CN108366394A - High energy efficiency wireless sensing network data transmission method based on time-space compression network code - Google Patents
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Abstract
The invention discloses a kind of high energy efficiency wireless sensing network data transmission method based on time-space compression network code, mainly solves the high energy consumption issues of data collection in wireless sense network, and improves the accuracy of data recovery.This method is to choose best next-hop both candidate nodes using two hop neighbor information on the basis of network code and compressed sensing are united, avoid the redundant transmission of data, reduce energy consumption；KSVD thoughts are utilized simultaneously, and best time observation matrix is obtained by training time sparse dictionary, so that the better rarefaction of data, to improve the accuracy of data recovery.The present invention can significantly decrease volume of transmitted data, reduce energy consumption, and improve the precision of data reconstruction at aggregation node, be suitable for the wireless sense network of extensive dense distribution.
Description
Technical Field
The invention relates to a high-energy-efficiency wireless sensor network data transmission method based on space-time compression network coding, and belongs to the technical field of wireless communication.
Background
In recent years, Wireless Sensor Networks (WSNs) have received great research attention in many applications such as military reconnaissance, environmental monitoring, security systems, and industrial automation. However, at present, the sensor devices are generally powered by batteries, and the places where the wireless sensor networks are arranged are generally inconvenient to reach, and once the batteries of the sensor devices are exhausted, the sensor devices mean that the nodes of the network die, so that the functions of the sensor networks are reduced or even lost. Energy becomes a bottleneck problem limiting wireless sensor networks. In the wireless sensor network, energy consumption of sensor nodes is mainly focused on wireless transmission of data, and therefore, a wireless transmission method with high energy efficiency becomes a key for solving the energy bottleneck problem.
In a wireless sensor network, sensing ranges of nodes are overlapped, and most of sensed physical data of the nodes are slowly changed along with time, so that acquired data have high time-space correlation, and therefore, a method for improving the energy efficiency of wireless transmission by using a data compression technology becomes feasible, wherein a Compression Sensing (CS) technology is widely used for reducing the number of data transmission so as to improve the energy efficiency of data transmission. Besides the traditional store-and-forward function, the nodes in the wireless sensor network also have the computing processing capacity, and in addition to the broadcasting characteristic of wireless transmission in the network and the dynamic characteristic of network links, network coding technology is also proposed to reduce the energy consumption of the nodes and improve the network capacity.
To reduce the Energy consumption to a greater extent, methods combining the above two technologies are attracting attention, for example, methods combining Compressed Sensing and network coding proposed by x.yang et al (x.yang et al, China, IEEE Transactions on Wireless Communications vol.12, No.10, pp.5087-5099, October 2013, "Energy-Efficient Distributed Data storage for Wireless sensors Networks Based on Compressed Sensing and network coding", which reduce the number of Data packet transmissions by designing conditions for combining codes and forwarding probabilities of receiving nodes, thereby reducing the Energy consumption. However, since the relay nodes in the transmission process of this method are blindly selected, redundant transmission inevitably occurs. For this reason, Y.ZHou et al (Y.ZHou et al, China, Ksi transformation on Internet & information System9.1(2017):2488-2511, "Improved Compressed Network Coding Scheme for Energy-Efficient data communication in Wireless Sensor Network") proposes a Compressed Network Coding method with optimal relay node selection, which avoids Energy consumption caused by redundant transmission and further reduces Energy consumption. However, both methods only consider the spatial correlation between Sensor Data, and b.gong et al (b.gong et al, China, ieee communications Letters, vol.19, No.5, pp.803-806, May 2015, "spatial comprehensive Network Coding for Energy-Efficient Distributed Data Storage in wireless sensors Networks") proposes to further improve Energy efficiency by simultaneously using the spatial correlation and the temporal correlation of Data.
Gong et al, however, proposes a scheme based on the assumption that data has ideal sparsity when both the temporal and spatial sparse dictionaries are overcomplete DCT matrices, but in practice, data is less sparsity when both the temporal and spatial sparse dictionaries are overcomplete DCT matrices, and CS cannot achieve reliable data recovery. Therefore, C.Wang et al (C.Wang et al, China,2015IEEE 81st temporal Technology Conference (VTC Spring), Glasgow,2015, pp.1-6, "Practical adaptive comparative Network Coding for Energy-Efficient Distributed Data Storage in Wireless Sensors Networks") provides a method for designing sparse dictionaries and measurement matrices to improve the accuracy of Data recovery. However, this method is low in energy efficiency and further improvement is required.
Disclosure of Invention
The invention aims to provide a high-energy-efficiency wireless sensor network data transmission method based on space-time compression network coding, and relates to a wireless sensor network data transmission method and reconstruction and recovery of data at a sink node. The method further reduces the data transmission times by using an optimal relay node selection method on the basis of using compressed network coding, and improves the data reconstruction precision by redesigning a time sparse dictionary and a measurement matrix.
The invention adopts the following technical scheme for solving the technical problems:
the invention provides a high-energy-efficiency wireless sensor network data transmission method based on space-time compression network coding, which comprises the following steps:
step 1: initializing a data packet of each node in the network, wherein the data packet of each node consists of four parts, namely a next node ID, a coefficient of a current node, the current node ID and data of the current node, and a data packet P (i) of a node i is initialized as follows: next nodeCoefficient of node iIDP (i) of node i, mem ═ i, data of node ii is 1,2, …, N is the total number of nodes,randomly and equally probabilistically selecting from { +1, -1}, XiThe original data sequence of T instants obtained for node i and having dimensions T x 1,for the time observation matrix, T1 denotes XiDimension after time compression;
step 2: selecting a source node from the initialized nodes in the step 1, and determining that a certain node is a source node if the certain node is greater than a preset probability p;
and step 3: selecting a candidate node k of a next hop of a node i to be broadcasted according to two-hop neighbor information before broadcasting, and storing the candidate node k into a data packet, wherein P (i).
And 4, step 4: after receiving the data packet of the node i, the node j compares the information from the same node in the P (j) and the P (i), if the information is not from the same node in the P (j) and the P (i)The data of node i is merged into node j, and the data packet P (j) for updating node j is:
and 5: step 4, all the nodes which update the data packets judge whether the node is selected by a father node i, if the node is selected, namely P (i), nex is j, the selected node becomes a node to be broadcasted, and the step 3 is repeated until no node needs to be broadcasted;
step 6: after the whole broadcasting process is finished, the sink node collects M data packets from the terminal node of network transmission to form a mapping matrixAnd original data are reconstructed by adopting a compressed sensing decoding method.
As a further technical solution of the present invention, in step 1Randomly and equally probabilistically selecting from { +1, -1 }.
As a further technical scheme of the invention, the design method of the time observation matrix comprises the following steps:
(1) building a database based on historical dataInitializing a time sparse dictionary Ψ representing the sensing data of N nodes in the network at the past T timestAnd space sparse dictionary ΨsAre all overcomplete DCT matrices, then X is represented as:
wherein,the time sparse dictionary is a sparse matrix, and K is the length of the time sparse dictionary;
(2) sparse dictionary Ψ for time using KSVD idea using database XtUpdating to obtain an updated time sparse dictionary Ψt′；
(3) Initializing a time observation matrix to a random gaussian matrix phit 0Let At＝Φt 0Ψt', make an errorMinimum phitI.e. the designed time observation matrix.
As a further technical solution of the present invention, the method for selecting the candidate node k of the next hop in step 3 includes the following two cases:
(1) when the node i is a source node:
k＝argmaxk|Ω(k)\Ω(i)|
s.t.k∈Ω(i)
in the formula, Ω (-) represents a set of neighbor nodes, \ represents a difference set of the two sets, | · | represents the number of elements in the set;
(2) when the node i is an intermediate node:
k＝argmaxk|Ω(k)\Ω(f)|
s.t.k∈Ω(i)\Ω(f)
in the formula, the node f represents a parent node of the intermediate node i.
As a further aspect of the present invention, in step 6X represents the sensing data of N nodes in the network at the past T moments, phisRepresenting a spatial observation matrix.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects: the technical scheme adopted by the invention is a data transmission method based on space-time compression network coding and applied to a wireless sensor network, and the optimal relay node is selected by utilizing two-hop neighbor information in the data transmission process, so that the energy consumption caused by redundant transmission is avoided; by utilizing the KSVD thought, the optimal time observation matrix is obtained by training the time sparse dictionary, so that the data is better sparse, the accuracy of data recovery is improved, and the method has certain practical value.
Drawings
FIG. 1 is a node distribution diagram according to the present invention.
FIG. 2 is a flow chart of the present invention.
Fig. 3 is a sparse transformation of a signal under an original temporal sparse dictionary and a redesigned temporal sparse dictionary, wherein (a) the original temporal sparse dictionary is, and (b) the redesigned temporal sparse dictionary is.
Fig. 4 is a graph comparing the total number of transmissions of the present technology with the prior art method.
Fig. 5 is a graph comparing the total number of receptions of the present technology with a prior method.
Fig. 6 is a diagram comparing the MSE of the present technology to that of the prior art method.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
how to design an energy-efficient data transmission scheme in a wireless sensor network becomes a hot point of research in the field of wireless transmission in recent years. Research shows that the compression sensing technology and the network coding technology can effectively compress the data volume required to be transmitted, reduce the communication times and further improve the energy efficiency of data collection.
The research scene of the invention is based on the wireless sensor network which is researched in a popular large-scale intensive distribution in recent years, the invention utilizes the optimization theory, uses two-hop neighbor information to select the optimal next-hop candidate node, avoids redundant transmission of data, reduces energy consumption, and simultaneously utilizes the KSVD thought to obtain the optimal time observation matrix by training a time sparse dictionary so as to ensure better sparseness of the data and improve the accuracy of data recovery. The innovation points of the invention are as follows: (1) the collection of data is realized by combining compressed sensing and network coding technology; (2) designing a method for selecting an optimal relay node in a data communication process by using two-hop neighbor information; (3) and obtaining an optimal time observation matrix by training a time sparse dictionary. The invention can effectively improve the energy efficiency of data collection of the wireless sensor network and has certain practical application value.
The invention discloses a high-energy-efficiency wireless sensor network data transmission method based on space-time compressed network coding, which is a method combining linear network coding and compressed sensing, and a space observation matrix phi for compressed sensing reconstruction is formed by utilizing the linear network coding in the process of network data transmissionsAnd simultaneously redesigning a time observation matrix phi for compressed sensing reconstruction based on the trained time sparse dictionarytThen, the measuring data with space-time correlation in the network is compressed in space-time by using the time and space observation matrixes, and the measuring data is collected at the convergent nodeAnd recovering the original data by a compressed sensing decoding method.
The method further reduces the data transmission times by using an optimal relay node selection method on the basis of using compressed network coding, and improves the data reconstruction precision by redesigning a time sparse dictionary and a measurement matrix. The method comprises the following steps:
step 1: initializing a data packet of each node in the network, wherein the data packet of each node consists of four parts, namely a next node ID, a coefficient of a current node, the current node ID and data of the current node, and a data packet P (i) of a node i is initialized as follows: next nodeCoefficient of node iIDP (i) of node i, mem ═ i, data of node ii is 1,2, …, N is the total number of nodes,randomly and equally probabilistically selecting from { +1, -1}, XiThe original data sequence of T instants obtained for node i and having dimensions T x 1,for the redesigned time observation matrix, T1 denotes XiDimension after time compression, ΦtThe design method comprises the following steps:
(1) building a database based on historical dataInitializing a time sparse dictionary Ψ representing the sensing data of N nodes in the network at the past T timestAnd space sparse dictionary ΨsAre all overcompleteDCT matrix, then X can be represented as:
wherein,is a sparse matrix;
(2) sparse dictionary Ψ for time using KSVD idea using database XtIs updated, i.e.
In the above formula, #lDenotes ΨtColumn l, θlColumn l, representing Θ, willAll non-zero elements in (A) construct a new matrixWherein N isl0Is composed ofNumber of non-zero elements in (E), then pair El0Rl0＝UΛVTCarrying out SVD (singular value decomposition), solving left and right singular vectors corresponding to the maximum singular value, respectively assigning the left and right singular vectors to corresponding columns of the dictionary and corresponding rows of the sparse matrix, and repeating the process until the initial time sparse dictionary psi is usedtUpdating each column, and then performing iteration by using the updated dictionary so as to obtain a better sparse matrix theta;
(3) initializing a time observation matrix to a random gaussian matrix phit 0Let At＝Φt 0Ψt', make an errorMinimum phitI.e. the designed time observation matrix.
Step 2: and (3) selecting a source node from the initialized nodes in the step (1), and if a certain node is greater than the preset probability p, determining the node as the source node.
And step 3: selection and broadcasting of relay nodes: before broadcasting, any node i to be broadcasted needs to select a candidate node k of the next hop according to the two-hop neighbor information, and stores the candidate node k into a data packet P (i).
Due to the difference of the nodes i, different next hop selection methods are required, namely:
(1) when the node i is the source node,
k＝argmaxk|Ω(k)\Ω(i)|
s.t.k∈Ω(i)
in the formula, Ω (-) represents a set of neighbor nodes, \ represents a difference set of the two sets, | · | represents the number of elements in the set;
(2) when the node i is an intermediate node,
k＝argmaxk|Ω(k)\Ω(f)|
s.t.k∈Ω(i)\Ω(f)
in the formula, the node f represents a parent node of the intermediate node i.
And 4, step 4: the receiving node judges whether to merge the data packet: any node j receives the data packet of the node i, compares whether the information from the same node exists in the node P (j) and the node P (i), and if the information is not the information from the same node, the node P (j) and the node P (i) are connected with each otherThe data of node i is merged into node j, and the data packet P (j) for updating node j is:
and 5: and 4, judging whether the node is selected by the father node i of the node by all the nodes which update the data packets in the step 4, if so, namely P (i) nex is j, enabling the node to be a node to be broadcasted, and repeating the step 3 until no node needs to be broadcasted.
Step 6: after the whole broadcasting process is finished, the sink node collects M data packets from the terminal node of network transmission to form a mapping matrixAnd original data are reconstructed by adopting a compressed sensing decoding method.
Considering a wireless sensor network of a unit area, which includes N sensor nodes randomly and uniformly distributed, as shown in fig. 1, a random observation time slot is T, and a network communication distance is rtFirstly, a time sparse dictionary psi initialized to an overcomplete DCT matrix by using data pairs of N nodes in the network at the past T momentstTraining, and obtaining corresponding time observation matrix by using the trained dictionaryT1 represents the dimension of the original data sequence at T moments acquired by the node after time compression.
When data transmission is carried out, each node broadcasts a hello signal, and any node i learns the neighbor node omega (i). Each node broadcasts a hello signal with its neighbor list again, and each node learns the information of the two-hop neighbor nodes, namely the neighbor nodes of the neighbor nodes.
After the data collection command is obtained, the method of the present invention is used for data transmission, and the data transmission flow is shown in fig. 2. The node marked with the number i senses that the data at T moments are XiThen the original data vector is X ═ X1,X2,…,XN]. After the transmission process is finished, the sink node obtains M packets, which are set as P' (1), …,p ' (M) }, the observed data vector Y is [ P ' (1). dat, …, P ' (M). dat)]While generating spatial observation matrices using packet contentsSpecifically, P ' (M) is the mth packet of the M packets obtained, and the sparse spatial observation matrix Φ is generated by using the elements in P ' (M). mem and P ' (M). coesLine m ofWhere the elements in P' (m) mem correspond to sparse vectorsThe position of the middle non-0 value, and the element in P' (m). coe corresponds to the value of the non-0 value. Finally, theAnd reconstructing an original data vector x by a compressed sensing decoding method.
Simulation result
The performance of the invention is analyzed in conjunction with simulations. In the simulation, a wireless sensor network S of a unit area is considered to be 1 × 1, where the number N of sensor nodes is 300, and an observation time slot T is 30. Communication distance rtThe value range is [0.05, 0.085 ]]And selecting the preset probability p of the source node as 0.18.
FIG. 3 is a transformation of data under an original time sparse dictionary and a transformation under a redesigned time sparse dictionary, wherein (a) the original time sparse dictionary is, and (b) the redesigned time sparse dictionary is. It can be seen that the sparsity of the data is better under the redesigned time sparse dictionary transformation, which will make the reconstructed data more accurate.
Fig. 4 is a graph of total transmit times performance of the present technology and prior art methods, and fig. 5 is a graph of total receive times performance of the present technology and prior art methods. Among them, method 1 is the method proposed by b.gong et al, method 2 is the method proposed by y.zhou et al, and method 3 is the method proposed by c.wang et al. As can be seen from fig. 4 and 5, the inventive technique can reduce the number of communications by about 65% and 70% respectively, and significantly reduce the energy consumption when compared with methods 1 and 2.
FIG. 6 shows a prior art method of the present invention at rtMSE performance plot for 0.05. As can be seen, the MSE of the present technique is always superior to the other three methods as the number of reconstructed packets M increases. This is mainly due to the better sparsity of the data under the redesigned time sparse dictionary. That is to say, the invention greatly reduces the energy consumption in the data transmission process and simultaneously improves the accuracy of data reconstruction.
The invention discloses a high-energy-efficiency wireless sensor network data transmission method based on space-time compression network coding, which mainly solves the problem of high energy consumption of data collection in a wireless sensor network and improves the accuracy of data recovery. The method is based on the combination of network coding and compressed sensing, and selects the best next hop candidate node by using the two-hop neighbor information, thereby avoiding the redundant transmission of data and reducing the energy consumption; meanwhile, by utilizing the KSVD thought, an optimal time observation matrix is obtained by training a time sparse dictionary, so that the data is better sparse, and the accuracy of data recovery is improved. The invention can obviously reduce the data transmission quantity, reduce the energy consumption and improve the data reconstruction precision at the sink node, and is suitable for the large-scale densely distributed wireless sensor network.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the modifications or substitutions within the technical scope of the present invention are included in the scope of the present invention, and therefore, the scope of the present invention should be subject to the protection scope of the claims.
Claims (5)
1. The method for transmitting the data of the high-energy-efficiency wireless sensor network based on the space-time compression network coding is characterized by comprising the following steps of:
step 1: initializing a data packet of each node in the network, wherein the data packet of each node consists of four parts, namely a next node ID, a coefficient of a current node, the current node ID and data of the current node, and a data packet P (i) of a node i is initialized as follows: next nodeCoefficient of node iIDP (i) of node i, mem ═ i, data of node iN is the total number of the nodes,randomly and equally probabilistically selecting from { +1, -1}, XiThe original data sequence of T instants obtained for node i and having dimensions T x 1,for the time observation matrix, T1 denotes XiDimension after time compression;
step 2: selecting a source node from the initialized nodes in the step 1, and determining that a certain node is a source node if the certain node is greater than a preset probability p;
and step 3: selecting a candidate node k of a next hop of a node i to be broadcasted according to two-hop neighbor information before broadcasting, and storing the candidate node k into a data packet, wherein P (i).
And 4, step 4: after receiving the data packet of the node i, the node j compares the information from the same node in the P (j) and the P (i), if the information is not from the same node in the P (j) and the P (i)The data of node i is merged into node j, and the data packet P (j) for updating node j is:
and 5: step 4, all the nodes which update the data packets judge whether the node is selected by a father node i, if the node is selected, namely P (i), nex is j, the selected node becomes a node to be broadcasted, and the step 3 is repeated until no node needs to be broadcasted;
step 6: the whole broadcast isAfter the end of the process, the sink node collects M data packets from the terminal node of the network transmission to form a mapping matrixAnd original data are reconstructed by adopting a compressed sensing decoding method.
2. The method for transmitting data of energy-efficient wireless sensor network based on space-time compression network coding according to claim 1, wherein in step 1, the data is transmittedRandomly and equally probabilistically selecting from { +1, -1 }.
3. The method for transmitting the data of the energy-efficient wireless sensor network based on the space-time compression network coding according to claim 1, wherein the method for designing the time observation matrix comprises the following steps:
(1) building a database based on historical dataInitializing a time sparse dictionary Ψ representing the sensing data of N nodes in the network at the past T timestAnd space sparse dictionary ΨsAre all overcomplete DCT matrices, then X is represented as:
wherein,the time sparse dictionary is a sparse matrix, and K is the length of the time sparse dictionary;
(2) sparse dictionary Ψ for time using KSVD idea using database XtUpdating to obtain an updated time sparse dictionary Ψt′；
(3) Time observation matrix is firstInitialisation to a random gaussian matrix phit 0Let At＝Φt 0Ψt', make an errorMinimum phitI.e. the designed time observation matrix.
4. The method for transmitting data of an energy-efficient wireless sensor network based on space-time compression network coding according to claim 1, wherein the method for selecting the candidate node k of the next hop in step 3 includes the following two cases:
(1) when the node i is a source node:
k＝argmaxk|Ω(k)\Ω(i)|
s.t.k∈Ω(i)
in the formula, Ω (-) represents a set of neighbor nodes, \ represents a difference set of the two sets, | · | represents the number of elements in the set;
(2) when the node i is an intermediate node:
k＝argmaxk|Ω(k)\Ω(f)|
s.t.k∈Ω(i)\Ω(f)
in the formula, the node f represents a parent node of the intermediate node i.
5. The method for transmitting data of energy-efficient wireless sensor network based on space-time compression network coding according to claim 1, wherein in step 6X represents the sensing data of N nodes in the network at the past T moments, phisRepresenting a spatial observation matrix.
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Cited By (4)
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| CN110012488A (en) * | 2019-05-10 | 2019-07-12 | 淮阴工学院 | A kind of compressed data collection method of mobile wireless sensor network |
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