CN111278079A - Layered cooperative routing method and system for underwater self-organizing network - Google Patents

Layered cooperative routing method and system for underwater self-organizing network Download PDF

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CN111278079A
CN111278079A CN202010064491.1A CN202010064491A CN111278079A CN 111278079 A CN111278079 A CN 111278079A CN 202010064491 A CN202010064491 A CN 202010064491A CN 111278079 A CN111278079 A CN 111278079A
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胡一帆
陈露
孙玉娇
刘海林
吕斌
陈杰
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Oceanographic Instrumentation Research Institute Shandong Academy of Sciences
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Abstract

The invention discloses a layered cooperative routing method and a layered cooperative routing system for an underwater self-organizing network, wherein nodes in an underwater self-organizing network cluster forward acquired data to corresponding cluster head nodes, the cluster head nodes transmit the data to the cluster head nodes on the upper layer, and the last cluster head node forwards the data to a sink node through the nodes on the water surface layer; the nodes in the cluster transmit the acquired data to the corresponding cluster head nodes, and the nodes participating in communication are set as: a source node, a relay node and a destination node; the source node broadcasts to the relay node and the destination node at the same time, threshold value prejudgment is carried out at the destination node, and the quality of a transmission signal is judged by utilizing a noise threshold; and if the quality of the signal received by the destination node is lower than the noise threshold value, the relay node is started to carry out cooperative transmission of the data. The method improves the data transmission rate and the survival capacity of the network, and the formed system provides important guarantee for the application of the underwater self-organizing network in the actual submarine observation network.

Description

Layered cooperative routing method and system for underwater self-organizing network
Technical Field
The disclosure relates to the technical field of underwater self-organizing networks, in particular to a layered cooperative routing method and system for an underwater self-organizing network.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The ocean area is wide in resources, and the detection of the ocean becomes a research hotspot at present. With the development of science and technology, the self-organizing network is enough suitable for the underwater environment to form the underwater self-organizing network. The underwater self-organizing network is usually applied to a kelp observation network, mainly in a deep water environment, and underwater acoustic communication is usually adopted because radio waves are quickly attenuated due to absorption of seawater. Underwater acoustic signals have long end-to-end delays and due to attenuation bandwidth limitations, high quality underwater routing mechanisms are a major issue of research. During underwater data transmission, data signals suffer from fading and path loss, cannot successfully reach a destination or have high error rate.
In the course of implementing the present disclosure, the inventors found that the following technical problems exist in the prior art:
the underwater routing protocols can be divided into various types, and are divided into a cooperative routing protocol and a universal routing protocol according to whether a cooperative mechanism is added. Regarding the universal routing, depth-Based dbr (depth Based routing) is one of the classical algorithms, the depth of a sensing node is used as a forwarding basis, and the algorithm is simple and easy to understand but has high energy consumption. Gul et al therefore proposes a lightweight deep routing protocol (LDBR) that achieves energy minimization without incorporating a route fault tolerance and recovery algorithm. Liu et al propose an energy-saving cross-layer routing protocol (RECRP) that does not require consideration of node location to save energy, but does not consider link transmission quality, considering cross-layer transmission. The s.h.ahmed proposes an intelligent protocol for directed flooding, including angle adaptation and threshold adaptation, which dynamically reflects QoS requirements, but increases communication overhead. Jianan et al propose a routing protocol based on vectors and energy, determining priority according to vector distance; and then forwarding is carried out by combining energy without considering the number of node survival and death. With respect to cooperative routing, transmission reliability and data integrity are improved but network energy consumption is also increased by the relay nodes and the master node for source-to-sink data transmission. Ahmed et al propose a self-adaptive cooperative protocol, where nodes match a unidirectional antenna for cooperative transmission to reduce network overhead, but increase end-to-end delay. T.tayyaba et al reduce cooperative communication energy consumption by introducing mobile sink nodes, and this limitation needs to be further overcome because relay selection seriously affects network performance. Ahmad et al propose a Co-efficient energy-saving routing protocol (Co-EEUWSN), jointly optimize the physical layer transmission power and the network layer, improve the data arrival rate, but introduce the noise problem. In summary, a more suitable routing method for an underwater self-organizing network comprehensively considering the problems of energy consumption, signal-to-noise ratio, transmission rate and the like is needed to be applied to a submarine observation network.
Disclosure of Invention
In order to solve the defects of the prior art, the disclosure provides a layered cooperative routing method and system for an underwater self-organizing network;
in a first aspect, the present disclosure provides a hierarchical collaborative routing method for an underwater self-organizing network;
the layered cooperative routing method for the underwater self-organizing network comprises the following steps:
dividing partial nodes of the underwater self-organizing network into cluster head nodes and cluster internal nodes; the cluster nodes are mutually cooperated and communicated, the collected data are forwarded to the corresponding cluster head nodes, the cluster head nodes integrate the data and transmit the data to the cluster head nodes on the upper layer, after the data are forwarded layer by layer, the last cluster head node transmits the data to the nodes on the water surface layer, and the nodes on the water surface layer forward the data to the nearest sink node on the water surface;
the cluster nodes are mutually cooperated and communicated, and the collected data is forwarded to the corresponding cluster head node, and the method comprises the following steps: the nodes participating in the communication are set as follows: a source node, a relay node and a destination node;
the source node broadcasts to the relay node and the destination node at the same time, threshold value prejudgment is carried out at the destination node, and the quality of a transmission signal is judged by utilizing a noise threshold;
and if the quality of the signal received by the destination node is lower than the noise threshold value, the relay node is started to carry out cooperative transmission of the data.
In a second aspect, the present disclosure further provides a layered collaborative routing system for an underwater self-organizing network;
the layered cooperative routing system facing the underwater self-organizing network comprises:
cluster head nodes and cluster internal nodes are deployed on the underwater self-organizing network, cluster areas are selected for each layer, and cluster head nodes are selected for each cluster area; each cluster area comprises a cluster head node and a plurality of cluster nodes; the cluster nodes are mutually cooperated and communicated, the collected data are forwarded to the corresponding cluster head nodes, the cluster head nodes integrate the data and transmit the data to the cluster head nodes on the upper layer, after the data are forwarded layer by layer, the last cluster head node transmits the data to the nodes on the water surface layer, and the nodes on the water surface layer forward the data to the nearest sink node on the water surface;
the cluster nodes are mutually cooperated and communicated, and the collected data is forwarded to the corresponding cluster head node, and the method comprises the following steps: the nodes participating in the communication are set as follows: a source node, a relay node and a destination node;
the source node broadcasts to the relay node and the destination node at the same time, threshold value prejudgment is carried out at the destination node, and the quality of a transmission signal is judged by utilizing a noise threshold;
and if the signal quality received by the destination node is lower than the noise threshold value, starting the relay node to perform cooperative transmission of data, namely backing up and retransmitting the data packet.
Compared with the prior art, the beneficial effect of this disclosure is:
1. aiming at the stability and the energy consumption of the underwater self-organizing network, the layered cooperative routing method is provided by the disclosure. The accuracy of the arriving data packet is improved by utilizing the cooperative routing, and the problem of energy consumption is balanced by the hierarchical routing.
2. The relay cooperation based on the cooperative routing is added to the data transmission stage, so that the source node can send the same data through a plurality of paths, and the target node can receive the low-error-rate data packet. Compared with the traditional layered protocol, the cooperative routing can replace the ordinary multi-hop transmission, and the link quality of the underwater channel is better ensured.
3. An average clustering algorithm is used to cluster the nodes, and conditional probabilities can be used to select a clustering header. In the data transmission process, the relay node amplifies the signals and backs up the data packets to avoid packet loss, so that the data transmission rate and the survival capacity of the network are improved, and an important guarantee is provided for the application of the underwater self-organizing network to an actual submarine observation network.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a model of an underwater self-organizing network according to a first embodiment;
FIG. 2 is a first embodiment of an underwater cooperative routing model;
FIG. 3 is a diagram of intra-cluster cooperative routing according to a first embodiment;
fig. 4 is a diagram illustrating relay/destination node selection according to the first embodiment;
fig. 5 is a flow chart of the method of the first embodiment.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The first embodiment provides a layered cooperative routing method for an underwater self-organizing network;
the layered cooperative routing method for the underwater self-organizing network comprises the following steps:
dividing partial nodes of the underwater self-organizing network into cluster head nodes and cluster internal nodes; the cluster nodes are mutually cooperated and communicated, the collected data are forwarded to the corresponding cluster head nodes, the cluster head nodes integrate the data and transmit the data to the cluster head nodes on the upper layer, after the data are forwarded layer by layer, the last cluster head node transmits the data to the nodes on the water surface layer, and the nodes on the water surface layer forward the data to the nearest sink node on the water surface;
the cluster nodes are mutually cooperated and communicated, and the collected data is forwarded to the corresponding cluster head node, and the method comprises the following steps: the nodes participating in the communication are set as follows: a source node, a relay node and a destination node;
the source node broadcasts to the relay node and the destination node at the same time, threshold value prejudgment is carried out at the destination node, and the quality of a transmission signal is judged by utilizing a noise threshold;
and if the quality of the signal received by the destination node is lower than the noise threshold value, the relay node is started to carry out cooperative transmission of the data.
Further, the method for dividing part of nodes of the underwater self-organizing network into cluster head nodes and cluster internal nodes comprises the following specific steps:
s1: deploying sensor nodes underwater to form an underwater self-organizing network;
s2: dividing the underwater self-organizing network into a plurality of layers, wherein the layer closest to the water surface is a water surface layer, nodes in the water surface layer do not divide a cluster region, a cluster region is selected for each layer other than the water surface layer, and a cluster head node is selected for each cluster region; each cluster area comprises a cluster head node and a plurality of cluster nodes.
Further, the method further comprises: after completing one round of data transmission, the cluster head node CH will determine its own cluster average energy from the remaining energy of the cluster members (if this energy is less than the network threshold energy), the cluster will be reconstructed at this layer, and the cluster head node CH will be reselected. Routing information is also updated.
Further, in S1, deploying sensor nodes underwater to form an underwater ad hoc network; the method comprises the following specific steps:
and anchoring the sensor nodes under water randomly to form a self-organizing network.
Furthermore, each sensor node is also provided with a depth sensor, and the depth sensor is used for detecting the depth of the current sensor node under water.
Further, in S2, the underwater self-organizing network is divided into a plurality of layers, and the specific steps include:
and dividing the underwater self-organizing network into a plurality of layers according to the set communication radius.
Further, in S2, the underwater self-organizing network is divided into a plurality of layers, and the specific steps include:
LN＝Darea/W (1)
wherein LN represents the number of tiers; dareaRepresenting the depth of a monitoring area, and W represents the communication diameter of the sensor node;
Figure BDA0002375537300000061
wherein N isnumSequence number, N, representing the layer in which the sensor node is locateddptRepresenting the depth at which the sensor node is located.
Further, in S2, the selecting a cluster area for each layer of the non-water surface layer specifically includes: and selecting cluster regions for each layer of the non-water surface layer by adopting a clustering algorithm.
Further, a clustering algorithm is adopted for each layer of the non-water surface layer to select cluster regions, and the method specifically comprises the following steps:
s201: selecting a node as an initial cluster center K1Distance K from1The farthest node is used as the second clustering center K2；
S202: selecting a distance K1And K2All the farthest nodes are taken as a third clustering center K3，K3Is min (d (K)3,K1),d(K3,K2) Maximum of);
selecting a distance K1、K2And K3All the farthest nodes are taken as a fourth clustering center K4Selecting min (d (K)4,K1),d(K4,K2),d(K4,K3) Maximum value in) as K4；
By analogy, all K clustering centers are selected;
s203: after K initial clustering centers are obtained, clustering is carried out on the sensor nodes of each layer by using an average clustering algorithm to obtain a cluster region.
Further, in S2, the electing a cluster head node for each cluster region specifically includes: and (4) selecting the cluster head by using a Bayesian formula.
Further, a Bayesian formula is utilized to elect the cluster head, and the specific steps comprise:
s211: calculating Bayes probability according to the residual energy, the energy consumption rate and the link quality of each sensor node in the cluster;
s212: regarding the nodes with the Bayesian probability being larger than or equal to the Bayesian probability as cluster head nodes;
s213: and regarding the nodes with the probability less than the Bayesian probability as the nodes in the cluster.
It should be understood that a bayesian probability is calculated according to the attribute values of the residual energy, the energy consumption rate, the link quality and the like of each sensor node in the cluster, and the bayesian probability is as follows:
Figure BDA0002375537300000071
wherein, PiPosterior probability of electing cluster head for ith node, PijI.e. the probability of the jth attribute of the ith node, and a is the number of the node attributes.
Further, the method further comprises: if the quality of the signal received at the destination node is above the noise threshold, the transmission is made directly.
Further, the quality of the signal received at the destination node is a ratio of a power value of the signal received at the destination node to a noise power value.
Further, if the signal quality received at the destination node is lower than the noise threshold, the relay node is started to perform cooperative transmission of data, which is expressed by the formula:
yRD(f)＝αySR(f)gRD+nRD(f) (4)
yD＝ySD(f)+yRD(f) (5)
wherein α is an amplification factor, and is set
Figure BDA0002375537300000081
EbFor the transmitted signal energy, N0Is the power spectral density of the noise.
Further, the selection criteria of the relay node and the destination node are as follows:
each node acquires own depth information and depth information of other nodes in the data transmission process, and stores the node closest to the node to the neighbor node set of the node;
the relay node and the destination node are selected based on a Signal-Noise-Ratio (SNR) criterion.
Further, the selecting a relay node and a destination node based on the SNR criterion specifically includes:
presetting a depth referencedthSelection is at dthM sensor nodes with the highest residual energy simultaneously in a specified boundary are used as candidate relay nodes;
is selected to be located at dthN sensor nodes with the highest residual energy outside the specified boundary are used as candidate destination nodes;
screening out a final destination node from the candidate destination nodes;
and after the final destination node is selected, the final relay node is screened out from the candidate relay nodes by combining the depth difference between the source node and the destination node.
Screening out a final destination node from the candidate destination nodes; the selection function is:
Figure BDA0002375537300000082
f(vD) V corresponding to the maximum valueDNamely the optimal destination node.
After the final destination node is selected, the final relay node is screened out from the candidate relay nodes by combining the depth difference between the source node and the destination node; the selection function is:
Figure BDA0002375537300000091
wherein, f (v)D) Is a destination node vdSelection function, f (v)R) Is a relay node selection function, pDThe target node density, i.e. the ratio of the number of nodes covered by the transmission radius to the number of nodes in the layer, is expressed as rhoD＝Nr/Ni。ρRIs the density of the relay node, i.e. the set depth value
Figure BDA0002375537300000092
The ratio of the number of nodes covered to the number of nodes in the transmission radius region is expressed as
Figure BDA0002375537300000093
f(vR) Maximum value correspondenceV isRNamely the optimal relay node.
An underwater network model:
for underwater ad hoc networks, the main goal of routing protocol design is to reduce network energy consumption and increase throughput. The energy of the sensor nodes deployed underwater is restricted, and the batteries are difficult to replace. Whereas in terms of energy saving a cluster-based approach has proven feasible. The present disclosure first determines an underwater network model, as shown in fig. 1, by dividing the network in a hierarchical manner. The sensor nodes are arbitrarily anchored in the underwater self-organizing network. The depth information of each node is known and is obtained by a depth sensor installed on the node. And considering energy balance, nodes close to the water surface are not clustered and are defined as a water surface layer. The main work of the water surface layer is to transmit the data packet transmitted by the underwater cluster head to the sink node. And other nodes carry out network layering according to a given communication radius, and each cluster comprises a cluster head node (CH) and a plurality of intra-cluster nodes. The cluster nodes cooperate with each other to communicate and finally forward to the cluster head, and the cluster head nodes forward the information to the nearest sink node layer by layer after the information is fused. The underwater sound communication is adopted underwater by the sink node on the water surface, and the radio communication mode can be adopted on the water surface. When all the nodes finish the transmission to the sink node, one cycle is defined as one round.
An underwater cooperative routing model:
in an underwater ad hoc network, the environment is complicated, and direct communication between nodes may be lost due to the influence of sea water currents or may be hindered by plankton. Thus, collaboration is an effective way to avoid such problems. As shown in fig. 2, the cooperative routing model is composed of a source node, a destination node, and a relay node. The source node broadcasts to the relay node and the target node at the same time, the threshold value prejudgment is carried out at the target node, and the SNR noise threshold gamma is utilized0To determine the quality of the transmitted signal. And when the quality of the signal received by the destination node is lower than the threshold value, the relay node is started to carry out cooperative transmission. Considering the problem of saving node energy, each relay node is stipulated to be retransmitted only once.
In the first phase of cooperative routing: the source node first broadcasts to the relay node and the destination node.
ySR(f)＝xSgSR+nSR(f)，ySD(f)＝xSgSD+nSD(f) (8)
Underwater acoustic communications are susceptible to a variety of noise sources, such as ocean bottom turbulence, vessel motion, wind waves and turbulence, and the like. Taking these factors into account, the present disclosure presents the following general underwater noise formula:
n(f)＝nT(f)+nS(f)+nW(f)+nTh(f) (9)
wherein:
Figure BDA0002375537300000101
TABLE 1. associated parameter symbolic definition of underwater cooperative routing
Figure BDA0002375537300000102
Figure BDA0002375537300000111
In the second stage of cooperative routing: the destination node firstly performs threshold gamma 0 initial judgment, notifies the relay node (the selection of the relay node will be given in the subsequent part) of the signal lower than the threshold to perform cooperative retransmission, the relay transmission adopts amplification forwarding, and the maximum ratio combination is performed at the destination node. The received direct transmission signal ySD is expressed in the form:
Figure BDA0002375537300000112
the complex channel fading experienced between nodes can be represented in complex form:
Figure BDA0002375537300000113
|gSDi is the fading amplitude corresponding to the rayleigh distribution, g to (0,σ2)，σ2＝E[|gSD|2]＝1，θSDis the phase. The power received at the destination node is then
Figure BDA0002375537300000114
At this time, signal-to-noise ratio (SNR) threshold judgment is performed:
Figure BDA0002375537300000115
wherein, PnRepresenting the noise power.
If gamma at this timeSDLower than a set initial value gamma0Then cooperative routing is performed, expressed as follows:
yRD(f)＝αySR(f)gRD+nRD(f) (4)
yD＝ySD(f)+yRD(f) (5)
wherein α is an amplification factor, and is set
Figure BDA0002375537300000121
EbFor transmitting the energy of the signal, N0Is the power spectral density of the noise.
The complete process of the underwater hierarchical cooperative routing algorithm comprises the following steps:
energy consumption is often an urgent issue for underwater ad hoc networks. Due to the corrosiveness of seawater, underwater nodes are often corroded and cannot be recycled, and batteries are difficult to replace. The present disclosure rationally optimizes network consumption by building an appropriate underwater energy model. The underwater routing protocol needs to solve the communication problem caused by dynamic network topology, and the communication between nodes is influenced by seawater flow. Secondly, point-to-point node communication is not stable enough, packet loss problems exist, and the quality of a link affects the performance of the whole network. Therefore, the present disclosure provides a hierarchical routing protocol based on cooperation, which utilizes cooperative routing to improve accuracy of arriving data packets, and the hierarchical routing balances energy consumption problems at the same time. The whole protocol is divided into two stages: a layering phase and a transmission phase. The layering stage is responsible for network division and node clustering, and the transmission stage is responsible for cooperative routing and data forwarding. These two phases are described in detail below:
network layering stage:
considering energy balance, the monitoring network is divided into layers of the same scale. The number of layers LN can be set from LN to DareaCalculated as,/W, where DareaThe depth of the monitored area is represented, and W-2 x r represents the coverage area with the communication radius of the node. The first layer is defined as a water surface layer, and information is directly transmitted to the aggregation node closest to the first layer without clustering nodes in the layer. A depth sensor is arranged in each sensor node, the depth of each node is sensed by the sensor, and then the number of layers (namely layer serial number) where each node is located is obtained according to the following formula:
Figure BDA0002375537300000122
wherein N isnumSequence number representing the layer in which the node is located, NdptRepresenting the depth at which the node is located.
And repeatedly executing the calculation, and obtaining the layer sequence number of each node and storing the layer sequence number in the data packet list. By this time, all layering is complete.
Next, the present disclosure details the process of selecting cluster regions using an average clustering algorithm and electing a cluster head node (CH) using a bayesian formula.
Assuming that in an ideal environment, the number of initial clustering centers in each layer is K, the total number of nodes is N, and then the total number of nodes in each layer is NiN/LN where i ∈ { 2.
Ideally, the optimal number of K is defined as:
Figure BDA0002375537300000131
wherein, W is the node communication range, and L multiplied by L is the monitoring network range.
Considering node maldistribution, the algorithm is based on surrounding nodesThe density of points ρ (the ratio of the number of nodes within the sensing radius to the total number of nodes) selects the initial cluster center. Firstly, a node is selected as an initial cluster center K1Distance K from1The farthest node is used as the second clustering center K2. Then selecting the distance K1And K2All the farthest nodes are taken as a third clustering center K3It is min (d (K)3,K1),d(K3,K2) Maximum value of). Selecting min (d (K)4,K1),d(K4,K2),d(K4,K3) Maximum value in) as K4. And finally, selecting all K clustering centers according to the rule. Note that cluster regions are selected in this manner for each layer until all selections are made.
After K initial cluster centers are obtained, the network is clustered using an average clustering algorithm, where the variance (sum of squared errors in the cluster) is used as a standard metric function, which can be defined as:
Figure BDA0002375537300000132
wherein x ∈ RiIndicating that the sending distance of the node is within the communication range, X represents the distance between the node and the sink node, XiDepth information for k cluster regions in i ∈ {1, 2.
The convergence domain is defined as follows:
|E1-E2|<ε (16)
wherein ε is the minimum value. E1Representing the current metric function, E2Representing the metric function of the previous round. And judging whether the clustering region meets the requirement of a convergence domain by using a standard metric function, and if not, re-dividing the region until the condition is met.
Then, a cluster head is selected by using a Bayesian formula, and each node calculates the effective time H of each nodetIntegrating cluster head election time Ti and residual energy, HtIs derived from the following formula:
Figure BDA0002375537300000141
wherein, delta [1,0.5 ]]Any value, E, representing avoidance of nodes having similar residual energy collisionsrRefers to its residual energy, E0Refers to its initial energy. From the above equation, it can be seen that the higher the residual energy is, the shorter the effective time thereof is, the higher the possibility of electing the cluster head is. Node is at HtWhen the cluster head is selected, the competitive cluster head is automatically abandoned after the specified time.
The Bayesian probability is calculated according to the properties of the residual energy, the energy consumption rate and the link quality of each node.
The calculation results in two cases: node niIs the cluster head probability P (n)iH), or cluster membership probability P (n)i＝H′)。
The present disclosure calculates the probability of becoming cluster head nodes of each other in a cluster, and this maximum probability is based on its attribute values.
The prior probability P (n) of the node electing the cluster head under the premise of not knowing the node attributeiH), the posterior probability P (n) of electing cluster head under the precondition of knowing the properties of node residual energy, energy consumption rate and link qualityi＝H|xij)，xijRepresents the ith node xiThe jth attribute of (2). For the same reason, P (n)iH') and P (n)i＝H′|xij) And probability P (x) of node attribute setij)。
P(xij|ni＝H)＝(P(ni＝H|xij)*P(xij))/P(ni＝H) (18)
Now, there are only two cases: becomes H or H'.
P(ni＝H|xij)+P(ni＝H′|xij) 1 thus has P (n)i＝H)+P(ni＝H′)＝1。
Assuming that the cluster is known not to be a cluster head node, the posterior probability that the cluster has a set of possible attribute values is:
Figure BDA0002375537300000151
for convenience of representation hereinafter, xi1,xi2,......,xia＝XiIs the set of attribute values for this node. The following two equations can be obtained according to the above equation:
Figure BDA0002375537300000152
Figure BDA0002375537300000153
next, attribute set XiAs known from the packet acquisition, the probability of the node becoming a cluster head is:
Figure BDA0002375537300000154
given a set of attribute sets X for a nodeiIt can therefore be in the H or H' state:
P(Xi)＝P(Xi|ni＝H)*P(ni＝H)+P(Xi|ni＝H′)*P(ni＝H′) (23)
therefore, the probability of occurrence of these two states is equal, and then combining the above formula, the probability of the node becoming a cluster head is as follows:
Figure BDA0002375537300000155
simplified by the above formula, PiI.e. the posterior probability, P, of the node electing cluster headijI.e. the probability that the node attribute is known
Figure BDA0002375537300000156
The present disclosure performs reciprocal analysis of the above formula followed by elimination
Figure BDA0002375537300000157
The logarithmic analysis can be carried out to obtain:
Figure BDA0002375537300000161
then according to the logarithmic property, the following can be obtained:
Figure BDA0002375537300000162
therefore, PiComprises the following steps:
Figure BDA0002375537300000163
wherein P isiPosterior probability of electing cluster head for ith node, PijI.e. the probability of the jth attribute of the ith node, and a is the number of the node attributes. Order to
Figure BDA0002375537300000164
Then
Figure BDA0002375537300000165
Setting PiAnd selecting each cluster region according to the probability, and repeatedly executing until each layer is selected.
And (3) network transmission stage:
in an underwater acoustic communication network, as the propagation rate of an underwater acoustic signal is far lower than that of a radio wave, the propagation delay under water is greatly increased, and data packets can generate collision problems in the same time slot. Secondly, the propagation of sound waves in water can generate Doppler scaling influence, so that the transmission signals are diffused and cannot be transmitted to a specified range. The present disclosure considers the characteristics of the underwater acoustic channel, assuming that the transmitted data is QPSK modulated, and employs OFDM to mitigate inter-symbol interference. The method is mainly characterized in that an intra-cluster transmission mode is improved, and different from the traditional underwater transmission mode, the backup retransmission operation is carried out by considering the addition of a cooperative route and utilizing cooperative transmission consisting of a source node and a relay node.
As shown in fig. 3, in the data transmission phase, each node broadcasts to obtain depth information of itself and other nodes, and stores the closest node (using the depth difference between two nodes as the measurement standard) to its neighbor node set. Next, the relay node and the destination node are selected. In the conventional cooperative routing, only the depth and the residual energy of the nodes are generally considered to select the relay node. Therefore, it has not been assumed how the relay node should select when the quality of the transmission link is lower than normal. Thus, the present disclosure makes it more reliable to select a relay node based on signal-to-noise ratio (SNR) criteria.
The present disclosure presets a depth reference dthD being 3/4 of radius of transmissionthThe relay node is located at dthThe node within the specified boundary having the highest remaining energy at the same time. The target node is located at dthThe node outside the specified boundary having the highest remaining energy.
Then, the method screens out more suitable relay nodes and destination nodes by combining the self depth of the nodes, the density of surrounding nodes and SNR standards. And after the partner node is selected, executing cooperative routing. As shown in fig. 4, the partner node selects a graph.
Selecting destination node, and combining depth difference D of source node and destination nodeSDGiving a relay node selection formula:
Figure BDA0002375537300000171
Figure BDA0002375537300000172
wherein, f (v)D) Is the destination node selection function, f (v)R) Is a relay node selection function.
ρDThe target node density, i.e. the ratio of the number of nodes covered by the transmission radius to the number of nodes in the layer, is expressed as rhoD＝Nr/Ni。ρRIs the density of the relay node, i.e. the set depth value
Figure BDA0002375537300000173
The ratio of the number of nodes covered to the number of nodes in the transmission radius region is expressed as
Figure BDA0002375537300000174
The link quality is judged by the SNR standard and is expressed as:
Figure BDA0002375537300000175
after the relay node is successfully selected, the method waits, whether the quality of the direct transmission link at the moment meets a threshold value is verified at a target node, and otherwise, the relay node is used for executing backup retransmission operation. And performing cooperative communication among nodes in the cluster, and transmitting layer by layer until the last target node is a CH. Data fusion is already performed at the destination node before sending the data packet to the CH, which reduces the processing tasks of the CH. The CH is only responsible for transmitting data to the surface, while the surface nodes transmit directly to the sink node. Intra-cluster cooperation can ensure reliability of data transmission compared to a non-cooperative protocol, and preprocessing can reduce the burden of CH and network outage time. A cooperative routing diagram is shown in fig. 5, along with a proposed protocol flow diagram.
After completing a round, the CH will determine its own cluster mean energy from the remaining energy of the cluster members (if that energy is less than the network threshold energy), the cluster will reconstruct at that level, and the CH will be reselected. Routing information is also updated.
The method adds a relay cooperation model based on cooperative routing to a data transmission stage, so that a source node can send the same data through a plurality of paths, and a target node receives a low-error-rate data packet. Compared with the traditional layered protocol, the cooperative routing can replace the ordinary multi-hop transmission, and the link quality of the underwater channel is better ensured. An average clustering algorithm is used to cluster the nodes, and conditional probabilities can be used to select a clustering header. In the data transmission process, the relay node amplifies the signals and backs up the data packets to avoid packet loss, so that the data transmission rate and the survival capacity of the underwater self-organizing network are improved, and an important guarantee is provided for the application of the underwater self-organizing network to an actual seabed observation network.
The second embodiment also provides a layered cooperative routing system facing the underwater self-organizing network;
the layered cooperative routing system facing the underwater self-organizing network comprises:
cluster head nodes and cluster internal nodes are deployed on the underwater self-organizing network, cluster areas are selected for each layer, and cluster head nodes are selected for each cluster area; each cluster area comprises a cluster head node and a plurality of cluster nodes; the cluster nodes are mutually cooperated and communicated, the collected data are forwarded to the corresponding cluster head nodes, the cluster head nodes integrate the data and transmit the data to the cluster head nodes on the upper layer, after the data are forwarded layer by layer, the last cluster head node transmits the data to the nodes on the water surface layer, and the nodes on the water surface layer forward the data to the nearest sink node on the water surface;
the cluster nodes are mutually cooperated and communicated, and the collected data is forwarded to the corresponding cluster head node, and the method comprises the following steps: the nodes participating in the communication are set as follows: a source node, a relay node and a destination node;
the source node broadcasts to the relay node and the destination node at the same time, threshold value prejudgment is carried out at the destination node, and the quality of a transmission signal is judged by utilizing a noise threshold;
and if the quality of the signal received by the destination node is lower than the noise threshold value, the relay node is started to carry out cooperative transmission of the data.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (10)

1. The layered cooperative routing method for the underwater self-organizing network is characterized by comprising the following steps:
dividing partial nodes of the underwater self-organizing network into cluster head nodes and cluster internal nodes; the cluster nodes are mutually cooperated and communicated, the collected data are forwarded to the corresponding cluster head nodes, the cluster head nodes integrate the data and transmit the data to the cluster head nodes on the upper layer, after the data are forwarded layer by layer, the last cluster head node transmits the data to the nodes on the water surface layer, and the nodes on the water surface layer forward the data to the nearest sink node on the water surface;
the cluster nodes are mutually cooperated and communicated, and the collected data is forwarded to the corresponding cluster head node, and the method comprises the following steps: the nodes participating in the communication are set as follows: a source node, a relay node and a destination node;
the source node broadcasts to the relay node and the destination node at the same time, threshold value prejudgment is carried out at the destination node, and the quality of a transmission signal is judged by utilizing a noise threshold;
and if the quality of the signal received by the destination node is lower than the noise threshold value, the relay node is started to carry out cooperative transmission of the data.
2. The method as claimed in claim 1, wherein the dividing of the partial nodes of the underwater ad-hoc network into cluster head nodes and intra-cluster nodes comprises the following steps:
s1: deploying sensor nodes underwater to form an underwater self-organizing network;
s2: dividing the underwater self-organizing network into a plurality of layers, wherein the layer closest to the water surface is a water surface layer, nodes in the water surface layer do not divide a cluster region, a cluster region is selected for each layer other than the water surface layer, and a cluster head node is selected for each cluster region; each cluster area comprises a cluster head node and a plurality of cluster nodes.
3. The method of claim 1, further comprising: after completing one round of data transmission, the cluster head node CH determines its own cluster average energy according to the remaining energy of the cluster members, if the own cluster average energy is less than the network threshold energy, the cluster is reconstructed at the layer, and the cluster head node CH is reselected; the routing information is also updated.
4. The method of claim 2, wherein the underwater ad hoc network is divided into a plurality of layers, comprising the steps of:
and dividing the underwater self-organizing network into a plurality of layers according to the set communication radius.
5. The method of claim 2, wherein the cluster regions are selected for each of the non-aqueous layers by: and selecting cluster regions for each layer of the non-water surface layer by adopting a clustering algorithm.
6. The method as claimed in claim 2, wherein the selecting of the cluster head node for each cluster region is specifically: and (4) selecting the cluster head by using a Bayesian formula.
7. The method of claim 6, wherein a bayesian formula is used to elect a cluster head, comprising the steps of:
s211: calculating Bayes probability according to the residual energy, the energy consumption rate and the link quality of each sensor node in the cluster;
s212: regarding the nodes with the Bayesian probability being larger than or equal to the Bayesian probability as cluster head nodes;
s213: and regarding the nodes with the probability less than the Bayesian probability as the nodes in the cluster.
8. The method of claim 1, wherein the selection criteria for the relay node and the destination node are:
each node acquires own depth information and depth information of other nodes in the data transmission process, and stores the node closest to the node to the neighbor node set of the node;
the relay node and the destination node are selected based on a signal-to-noise ratio criterion.
9. The method of claim 8, wherein selecting the relay node and the destination node based on the SNR criterion comprises:
the depth reference d is presetthSelection is at dthM sensor nodes with the highest residual energy simultaneously in a specified boundary are used as candidate relay nodes;
is selected to be located at dthN sensor nodes with the highest residual energy outside the specified boundary are used as candidate destination nodes;
screening out a final destination node from the candidate destination nodes;
and after the final destination node is selected, the final relay node is screened out from the candidate relay nodes by combining the depth difference between the source node and the destination node.
10. The layered cooperative routing system facing the underwater self-organizing network is characterized by comprising:
cluster head nodes and cluster internal nodes are deployed on the underwater self-organizing network, cluster areas are selected for each layer, and cluster head nodes are selected for each cluster area; each cluster area comprises a cluster head node and a plurality of cluster nodes; the cluster nodes are mutually cooperated and communicated, the collected data are forwarded to the corresponding cluster head nodes, the cluster head nodes integrate the data and transmit the data to the cluster head nodes on the upper layer, after the data are forwarded layer by layer, the last cluster head node transmits the data to the nodes on the water surface layer, and the nodes on the water surface layer forward the data to the nearest sink node on the water surface;
the cluster nodes are mutually cooperated and communicated, and the collected data is forwarded to the corresponding cluster head node, and the method comprises the following steps: the nodes participating in the communication are set as follows: a source node, a relay node and a destination node;
the source node broadcasts to the relay node and the destination node at the same time, threshold value prejudgment is carried out at the destination node, and the quality of a transmission signal is judged by utilizing a noise threshold;
and if the signal quality received by the destination node is lower than the noise threshold value, starting the relay node to perform cooperative transmission of data, namely backing up and retransmitting the data packet.
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* Cited by examiner, † Cited by third party
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CN112073939A (en) * 2020-08-19 2020-12-11 青岛杰瑞自动化有限公司 Communication method and system based on ocean floating platform
CN114390469A (en) * 2022-03-23 2022-04-22 青岛科技大学 Service life prolonging method of three-dimensional columnar underwater acoustic sensor network based on cross-layer cooperation
CN115665756A (en) * 2022-10-24 2023-01-31 天津大学 Marine environment-oriented relay node deployment method for underwater wireless sensor network

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112073939A (en) * 2020-08-19 2020-12-11 青岛杰瑞自动化有限公司 Communication method and system based on ocean floating platform
CN114390469A (en) * 2022-03-23 2022-04-22 青岛科技大学 Service life prolonging method of three-dimensional columnar underwater acoustic sensor network based on cross-layer cooperation
CN114390469B (en) * 2022-03-23 2022-07-12 青岛科技大学 Service life prolonging method of three-dimensional columnar underwater acoustic sensor network based on cross-layer cooperation
CN115665756A (en) * 2022-10-24 2023-01-31 天津大学 Marine environment-oriented relay node deployment method for underwater wireless sensor network

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