CN111431630B - AUV (autonomous underwater vehicle) cooperation source node position privacy protection method based on anonymous cluster in UASNs (Universal asynchronous receiver network) - Google Patents
AUV (autonomous underwater vehicle) cooperation source node position privacy protection method based on anonymous cluster in UASNs (Universal asynchronous receiver network) Download PDFInfo
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Abstract
The invention discloses an AUV (autonomous Underwater vehicle) cooperation source node position privacy protection method based on anonymous clusters in UASNs (unmanned underwater vehicles), which comprises the following steps of: firstly, in a network pre-deployment stage, nodes are deployed randomly and divided into environment monitoring nodes and pipeline monitoring nodes according to different functions, the whole network is divided into a static layer and a dynamic layer according to an Eckman drift model, the nodes of the static layer form clusters according to a certain mode, and the dynamic layer does not form clusters; secondly, balancing network flow in order to cope with backtracking attack of an attacker, forwarding data in an anonymous cluster mode, and selecting a false source node; and finally, collecting data packets of real and false source nodes by the two AUVs respectively, wherein a Q-learning method is adopted for path planning of the AUVs. The method and the device can prevent backtracking attack of an attacker, improve the efficiency of underwater data collection, and enhance the privacy protection of the underwater environment on the source node.
Description
Technical Field
The invention belongs to the field of underwater acoustic sensor networks, and particularly relates to an AUV (autonomous underwater vehicle) cooperation source node position privacy protection method based on anonymous clusters in UASNs.
Background
The world is tightening the research on marine resources to relieve the situation of increasingly scarce land resources, and in the research on oceans, the role of the UASNs (underwater acoustic sensor network) is increasingly important. The underwater oil and gas resources are rich, and people use pipelines underwater to convey the oil and gas resources on the seabed to the water surface for use. However, due to the particularity of the underwater environment and the corrosiveness of seawater, the pipeline is easily damaged and is utilized maliciously by attackers. Nodes are usually deployed underwater for monitoring, but once the position of a source node is found, the failure of a pipeline is extremely easy to attack and use, so that source position privacy protection is needed. Source location privacy protection is an important means for wireless sensor network security protection. With the development of underwater acoustic sensor networks, the security and confidentiality of the underwater acoustic sensor networks are more and more emphasized. The source position privacy of the underwater acoustic sensor network originates from the source position privacy protection of the wireless sensor network, and the existing source position privacy protection algorithm is usually developed from the wireless sensor network.
In the research of location privacy protection, a network model and an attack model are first designed. Based on a network model and an attack model, the deep research of different location privacy protection technologies effectively avoids the leakage of sensitive location information of the network, such as the location of a source node or a base station. In addition, the location privacy protection technology also needs to consider network energy consumption, communication delay, data query precision, reliability and other factors.
The earliest proposed model for protecting location privacy is a panda hunter game, so that the scene of location privacy is clearer. In the panda hunter game model, many small sensor nodes are randomly distributed in the habitat for monitoring pandas. Once a panda appears, the surrounding sensor nodes (called source nodes) will quickly monitor it and send information (e.g., the panda's location) to the sink via multi-hop communication or routing paths. In addition, there is a hunter in a panda hunter game that can move freely in the network and monitor local wireless communications. By monitoring the network flow and tracking the routing path, a hunter can find the accurate position of the source node and finally find the panda.
In recent years, relevant research documents for privacy protection of the source location of a wireless sensor network are as follows:
1. lacing et al, in "Optimal Privacy monitoring Routing for Wireless Network," uses a statistical decision framework to optimize the Privacy of a Routing protocol given utility (or cost) constraints. A Bayesian Maximum A Posteriori (MAP) estimation is considered to monitor attacks of inter-node communication, having a global adversary of lossless and lossy observation; a decision framework is introduced to deal with the attack, the privacy utility balance problem is used as a linear programming, and the problem is effectively solved.
2. Wang et al in Source-Location Privacy Protection in Wireless Sensor Networks protects SLP under a hybrid attack threat with global and local features, proposes a message mapping method, and adds a dummy packet to hide the Source Location. Firstly, a lightweight message sharing scheme based on a congruence equation is designed. Second, each message is mapped to a set of shares, with shorter shares enabling it to be processed and transmitted in a power efficient manner. In addition, the proposed message sharing scheme can tolerate the unreliability of the sensor nodes and provide a more reliable data transmission mechanism for the network. Third, the source node builds a cloud around itself to hide its location based on shared and virtual packages. The radio behavior of the nodes in the cloud is carefully arranged to hide the true share from adversaries and to make the nodes in the cloud statistically indistinguishable. Finally, a random routing algorithm is integrated into the proposed scheme, the true shares are transferred from the false source node to the sink node, where the original message is reconstructed from the received shares.
3. In view of the time correlation between nodes, Mayank et al propose in Using Data Mules to prior Source Location Privacy in Wireless Sensor Networks, Using a Data mule in the Source Location Privacy protection, in which the protection of the Source Location is converted into the protection of the moving trajectory of the Data mule. Firstly, a real semi-global interception attack model is proposed and the effectiveness of the attack model in destroying the existing source position keeping technology is proved. Furthermore, an alpha angle anonymity model is defined for measuring source location privacy for semi-global eavesdroppers. A new protocol Mule Save Source (MSS) is designed to maintain alpha angle anonymity by leveraging the legacy functionality of the data Mule. Finally, the delay caused by using the data mule in the MSS is theoretically analyzed, and the balance between the delay and the privacy protection under different data mule mobility modes is researched through a large amount of simulation. The delay in the MSS is classified as a delay mainly caused by the buffering time of the source sensor and the data mule. Based on this result, two improvements have been proposed for multi-user storage systems: multi-user storage source shortest path (MSS-SP) and multi-user storage source two-level (MSS-TL), both of which are intended to reduce the overall latency by reducing the buffering time of the data multi-user and source.
4. Proaniao et al propose a service Decorrelation technique in Traffic Decorrelation Techniques for counting a Global Eave dropper in WSNs to reduce communication overhead, transmission delay, active nodes and interception probability during transmission. A general traffic analysis method is designed to infer context information by correlating transmission time and eavesdropping location. High communication and delay overhead is incurred as most existing strategies either fail to provide adequate protection. Partitioning the WSN into a minimal set of connected dominants that run in a round-robin fashion allows for a reduction in the number of traffic sources active at a given time while providing routing paths to any node in the WSN.
5. Wang et al, "All-Direction Random Routing for Source-Location Privacy protection against social Sensor Networks" propose a scheme for Protecting Source Location Privacy by using an omnibearing Random Routing algorithm. The scheme selects a plurality of random proxy nodes for packet transmission and processes a new enemy model parasitic node. The routing path may bypass the aggregation areas of these parasitic nodes during transmission. However, when the parasitic nodes are scattered, further research is required to verify that the scheme is effective.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: and selecting a specific underwater model, and selecting clustering according to different characteristics of different layers in the network model. After network clustering, selecting a proper position privacy protection technology to carry out underwater source node position privacy protection. And in the process of forming the anonymous cluster, balancing the network traffic. Meanwhile, in the data packet collection stage, two AUVs are adopted for collection, and data packets from real and false source nodes are collected respectively, so that the condition that the source node position is exposed due to the fact that the AUVs are tracked can be avoided, and the attack difficulty of an attacker is increased.
The technical purpose is achieved, the technical effect is achieved, the invention provides a source node position privacy protection method based on anonymous cluster AUV cooperation in UASNs, and the method is realized through the following technical scheme:
the method comprises the following steps: network pre-deployment
Underwater oil pipelines can be subjected to various attacks, including the situations of ship sinking, fish collision and the like, so that the pipelines are broken, and oil is leaked; in order to monitor the water quality near an underwater pipeline and an oil well, sensor nodes are randomly deployed underwater, the nodes are divided into environment monitoring nodes and pipeline monitoring nodes according to different functions of the nodes, and an attacker adopts a combination attack combining backtracking attack and selective forwarding;
a source node in the network is a node which detects that the state of the pipeline is changed; according to actual deployment of an oil pipeline of an underwater oil well, the whole network is divided into a dynamic layer and a static layer based on an Eckmann drift model, cluster heads of two types of nodes are respectively selected by a base station on the static layer and form clusters, and the dynamic layer nodes are close to receiver nodes and directly transmit data to Sink nodes through multi-hop forwarding;
step two: anonymous cluster mechanism
The source node generates n pieces of sub information from the real data packet through a sharing mechanism, specifies an average hop count, and randomly sets the hop count of each piece of sub information according to the average hop count; the source node respectively forwards the sub-information to the neighbor nodes, and meanwhile, false information with the same length as the sub-data packets is generated in the transmission process and transmitted in the network; when the sub information is transmitted once, the corresponding hop count is reduced by 1, and when the hop count is reduced to 0, the node where the sub information arrives is a false source node;
step three: data collection phase
In a static layer, when a source node appears, the source node sends beacon information to a cluster head, the cluster head node exchanges identity with the source node after receiving the information, meanwhile, a false source node is selected in a cluster in an anonymous cluster mode, a message for constructing the anonymous cluster is broadcasted, after the node receives the message for constructing the anonymous cluster, the cluster head fails, the false source node collects monitoring data of surrounding nodes, an AUV is waited for data collection, when the AUV reaches the cluster, a true and false source node sends an identification ID (identity) and a node ID to the AUV, the AUV analyzes after receiving the ID, and a responsible node is selected for data collection;
after the dynamic layer node receives the data from the static layer AUV, the neighbor nodes are randomly selected for data forwarding except for the nodes close to the water surface, the neighbor nodes receiving the data perform the same operation until the data reaches the Sink node, and the data collected by the dynamic layer source node is transmitted in the same mode.
The privacy protection method is suitable for monitoring the underwater oilfield pipeline in real time and protecting the position of a fault point.
The reason for the random deployment of the nodes in the step one is that the nodes are deployed along the pipeline and are easily discovered and attacked by attackers, and the random deployment of the nodes can increase transmission paths and collect water quality information of the oil production platform to prevent pollution. When the static layer nodes are clustered, the environment monitoring nodes and the pipeline monitoring nodes form clusters respectively. The environment monitoring node and the pipeline monitoring node are distinguished by being less than the network range away from the pipeline
The nodes are divided into pipeline monitoring nodes, and the rest are environment monitoring nodes.
The sharing mechanism in the second step is to divide the data packets into equal-length segments, and the length of each sub-data packet is integral
Meanwhile, an identification ID is added to the head of each subset for distinguishing the partial data packet from the whole data packet. The average number of hops set for the sub information is determined according to the size of the cluster where it is located, and it is ensured that the radius of the cluster can be spanned.
The data collection process in the third step is as follows:
the static layer data collection is carried out by adopting two AUVs in a cooperation mode, one AUV is specially used for collecting data of a real source node, the data are directly floated to the water surface after the collection is finished, the other AUV is used for sequentially collecting data packets of a false source node and transmitting the collected data packets to a nearest dynamic layer node, and when the energy is lower than 20% of the initial energy, the data packets float to the water surface for energy supplement;
the data collection of the dynamic layer mainly adopts a multi-hop forwarding mode among nodes, and sends the data to all neighbors of the receiver nodes close to the dynamic layer according to the maintained neighbor list until the data reaches the receiver.
The path planning of the AUV adopts a Q-learning method, which is a mature learning algorithm and will not be described in detail herein.
The method and the device can prevent backtracking attack of an attacker, improve the efficiency of underwater data collection, and enhance the privacy protection of the underwater environment on the source node.
Drawings
Fig. 1 is an application scenario of the entire privacy protection method in the present invention;
FIG. 2 is a diagram of a network model of the method of the present invention;
fig. 3 is a schematic diagram of an anonymous clustering mechanism.
Detailed Description
The invention is further explained below with reference to the drawings and examples.
The method comprises the following steps: network pre-deployment
In the case of the underwater drilling platform shown in fig. 1, the underwater oil pipeline may be subjected to various attacks, including sinking, fish collision, etc., which may result in pipeline rupture and oil leakage; in order to monitor the water quality near an underwater pipeline and an oil well, sensor nodes are randomly deployed underwater, the nodes are divided into environment monitoring nodes and pipeline monitoring nodes according to different functions of the nodes, and an attacker adopts a combination attack combining backtracking attack and selective forwarding;
a source node in the network is a node which detects that the state of the pipeline is changed; according to the actual deployment of an oil pipeline of an underwater oil well, as shown in fig. 2, the whole network is divided into a dynamic layer and a static layer based on an ekmann drift model, cluster heads are respectively selected by a base station at the static layer, clusters are formed, and data are directly transmitted to Sink nodes through multi-hop forwarding because nodes of the dynamic layer are close to receiver nodes;
step two: anonymous cluster mechanism
As shown in fig. 3, the source node generates n pieces of sub information from the real data packet through a sharing mechanism, specifies an average hop count, and randomly sets the hop count of each piece of sub information according to the average hop count; the source node respectively forwards the sub-information to the neighbor nodes, and meanwhile, false information with the same length as the sub-data packets is generated in the transmission process and transmitted in the network; when the sub information is transmitted once, the corresponding hop count is reduced by 1, and when the hop count is reduced to 0, the node where the sub information arrives is a false source node;
step three: data collection phase
In a static layer, when a source node appears, the source node sends beacon information to a cluster head, the cluster head node exchanges identity with the source node after receiving the information, meanwhile, a false source node is selected in a cluster in an anonymous cluster mode, a message for constructing the anonymous cluster is broadcasted, after the node receives the message for constructing the anonymous cluster, the cluster head fails, the false source node collects monitoring data of surrounding nodes, an AUV is waited for data collection, when the AUV reaches the cluster, a true and false source node sends an identification ID (identity) and a node ID to the AUV, the AUV analyzes after receiving the ID, and a responsible node is selected for data collection;
after receiving the data from the AUV of the static layer, the nodes of the dynamic layer randomly select neighbor nodes for data forwarding except for transmitting to the nodes close to the water surface, the neighbor nodes receiving the data perform the same operation until the data reaches the Sink node, and the data collected by the source node of the dynamic layer is transmitted in the same way;
the reason for the random deployment of the nodes in the step one is that the nodes are deployed along the pipeline and are easily discovered and attacked by attackers, and the random deployment of the nodes can increase transmission paths and collect water quality information of the oil production platform to prevent pollution. When static layer nodes are clustered, environment monitoring nodes and pipeline monitoring nodesThe dots form clusters, respectively. The environment monitoring node and the pipeline monitoring node are distinguished by being less than the network range away from the pipeline
The nodes are divided into pipeline monitoring nodes, and the rest are environment monitoring nodes.
The sharing mechanism in the second step is to divide the data packets into equal-length segments, and the length of each sub-data packet is integral
Meanwhile, an identification ID is added to the head of each subset for distinguishing the partial data packet from the whole data packet. The average number of hops set for the sub information is determined according to the size of the cluster where it is located, and it is ensured that the radius of the cluster can be spanned.
The data collection process in the third step is as follows:
the static layer data collection is carried out by adopting two AUVs in a cooperation mode, one AUV is specially used for collecting data of a real source node, the data are directly floated to the water surface after the collection is finished, the other AUV is used for sequentially collecting data packets of a false source node and transmitting the collected data packets to a nearest dynamic layer node, and when the energy is lower than 20% of the initial energy, the data packets float to the water surface for energy supplement;
the data collection of the dynamic layer mainly adopts a multi-hop forwarding mode among nodes, and sends the data to all neighbors of the receiver nodes close to the dynamic layer according to the maintained neighbor list until the data reaches the receiver.
Claims (9)
1. A privacy protection method for AUV (autonomous Underwater vehicle) cooperation source node positions based on anonymous clusters in UASNs (unmanned aerial vehicle systems), is characterized by comprising the following steps:
the method comprises the following steps: network pre-deployment
In order to monitor the water quality near an underwater pipeline and an oil well, sensor nodes are randomly deployed underwater, the nodes are divided into environment monitoring nodes and pipeline monitoring nodes according to different functions of the nodes, and an attacker adopts a combination attack combining backtracking attack and selective forwarding;
a source node in the network is a node which detects that the state of the pipeline is changed; according to actual deployment of an oil pipeline of an underwater oil well, the whole network is divided into a dynamic layer and a static layer based on an Eckmann drift model, cluster heads of two types of nodes are respectively selected by a base station on the static layer and form clusters, the nodes of the dynamic layer are close to a receiver and do not form clusters, and data are directly transmitted to Sink nodes through multi-hop forwarding;
step two: anonymous cluster mechanism
The source node generates n pieces of sub information from the real data packet through a sharing mechanism, specifies an average hop count, and randomly sets the hop count of each piece of sub information according to the average hop count; the source node respectively forwards the sub-information to the neighbor nodes, and meanwhile, false information with the same length as the sub-data packets is generated in the transmission process and transmitted in the network; when the sub information is transmitted once, the corresponding hop count is reduced by 1, and when the hop count is reduced to 0, the node where the sub information arrives is a false source node;
step three: data collection phase
In a static layer, when a source node appears, the source node sends beacon information to a cluster head, the cluster head node exchanges identity with the source node after receiving the information, meanwhile, a false source node is selected in a cluster in an anonymous cluster mode, a message for constructing the anonymous cluster is broadcasted, after the node receives the message for constructing the anonymous cluster, the cluster head fails, the false source node collects monitoring data of surrounding nodes, an AUV is waited for data collection, when the AUV reaches the cluster, a true and false source node sends an identification ID (identity) and a node ID to the AUV, the AUV analyzes after receiving the ID, and a responsible node is selected for data collection;
after the dynamic layer node receives the data from the static layer AUV, the neighbor nodes are randomly selected for data forwarding except for transmitting to the node close to the water surface, the neighbor nodes receiving the data perform the same operation until the data reaches the Sink node, and the data collected by the dynamic layer node is transmitted in the same way.
2. The AUV collaboration source node location privacy protection method in UASNs based on anonymous clusters as recited in claim 1, wherein the privacy protection method is suitable for real-time monitoring of underwater oilfield pipelines and protection of fault point locations.
3. The AUV (autonomous Underwater vehicle) cooperation source node position privacy protection method in UASNs (unmanned aerial vehicle) according to claim 1, wherein when the static layer nodes in the step one are clustered, the environment monitoring nodes and the pipeline monitoring nodes form clusters respectively.
4. The AUV collaboration source node location privacy protection method in UASNs as claimed in claim 1, wherein the environment monitoring node and the pipeline monitoring node in the step one are distinguished by being less than network range from the pipeline
The nodes are divided into pipeline monitoring nodes, and the rest are environment monitoring nodes.
5. The AUV (autonomous Underwater vehicle) cooperation source node location privacy protection method in UASNs (UASNs) according to claim 1, wherein the sharing mechanism in the second step is to fragment packets equally long, and each sub-packet is of an integral length
Meanwhile, an identification ID is added to the head of each subset for distinguishing the partial data packet from the whole data packet.
6. The method for privacy protection of AUV (autonomous Underwater vehicle) cooperation based on anonymous clusters in UASNs according to claim 1, wherein the determination of the average hop count in step two is determined according to the size of the cluster and ensures that the radius of the cluster can be spanned.
7. The AUV cooperative source node position privacy protection method in UASNs according to claim 1, wherein the static layer data collection in step three is performed by two AUVs in cooperation, one AUV is dedicated to collect data of a real source node, and after the collection is completed, the AUV directly floats to the water surface, the other AUV sequentially collects data packets of a false source node, transmits the collected data packets to a nearest dynamic layer node, and floats to the water surface for energy supplement when the energy is lower than 20% of the initial energy;
when the AUV responsible for collecting the data of the false source node does not sense the node in the communication range, the AUV directly floats to the water surface.
8. The method as recited in claim 1, wherein the dynamic layer node in step three maintains a neighbor list for recording the hop count of the neighbor node to the receiver node and dynamically updates.
9. The AUV collaboration-based source node location privacy protection method in UASNs as claimed in claim 7, wherein the AUV adopts a Q-learning method for path planning.
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