 A CN 202110482222A CN 113347677 A CN113347677 A CN 113347677A
- Authority
- CN
- China
- Prior art keywords
- node
- path
- nodes
- fitness
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004891 communication Methods 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 25
- 239000002245 particle Substances 0.000 title claims abstract description 17
- 238000005457 optimization Methods 0.000 title abstract description 14
- 230000005540 biological transmission Effects 0.000 claims abstract description 28
- 238000013139 quantization Methods 0.000 claims description 7
- 208000003443 Unconsciousness Diseases 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000004364 calculation method Methods 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 3
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000011664 nicotinic acid Substances 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention provides a multi-node communication method based on a particle swarm optimization, which solves the problems that the channel load and the competition risk are increased due to randomness and an unconscious information transmission mode in the conventional multi-node communication network. The method comprises the following steps: 1) loading preset parameters; 2) all nodes access the network and send broadcast messages; 3) generating an initial node and a target node; 4) the starting node randomly designates a known node as a relay node and sends information; after receiving the information, the appointed relay node continues to appoint the next-stage relay node and sends the information to the next-stage relay node; the nodes are sequentially transmitted until the target node is reached; 5) calculating the path fitness; 6) updating the path with high fitness into an individual optimal path; 7) and judging the path exploration capability. The invention optimizes the point-to-point information transmission path in the multi-node network by the path optimization technology of the particle swarm optimization, reduces the channel occupation time and improves the information transmission efficiency on the premise of ensuring the transmission reliability.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a multi-node communication method based on a particle swarm algorithm.
Background
By virtue of multi-hop interconnection and network topology characteristics, the multi-node communication network becomes an effective solution for wireless access networks such as family, community, enterprise and metropolitan area networks. In the implementation of a multi-node communication network, a point-to-point information transmission path planning strategy directly influences the channel utilization rate. Common multi-node communication networks are mostly in a random or unconscious information transmission mode, so that the channel load and the competition risk are increased, the bandwidth utilization rate is reduced, and higher performance and value are lacked. Therefore, how to reduce the channel load, and reduce the wasted bandwidth of contention and backoff are the core concerns for measuring the performance and value of the network.
Disclosure of Invention
The invention provides a multi-node communication method based on a particle swarm algorithm, aiming at solving the technical problems that the channel load and the competition risk are increased and the bandwidth utilization rate is reduced due to a random or unconscious information transmission mode in the conventional multi-node communication network.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a multi-node communication method based on a particle swarm algorithm is characterized by comprising the following steps:
1) initialization parameters
After the node equipment is started, loading preset parameters, wherein the preset parameters comprise an optimal path failure threshold TmaxLink quality weighting factor c1Path optimal weight factor c2And fitness returns to the time to live (TIL);
2) initializing node locations
All nodes access the network and sequentially send broadcast messages; each node records the positions of other nodes and the quality of a communication link according to the received broadcast information; wherein the number n of nodes in the network is more than or equal to 3;
3) task publishing
Generating an initial node and a target node according to the actual information transmission task; or when the network is idle, randomly generating an initial node and a target node with exploration capacity;
4) path search
The starting node randomly designates other known nodes which are not designated as relay nodes and sends information; after receiving the information, the appointed relay node continues to appoint the next-stage relay node and sends the information to the next-stage relay node; the nodes are sequentially transmitted until the target node is reached;
5) return path fitness
After receiving the information, the target node returns path information according to the original path; the start node then calculates the fitness Fit of the path according to the fitness function:
wherein: q. q.siThe link quality quantization value between two nodes, i is 3, …, n;
m is the number of nodes passed by the path;
if the initial node does not receive the path information returned by the target node within the TIL time, judging that the path communication fails, and returning to the step 4);
6) updating individual optimal paths
The initial node records the current path and the corresponding fitness, compares the current path with the fitness of the individual historical optimal path, and updates the current path to be the individual optimal path if the current path fitness is higher than the individual historical optimal path fitness; if the current path fitness is equal to or lower than the individual historical optimal path fitness, keeping the original individual optimal path;
7) decision path exploration capability
Judging whether the starting node finishes the operation of designating all the known nodes as the relay nodes, if so, temporarily canceling the path searching capability until the fitness drops to exceed the optimal path failure threshold T when the node transmits information according to the optimal path at a certain timemaxRe-endowing the node with path searching capability; if the starting node still has a known node which is not designated as a relay node, the path searching capability of the node is kept to explore whether other more optimal paths exist;
after the judgment is finished, returning to the step 3); and ending the path exploration work until the equipment needs to be powered off or receives a stop command.
Further, in step 4), the principle of designating the relay node is as follows:
A) the peer node of the superior node can not be selected as the next-level relay node;
B) the relay nodes autonomously select the next-stage relay node according to the individual optimal path;
C) and when the relay node does not have the optimal path to the target node, preferentially selecting the lower node with good communication quality.
Further, in step 1), the link quality weighting factor c1Is the proportion of the link quality quantization mean value between nodes in the path, c is more than 0.51＜2；
The path optimal weight factor c2C is the proportion of the number of nodes in the path, and c is more than 0.52＜2。
Further, in step 2), the broadcast message is sent in a time division manner.
Compared with the prior art, the invention has the advantages that:
1. the multi-node communication method optimizes the point-to-point information transmission path in the multi-node network through the path optimization technology of the particle swarm optimization, reduces the collision probability and improves the channel utilization rate.
2. The optimal path searched by the multi-node communication method takes the quality of a transmission link into consideration, ensures the reliability of transmission and reduces the retransmission probability; meanwhile, the number of nodes on the path is also considered, and fewer relay nodes can enable information to reach a target node more quickly, so that the information transmission efficiency among the nodes is improved, and the time delay of information transmission is reduced.
3. The multi-node communication method of the invention adds a path optimization algorithm under the existing node access and collision avoidance mechanism, so the method of the invention can be directly used for determining the optimal transmission path without modifying the original transmission scheme.
4. The fitness calculation formula of the multi-node communication method is simple and effective, and compared with other conventional calculation functions, the multi-node communication method has the advantages of small calculation amount and easiness in implementation.
Drawings
FIG. 1 is a flow chart of a multi-node communication method based on particle swarm optimization according to the present invention;
fig. 2 is a flow chart of information transfer path optimization in the embodiment of the invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific embodiments.
The particle swarm algorithm is a bionic algorithm, and intelligent optimization search is generated through cooperative cooperation among particles in a swarm. In a multi-node communication network, each node can be regarded as a particle in the network, and the optimal path for information transmission between any two nodes is searched through information interaction between the nodes. Therefore, the invention provides a multi-node communication method based on the particle swarm optimization idea on the basis of a multi-node communication network with the existing node access and collision back-off mechanism.
The multi-node communication method of the invention comprises the following steps:
1) initialization parameters
The node equipment is started, and once the node equipment is in a starting state, the preset parameters are loaded immediately, wherein the preset parameters comprise an optimal path failure threshold TmaxLink quality weighting factor c1Path optimal weight factor c2And fitness returns to the time to live (TIL);
optimal path failure threshold Tmax: when the node finishes the path search and transmits information according to the optimal path, the path fitness is allowed to be reduced to the maximum value;
link quality weighting factor c1: the link quality quantization mean value between nodes in the path accounts for the proportion. The larger the value is, the more important the link quality is in the path search, in this embodiment, c is more than 0.51＜2；
Path optimal weight factor c2: the number of nodes in the path accounts for the proportion. The larger the value is, the more important the number of nodes passed by the path is, and the reality isIn the examples, 0.5 < c2＜2。
2) Initializing node locations
The network is initialized (the number of nodes n in the network is more than or equal to 3). All nodes enter the network according to the access rule, and the broadcast messages containing the self-position information are sequentially sent in a time division mode, so that all nodes in the network can acquire the positions of other nodes communicating with the nodes. And each node records the positions of other nodes and the quality of a communication link according to the received broadcast information.
3) Task publishing
Generating an initial node and a target node according to an actual information transmission task, and recording path information in the effective information transmission process; or when the network is idle, randomly generating an initial node and a target node with exploration capability for path search and trying whether other more paths exist.
4) Path search
In the information transmission process, the starting node randomly designates other known nodes (other nodes capable of normally communicating) which are not designated as relay nodes, sends information and carries out point-to-point information transmission; after receiving the information, the appointed relay node continues to appoint the next-stage relay node and sends the information to the next-stage relay node; and sequentially transmitting the nodes until the target node is reached.
The principle to be followed for selecting the next-stage relay node is as follows:
a) the peer node of the superior node can not be selected as the next-level relay node, so that the redundant path is avoided; taking the node positions in fig. 2 as an example, assuming that the starting node of the current task is node 1 and the target node is node 8, and defining the distance between node 1 and node 2 as a unit transmission distance, each node can communicate with the neighboring node with the approximate unit transmission distance. The information transmission path is transmitted from the originating node 1 to the node 2 and then to the node 5. In order to avoid generating redundant routes, the node 5 is allowed to be transmitted to the node 7 or the node 6, but the peer node 4 of the superior node 2 cannot be selected as the next-stage relay node.
b) In order to realize the information sharing of all the nodes, the relay nodes autonomously select the next-stage relay node according to the individual optimal path.
c) The link quality is an important index for evaluating the fitness, and when the relay node does not have the optimal path to the target node, the next node with better notification quality should be preferentially selected, and the next node with better communication quality should be preferentially selected, so as to improve the transmission reliability.
5) Return path fitness
And after receiving the information, the target node returns the path information according to the original path. The start node then calculates the fitness of the path according to a fitness function. In the path, the smaller the number of nodes transmitted from the starting node to the target node is, the higher the communication quality is, the higher the fitness of the path is; conversely, the lower the fitness of the node. The path fitness is obtained by jointly calculating the current speed, the number of passing nodes and the link quality quantization value, and the fitness Fit calculation formula is as follows:
wherein q isiThe link quality quantization value between two nodes, i is 3, …, n; and m is the number of nodes passed by the path.
If the initial node does not receive the path information returned by the target node within the TIL time, judging that the path communication fails, and returning to the step 4);
6) updating individual optimal paths
The starting node records the current path and the corresponding fitness and compares the current path with the fitness (pbest) of the individual historical best path:
if the current path fitness is higher than the individual historical optimal path fitness, updating the current path to be the individual optimal path;
and if the current path fitness is lower than the individual historical optimal path fitness, the current path fitness is not updated, and the original individual optimal path is kept.
7) Decision path exploration capability
Judging whether the starting node finishes the operation of designating all known nodes as relay nodes or not, and temporarily canceling the path searching capability of the starting node if the starting node already designates all the known nodes; until the node transmits information according to the optimal path at a certain time, the fitness changes (reduces) and exceeds the failure threshold T of the optimal pathmaxAnd then, the path searching capability of the node is endowed again. If the starting node still has an unspecified known node, the path searching capability of the node is maintained, and whether other paths are more optimal or not is tried;
after the judgment is finished (the invalid node competes with other nodes fairly, the nodes with the exploration capacity are less and less along with the exploration, and the probability that the invalid node becomes the initial node at random is higher), the step 3 is returned; and ending the path exploration work until the equipment needs to be powered off or receives a stop command.
The method adds the point-to-point information transmission path optimization method to the multi-node communication network, and searches the unique optimal path in numerous and disorderly transmission routes, thereby avoiding the occurrence of low-efficiency channel occupation and improving the communication efficiency.
In fig. 2, the network contains 9 nodes, the current transfer task is from node 1 to node 8, and 3 paths marked in the figure are effective transfer paths after irrational paths are removed. Wherein, path 1 passes through 4 nodes, path 2 passes through 2 nodes, and path 3 passes through 3 nodes, if it is assumed that the communication link quality of 3 paths is similar, and the number of nodes passed by path 2 is the least, so path 2 is the optimal path.
The method of the invention adds the path optimization algorithm under the existing node access and collision avoidance mechanism, so the original transmission scheme is not required to be modified, and the method can be used only by adding the method.
The above description is only for the preferred embodiment of the present invention and does not limit the technical solution of the present invention, and any modifications made by those skilled in the art based on the main technical idea of the present invention belong to the technical scope of the present invention.
Claims (4)
1. A multi-node communication method based on a particle swarm algorithm is characterized by comprising the following steps:
1) initialization parameters
After the node equipment is started, loading preset parameters, wherein the preset parameters comprise an optimal path failure threshold TmaxLink quality weighting factor c1Path optimal weight factor c2And fitness returns to the time to live (TIL);
2) initializing node locations
All nodes access the network and sequentially send broadcast messages; each node records the positions of other nodes and the quality of a communication link according to the received broadcast information; wherein the number n of nodes in the network is more than or equal to 3;
3) task publishing
Generating an initial node and a target node according to the actual information transmission task; or when the network is idle, randomly generating an initial node and a target node with exploration capacity;
4) path search
The starting node randomly designates other known nodes which are not designated as relay nodes and sends information; after receiving the information, the appointed relay node continues to appoint the next-stage relay node and sends the information to the next-stage relay node; the nodes are sequentially transmitted until the target node is reached;
5) return path fitness
After receiving the information, the target node returns path information according to the original path; the start node then calculates the fitness Fit of the path according to the fitness function:
wherein: q. q.siThe link quality quantization value between two nodes, i is 3, …, n;
m is the number of nodes passed by the path;
if the initial node does not receive the path information returned by the target node within the TIL time, judging that the path communication fails, and returning to the step 4);
6) updating individual optimal paths
The initial node records the current path and the corresponding fitness, compares the current path with the fitness of the individual historical optimal path, and updates the current path to be the individual optimal path if the current path fitness is higher than the individual historical optimal path fitness; if the current path fitness is equal to or lower than the individual historical optimal path fitness, keeping the original individual optimal path;
7) decision path exploration capability
Judging whether the starting node finishes the operation of designating all the known nodes as the relay nodes, if so, temporarily canceling the path searching capability until the fitness drops to exceed the optimal path failure threshold T when the node transmits information according to the optimal path at a certain timemaxRe-endowing the node with path searching capability; if the starting node still has a known node which is not designated as a relay node, the path searching capability of the node is kept to explore whether other more optimal paths exist;
after the judgment is finished, returning to the step 3); and ending the path exploration work until the equipment needs to be powered off or receives a stop command.
2. The particle swarm algorithm-based multi-node communication method according to claim 1, wherein: in step 4), the principle of designating the relay node is as follows:
A) the peer node of the superior node can not be selected as the next-level relay node;
B) the relay nodes autonomously select the next-stage relay node according to the individual optimal path;
C) and when the relay node does not have the optimal path to the target node, preferentially selecting the lower node with good communication quality.
3. The particle swarm algorithm-based multi-node communication method according to claim 1 or 2, wherein: in step 1), the link quality weighting factor c1Is the proportion of the link quality quantization mean value between nodes in the path, c is more than 0.51＜2；
The path optimal weight factor c2C is the proportion of the number of nodes in the path, and c is more than 0.52＜2。
4. The particle swarm algorithm-based multi-node communication method according to claim 3, wherein: in step 2), the broadcast message is sent in a time division manner.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110482222.1A CN113347677B (en) | 2021-04-30 | 2021-04-30 | Multi-node communication method based on particle swarm optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110482222.1A CN113347677B (en) | 2021-04-30 | 2021-04-30 | Multi-node communication method based on particle swarm optimization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113347677A true CN113347677A (en) | 2021-09-03 |
| CN113347677B CN113347677B (en) | 2023-12-08 |
Family
ID=77469324
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110482222.1A Active CN113347677B (en) | 2021-04-30 | 2021-04-30 | Multi-node communication method based on particle swarm optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113347677B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115086229A (en) * | 2022-04-29 | 2022-09-20 | 珠海高凌信息科技股份有限公司 | SDN network multi-path calculation method based on evolutionary algorithm |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105430706A (en) * | 2015-11-03 | 2016-03-23 | 国网江西省电力科学研究院 | WSN (Wireless Sensor Networks) routing optimization method based on improved PSO (particle swarm optimization) |
| CN106792963A (en) * | 2016-12-06 | 2017-05-31 | 淮阴工学院 | A kind of car networking sub-clustering car car multi-hop routing method based on particle group optimizing |
| CN108156613A (en) * | 2017-11-26 | 2018-06-12 | 南京邮电大学 | A kind of relay node distribution method in unmanned plane relay multi-hop communication system |
| CN108183860A (en) * | 2018-01-19 | 2018-06-19 | 东南大学 | Two-dimentional network-on-chip adaptive routing method based on particle cluster algorithm |
| US20190086938A1 (en) * | 2015-07-27 | 2019-03-21 | Genghiscomm Holdings, LLC | Airborne Relays in Cooperative-MIMO Systems |
| CN111065147A (en) * | 2019-12-29 | 2020-04-24 | 高小翎 | Method for improving routing transmission quality of wireless distributed sensor network |
-
2021
- 2021-04-30 CN CN202110482222.1A patent/CN113347677B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190086938A1 (en) * | 2015-07-27 | 2019-03-21 | Genghiscomm Holdings, LLC | Airborne Relays in Cooperative-MIMO Systems |
| CN105430706A (en) * | 2015-11-03 | 2016-03-23 | 国网江西省电力科学研究院 | WSN (Wireless Sensor Networks) routing optimization method based on improved PSO (particle swarm optimization) |
| CN106792963A (en) * | 2016-12-06 | 2017-05-31 | 淮阴工学院 | A kind of car networking sub-clustering car car multi-hop routing method based on particle group optimizing |
| CN108156613A (en) * | 2017-11-26 | 2018-06-12 | 南京邮电大学 | A kind of relay node distribution method in unmanned plane relay multi-hop communication system |
| CN108183860A (en) * | 2018-01-19 | 2018-06-19 | 东南大学 | Two-dimentional network-on-chip adaptive routing method based on particle cluster algorithm |
| CN111065147A (en) * | 2019-12-29 | 2020-04-24 | 高小翎 | Method for improving routing transmission quality of wireless distributed sensor network |
Non-Patent Citations (3)
| Title |
|---|
| ASHA G.R.: "An Energy aware Routing Mechanism in WSNs using PSO and GSO Algorithm", 《2018 5TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND INTEGRATED NETWORKS》 * |
| BAO YU: "Relay Node Deployment for Wireless Sensor Networks Based on PSO", 《2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY》 * |
| 单蓉;李涛;: "基于支持向量机的移动无线传感器网络可靠性研究", 工业仪表与自动化装置, no. 06 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115086229A (en) * | 2022-04-29 | 2022-09-20 | 珠海高凌信息科技股份有限公司 | SDN network multi-path calculation method based on evolutionary algorithm |
| CN115086229B (en) * | 2022-04-29 | 2023-07-11 | 珠海高凌信息科技股份有限公司 | SDN network multipath calculation method based on evolutionary algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113347677B (en) | 2023-12-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103052129B (en) | Energy-saving route setup and power distribution method in wireless multi-hop relay network | |
| CN107182074B (en) | A kind of routing optimal path choosing method based on Zigbee | |
| CN111970658A (en) | Unmanned aerial vehicle swarm formation network routing method based on optimal rigid graph | |
| CN108900996A (en) | A kind of wireless sensor network data transmission method based on the double-deck fuzzy algorithmic approach | |
| CN110461018B (en) | Opportunistic network routing forwarding method based on computable AP | |
| CN113260012A (en) | Unmanned aerial vehicle cluster topology control method based on position track prediction | |
| CN111385201A (en) | RPL routing method based on bidirectional father node decision | |
| Li et al. | A reliable and efficient forwarding strategy in vehicular named data networking | |
| CN113347677A (en) | Multi-node communication method based on particle swarm optimization | |
| CN110932969B (en) | Advanced metering system AMI network anti-interference attack routing algorithm for smart grid | |
| CN111818484B (en) | Safety route control method for Internet of vehicles | |
| CN109257830A (en) | In-vehicle networking self-adoptive retreating method based on QoS | |
| CN110808911B (en) | Networking communication routing method based on ant colony pheromone | |
| CN116528313A (en) | Unmanned aerial vehicle low-energy-consumption rapid routing method for task collaboration | |
| Nisha et al. | An energy efficient self organizing multicast routing protocol for internet of things | |
| Biradar et al. | Multipath Load Balancing in MANET via Hybrid Intelligent Algorithm | |
| CN110099410B (en) | DTN distributed caching method and device for temporary empty vehicle ground network | |
| CN114867081A (en) | Mobile ad hoc network multi-source transmission routing method based on relay unmanned aerial vehicle node | |
| CN115226178A (en) | Wireless sensor network routing protocol optimization method based on particle swarm | |
| CN115361721B (en) | IFTOPSIS-based multipath routing formation method for distributed unmanned aerial vehicle | |
| CN115379526B (en) | Positioning-free efficient underwater acoustic sensor network transmission method | |
| CN113938975B (en) | Mobile sensing network route optimization method based on competition window ant colony clustering | |
| CN113709036B (en) | Route improvement method of Spray and Wait based on node history encounter information | |
| CN102724731B (en) | Epidemic routing algorithm with adaptive capacity | |
| CN118215099A (en) | Flight ad hoc network inter-cluster routing method and system based on mixed ant colony |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |