CN104244358A - Energy-saving routing strategy of wireless sensing network based on distributed compressed sensing (DCS) - Google Patents

Abstract

The invention relates to an energy-saving routing strategy of a wireless sensing network based on distributed compressed sensing (ESRS). The energy-saving routing strategy includes the steps that nodes of the wireless sensing network are clustered, cluster head nodes are selected, data information of the cluster head nodes in a WSM is compressed, the data information is transmitted to a base station through a wireless transmission protocol, and an original data sequence is reconstructed in a reconstruction method. Data processing complexity of the nodes can be effectively reduced in the small-scale wireless sensing network, energy consumption of the network nodes is balanced and the life cycle of the network is prolonged.

Description

A kind of wireless sense network Energy Saving Based Routing Strategy based on DCS

Technical field

The present invention relates to a kind of radio sensing network Energy Saving Based Routing Strategy, particularly relate to a kind of radio sensing network Energy Saving Based Routing Strategy based on distributed compression perception, belong to technology of wireless sensing network field.

Background technology

Nearly ten years, the focus of research is progressively become in the application of wireless sensor network in precision agriculture.In agricultural environment, often sensor node quantity is many, and distribution density is high, and the too early death of node can cause network failure, shortens network life.Therefore, how the energy ezpenditure of balanced node is the research emphasis of wireless sensor network in agricultural application to extend the life span of network.And in the network environment that sensor node is densely distributed, particularly for large-scale wireless sensing network, node is collected the data that monitor and is sent to base station.Have a large amount of transfer of data in network, large-scale network needs internodal mutual forwarding simultaneously.The demand of network to bandwidth sum energy is huge, so we should reduce the amount of communications in network as much as possible, carries out corresponding compression to the data of signal, thus the load of the reduction network of maximum possible.

Data for transmission signal need the problem of compression, and DCS (Distributed Compressed Sensing, DCS) concept is suggested to be applied in wireless sense network carries out compression sampling to ensemble.The basic thought of DCS is: if having several signal all can under a certain sparse base rarefaction representation, and it is interrelated between these signals, each signal incoherent base of this sparse base just can compress the data gathered by sensor node, decoding end is forwarded to after coding, under signal meets certain joint sparse model case, the measured value containing low volume data after decoding end utilizes coding recovers each the original signal in WSN.

Existing have following several about the research of compressed sensing in wireless sense network:

Self adaptation Bayes compression sensing method, Bayesian learning framework is used to rebuild initial observation vector, the posterior probability density about primary signal of recycling gained solves the optimum projection vector next time projected, and can both collect maximum information to make to project each time.Adopt adaptive projection vector to reduce reconstruction error, but the projection vector of this adaptive approach is intensive, and does not consider routing issue.

Based on Bayes's compressed sensing method for reconstructing, the method, to maximize obtained information quantity under specific energy consumption for target, proposes a kind of greedy method solving sparse projection vector.But owing to being greedy, the complexity of method is very large; And the establishing target of this projection vector easily only selects a hop neighbor node of Sink as projection objects, reduces the quality of reconstruction.

Based on the integrated processes of LEACH method and Bayes's compressed sensing, achieve effective combination of LEACH and Bayes CS, realize the detection to wireless sensor network target source.Wherein, LEACH method is carried out sub-clustering to wireless sensor network node and is selected leader cluster node, concentrates on leader cluster node by the information of bunch interior nodes, simultaneously only leader cluster node to base-station node transmission of information.Original signal is reconstructed in the data that base-station node receives.But weak point is the program, and what adopt is LEACH cluster-dividing method, and the cluster-leader selected of LEACH method is too random, can not take into account the equilibrium of node energy consumption, need to improve.

Summary of the invention

Technical problem: the object of the invention is, a kind of wireless sense network Energy Saving Based Routing Strategy based on DCS (An Energy-Saving Routing Strategy Based On Distributed Compressed Sensing is proposed, ESRS), the data message of the leader cluster node in WSN is compressed, reduce energy ezpenditure, extend network life cycle.

Technical scheme: method of the present invention is a kind of method of tactic, comprises the steps:

Step one, selected transducer energy consumption model, be wireless intensive sensing network based on selected application scenarios, select free space model.

According to the environment scene used, selected transducer energy consumption model is free space model, is calculated as follows the energy consumption of transmission k-bit data:

$\begin{array}{c}{E}_{\mathrm{Tx}}(k,d)=E_{\mathrm{Tx}-\mathrm{elc}}\left(k\right)+E_{\mathrm{Tx}-\mathrm{mp}}(k,d)\\ =\left\{\begin{array}{c}{\mathrm{kE}}_{\mathrm{elec}}+k{\mathrm{\&epsiv;}}_{\mathrm{fs}}{d}^2,d<d_0\\ {\mathrm{kE}}_{\mathrm{elec}}+{\mathrm{k\&epsiv;}}_{\mathrm{mp}}{d}^4,d\&GreaterEqual;d_0\end{array}\right.\end{array}$

E _Rx(k,d)＝E _Rx-elec(k)＝kE _elec

Wherein, node sends the energy consumption E of data _txcomprise radiating circuit loss and power amplification loss two parts, node receives the energy consumption E of data _rxfor receiving circuit loss; Suppose that the energy consumption of transmission circuit or receiving circuit is E _elec=50nJ/bit; As transmission range d<d ₀with d>=d ₀time, the coefficient of energy dissipation of transmission amplifying circuit is respectively ε _fs=10pJ/bit/m ²and ε _mp=0.0013pJ/bit/m ⁴, critical distance

Step 2, sensor node carry out Data Collection, and adopt EBCR routing policy to carry out the selection of bunch head, after cluster, member node will collect data and send to leader cluster node.

According to the principle that whole network energy consumption is minimum, be calculated as follows more excellent bunch of head number K _opt:

$K_{\mathrm{opt}}=\frac{R}{(D_{\max }-D_{\min })}\sqrt{\frac{2N}{\mathrm{\&pi;}}}$

Wherein, R is zone radius, and N is network node sum, D _maxand D _minthe nodes distance minimum and maximum to base station respectively.

Step 3, selected orthogonal basis are sparse base ψ, at leader cluster node place to signal x _i, i=1,2 ... K carries out joint sparse conversion, and the joint sparse of trying to achieve signal represents W.

The sparse signal model that the present invention chooses is JSM-1 joint sparse model.In JSM-1 joint sparse model, each signal comprises two parts: the common sparse part that all signals have and the only self-contained unique portion of each signal, that is:

x _j＝z _c+z _j,j∈{1,2,…J}

And the common ground of signal and unique portion can carry out sparse expression on certain base, that is:

z _c＝Ψθ _c,||θ _c|| ₀＝K _c

And

z _j＝Ψθ _j,||θ _j|| ₀＝K _j

Wherein, signal z _call x _jpublic part and degree of rarefication on sparse base Ψ is K _c, signal z _jthen each x _junique portion and degree of rarefication on same sparse base is K _j.

Step 4, structure weight matrix Φ are calculation matrix, obtain measured value matrix Y=[y _c, y ₁, y ₂... y _j].

Data after compression are sent to base station by step 5, employing EBCR routing policy.

Step 6, base-station node reconstruct the signal vector of each node, adopt synchronous orthogonal matching pursuit method (Simultaneous Orthogonal Matching Pursuit, SOMP) to try to achieve the approximation of θ after, thus recover primary signal , realize the Accurate Reconstruction of joint sparse signal.

Accompanying drawing explanation

Fig. 1 is the flow chart of routing policy ESRS of the present invention.

Fig. 2 is the Network Survivability number of nodes trend graph of the present invention and existing route strategy.

Fig. 3 is the comparison diagram of the network of network dump energy of the present invention and existing route strategy.

Fig. 4 is the comparison diagram of the residue energy of node standard deviation of the present invention and existing route strategy.

Embodiment

Be described principle of the present invention and feature below in conjunction with accompanying drawing, example, only for explaining the present invention, is not intended to limit scope of the present invention.

Be the process of the Energy Saving Based Routing Strategy based on DCS of the present invention shown in Fig. 1, sensor node carries out the selection of bunch head according to EBCR routing policy, and after cluster, member node will collect data and send to leader cluster node.

Have K node in supposing in network bunch, each node carries out Data Collection, and the signal vector length of each node is N, and after sub-clustering, collection data are sent to leader cluster node by member node, and the data of leader cluster node just can be expressed as the matrix of a N × K:

$\begin{array}{c}D=\left[\begin{array}{cccc}{x}_1& x_2& ...& x_k\end{array}\right]\\ =\left[\begin{array}{cccc}{x}_{11}& x_{12}& ...& x_{1k}\\ x_{21}& x_{22}& ...& x_{2k}\\ .& .& .& \\ .& .& .& \\ .& .& .& \\ x_{j1}& x_{j2}& ...& x_{\mathrm{jk}}\\ x_{N1}& x_{N2}& ...& x_{\mathrm{Nk}}\end{array}\right]\end{array}$

X _i, i=1,2 ... K is each node data.Every a line x of matrix D ^j, j=1,2 ... N and each row x ⁱ, j=1,2 ... the signal vector obtained after what K represented respectively is in network data and single-sensor node sample that all sensors node samples simultaneously to be obtained.

As Fig. 1, the idiographic flow of strategy of the present invention is:

Step one: sensor node carries out Data Collection, adopt EBCR routing policy formula to carry out the selection of bunch head, after cluster, member node will collect data and send to leader cluster node.

Step 2: selected orthogonal basis is sparse base ψ, at leader cluster node place to signal x _i, i=1,2 ... K carries out joint sparse conversion, and the joint sparse of trying to achieve signal represents W.

Step 3: building weight matrix Φ is calculation matrix, obtains measured value matrix Y=[y _c, y ₁, y ₂... y _j]

Step 4: adopt EBCR routing policy that the data after compression are sent to base station.

Step 5: base-station node reconstructs the signal vector of each node, adopts synchronous orthogonal matching pursuit method SOMP to try to achieve the approximation of θ after, thus recover primary signal .

Beneficial effect of the present invention is: for the feature that wireless sensor network node finite energy, hardware resource are limited, on the basis of EBCR routing policy, introduce distributed compression cognition technology, devise a kind of wireless sense network Energy Saving Based Routing Strategy ESRS based on DCS.This strategy combines distributed compression cognition technology, is carried out compressing to reduce volume of transmitted data by the data message of leader cluster node in WSN, through wireless transmission protocol by data information transfer to base station, then reconstruct former data sequence by reconstructing method.

By emulating ESRS routing policy, compare The number of nodes alive (network life), Remaining energy in network (residue of network organization energy) and Standard deviation of residual energy (residue energy of node standard deviation) and LEACH, HEED, EEUC, EBCR have carried out emulation experiment.

As seen from Figure 2, owing to failing to consider the energy ezpenditure of nodes, the surviving node number of LEACH, HEED is taken turns left and right 200 and is just started rapid drawdown; Under comparing, EEUC take into account the dump energy of cluster head, surviving node number in network obviously increases, and EBCR and ESRS has just taken into account the distance of the dump energy of node, the energy consumption speed of node and node and base station when cluster-leader selected, the effective equilibrium energy ezpenditure of network, network energy be uniformly distributed and be efficiently utilized, thus extending the time-to-live of node and whole network; Can apparently higher than EBCR and EEUC at the Network Survivability nodes of network operation ESRS scheme in early stage, this is because context of methods has carried out compressing process to the data of node, just can well equalizing network energy consumption in the early stage of the network operation, thus have in early stage and have more surviving node than EEUC, EBCR.

Fig. 3 display be the residue of network organization energy percentage of LEACH, HEED, EBCR, ESRS.As seen from the figure, dump energy in the whole network of ESRS has the raising of certain amplitude, reason combines compress technique, although node computation complexity can be made to increase, but greatly reduce and need data volume to be processed, save the consumption of node energy generally, thus more dump energy can be had in whole network, and then improve the network performance of WSN.

Fig. 4 compared for the residue energy of node standard deviation of HEED, EEUC, EBCR, ESRS, and as can be seen from the figure, along with the increase of the wheel number of the network operation, the dump energy difference of each node increases thereupon.Wherein the residue energy of node standard deviation of EEUC, EBCR and ESRS is less, the residue energy of node standard deviation of HEED is maximum, HEED is not owing to considering the energy of the whole network, leader cluster node is compared with the more energy of other node consumption, along with increasing of death nodes, dump energy standard deviation increases, and node energy skewness weighs.EBCR, when bunch head is selected, well take into account the balancing energy of network, so nodes dump energy standard deviation is less.But the data of ESRS to leader cluster node are compressed, and greatly reduce the data volume in network, the dump energy standard deviation of node is less, and the effect of ESRS balancing energy is further enhanced, thus has more surviving node in the later stage.

Simulation result shows at small-scale radio sensing network, and the energy ezpenditure in ESRS routing policy is more balanced than EBCR network, extends the life cycle of network, have more surviving node in network, improve the network performance of wireless sense network.

Claims (4)

1., based on a wireless sense network Energy Saving Based Routing Strategy of DCS, it is characterized in that comprising the steps:

Step one, selected transducer energy consumption model;

Step 2, sensor node carry out Data Collection, and adopt EBCR routing policy to carry out the selection of bunch head, after cluster, member node will collect data and send to leader cluster node;

Step 3, selected orthogonal basis are sparse base ψ, at leader cluster node place to signal x _i, i=1,2 ... K carries out joint sparse conversion, and the joint sparse of trying to achieve signal represents W;

Step 4, structure weight matrix Φ are calculation matrix, obtain measured value matrix Y=[y _c, y ₁, y ₂... y _j];

Data after compression are sent to base station by step 5, employing EBCR routing policy;

Step 6, base-station node reconstruct the signal vector of each node, adopt synchronous orthogonal matching pursuit method SOMP to try to achieve the approximation of θ after, thus recover primary signal .

2. the Energy Saving Based Routing Strategy of a kind of wireless sense network based on DCS according to claim 1, is characterized in that, in described step 2, according to the principle that whole network energy consumption is minimum, is calculated as follows more excellent bunch of head number K _opt:

$K_{\mathrm{opt}}=\frac{R}{(D_{\max }-D_{\min })}\sqrt{\frac{2N}{\mathrm{\&pi;}}}$

Wherein, R is zone radius, and N is network node sum, D _maxand D _minthe nodes distance minimum and maximum to base station respectively.

3. a kind of wireless sense network Energy Saving Based Routing Strategy based on DCS according to claim 1 and 2, is further characterized in that, in described step 3 and step 4, the sparse signal model chosen is JSM-1 joint sparse model, at leader cluster node place to signal x _i, i=1,2 ... K carries out joint sparse conversion, and the joint sparse of trying to achieve signal represents W, and building weight matrix Φ is calculation matrix, obtains measured value matrix Y=[y _c, y ₁, y ₂... y _j].

4. according to the Energy Saving Based Routing Strategy of a kind of wireless sense network based on DCS described in claim 3, be further characterized in that, adopt synchronous orthogonal matching pursuit method at base-station node, realize the Accurate Reconstruction of joint sparse signal.