CN106209261B - The mobile data collection method of three-dimensional UASNs based on probability neighborhood grid - Google Patents

Abstract

The invention discloses a three-dimensional UASNs mobile data collection method based on a probabilistic neighborhood grid, including: according to the characteristics of a 3D underwater acoustic sensor network, comprehensively considering sound wave fading, ocean current surface activities, turbulent noise, wind, thermal noise, etc. Factors, build a 3D underwater acoustic sensor network probabilistic communication model; based on the constructed 3D underwater acoustic sensor network probabilistic communication model, divide the network into probabilistic neighborhood grids; plan AUV data collection paths based on probabilistic neighborhood grids , to collect data across the network. Therefore, the beneficial effects of the present invention are: adopting the probabilistic underwater acoustic communication model, the data collection distance can be flexibly adjusted according to the probability requirements; using AUV for data collection, effectively reducing the energy consumption of sensor nodes for data transmission, and prolonging the network life; By dividing the network into small grids, the AUV only needs to traverse the center of the grid to complete data collection, which can be effectively applied to underwater acoustic sensor networks with unknown node deployment information; by changing the value of the data transmission success rate p and the data transmission wheel The number provides an effective solution to balance information gain and data delay.

Description

Mobile Data Collection Method for 3D UASNs Based on Probabilistic Neighborhood Grid

technical field

The invention belongs to the field of underwater acoustic sensor networks, in particular to a method for collecting mobile data of three-dimensional UASNs based on probabilistic neighborhood grids.

Background technique

Underwater data collection is of great significance to the application of underwater acoustic sensor networks (UASNs). Whether it is the monitoring and management of the underwater environment or the monitoring and early warning of underwater disasters, people need to use UASNs to collect and obtain Perceive the interest information in the monitoring area, and then analyze, process, store and mine the information, and finally make a reasonable and effective decision. In many applications of UASNs, data collection requires the transmission of a large amount of sensing data, and the transmission of a large amount of sensing data in the network will generate a large amount of communication overhead. In addition, since node energy is supplied by batteries with limited power rather than continuous supply, in order to obtain more detection data in the detection area and ensure the effectiveness of the network, prolonging the life of the network is the primary task. Therefore, how to extend the lifetime of the network as much as possible while ensuring the information gain is a very challenging problem.

At present, the relevant research literature on the data collection method of underwater acoustic sensor network is as follows:

1. The article "Data Collection with Multiple MobileActors in Underwater Sensor Networks" published by Wang et al. on the "International Conference on Distributed Computing Systems Workshops" in 2008 proposed an underwater data that uses multiple mobile actors to obtain high-time precision data Collection scheme. The scheme mainly includes three algorithms: region division and actors dispersal algorithm, sub-region optimization algorithm, and virtual cluster generation algorithm. The scheme first divides the network into 4 ⁿ areas according to the position of the boundary nodes, then estimates the collection delay of each sub-area according to the number of nodes, and optimizes the sub-areas, and then deploys actors to each sub-area according to a certain location strategy Sub-areas, establish virtual clusters for data collection.

2. The article "A Distributed Energy-Aware Routing Protocol for Underwater Wireless SensorNetworks" published by Domingo et al. in "Wireless Personal Communications" in 2011 proposed an energy-efficient distributed clustering scheme DUCS, which uses clustering And data aggregation data to eliminate redundant information, so as to achieve the purpose of reducing network energy consumption. Although clustering is an effective way to optimize the total energy consumption of large-scale networks, this method will cause the problem of uneven energy consumption of cluster head nodes.

3. The article "Underwater Data Collection using Robotic SensorNetworks" published by Hollinger et al. in "IEEE Journal on Selected Areas in Communications" in 2012 proposed a scheme for collecting data from underwater acoustic sensor networks using AUV. In this scheme, the problem of planning AUV paths for underwater data collection and minimizing path consumption while maximizing data collection is defined as the data collection problem under communication constraints (CC-DCP), and the CC-DCP problem is formulated and proposed A heuristic approximation algorithm, and finally three 2D heuristic path planning schemes suitable for different scenarios are proposed.

4. The article "AEDG: AUV-aided Efficient Data Gathering Routing Protocol for Underwater WirelessSensor Networks" published by Ilyas et al. in "Procedia Computer Science" in 2015 proposed the AUV-assisted data collection method AEDG, whose purpose is to realize UASNs reliable data collection. In AEDG, the gateway uses the shortest path tree algorithm to collect node data, and then the AUV collects data from the gateway along a preset elliptical trajectory. This method can effectively balance energy consumption and prolong the life cycle of the network. However, AEDG is based on a deterministic communication model, and in the practical application of UASNs, the success rate of data transmission decreases with distance.

5. The article "A Distributed Data-Gathering Protocol Using AUV in Underwater Sensor Networks" published by Khan et al. in "Sensors" in 2015 proposed a distributed data collection scheme AUV-PN. In this scheme, AUVs perform two phases: Network Partitioning Tour (NPT) and Data Collection Tour (DGT). In the NPT stage, AUV first divides the entire network into multiple clusters, and each cluster selects a cluster head node CH according to the LEACH protocol; then, CH further divides the cluster into multiple sub-clusters, and assigns a path-node to each sub-cluster (PN) to collect the local data of the member nodes MN in the sub-cluster. After dividing the network, AUV starts to perform DGT. In this solution, AUV can collect data of the whole network only by accessing CH and PN, which effectively shortens the data collection time. However, in this scheme, PN needs to collect all the data in the sub-cluster, and the selection of PN only considers the total energy consumption of data uploading in the sub-cluster, and does not consider the problem of remaining energy. The additional communication overhead will lead to premature PN Death affects the life cycle of the entire network.

6. The article "Adaptive data collection in sparse underwater sensor networks using mobileelements" published by Jalaja et al. in "Lecture Notes in Computer Science" in 2015 proposed a mobile-assisted adaptive data collection method, which uses mobile elements to Reduce network energy consumption and reduce data delay through a polling mechanism. However, since the mobile elements in this method need to move to all nodes for data collection, the data delay is still large despite the polling mechanism.

To sum up, the common problems in data acquisition based on mobile elements in the current underwater acoustic sensor network are:

1) The design of most underwater acoustic sensor network data collection schemes is based on the ideal deterministic underwater acoustic communication model, but in practical applications, the data transmission success rate of the underwater acoustic channel is attenuated with the distance, when the data transmission fails , data collection will not be complete;

2) The cluster-based data collection method will increase the energy consumption of cluster head nodes, which will eventually lead to uneven energy consumption of the network and reduce the life of the network;

3) Most mobile-assisted data collection methods assume that sensor nodes are deployed on the same plane, which cannot be effectively applied to 3D water environments;

4) It is highly dependent on node deployment information, and for networks with unknown node deployment information, the data collection method cannot be realized.

Contents of the invention

In order to solve many problems and deficiencies in the existing underwater acoustic sensor network data collection technology, the present invention proposes a three-dimensional UASNs mobile data collection method based on probabilistic neighborhood grid, mainly by dividing the network into probabilistic neighborhood network Grid, the AUV reaches the center of each probabilistic neighborhood grid to collect node data, so as to effectively balance the load, reduce node energy consumption, and prolong the life of the network.

Realize above-mentioned technical purpose, reach above-mentioned technical effect, the present invention realizes through the following technical solutions:

A mobile data collection method for three-dimensional UASNs based on a probabilistic neighborhood grid, which specifically includes four steps:

(1) Construction of network probabilistic communication model: According to the characteristics of 3D UASNs, comprehensively considering factors such as acoustic fading, ocean current surface activity, turbulent noise, wind, thermal noise, etc., construct a probabilistic communication model of 3D UASNs;

(2) Network division: Based on the constructed probabilistic communication model and the required data transmission success rate p, the network is divided into probabilistic neighborhood grids of the same size;

(3) Grid traversal path planning: Based on the already divided probability neighborhood grid, the Layered-Scan method is used to traverse each probability neighborhood grid layer by layer to determine the path of the AUV;

(4) Data collection: AUV traverses all probabilistic neighborhood grids along the determined path, and starts the data collection process. When AUV is close to the center of the small grid, the scheduling protocol is used to collect the data of the current probabilistic neighborhood grid.

The data collection method described can be generally applied to 3D UASNs whose node deployment information is unknown.

In the probabilistic communication model of the underwater acoustic sensor network described in step (1), the success rate of data transmission decays with the increase of the transmission distance.

The probability neighborhood described in step (2) is defined as: the probability neighborhood Ψ _n is the set of all positions x _v whose data transmission success rate P(x _v , x _n )≥p to the position x _v in the three-dimensional UASNs.

In order to ensure that the successful data transmission rate of any node in the probabilistic neighborhood grid is not less than p, the probabilistic neighborhood grid described in step (2) is completely contained in the inscribed regular hexahedron of the probabilistic neighborhood.

Therefore, the calculation method of the probabilistic neighborhood grid is: the maximum side length of the probabilistic neighborhood grid is Where d_p is the transmission distance corresponding to the data transmission success rate p; the three-dimensional UASNs are divided into k*k*k probability neighborhood grids, where where L is the network edge length; the final probabilistic neighborhood grid edge length is

The data collection scheduling protocol described in step (4) is based on the time division multiple access mechanism, and specifically includes three stages:

(4-1) Initial stage: All nodes deployed in the network are in an inactive state. When the AUV is close to the center of the probabilistic neighborhood grid, the AUV broadcasts a high-power Wake-up control packet containing the initial scheduling information of the nodes. The high-power Wake-up control packet can trigger the nodes in the current probability neighborhood grid to enter the active state;

(4-2) Scheduling phase: The node that receives the Wake-up packet judges whether it is in the current probability neighborhood grid, and if so, turns to the active state, and according to the time slot allocated in the Wake-up packet, the Reply to AUV with an acknowledgment packet ACK, after that, AUV reassigns time slots according to the information fed back by each node, and sends new transmission scheduling information to the nodes;

(4-3) Data transmission stage: According to the new transmission scheduling information, the nodes transmit their stored data packets to the AUV. After the data transmission of all nodes is completed, the AUV reschedules the data transmission protocol for the next round of data Transfer until all transfer rounds are complete.

In the data transmission stage, the number of data transmission rounds is preset according to the user's information gain and data delay requirements. By increasing the number of data transmission rounds, the information gain can be increased while maintaining a small data delay.

Compared with the existing underwater acoustic sensor network data collection method, the positive effects of the present invention are:

(1) Using a probabilistic underwater acoustic communication model, the data collection distance can be flexibly adjusted according to the probability requirements;

(2) The use of AUV for data collection effectively reduces the energy consumption of sensor nodes for data transmission and prolongs the life of the network;

(3) By dividing the network into small grids, AUV only needs to traverse the center of the grid to complete data collection, which can be effectively applied to underwater acoustic sensor networks with unknown node deployment information;

(4) By changing the value of the data transmission success rate p and the number of data transmission rounds, an effective solution for balancing information gain and data delay is provided.

Description of drawings

Fig. 1 is the schematic flow chart of whole data collection method among the present invention;

Fig. 2 is a schematic diagram of probabilistic neighborhood grid calculation in the present invention;

Fig. 3 is a schematic diagram of grid division in the present invention;

Fig. 4 is the schematic diagram of the path of AUV traversal probability neighborhood grid among the present invention;

Fig. 5 is a schematic diagram of the frame structure of the data scheduling protocol in the present invention.

Detailed ways

The present invention will be further described below in conjunction with accompanying drawing.

As shown in Figure 1, it is a flow chart of a three-dimensional UASNs mobile data collection method based on a probabilistic neighborhood grid, which specifically includes the following four steps:

(1) Construction of network probabilistic communication model: According to the characteristics of 3D UASNs, comprehensively considering factors such as acoustic fading, ocean current surface activity, turbulent noise, wind, thermal noise, etc., construct a probabilistic communication model of 3D UASNs;

(2) Network division: Based on the constructed probabilistic communication model and data transmission success rate p, the network is divided into probabilistic neighborhood grids of the same size;

(3) Grid traversal path planning: Based on the already divided probability neighborhood grid, the Layered-Scan method is used to traverse each probability neighborhood grid layer by layer to determine the path of the AUV;

(4) Data collection: AUV traverses all probabilistic neighborhood grids along the determined path, and starts the data collection process. When AUV is close to the center of the small grid, the scheduling protocol is used to collect the data of the current probabilistic neighborhood grid.

In the probabilistic communication model of the underwater acoustic sensor network described in step (1), the success rate of data transmission decays with the increase of the transmission distance.

Figure 2 is a schematic diagram of calculating the grid size of the probability neighborhood. Among them, the probability neighborhood is defined as: the probability neighborhood Ψ _n is the set of all positions x _v whose data transmission success rate P(x _v , x _n )≥p to the position x _v in the three-dimensional UASNs.

In order to ensure that the successful data transmission rate of any node in the probabilistic neighborhood grid is not less than p, the probabilistic neighborhood grid must completely include the probability neighborhood sphere at the center of the grid, and the maximum grid size is the probability neighborhood sphere Connect the regular hexahedron.

Therefore, the specific calculation method of the probabilistic neighborhood grid is: the maximum side length of the probabilistic neighborhood grid is Where d_p is the transmission distance corresponding to the data transmission success rate p;

The 3D underwater acoustic sensor network is divided into k*k*k probability neighborhood grids, where where L is the network side length;

The final probabilistic neighborhood grid side length is

Figure 3 is a schematic diagram of grid division. The entire 3D UASNs are finally evenly divided into k*k*k probabilistic neighborhood grids with side length l.

Figure 4 shows the traversal path of the probabilistic neighborhood grid. By dividing the network into k layers, each layer can be regarded as a 2D plane, and the AUV finally moves along the Scan path at the center of each layer.

Figure 5 is a schematic diagram of the frame structure of the data scheduling protocol. The data collection and scheduling protocol is based on the time division multiple access mechanism, and specifically includes three stages:

1) Initial stage: All nodes deployed in the network are in an inactive state. When the AUV is close to the center of the probabilistic neighborhood grid, the AUV broadcasts a high-power Wake-up control packet containing the node’s initial scheduling information. The high-power The Wake-up control package can trigger the nodes in the current probability neighborhood grid to enter the active state;

2) Scheduling stage: The node that receives the Wake-up packet judges whether it is in the current probability neighborhood grid, and if so, turns to the active state, and responds to an AUV in sequence according to the time slot allocated in the Wake-up packet After confirming the packet ACK, AUV reassigns the time slot according to the information fed back by each node, and sends the new transmission scheduling information to the node;

3) Data transmission stage: According to the new transmission scheduling information, the nodes transmit their stored data packets to the AUV. After the data transmission of all nodes is completed, the AUV reschedules the data transmission protocol for the next round of data transmission until all The number of transfer rounds is complete.

Wherein, in the data transmission stage, the number of data transmission rounds is preset according to the user's information gain and data delay requirements. By increasing the number of data transmission rounds, the information gain can be improved while maintaining a small data delay. .

Claims (7)

1. a mobile data collection method based on the three-dimensional UASNs of the probability neighborhood grid, is characterized in that, specifically comprises four steps: (1) Construction of network probabilistic communication model: According to the characteristics of 3D UASNs, comprehensively considering the factors of acoustic fading, ocean current surface activity, turbulent noise, wind, and thermal noise, a probabilistic communication model of 3D UASNs is constructed; (2) Network division: based on the probabilistic communication model constructed and the required data transmission success rate p, the network is divided into probabilistic neighborhood grids of the same size; the probabilistic neighborhood is defined as: the probabilistic neighborhood Ψ _n is The set of all positions x _v whose data transmission success rate P(x _v , x _n )≥p to position x _v in the 3D underwater acoustic sensor network; (3) Grid traversal path planning: Based on the already divided probability neighborhood grid, the Layered-Scan method is used to traverse each probability neighborhood grid layer by layer to determine the path of the AUV; (4) Data collection: AUV traverses all probabilistic neighborhood grids along the determined path, and starts the data collection process. When AUV is close to the center of the small grid, the scheduling protocol is used to collect the data of the current probabilistic neighborhood grid. 2. The mobile data collection method of three-dimensional UASNs based on probabilistic neighborhood grids according to claim 1, wherein the data collection method is suitable for networks with unknown node deployment information. 3. the mobile data collection method of the three-dimensional UASNs based on probabilistic neighborhood grid according to claim 1, is characterized in that, the feature of described UASNs probabilistic communication model is: data transmission success rate decays with distance. 4. the mobile data collection method of the three-dimensional UASNs based on probabilistic neighborhood grid according to claim 1, is characterized in that, the feature of the described probability neighborhood grid of step (2) is: the probability neighborhood grid is An inscribed regular hexahedron completely contained in the probability neighborhood. 5. the mobile data collection method of the three-dimensional UASNs based on probability neighborhood grid according to claim 4, is characterized in that, the calculation method of described probability neighborhood grid is: The maximum edge length of the probabilistic neighborhood grid is Where d_p is the transmission distance corresponding to the data transmission success rate p; The three-dimensional UASNs network is divided into k*k*k probability neighborhood grids, where where L is the network side length; The final probabilistic neighborhood grid side length is 6. the mobile data collection method based on the three-dimensional UASNs of probabilistic neighborhood grid according to claim 1, is characterized in that, the data collection scheduling agreement described in step (4) is based on time division multiple access mechanism, specifically comprises three stages: (4-1) Initial stage: All nodes deployed in the network are in an inactive state. When the AUV is close to the center of the probabilistic neighborhood grid, the AUV broadcasts a high-power Wake-up control packet containing the initial scheduling information of the nodes. The high-power Wake-up control packet can trigger the nodes in the current probability neighborhood grid to enter the active state; (4-2) Scheduling phase: The node that receives the Wake-up packet judges whether it is in the current probability neighborhood grid, and if so, turns to the active state, and according to the time slot allocated in the Wake-up packet, the Reply to AUV with an acknowledgment packet ACK, after that, AUV reassigns time slots according to the information fed back by each node, and sends new transmission scheduling information to the nodes; (4-3) Data transmission stage: According to the new transmission scheduling information, the nodes transmit their stored data packets to the AUV. After the data transmission of all nodes is completed, the AUV reschedules the data transmission protocol for the next round of data Transfer until all data transfer rounds are complete. 7. The mobile data collection method of three-dimensional UASNs based on probabilistic neighborhood grid according to claim 6, characterized in that, the number of data transmission rounds is preset according to the user's needs, by increasing the number of data transmission rounds The number increases the information gain while keeping the data delay small.