24 results about "Underwater wireless sensor networks" patented technology

The invention relates to an underwater wireless sensor network path sensing routing method based on deep reinforcement learning, which comprises the following steps that: firstly, a node constructs and periodically updates a transmission preference model based on local and neighbor node interaction information; secondly, deploying a deep reinforcement learning model at each underwater sensor node to perform distributed routing strategy learning; and finally, generating a global guide vector by the sink node according to the routing path information of the received data packet, reversely spreading the global guide vector to the source node, fusing the global guide vector with a local transmission preference vector of the node to generate a guide reward, optimizing the deep reinforcement learning model, and updating a routing strategy. According to the method, the problems of difference and complexity of underwater transmission tasks can be solved, and the network data transmission efficiency and the overall service quality are improved in combination with local preference and global guidance while the node online learning is kept to adapt to the dynamic underwater environment.

The present invention discloses a method for deploying underwater wireless sensor network target coverage based on seabed topography, comprising: considering the target coverage problem of the underwater wireless sensor network based on the seabed topography, constructing an integer nonlinear programming model (INLP); and solving the integer nonlinear programming model (INLP) using the Harris Eagle optimization algorithm. The present invention innovatively incorporates seabed topography features, uses the underwater acoustic simulator BELLHOP3D to calculate the loss of underwater acoustic signals, and further derives the transmission success rate of the link, thereby effectively improving the transmission quality of the underwater communication system. By constructing the integer nonlinear programming model, the deployment positions of underwater sensor nodes are optimized, maximizing coverage and uniformity, and resolving the problem that traditional underwater network deployment methods fail to fully consider the impact of complex seabed topography on signal transmission.

The application discloses a kind of underwater wireless sensor network node positioning method based on matrix completion, steps are as follows: S1: the Euclidean distance between two different sensor nodes is obtained by TOA ranging method, so as to obtain the Euclidean distance matrix of underwater sensor network node;S2: the Euclidean distance matrix is recovered and completed using the matrix completion algorithm based on non-convex rank approximation, and the distance measurement between all nodes in the network is obtained;S3: the relative coordinates of all nodes underwater are calculated using MDS-MAP algorithm, the relative coordinates of conventional nodes are converted into absolute coordinates by the position of a part of anchor nodes, and the coordinates of conventional nodes are output.The application can not only accurately recover and complete the missing distance matrix, but also effectively handle the influence of Gaussian noise and outlier noise, with good robustness and accuracy.Using the position information of a small number of anchor nodes, the relative position is converted into absolute position, with high node positioning accuracy.

This invention discloses a clustering method for underwater wireless sensor networks based on improved AP clustering, which relates to the field of underwater wireless sensor network topology optimization. By acquiring node status information in the underwater wireless sensor network and initializing node feature data, the method improves its adaptability to underwater environments by comprehensively considering inter-node distance, received signal strength, and node residual energy. It also employs an energy and position-aware mechanism to iteratively update attraction and affiliation information to form a preliminary cluster structure, effectively addressing node energy consumption and position changes in complex underwater environments. Based on the node communication range, preference values ​​are updated to adjust the cluster structure to ensure that no nodes leave the cluster. A comprehensive weight function for cluster head updates is designed to avoid bias due to a single factor. This invention can better adapt to complex underwater environments, effectively optimize network topology, balance sensor node energy consumption, and improve the stability and energy efficiency of underwater wireless sensor networks.

The invention belongs to the technical field of communication, and particularly relates to a defense method and system for a malicious propagation model of an underwater wireless sensor network, and the method comprises the steps: dividing nodes into sensor nodes and autonomous underwater vehicles, and determining the propagation relation between the states; constructing a heterogeneous underwater wireless sensor network malicious program propagation model; calculating each stable point of which the state change rate is 0 in the constructed heterogeneous underwater wireless sensor network malicious program propagation model; and calculating a basic regeneration number according to the newly added Bayer call rate matrix and the state transition rate matrix of the heterogeneous node at the stable point. According to the method, the dynamic change process of the internal and external states of the heterogeneous nodes with different characteristics is reflected, the stable point representing that the malicious program is finally extinguished or popular is obtained, the condition for judging the propagation stable state of the malicious program of the heterogeneous underwater wireless sensor network is given, a theoretical basis is provided for propagation of the malicious program, and the method is suitable for popularization and application of the heterogeneous underwater wireless sensor network. And the improved SCSO algorithm realizes an optimal control strategy.

The present application discloses a data display method, device, equipment and medium, which relate to the technical field of underwater wireless sensor networks. The method is applied to a shore-based platform, by receiving underwater data transmitted by a main base station; wherein the underwater data at least includes detection data, communication data and navigation data of each node in the underwater wireless sensor network; pre-processing the underwater data; transmitting the pre-processed underwater data to a database server, so that the display terminal can obtain the underwater data through the database server and display it. It can be seen that the above scheme directly obtains the underwater data of the underwater wireless sensor network and pre-processes the underwater data, so that the display terminal can better display the underwater environment. It solves the problem of underwater environment visualization, realizes the display of multiple types of data such as communication data, detection data and navigation data in the underwater environment, and restores the underwater scene in real time and realistically.

This invention discloses a target tracking method, apparatus, electronic device, and medium for underwater wireless sensor networks under measurement delay conditions. The method includes: constructing an underwater measurement delay model based on the delay caused by underwater acoustic propagation and sensor measurements under this delay; designing an augmented state estimator based on the underwater measurement delay model to compensate for the delay caused by underwater acoustic propagation; decoupling the augmented state estimator and extending it into a distributed augmented state estimator suitable for underwater wireless sensor networks; setting the morphological characteristics, dynamic parameters, initial state, and reference trajectory of the underwater target; and deploying the underwater wireless sensor network to perform target tracking using the distributed augmented state estimator. This invention can effectively compensate for the impact of measurement delay caused by slow underwater acoustic propagation on the accuracy and stability of underwater target tracking, keeping the tracking error within a small range.

The invention discloses a soft synchronization TDMA (Time Division Multiple Access) protocol implementation method for an underwater wireless sensor network, belongs to the technical field of underwater wireless communication, and solves the problems of difficulty in synchronization and high conflict rate of the existing UWSNs in an underwater sound environment with long propagation delay. According to the method, the initial time of an active window is distributed to each sensor node in the network at equal intervals, a periodic frame structure with the windows capable of being overlapped is established, and flexible adjustment of the frame length is achieved so as to optimize time delay; window random jitter is introduced to realize cross-period decoupling of the synchronization error, and the cumulative effect of the synchronization error is relieved; an access moment is randomly selected in an active window according to truncation cosine-distribution, and data packet transmission is performed at the access moment in the window, so that the conflict rate is remarkably reduced, the throughput and the channel utilization rate are improved, the end-to-end delay is shortened, and the access fairness is improved on the premise that strict global synchronization is not needed; the method is suitable for resource-limited underwater sensor nodes and UWSNs scenes such as marine environment monitoring, deep sea exploration and national defense communication.

The invention relates to a data acquisition and transmission system. The data acquisition and transmission system comprises a sea-based information acquisition unit, underwater wireless sensor network equipment and a shore-based data processing unit which perform data interaction through a preset integrated data link, the underwater wireless sensor network equipment acquires deep marine environment data and transmits the deep marine environment data to the sea-based information acquisition unit; the sea-based information acquisition unit is used for acquiring offshore data or receiving deep marine environment data acquired by the underwater wireless sensor network equipment; after the deep ocean environment data and the offshore data are preprocessed, the preprocessed deep ocean environment data and the preprocessed offshore data are transmitted to the shore-based data processing unit; and the shore-based data processing unit receives the preprocessed deep marine environment data and the preprocessed offshore data sent by the sea-based information acquisition unit, and carries out fusion processing and storage on the preprocessed deep marine environment data and the preprocessed offshore data. Therefore, the operation stability of the data acquisition and transmission system can be improved.

The present invention discloses a three-dimensional underwater target tracking method based on an improved particle filter, belonging to the technical field of underwater wireless sensor networks. The method comprises the following steps: collecting three-dimensional target tracking data of an underwater target; constructing a target tracking model and a measurement model based on the three-dimensional target tracking data; improving the particle filter algorithm based on an unscented Kalman filter, constructing an importance density function based on the improved particle filter algorithm, the target tracking model, and the measurement model, and resampling the particles; introducing a dynamic adaptive hierarchical weighting factor to correct the weights of different particles, and normalizing the corrected particle weights; performing anomaly detection on the three-dimensional target tracking data based on an optimized Grubbs criterion; and fusing the data of trusted nodes using an information entropy weighting strategy to obtain a fused target state estimate, thereby achieving three-dimensional tracking of the underwater target. The present invention can achieve high-precision and high-reliability three-dimensional underwater target tracking.

The present application discloses a data transmission method, apparatus, equipment and medium for an underwater wireless sensor network, which relates to the technical field of data transmission. The method configures a MAC protocol and basic protocol information, defines a node state calculation formula, a node action calculation formula and a reward function, and adds a decision network to a time slot model; calculates an initial node state and an initial node action, and if the initial node action is to send data, performs data communication with other sending nodes; after obtaining confirmation characters from other sending nodes, calculates a reward value and updates the initial node state; updates the decision network using the initial node state, initial node action, reward value and updated node state; inputs current node data into the updated decision network, outputs a node data transmission strategy, and performs data transmission between the local and receiving nodes in the underwater wireless sensor network according to the node data transmission strategy, thereby maximizing network throughput and improving data transmission efficiency of the underwater wireless sensor network.

The invention relates to a double-perception online fountain code transmission system and method based on deep reinforcement learning. The system is applied to an underwater wireless sensor network, and comprises a network modeling module which is used for constructing an underwater acoustic sensor network model of a three-dimensional hierarchical clustering topology by adopting a fuzzy clustering algorithm according to spatial distribution and residual energy of nodes, and determining a transmission mechanism of half-duplex underwater acoustic communication; the reinforcement learning optimization module is used for modeling a decoding process of an online fountain code into a Markov decision process and training an agent in combination with a deep learning algorithm to generate an optimal coding degree sequence; and the coding transmission execution module is used for executing the optimal coding degree sequence to generate a coding data packet, performing multi-hop transmission, and performing coding degree switching based on limited feedback triggering until the aggregation node recovers all original data. Based on a half-duplex underwater acoustic communication mechanism, channel conflicts are avoided, the optimal coding degree sequence can be dynamically matched with a decoding state, and the data transmission reliability in an extreme underwater environment is improved.

The invention discloses a multi-AUV (Autonomous Underwater Vehicle) collaborative underwater data acquisition system and a path optimization method, and belongs to the technical field of underwater wireless sensor networks. The system comprises a fixed sensor node, a data acquisition layer, a data integration layer, a cross-medium transmission layer, a dynamic region division module and a path optimization module. The fixed sensor node is deployed in a shallow submerged reef sea area of the South China Sea, and transmits data to the peripheral AUV through single-hop underwater acoustic communication; the central AUV is interconnected with the peripheral AUV through a dynamic telescopic optical fiber link to fuse multi-source data; and the mobile AUV uses blue-green laser communication to enable the data to float upwards to the water surface base station to be transmitted back. The dynamic region division module divides a monitoring region into three fan-shaped sub-regions which are not overlapped with one another, and the geographic unit integrity constraint and the load balancing constraint are met; and the path optimization module plans a peripheral AUV path by adopting an information entropy enhanced ant colony algorithm, and path planning efficiency is remarkably improved by dynamically adjusting heuristic factors and triggering local search optimization.

This invention relates to a node clustering routing method and device for underwater wireless sensor networks. The underwater wireless sensor network includes a sink node, a base station, and multiple dynamic underwater nodes. The routing method is applied to the sink node and includes: establishing a connection with the base station; for each underwater node, forming multiple clusters through clustering; for each cluster, determining the corresponding cluster head node using a hybrid optimization algorithm combining fruit fly and firefly algorithms, and then establishing a connection with the corresponding cluster head node; determining the target cluster where the target underwater node is located, determining the optimal path from the cluster head node of the target cluster to the target underwater node using an adaptive step-size firefly algorithm, and then establishing a connection with the target underwater node based on the optimal path, thus establishing a route from the base station to the target underwater node. Compared with existing technologies, this invention has advantages such as strong search capability and fast convergence speed.

The application discloses a centralized trust evaluation method and device based on a cloud theory and a storage medium, belongs to the technical field of trust models of underwater wireless sensor networks, and particularly relates to a trust evaluation method of underwater wireless sensor networks. The method solves the problems of ignoring the fuzziness and uncertainty of trust and relying on a threshold and a standard trust cloud which are set by a person in a trust model for a clustered network topology. The method comprises the following steps: a sink node calculates and updates a packet cloud model, a data cloud model and an energy cloud model of all network nodes by using a reverse cloud algorithm; a mean square error between input samples and output samples is calculated as a reconstruction error; and the reconstruction error is input into an SVM model for node binary classification, so that a malicious node is identified. The centralized trust evaluation method and device based on the cloud theory and the storage medium are suitable for trust evaluation of nodes in a clustered network topology.

The application relates to a three-dimensional underwater wireless sensor network dynamic positioning method, which comprises the following steps: step S1, calculating the minimum hop number and the average hop distance between each unknown node and a beacon node in a three-dimensional underwater wireless sensor network; step S2, converting the signal strength between the unknown node and the beacon node into the communication radius distance between the two, improving the HH-VBF underwater routing protocol, selecting the next hop node and transmitting information by using the improved HH-VBF underwater routing protocol, and calculating the updated minimum hop number; step S3, calculating the average hop distance of each unknown node to the beacon node and optimizing the average hop distance; step S4, calculating the coordinates of the unknown node; and step S5, tracking the target of the unknown node. Compared with the prior art, the application realizes real-time positioning and accurate tracking of unknown sensor nodes in a dynamic three-dimensional underwater environment.

The invention relates to an underwater wireless sensor network resource allocation method based on multi-agent reinforcement learning, and aims to improve network fairness and long-term energy efficiency by optimizing power allocation. In consideration of the dynamic characteristics of the environment and the non-convexity of the problem, firstly, the problem is modeled as a Markov process, and a multi-agent resource scheduling scheme combined with priority experience playback is introduced. According to the method, the learning process is accelerated by efficiently utilizing key experience. Besides, in order to cope with credit distribution challenges in a multi-agent scene, the invention also provides a multi-agent resource scheduling scheme based on anti-factual rewards, which can evaluate contribution of each agent to global rewards, thereby improving learning efficiency and stability.

The cluster head election method for improving LEACH algorithm and the improved LEACH routing protocol method belong to the technical field of routing soft security of underwater wireless sensor network, and particularly relate to a cluster head election process of underwater wireless sensor network; solve the problem that the existing cluster-based routing protocol ignores the security problem and the security of the cluster head election process is low; the method comprises the following steps: each node updates its own mse according to the trust evaluation result of the sink node on itself; each node uses an improved cluster head election threshold function T imp (n) to calculate the cluster head election threshold; each node compares the random number generated by itself with the cluster head election threshold; if it is less than the cluster head election threshold, the node becomes the cluster head node in the current large period. The improved cluster head election method for improving LEACH algorithm and the improved LEACH routing protocol method are suitable for providing a cluster-based routing protocol.

The application discloses a prediction compensation type opportunity routing forwarding timing control method for a mobile underwater wireless sensor network, and relates to the technical field of communication control, and comprises the following steps: S1, acquiring a current sending node candidate node list, predicting the position of the candidate node at a data packet predicted arrival time and a predicted propagation distance; S2, generating a priority adjustment factor through fuzzy logic, and then obtaining a dynamic comprehensive priority; S3, performing time reversal channel prediction by using a detection signal of the candidate node, obtaining a future propagation delay, and calculating a dynamic propagation delay compensation base value according to non-collision constraints of all candidate node pairs; S4, calculating a waiting time for each candidate node according to the dynamic comprehensive priority, the dynamic propagation delay compensation base value, a relative radial velocity and the predicted propagation distance; and S5, encapsulating the candidate node list and the waiting time into a data packet header and broadcasting, and the candidate node analyzes the own waiting time and starts a local timer, and inhibits forwarding or executes forwarding.

This application provides an image transmission method, system, device, and medium for underwater wireless sensor networks. The method includes: acquiring an original image; using the original image as image blocks selected in the first screening, and further screening based on target conditions to process image blocks that do not meet the target conditions into N sub-blocks, and using these N sub-blocks as image blocks selected in the next screening for further screening, until a final screening is completed; acquiring the hierarchical information of each sub-block in the original image; encoding the sub-blocks that meet the target conditions to obtain encoded sub-blocks; and transmitting the encoded information stream to a central node based on the hierarchical information, in the order from the first layer to the last layer, using a trained deep learning joint source-channel model encoding. This application enables high-quality image transmission in underwater wireless sensor networks.

The invention discloses an underwater wireless sensor network routing method based on hole prediction and MARCOS multi-attribute decision. The method comprises the following steps: establishing a neighbor list; storing and updating the information received from the neighbor node; the method comprises the following steps: predicting whether adjacent nodes possibly escape from a node communication radius or not by establishing a safety area in a node communication range and dividing an escape area, and quantitatively evaluating the communication performance of the adjacent nodes from a safety area score, an escape area score and a forwarding performance score based on neighbor list information; introducing a multi-attribute decision-making method to dynamically fuse the safe area score, the escape area score and the forwarding performance score, and calculating a comprehensive forwarding priority; when a node forwards a data packet, the data packet is added according to a candidate node list of multi-attribute decision sorting, a next-level node carries out receiving and first-level time slice waiting, and meanwhile second-level time slice waiting is started according to whether forwarding succeeds or not. According to the method, the routing decision adaptivity is high, and the problems of high processing overhead and the like caused by routing holes are solved from the source.

The invention provides an underwater node positioning method and electronic equipment, and relates to the technical field of underwater wireless sensor networks. The method comprises the following steps: acquiring an initially received RSSI signal corresponding to each node of an underwater wireless sensor network, inputting the RSSI signal into a two-stage filtering model, outputting a filtered RSSI signal, and carrying out mean-median double-weight fusion to obtain a corrected RSSI signal; wherein the two-stage filtering model comprises IHADF and Gaussian filtering; real-time hydrological parameters are obtained, the corrected RSSI signals and the real-time hydrological parameters are input into the dynamic sound propagation path loss model, and an inter-node distance estimation value is output; the hydrological parameters comprise temperature, salinity and depth; and performing dimension reduction mapping based on the inter-node distance estimation value and the depth data corresponding to the nodes, mapping to a plane where the two-dimensional plane of the unknown node is located, and solving the three-dimensional coordinates of the unknown node by adopting a self-adaptive filtering algorithm fusing depth perception and sound velocity profile dynamic compensation. The problem that a traditional positioning algorithm is insufficient in adaptability can be systematically solved.

The invention relates to a multi-source iterative positioning method for an ocean wireless sensor network under a non-line-of-sight and layered propagation condition, which comprises the following steps: acquiring observation information received by a target node to be estimated from RSS and TOA of anchor nodes, introducing an NLOS error, and constructing an initial signal power model and an initial distance model; modeling a propagation path of the signal, and obtaining a compensation form of signal path propagation under the layering effect based on a modeling result; converting the initial signal power model and the initial distance model based on the compensation form to obtain a correction model, and obtaining an initial positioning objective function based on the correction model; respectively calculating the weight coefficients of the RSS and TOA by using the distance characteristics measured by the RSS and TOA, and obtaining a positioning objective function based on the weight coefficients; and performing initial position estimation based on the positioning target function, and performing fine advancing to obtain a final positioning estimation value. Compared with the prior art, the influence of the layering effect and the NLOS error on the positioning accuracy is considered at the same time, and more accurate positioning estimation is achieved.

The present application relates to underwater wireless sensor network technical field, disclose a kind of malicious program propagation attack and defense game method in wisdom fishery, including the following operating steps: S1: construct the cell state conversion graph of AUV, UWSNs collaborative system attack and defense game model based on SIRQ infectious disease model;S2: according to cell state conversion relationship definition attack and defense game strategy parameter;S3: based on incomplete information game establishment function income table and income function formula;S4: income function formula solution mixed Nash equilibrium strategy solution;S5: through mixed Nash equilibrium strategy solution analysis cell behavior strategy.The present application is in SRQ neighbor cell model three-party attack and defense game, whether susceptible cell is detected or not, its expected income is negative, so immune cell will certainly adopt to send immune repair program, immune cell will adopt to send immune repair program behavior strategy, no longer need to satisfy necessary condition that susceptible cell is in detection state.