CN116684938A - Efficient data transmission system and method for wireless communication - Google Patents
Efficient data transmission system and method for wireless communication Download PDFInfo
- Publication number
- CN116684938A CN116684938A CN202310679339.8A CN202310679339A CN116684938A CN 116684938 A CN116684938 A CN 116684938A CN 202310679339 A CN202310679339 A CN 202310679339A CN 116684938 A CN116684938 A CN 116684938A
- Authority
- CN
- China
- Prior art keywords
- node
- data
- transmission
- module
- wireless communication
- 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.)
- Pending
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 117
- 238000004891 communication Methods 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000005457 optimization Methods 0.000 claims abstract description 25
- 238000003860 storage Methods 0.000 claims abstract description 16
- 238000001514 detection method Methods 0.000 claims abstract description 13
- 238000012544 monitoring process Methods 0.000 claims abstract description 10
- 238000012545 processing Methods 0.000 claims abstract description 9
- 238000012423 maintenance Methods 0.000 claims abstract description 6
- 238000004458 analytical method Methods 0.000 claims abstract description 5
- 238000012546 transfer Methods 0.000 claims description 24
- 238000012549 training Methods 0.000 claims description 17
- 230000008447 perception Effects 0.000 claims description 15
- 230000008667 sleep stage Effects 0.000 claims description 12
- 238000012360 testing method Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 8
- 238000012795 verification Methods 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000012790 confirmation Methods 0.000 claims description 4
- 230000002068 genetic effect Effects 0.000 claims description 4
- 206010000117 Abnormal behaviour Diseases 0.000 claims description 3
- 230000006399 behavior Effects 0.000 claims description 3
- 230000006835 compression Effects 0.000 claims description 3
- 238000007906 compression Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 210000002569 neuron Anatomy 0.000 claims description 3
- 238000005265 energy consumption Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 description 4
- 241000282414 Homo sapiens Species 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/104—Peer-to-peer [P2P] networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/30—Security of mobile devices; Security of mobile applications
- H04W12/37—Managing security policies for mobile devices or for controlling mobile applications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computer Security & Cryptography (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a high-efficiency data transmission system and a method for wireless communication, which belong to the technical field of wireless communication and comprise a network monitoring module, a node transmission module, a path adjustment module, a compressed sensing module, a sensing optimization module, a data recording module, a log detection module and a block storage module; the invention can update the transmission path, reduce the energy consumption of data transmission, effectively reduce the data processing capacity of each transmission node, improve the data processing efficiency, reduce the waiting time of users, improve the use experience, prevent related data from being maliciously tampered, ensure the safety of digital property and facilitate the performance analysis of network maintenance personnel on the wireless communication network.
Description
Technical Field
The present invention relates to the field of wireless communications technologies, and in particular, to a system and a method for efficient data transmission for wireless communications.
Background
The wireless communication network is composed of a large number of sensor nodes and is a plurality of self-organizing network systems deployed in the area needing to be monitored. The wireless communication network integrates a plurality of new technologies, has application characteristics different from the traditional network, has network characteristics different from the general network, changes the interaction mode between human beings and the nature, does not need the support of a fixed network, has the characteristics of quick point distribution, strong destructiveness and the like, and is considered to be one of the most important new technologies in the 21 st century; in recent years, with rapid development of wireless communication, micro-electro-mechanical systems, integrated circuits and other fields and continuous innovation of technology, human society activities are mainly performed by virtue of development and acquisition, transmission and processing of information resources. From the technical means, wireless communication networks are the main way and way for people to acquire information in the natural field, and have important influence on future life of people.
The existing high-efficiency data transmission system and method for wireless communication have the disadvantages of high data transmission energy consumption, high data processing capacity of each transmission node and low data processing efficiency; in addition, the existing high-efficiency data transmission system and method for wireless communication cannot prevent data from being tampered maliciously, digital property safety is poor, and maintenance personnel are inconvenient to conduct performance analysis.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a high-efficiency data transmission system and method for wireless communication.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a high-efficiency data transmission system for wireless communication comprises a network monitoring module, a node transmission module, a path adjustment module, a compressed sensing module, a sensing optimization module, a data recording module, a log detection module and a block storage module;
the network monitoring module is used for monitoring and collecting information of each node of the wireless communication network;
the node transmission module is used for sending transmission signals to each node;
the path adjustment module is used for optimizing the transmission path;
the compressed sensing module is used for receiving the transmission information of each node and performing compressed optimization on the transmission information;
the perception optimization module is used for adjusting and updating parameters of the compressed perception module;
the data recording module is used for recording the data transmission efficiency of each node of the wireless communication network;
the log detection module is used for performing risk detection on the transmission log;
the block storage module is used for carrying out block storage on the current wireless communication network transmission information.
As a further scheme of the present invention, the specific steps of the path adjustment module for optimizing the path are as follows:
step one: the path adjustment module traverses each node of the whole wireless communication network, counts the number of transmission equipment, sensor nodes, transfer nodes and sink nodes, and simultaneously collects each group of transmission path information during data transmission;
step two: integrating the transmission paths collected by the transmission devices into a group of path sets respectively, representing the sets as a population, generating a population matrix by combining a genetic algorithm, randomly selecting two groups of path individuals in the population, selecting a certain path from the two groups of path individuals respectively, and exchanging to obtain two new path individuals;
step three: and randomly selecting two paths in a group of path individuals to exchange until all paths in the path individuals are exchanged, traversing each node from the end point of the data transmission path, judging the node between the start point and the node as a redundant node if a certain node can be connected with the start point in a barrier-free manner, deleting the redundant nodes after the redundant node is confirmed to be finished, recalculating the fitness function of the paths, continuously optimizing the paths through continuous iteration, and simultaneously updating the data transmission path.
As a further scheme of the invention, the compressed sensing module performs the specific steps of compressing and optimizing the transmission information as follows:
step (1): the compressed sensing module collects original data sequences transmitted by transmission nodes and transit nodes reconstructed by all groups of sink nodes, distinguishes common data packets from abnormal data packets, sets an emergency queue and a conventional queue to respectively place the abnormal data packets and the common data packets, and the data packet forwarding priority in the emergency queue is higher than that of the conventional queue;
step (2): then detecting the state information of the transfer node, if the state of the transfer node is 0, judging that no data needs to be transmitted currently, if the state of the transfer node is 1, judging that the data needs to be transmitted currently, and simultaneously calculating the average waiting data packet queue length of the related transfer node;
step (3): each group of nodes are deployed on an Euclidean plane area of M by a random throwing mode, a non-directional connectivity graph is used as a topological structure of the wireless communication network, then the transit nodes collect data of all transmission nodes of the transit nodes and compress the data and the data monitored by the transit nodes into a new data packet, and meanwhile, time slot scheduling is carried out through a time division multiple access protocol, and reconstruction errors of data sequences of the transit nodes in a sink node are measured according to NMSE.
As a further aspect of the present invention, the specific calculation formula of the average waiting data packet queue length in step (2) is as follows:
wherein lambda represents the average arrival rate of the transmission node data packets; μ represents the average service rate of the packet; c represents channel capacity;
the NMSE in step (3) has the following specific definition formula:
wherein x is j Representing an original data sequence of surrounding environment parameters of the jth node;representing the original data sequence of the jth node reconstructed by the sink node.
As a further scheme of the invention, the specific steps of the perception optimization module adjustment optimization are as follows:
step I: the perception optimization module initializes a network connection weight in a specified interval of the compressed perception module, submits training samples from a set of input and output pairs during training, compares expected network output with actual network output, and calculates local errors of all neurons;
step II: after the local error exceeds a preset threshold value of a worker, training and updating the weight of the compressed sensing module according to a learning rule equation, and listing all possible data results according to a preset learning rate and step length;
step III: for each group of data, selecting any subset as a test set, selecting the rest subsets as training sets, detecting the test set after training a test model, and counting root mean square errors of detection results;
step IV: and replacing the test set with another subset, taking the rest subset as a training set, counting root mean square errors again until all data are predicted once, and selecting the corresponding combined parameter with the minimum root mean square error as the optimal parameter in the data interval and replacing the original parameter of the compressed sensing module.
A high-efficiency data transmission method for wireless communication is provided, which comprises the following steps:
(1) Collecting current wireless communication network node information and optimizing a transmission path;
(2) Constructing a compressed sensing model and carrying out parameter optimization on the model;
(3) Performing compression optimization on each node and simultaneously recording the node transmission efficiency;
(4) The transfer log is analyzed and risk transfer is interrupted while the block stores transfer information.
As a further aspect of the present invention, the specific analysis step of the transmission log in the step (3) is as follows:
the first step: the method comprises the steps that relevant log acquisition plug-ins are deployed in transmission systems of different transmission equipment or log data recorded in the transmission systems are obtained through a syslog server, and log data meeting preset conditions are screened out;
and a second step of: processing the residual log data into log data with a uniform format, matching the user operation behaviors recorded in the processed log data with abnormal behavior characteristics, generating corresponding alarm information according to the matching result, calculating the risk scores of all alarm information and outputting calculation results, feeding the alarm information back to related maintenance personnel, and interrupting related operation processes.
As a further aspect of the present invention, the block storage in step (3) specifically includes the following steps:
step I: the block storage module processes the current wireless communication network transmission information into a block meeting the condition, and when the block is connected to the network, each node in the block chain network generates a local public and private key pair as a self identifier in the network, and when one node waits for the local role to become a candidate node, the leader generation application information is broadcasted to other nodes in the network;
step II: when the candidate node becomes a leading node, the other nodes become trailing nodes, then the leading node broadcasts the block record information, the trailing nodes broadcast the received information to the other trailing nodes after receiving the information and record the repetition times, the information with the largest repetition times is used for generating a block head, and a verification application is sent to the leading node;
and III, step III: after the verification is passed, the leading node sends an adding command and enters a sleep stage, the leading node cannot be applied for becoming the leading node again in the sleep stage until the sleep stage is finished, and after the following node receives the confirmation information, each newly generated block group is added to the block chain and returns the candidate identity.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the system, each node of the whole wireless communication network is traversed through an adjusting module, iterative optimization is carried out on a data transmission path through a genetic algorithm, then a compressed sensing module collects each transmission node reconstructed by each group of aggregation nodes and an original data sequence transmitted by a transfer node, the ordinary data packet and the abnormal data packet are distinguished and processed, then the state information of the transfer node is detected, meanwhile, the average waiting data packet queue length in the transmission process of the relevant transfer node is calculated, each group of nodes is deployed on an Euclidean plane area of M by randomly scattering, a non-directional communication graph is used as a topological structure of the wireless communication network, then the transfer node collects data of all transmission nodes and compresses the data monitored by the non-directional communication graph and the data into a new data packet, and then a sensing optimization module initializes a network connection weight in a specified interval of the compressed sensing module and updates the module parameter information, so that the transmission path is updated, the data transmission energy consumption is reduced, meanwhile, the data processing capacity of each transmission node is effectively reduced, the data processing efficiency is improved, the user waiting time is shortened, and the user experience is improved.
2. The invention processes the current wireless communication network transmission information into the blocks meeting the conditions through the block storage module, meanwhile, when the blocks enter the network, each node in the block chain network generates a local public and private key pair as the identifier of the node in the network, when one node waits for the local role to become a candidate node, the node broadcasts the leading generation application information to other nodes in the network, after the candidate node becomes the leading node, the other nodes become the following nodes, the leading node broadcasts the block record information, after the following nodes receive the information, broadcasts the received information to the other following nodes and records the repetition times, and generates a block header by using the information with the maximum repetition times, and sends a verification application to the leading node, after the verification is passed, the leading node sends an addition command and enters a sleep stage, the node cannot be applied again in the sleep stage until the sleep stage is finished, after the following nodes receive the confirmation information, each newly generated block is added to the block chain and returns the candidate identity, thereby ensuring the digital property security, and facilitating the network maintenance personnel to perform performance analysis on the wireless communication network.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Fig. 1 is a system block diagram of an efficient data transmission system for wireless communication according to the present invention;
fig. 2 is a flow chart of a method for efficient data transmission for wireless communication according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1, a high-efficiency data transmission system for wireless communication includes a network monitoring module, a node transmission module, a path adjustment module, a compressed sensing module, a sensing optimization module, a data recording module, a log detection module, and a block storage module.
The network monitoring module is used for monitoring and collecting information of each node of the wireless communication network; the node transmission module is used for sending transmission signals to each node; the path adjustment module is used for optimizing the transmission path.
Specifically, the path adjustment module traverses each node of the whole wireless communication network, counts the number of transmission equipment, sensor nodes, transfer nodes and sink nodes, collects each group of transmission path information during data transmission, respectively integrates the transmission paths collected by each transmission equipment into a group of path sets, simultaneously represents the sets as a population, generates a population matrix by combining a genetic algorithm, randomly selects two groups of path individuals in the population, respectively selects a certain section of path from the two groups of path individuals, then performs exchange to obtain new two new path individuals, randomly selects two sections of paths in the group of path individuals for exchange until all paths in the path individuals are exchanged, starts traversing each node from the end point of a data transmission path, judges the node between the start point and the node as a redundant node if a certain node can be connected with the start point without barriers, deletes the redundant node and recalculates the adaptability function of the path after the redundant node is confirmed, continuously optimizes the path through continuous iteration, and simultaneously updates the data transmission path.
And the compressed sensing module is used for receiving the transmission information of each node and performing compressed optimization on the transmission information.
Specifically, the compressed sensing module collects the original data sequences transmitted by each transmission node and the transit node reconstructed by each group of sink nodes, and distinguishes the normal data packets from the abnormal data packets, meanwhile, an emergency queue and a normal queue are set to respectively place the abnormal data packets and the normal data packets, the data packet forwarding priority in the emergency queue is higher than that of the normal queue, then the transit node state information is detected, if the transit node state is 0, the current data transmission is judged, if the transit node state is 1, the current data transmission is judged, meanwhile, the average waiting data packet queue length of the relevant transit node is calculated, each group of nodes is deployed on an Euclidean plane area of M by random scattering, a non-directional connectivity graph is used as the topological structure of the wireless communication network, then the transit node collects the data of all transmission nodes and compresses the data monitored by itself into a new data packet, and meanwhile, time slot scheduling is carried out through a time division multiple access protocol, and the reconstruction error of the data sequence of the transit node at the sink node is measured according to NMS E.
It should be further noted that the specific calculation formula of the average waiting data packet queue length is as follows:
wherein lambda represents the average arrival rate of the transmission node data packets; μ represents the average service rate of the packet; c represents channel capacity;
the specific definition formula of the NMSE is as follows:
wherein x is j Representing an original data sequence of surrounding environment parameters of the jth node;representing the original data sequence of the jth node reconstructed by the sink node.
The perception optimization module is used for adjusting and updating the parameters of the compressed perception module.
Specifically, the perception optimization module initializes the network connection weight in a specified interval of the compressed perception module, submits a training sample from a set of input and output pairs during training, compares expected network output with actual network output, calculates local errors of all neurons, trains and updates the weight of the compressed perception module according to a learning rule equation after the local errors exceed a preset threshold of staff, lists all possible data results according to a preset learning rate and step length, selects any subset as a test set for each group of data, and the rest subset as a training set, detects the test set after training the test model, counts root mean square errors of the detection results, replaces the test set with another subset, and then takes the rest subset as the training set, counts root mean square errors again until all data are predicted once, and selects a corresponding combination parameter with the minimum root mean square errors as an optimal parameter in the data interval and replaces the original parameter of the compressed perception module.
The data recording module is used for recording the data transmission efficiency of each node of the wireless communication network; the log detection module is used for detecting risk of the transmission log; the block storage module is used for carrying out block storage on the current wireless communication network transmission information.
Example 2
Referring to fig. 2, a high efficiency data transmission method for wireless communication, the transmission method is specifically as follows:
and acquiring the current wireless communication network node information and optimizing a transmission path.
And constructing a compressed sensing model and carrying out parameter optimization on the model.
And carrying out compression optimization on each node and simultaneously recording the node transmission efficiency.
The transfer log is analyzed and risk transfer is interrupted while the block stores transfer information.
Specifically, the log detection module deploys related log acquisition plug-ins in transmission systems of different transmission devices or acquires log data recorded in the transmission systems through a syslog server, screens out the log data meeting preset conditions, processes the residual log data into log data in a unified format, matches user operation behaviors recorded in the processed log data with abnormal behavior features, generates corresponding alarm information according to the matching results, calculates risk scores of the alarm information and outputs calculation results, feeds the alarm information back to related maintenance personnel, and interrupts related operation processes.
Specifically, the block storage module processes the current wireless communication network transmission information into blocks meeting the conditions, when the blocks enter the network, each node in the block chain network generates a local public and private key pair as an identifier of the node in the network, when one node waits for the local role to become a candidate node, the node broadcasts the leading generation application information to other nodes in the network, after the candidate node becomes the leading node, the other nodes become the following nodes, the leading node broadcasts the block record information, after the following node receives the information, broadcasts the received information to the other following nodes and records the repetition times, and generates a block header by using the information with the maximum repetition times, and sends a verification application to the leading node, after the verification is passed, the leading node sends an addition command and enters a sleep stage, the node cannot apply for becoming the leading node again in the sleep stage until the sleep stage is ended, after the following node receives the confirmation information, each newly generated block is added to the block chain and returns the candidate identity.
Claims (8)
1. The efficient data transmission system for wireless communication is characterized by comprising a network monitoring module, a node transmission module, a path adjustment module, a compressed sensing module, a sensing optimization module, a data recording module, a log detection module and a block storage module;
the network monitoring module is used for monitoring and collecting information of each node of the wireless communication network;
the node transmission module is used for sending transmission signals to each node;
the path adjustment module is used for optimizing the transmission path;
the compressed sensing module is used for receiving the transmission information of each node and performing compressed optimization on the transmission information;
the perception optimization module is used for adjusting and updating parameters of the compressed perception module;
the data recording module is used for recording the data transmission efficiency of each node of the wireless communication network;
the log detection module is used for performing risk detection on the transmission log;
the block storage module is used for carrying out block storage on the current wireless communication network transmission information.
2. An efficient data transmission system for wireless communication as defined in claim 1, wherein the path adjustment module optimizes the path as follows:
step one: the path adjustment module traverses each node of the whole wireless communication network, counts the number of transmission equipment, sensor nodes, transfer nodes and sink nodes, and simultaneously collects each group of transmission path information during data transmission;
step two: integrating the transmission paths collected by the transmission devices into a group of path sets respectively, representing the sets as a population, generating a population matrix by combining a genetic algorithm, randomly selecting two groups of path individuals in the population, selecting a certain path from the two groups of path individuals respectively, and exchanging to obtain two new path individuals;
step three: and randomly selecting two paths in a group of path individuals to exchange until all paths in the path individuals are exchanged, traversing each node from the end point of the data transmission path, judging the node between the start point and the node as a redundant node if a certain node can be connected with the start point in a barrier-free manner, deleting the redundant nodes after the redundant node is confirmed to be finished, recalculating the fitness function of the paths, continuously optimizing the paths through continuous iteration, and simultaneously updating the data transmission path.
3. An efficient data transmission system for wireless communication as defined in claim 2, wherein the compressed sensing module performs the following steps of:
step (1): the compressed sensing module collects original data sequences transmitted by transmission nodes and transit nodes reconstructed by all groups of sink nodes, distinguishes common data packets from abnormal data packets, sets an emergency queue and a conventional queue to respectively place the abnormal data packets and the common data packets, and the data packet forwarding priority in the emergency queue is higher than that of the conventional queue;
step (2): then detecting the state information of the transfer node, if the state of the transfer node is 0, judging that no data needs to be transmitted currently, if the state of the transfer node is 1, judging that the data needs to be transmitted currently, and simultaneously calculating the average waiting data packet queue length of the related transfer node;
step (3): each group of nodes are deployed on an Euclidean plane area of M by a random throwing mode, a non-directional connectivity graph is used as a topological structure of the wireless communication network, then the transit nodes collect data of all transmission nodes of the transit nodes and compress the data and the data monitored by the transit nodes into a new data packet, and meanwhile, time slot scheduling is carried out through a time division multiple access protocol, and reconstruction errors of data sequences of the transit nodes in a sink node are measured according to NMSE.
4. A high efficiency data transmission system for wireless communication according to claim 3, wherein the average waiting data packet queue length in step (2) is specifically calculated as:
wherein lambda represents the average arrival rate of the transmission node data packets; μ represents the average service rate of the packet; c represents channel capacity;
the NMSE in step (3) has the following specific definition formula:
wherein x is j Representing an original data sequence of surrounding environment parameters of the jth node;representing the original data sequence of the jth node reconstructed by the sink node.
5. A high efficiency data transmission system for wireless communication according to claim 3, wherein said perceptual optimization module adjusts the optimization steps as follows:
step I: the perception optimization module initializes a network connection weight in a specified interval of the compressed perception module, submits training samples from a set of input and output pairs during training, compares expected network output with actual network output, and calculates local errors of all neurons;
step II: after the local error exceeds a preset threshold value of a worker, training and updating the weight of the compressed sensing module according to a learning rule equation, and listing all possible data results according to a preset learning rate and step length;
step III: for each group of data, selecting any subset as a test set, selecting the rest subsets as training sets, detecting the test set after training a test model, and counting root mean square errors of detection results;
step IV: and replacing the test set with another subset, taking the rest subset as a training set, counting root mean square errors again until all data are predicted once, and selecting the corresponding combined parameter with the minimum root mean square error as the optimal parameter in the data interval and replacing the original parameter of the compressed sensing module.
6. A method for efficient data transmission for wireless communication, the method comprising:
(1) Collecting current wireless communication network node information and optimizing a transmission path;
(2) Constructing a compressed sensing model and carrying out parameter optimization on the model;
(3) Performing compression optimization on each node and simultaneously recording the node transmission efficiency;
(4) The transfer log is analyzed and risk transfer is interrupted while the block stores transfer information.
7. The efficient data transmission method for wireless communication according to claim 6, wherein the transmission log specific analysis step in step (3) is as follows:
the first step: the method comprises the steps that relevant log acquisition plug-ins are deployed in transmission systems of different transmission equipment or log data recorded in the transmission systems are obtained through a syslog server, and log data meeting preset conditions are screened out;
and a second step of: processing the residual log data into log data with a uniform format, matching the user operation behaviors recorded in the processed log data with abnormal behavior characteristics, generating corresponding alarm information according to the matching result, calculating the risk scores of all alarm information and outputting calculation results, feeding the alarm information back to related maintenance personnel, and interrupting related operation processes.
8. The efficient data transmission method for wireless communication as recited in claim 6, wherein the block storage in step (3) comprises the specific steps of:
step I: the block storage module processes the current wireless communication network transmission information into a block meeting the condition, and when the block is connected to the network, each node in the block chain network generates a local public and private key pair as a self identifier in the network, and when one node waits for the local role to become a candidate node, the leader generation application information is broadcasted to other nodes in the network;
step II: when the candidate node becomes a leading node, the other nodes become trailing nodes, then the leading node broadcasts the block record information, the trailing nodes broadcast the received information to the other trailing nodes after receiving the information and record the repetition times, the information with the largest repetition times is used for generating a block head, and a verification application is sent to the leading node;
and III, step III: after the verification is passed, the leading node sends an adding command and enters a sleep stage, the leading node cannot be applied for becoming the leading node again in the sleep stage until the sleep stage is finished, and after the following node receives the confirmation information, each newly generated block group is added to the block chain and returns the candidate identity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310679339.8A CN116684938A (en) | 2023-06-08 | 2023-06-08 | Efficient data transmission system and method for wireless communication |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310679339.8A CN116684938A (en) | 2023-06-08 | 2023-06-08 | Efficient data transmission system and method for wireless communication |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116684938A true CN116684938A (en) | 2023-09-01 |
Family
ID=87790434
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310679339.8A Pending CN116684938A (en) | 2023-06-08 | 2023-06-08 | Efficient data transmission system and method for wireless communication |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116684938A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117014460A (en) * | 2023-09-28 | 2023-11-07 | 深圳市壹通道科技有限公司 | Distributed information management system based on 5G communication |
-
2023
- 2023-06-08 CN CN202310679339.8A patent/CN116684938A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117014460A (en) * | 2023-09-28 | 2023-11-07 | 深圳市壹通道科技有限公司 | Distributed information management system based on 5G communication |
| CN117014460B (en) * | 2023-09-28 | 2023-12-29 | 深圳市壹通道科技有限公司 | Distributed information management system based on 5G communication |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108366386B (en) | Method for realizing wireless network fault detection by using neural network | |
| CN114629802B (en) | Service awareness-based power communication backbone network quality assessment method | |
| CN111242171A (en) | Model training, diagnosis and prediction method and device for network fault and electronic equipment | |
| CN106533791B (en) | A kind of end-to-end QoS optimization device and method based on big data platform | |
| CN116684938A (en) | Efficient data transmission system and method for wireless communication | |
| CN113489619A (en) | Network topology inference method and device based on time series analysis | |
| CN112688822A (en) | Edge computing fault or security threat monitoring system and method based on multi-point cooperation | |
| CN111726768A (en) | Edge-oriented computation reliable data collection method based on compressed sensing | |
| CN114374969A (en) | Trust evaluation method in power Internet of things environment | |
| Al-Qurabat et al. | Distributed data aggregation protocol for improving lifetime of wireless sensor networks | |
| CN115665787A (en) | Low-overhead AMF network intelligent fault diagnosis method based on machine learning | |
| Alho et al. | Machine learning based mobile network throughput classification | |
| Becher et al. | Towards short-term wireless link quality estimation | |
| CN111601268A (en) | Cluster head selection method and device, terminal equipment and storage medium | |
| Kim et al. | Unsupervised anomaly detection and root cause analysis in mobile networks | |
| CN116720167B (en) | Role management system for multiple isolation management | |
| Wang et al. | Predictive modeling-based data collection in wireless sensor networks | |
| CN115002035A (en) | Power data transmission congestion evasion method based on service level | |
| CN110316229B (en) | Wireless attack detection method for CBTC system vehicle-ground wireless network | |
| Nguyen et al. | A decentralized Bayesian attack detection algorithm for network security | |
| CN114584345A (en) | Rail transit network security processing method, device and equipment | |
| WO2012116551A1 (en) | Multicast performance analysing method and system | |
| Srikanth et al. | GREEN COMP BASED ENERGY EFFICIENT DATA AGGREGATION ALGORITHM WITH MALICIOUS NODE IDENTIFICATION (GEED-M) FOR LIFETIME IMPROVEMENT IN WSN | |
| Bapu et al. | Malicious node detection through run time self-healing algorithm in WSN | |
| JP4299210B2 (en) | Network monitoring method and apparatus |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication |