CN103796273A - Energy-balanced clustering routing strategy for wireless sensor networks - Google Patents

Abstract

The invention relates to an EBCR. The EBCR comprises the following steps that residual energy, energy consumption speed and the distance between a node and a base station serve as parameters for selecting cluster heads, and the selecting process of the cluster heads is improved, so that more clusters are distributed near the base station, the area of the corresponding clusters is small, clusters with larger area are generated at the positions away from the base station, and effective balance of distribution of network energy and cluster heads is achieved. By means of the method, in a small scale wireless sensor network, energy consumption between the cluster heads can be effectively reduced, network energy consumption distribution is balanced, and the network life cycle is effectively prolonged.

Description

A kind of radio sensing network clustering routing strategy based on balancing energy

Technical field

The present invention relates to a kind of radio sensing network clustering routing strategy, relate in particular to a kind of radio sensing network clustering routing strategy based on balancing energy, belong to technology of wireless sensing network field.

Background technology

Nearly ten years, at wireless sensor network, the application in precision agriculture progressively becomes the focus of research.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, has shortened network life.Therefore the life span that, how energy consumption of balanced node extends network is the research emphasis of wireless sensor network in agricultural application.Due to the quantitative limitation of sensor node energy, its calculating, storage and communication capacity are all very limited, and each node can only get the information of localized network, thereby the networking communication protocol of moving on node can not be too complicated.

Consume inhomogeneous problem for node energy, existing many procotols have all proposed solution.Wherein LEACH, PEGASIS, HEED,, comparatively typical case of EEUC.

LEACH: its core concept is to allow each node elected cluster head in turn, thereby makes the energy in network run out possibility evenly.But LEACH still has weak point, such as cluster head node, not necessarily node, the cluster head node of dump energy maximum are spatially difficult to be uniformly distributed the energy that causes with single-hop transmission and consume inhomogeneous and be unfavorable for the expansion etc. of network.

PEGASIS:PEGASIS adopts chain structure transmission of data packets, and carries out data gathering to reduce energy consumption, but accumulation point is still random selection, cannot guarantee network energy consumption balance.

The cluster head election strategy of HEED:HEED has been considered the dump energy of node, but does not consider network in general structure, easily causes cluster head energy consumption inequality.

The reference factor that EEUC:EEUC algorithm is elected the distance of residue energy of node and nodal point separation base station as cluster head, and setpoint distance threshold value, distinguish communication mode between the two according to the distance of node and base station, cluster head number is more stable than LEACH algorithm and HEED algorithm, energy consumption is more even, and network lifecycle is longer.But EEUC algorithm relates to 4 parameters, need to manually choose, and practical operation is got up more difficult.

Summary of the invention

The object of the invention is, propose a kind of radio sensing network clustering routing strategy based on balancing energy (Energy ?BalancedClusteringRoutingStrategyforWirelessSensorNetwor ks, EBCR), can equalizing network energy distribution, extend network life cycle.

The technical scheme that the present invention solves the problems of the technologies described above is as follows: a kind of radio sensing network clustering routing strategy based on balancing energy, comprises the steps:

Step 1, selected transducer energy consumption model, be wireless intensive sensing network based on selected application scenarios, selects free space model;

Step 2, with reference to energy consumption model, calculate the energy consumption of cluster head node and non-cluster head node, ask for preferably cluster head number of network according to the principle of whole network energy consumption minimum;

Step 3, take this cluster head number as reference, calculate preferably cluster head ratio, in the improved threshold value formula of substitution;

Step 4, each node are preserved three energy informations: node current remaining E _current, the primary power E of node _max, the dump energy E of last round of beginning _last; The speed of dump energy and energy consumption is taken into account, is improved the threshold value formula T (n) that cluster head is selected, determine the high and energy consumption of dump energy slow between a proportion α preferably;

Step 5, emulation experiment are determined after the proportion of the high and energy consumption of dump energy between slow, by the distance factor of nodal point separation base station

join in improved threshold value formula T (n), realize the efficient balance that network energy and cluster head distribute.

The present invention proposes a kind of radio sensing network clustering routing strategy based on balancing energy.The method that the application of the invention proposes, can equalizing network energy distribution, extends network life cycle.

Accompanying drawing explanation

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

Fig. 2 is the comparison diagram of the residue of network organization energy percentage of the present invention and existing Stratified Strategy.

Fig. 3 is the comparison diagram of the Network Survivability number of nodes of the present invention and existing Stratified Strategy.

Fig. 4 is the comparison diagram of the cluster head number of nodes of the present invention and existing Stratified Strategy.

Fig. 5 is the comparison diagram of the residue energy of node standard deviation of the present invention and existing Stratified Strategy.

Fig. 6 is the every comparison diagram that sends data volume of taking turns of the node of the present invention and existing Stratified Strategy.

Embodiment

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

Shown in Fig. 1, be the process of the routing policy based on balancing energy of the present invention, networking initialization is, base station is fixed, and in network, all sensor nodes are all that same kind and primary power equate.Node has enough computing capabilitys, while coming into operation for the first time after network design, use a larger transmitted power to signal of all node broadcasts in network by base station, each sensor node receiving after this signal, can calculate according to the intensity that receives signal the approximate distance of it and base station.

Provide illustrating of several what's news below.

More excellent cluster head number K _opt:

$K_{\mathrm{opt}}=\frac{R}{(D_{\max }-D_{\min })}\sqrt{\frac{2N}{\mathrm{\π}}}---\left(1\right)$

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

Improve the threshold value formula T (n) that cluster head is selected: using the distance of residue energy of node, energy consumption speed and node and base station as the parameter of choosing cluster head, improve the selection course of cluster head, the account form of T (n) is:

$\begin{array}{c}T\left(n\right)=\left(\begin{array}{cc}\frac{p_{\mathrm{opt}}}{1-p_{\mathrm{opt}}\left[r\mathrm{mod}(1/p_{\mathrm{opt}})\right]}[\mathrm{\β}\·(\mathrm{\α}\·\frac{E_{\mathrm{currernt}}}{E_{\max }}+(1-\mathrm{\α})\left(1\frac{E_{\mathrm{last}}-E_{\mathrm{current}}}{E_{\max }}\right))+(1-\mathrm{\β})\left(\frac{D_{\max }-d}{D_{\max }-D_{\min }}\right)]& \mathrm{if}(n\∈G)\\ 0& \mathrm{else}\end{array},\right)\\ a\∈\left[\mathrm{0,1}\right]\end{array}---\left(2\right)$

Wherein, P _optbe the percentage that more excellent cluster head number accounts for all nodes, r is the number of times looping at present, E _currentnode current remaining, E _maxthe primary power of node, E _lastthe dump energy of last round of beginning, D _maxand D _minbe respectively the nodes distance minimum and maximum to base station, d is the distance of node to base station, α be the high and energy consumption of dump energy slow between a proportion preferably, a ∈ [0,1], β is proportion between capacity factor and distance factor, β ∈ [0,1].

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

1, selected transducer energy consumption model;

2, ask for preferably cluster head number of network according to the principle of whole network energy consumption minimum;

3,, take this cluster head number as reference, calculate preferably cluster head ratio;

4, the speed of dump energy and energy consumption is taken into account, is improved the threshold value formula T (n) that cluster head is selected, determine the high and energy consumption of dump energy slow between a proportion α preferably;

5, emulation experiment is determined after the proportion of the high and energy consumption of dump energy between slow, and the distance factor of nodal point separation base station is joined in improved threshold value formula T (n);

6, node is all dead, and overall process finishes.

It is below a concrete case study on implementation of the present invention.With reference to Fig. 5, the course of work is as follows:

1, base station calculates in network a preferably cluster head number according to formula (1);

2,, take this cluster head number as reference, calculate preferably cluster head ratio;

3, by dump energy E _currentspeed with energy consumption

apart from the factor

take into account, improve the threshold value formula T (n) that cluster head is selected, cluster head is selected to start;

4, bunch establishment stage, after selecting bunch head according to election of cluster head algorithm recited above, once node determined after bunch identity of oneself, just start to set up oneself bunch.Whether elected node will be the elected information broadcasting of oneself to all nodes in network, and node judges a bunch head after receiving broadcast message, to determine to add bunch.

5, stablize data transfer phase, leader cluster node calculates the timetable for time division multiplexing (TDMA) according to bunch member's number, and is broadcast to a bunch interior nodes, has started data transmission.

6, in each is taken turns, leader cluster node carries out data fusion to it collect the data that in all bunches, member sends in each frame after, with filtering garbage, and then sends to base station.When the data of all frames of taking turns are all after end of transmission, base station just sends synchronizing signals to all nodes after determining and oneself having received the data that each leader cluster node sends, and starts new one and takes turns communication.

7 until all node death, and overall process finishes.

The foregoing is only preferred embodiment of the present invention, in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.

Beneficial effect of the present invention is: can reduce energy consumption between cluster head, balanced network energy distribution, has effectively extended network life cycle.By use EBCR (Energy ?BalancedClusteringRouting StrategyforWirelessSensorNetworks, wireless sense network clustering routing strategy based on balancing energy) routing policy carries out emulation, relatively NetworkLifetime(network life), Numberofclusterhead(cluster head number of nodes) and Standarddeviationofresidualenergy(residue energy of node standard deviation), the every data volume of taking turns transmission of Amountofdataperround() and LEACH, HEED, EEUC carried out emulation experiment.

First determine proportion a.As seen from Figure 2, LEACH agreement is owing to not taking into full account the dump energy of cluster head node, and its energy consumes too fast; HEED, due to residue energy of node is taken into account, has reduced network energy consumption to a certain extent.But owing to not taking into full account network in general structure, in the steep increasing that has moved 650 energy consumptions of network while taking turns left and right, in the time of a=0.7, energy consumption is mild to some extent; In the time of a=0.9, after operation a period of time, also there is the increase of certain amplitude in network energy consumption.Consider a value 0.8.

As seen from Figure 3, EBCR 550 take turns before the survival nodes of network can be apparently higher than EEUC, this is because EBCR just takes distance to cause into account with capacity factor when cluster head is chosen, just can well equalizing network energy consumption in early stage of the network operation, thus more survival node had in early stage.

What Fig. 4 showed is the cluster head quantity that LEACH, HEED, EEUC, EBCR produce in 100round.As seen from the figure, because LEACH chooses a bunch head at random, so its produce a bunch number change amplitude very large, the LEACH that compares, HEED, EEUC, the cluster head quantity of EBCR is more stable, EEUC in choosing bunch head, reckon without cluster head compared with ratio of greater inequality example.

As shown in Figure 5, along with the increase of the wheel number moving, the dump energy difference of each node increases thereupon, and this is because cluster head node causes compared with the more energy of other node consumption.LEACH chooses cluster head at random, so caused the unbalanced of energy consumption, the cluster head of HEED is selected still with certain randomness, the phenomenon of energy consumption inequality still there will be, EEUC is by Uneven Cluster strategy, must be less near bunch scale of base station, in its bunch, member node number can be less, node from base station away from more forms larger bunch, therefore, its energy scale is poor relatively little, and the angle that the present invention selects from cluster head goes to realize this function, has also reached the poor relatively little result of energy scale.

As can be seen from Figure 6, HEED, EEUC, the every data volume of taking turns transmission of EBCR are in earlier stage more stable, and LEACH changes just very greatly, this is because LEACH does not consider energy problem on bunch head is selected, and EBCR and EEUC can maintain amount of communication data stable of long period.Strategy of the present invention is guaranteeing that on the basis of balancing energy, the reliability at networking is also better.

Simulation result shows at small-scale radio sensing network, uses this strategy can reduce energy consumption between cluster head, effectively extends network life cycle, balanced network energy distribution.

Claims (5)

1. the radio sensing network clustering routing strategy based on balancing energy, is characterized in that comprising the steps:

Step 1, selected transducer energy consumption model;

Step 2, ask for preferably cluster head number of network according to the principle of whole network energy consumption minimum;

Step 3, take this cluster head number as reference, calculate preferably cluster head ratio;

Step 4, dump energy and energy consumption speed are taken into account, are improved the threshold value formula T (n) that cluster head is selected, determine the high and energy consumption of dump energy slow between a proportion α preferably;

Step 5, emulation experiment are determined after the proportion between the high and energy consumption speed of dump energy, the distance parameter between node and base station are joined in improved threshold value formula T (n).

2. a kind of radio sensing network clustering routing strategy based on balancing energy according to claim 1, is characterized in that, in described step 1, selected transducer energy consumption model is free space model, is calculated as follows the energy consumption of transmission k-bit data:

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

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; The energy consumption of supposing transmission circuit or receiving circuit is E _elec=50nJ/bit; As transmission range d<d ₀and 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

Be further characterized in that, in described step 2, according to the principle of whole network energy consumption minimum, be calculated as follows more excellent cluster 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 _minrespectively the nodes distance minimum and maximum to base station.

3. a kind of radio sensing network clustering routing strategy based on balancing energy according to claim 2, is characterized in that, in described step 3, is calculated as follows preferably cluster head ratio P _opt:

$p_{\mathrm{opt}}=\frac{K_{\mathrm{opt}}}{N}$

Wherein, K _optfor more excellent cluster head number, N is network node sum.

4. a kind of radio sensing network clustering routing strategy based on balancing energy according to claim 3, is characterized in that, dump energy and energy consumption speed are taken into account, and the threshold value formula T (n) that improves cluster head selection is as follows:

$\begin{array}{c}T\left(n\right)=\left(\begin{array}{cc}\frac{p_{\mathrm{opt}}}{1-p_{\mathrm{opt}}\left[r\mathrm{mod}(1/p_{\mathrm{opt}})\right]}[\mathrm{\&alpha;}\&CenterDot;\frac{E_{\mathrm{currernt}}}{E_{\max }}+(1-\mathrm{\&alpha;})(1-\frac{E_{\mathrm{last}}-E_{\mathrm{current}}}{E_{\max }})]& \mathrm{if}(n\&Element;G)\\ 0& \mathrm{otherwise}\end{array}\right)\\ a\&Element;\left[\mathrm{0,1}\right]\end{array}$

Wherein, each node is preserved three energy informations: node current remaining E _current, the primary power E of node _max, the dump energy E of last round of beginning _last; α be the high and energy consumption of dump energy slow between a proportion preferably, a ∈ [0,1].

5. a kind of radio sensing network clustering routing strategy based on balancing energy according to claim 4, is characterized in that: will be apart from the factor

in the threshold value formula T (n) that considers to select in cluster head, when guaranteeing capacity factor, increase the possibility that becomes cluster head near base station, make to increase near the cluster head of base station, improved threshold value formula T (n) is as follows:

$\begin{array}{c}T\left(n\right)=\left(\begin{array}{cc}\frac{p_{\mathrm{opt}}}{1-p_{\mathrm{opt}}\left[r\mathrm{mod}(1/p_{\mathrm{opt}})\right]}[\mathrm{\&beta;}\&CenterDot;(\mathrm{\&alpha;}\&CenterDot;\frac{E_{\mathrm{currernt}}}{E_{\max }}+(1-\mathrm{\&alpha;})(1-\frac{E_{\mathrm{current}}}{E_{\max }}))+(1-\mathrm{\&beta;})\left(\frac{D_{\max }-d}{D_{\max }-D_{\min }}\right)]& \mathrm{if}(n\&Element;G)\\ 0& \mathrm{otherwise}\end{array}\right)\\ a\&Element;\left[\mathrm{0,1}\right]\end{array}$

Wherein, d is the distance of node to base station, gets a proportion β, β ∈ [0,1] between capacity factor and distance factor.

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