CN105636081A - Improved EAST (Efficient Data Collection Aware of Spatio-Temporal Correlation) algorithm based on grey model prediction - Google Patents

Abstract

The invention discloses an improved EAST (Efficient Data Collection Aware of Spatio-Temporal Correlation) algorithm based on grey model prediction, belonging to the technical field of wireless sensor networks. The algorithm comprises the following steps of step 1 establishing a temporal correlation model based on a grey model; step 2 establishing a spatial correlation model; and step 3 carrying out data transmission. The algorithm inherits a frame structure of the EAST algorithm, and meanwhile, the precision of the original algorithm is improved by utilizing the grey model and the system energy efficiency is improved by utilizing a priority selection model. By fully utilizing environmental data collected by nodes to carry out modelling through the grey model, when an exceptional event is detected, a grid-based cluster is established, the residual energy and distance factors are comprehensively considered from selection of a cluster head node and a leader, and lastly, data is transmitted by utilizing a theoretically optimum path. In comparison with the EAST algorithm, the provided GM-EAST algorithm has remarkable improvements in the aspects of data precision and energy efficiency.

Description

EAST innovatory algorithm based on Grey Model

Technical field

The present invention relates to the EAST innovatory algorithm based on Grey Model, belong to technology of wireless sensing network field.

Background technology

Wireless sense network is the network being made up of many wireless sensor nodes. The fields such as owing to sensor node is relatively inexpensive, and wireless communication technology is increasingly mature, and wireless sense network has had been applied in various actual scene, such as, military, medical treatment, scientific research. Its Main Function is to collect ambient data, is analyzed the data collected processing, thus the various changes occurred in response environment. Owing to sensor is usually powered with battery, and scene residing for them is generally of particularity and complexity, manually changes battery and be nearly impossible, so how save the energy content of battery, to extend Network morals, it is always up one popular research field of wireless sense network.

In order to save energy, extending Network morals, scholars go out to send solution problem from multiple angles. The data of all nodes, if any people's research and utilization cluster algorithm, are transferred to upper layer node with the thought of Delamination Transmission, until being transferred to sink node by ratio. Data transfer path is studied by some people, and data are transmitted by the path selecting consumed energy minimum. Some people utilizes the thought of data prediction, makes sink node and ordinary node run prediction algorithm simultaneously, within tolerable accuracy rating, prediction data is considered as the data collected.

Constantly there is scholar that the spatial coherence between temporal correlation and the node of the data that node is collected is studied in recent years, propose various algorithm, such as EEDC (Energy-EfficientDataCollection), SCCS (SpatiotemporalClusteringandCompressingSchemes), EAST (EfficientDataCollectionAwareofSpatio-TemporalCorrelation) scheduling algorithm, although they have certain effect to saving energy, but these algorithms have introduced again the delay of data transmission, the problem that the reduction of data precision etc. are new, thus have impact on the overall performance of network. one of representative as temporal correlation, EAST algorithm can effectively collect environment data in real time, but no matter precision or energy consumption aspect, and the method all has greatly improved space.

Summary of the invention

The present invention is directed to problem above, and develop the EAST based on Grey Model and improve (GM_EAST) algorithm. This algorithm inherits the frame structure of EAST algorithm, utilizes gray model to improve the precision of former algorithm simultaneously, utilizes priority voter model to improve decorum energy efficiency.

The present invention comprises the steps:

The first step: set up the temporal correlation model based on gray model,

Second step: spatial coherence model is set up,

3rd step: data are transmitted.

The principle of the invention and beneficial effect: labor is based on the pluses and minuses of the EAST algorithm of temporal correlation, and for deficiency in collecting data precision and energy consumption of EAST algorithm, it is proposed that innovatory algorithm. Make full use of, by gray model, the environmental data that node collects to be modeled, when detecting that anomalous event occurs, set up based on grid bunch, choosing of leader cluster node and leader, consider dump energy and distance factor, finally utilize theoretical optimal path transmission data. Compared to EAST algorithm, it is proposed to GM_EAST algorithm all increase significantly in data precision and energy efficiency.

Accompanying drawing explanation

Fig. 1 spatial coherence legend.

Fig. 2 mean square error comparison diagram. Make threshold epsilon value 0.1 to 1, and compare EAST algorithm and the square mean error amount of innovatory algorithm GM_EAST at each occurrence. The experimental result o of EAST algorithm represents, the experimental result * of GM_EAST algorithm represents.

Fig. 3 network lifecycle comparison diagram. Original EAST algorithm and innovatory algorithm GM_EAST are tested, often dead 10 nodes, passed experiment wheel number is recorded, until more than dead 50 nodes. In figure, dark cylindricality represents EAST algorithm, and light color cylindricality represents GM_EAST algorithm.

Fig. 4 total energy consumption comparison diagram. Original EAST algorithm and innovatory algorithm GM_EAST are tested, often takes turns through 500, record the energy expenditure of whole system. In figure, dark line represents EAST algorithm, and light line represents GM_EAST algorithm.

Detailed description of the invention

The present invention includes three steps: set up the temporal correlation model based on gray model, and spatial coherence model is set up, and data are transmitted.

The first step: set up the temporal correlation model stage based on gray model.

First, collect some historical datas, be respectively stored in member node queue Qm and sink node queue Qs, it is assumed that these historical datas are X⁽⁰⁾:

X⁽⁰⁾=(x⁽⁰⁾(1),x⁽⁰⁾(2),x⁽⁰⁾(3)��x⁽⁰⁾(t))(1)

x⁽⁰⁾(i), i=1,2,3 ... t represents the initial data in i moment, t sets up the number of initial data needed for model.

Then need that initial data queue is carried out Accumulating generation and obtain queue X⁽¹⁾:

X⁽¹⁾=(x⁽¹⁾(1),x⁽¹⁾(2),x⁽¹⁾(3)��x⁽¹⁾(t))(2)

Wherein:It is the result of Accumulating generation, x⁽⁰⁾I () is the initial data in i moment in formula (1).

Then can show that single argument single order gray model is:

$\frac{{\mathrm{dX}}^{\left(1\right)}}{dt}+{\mathrm{aX}}^{\left(1\right)}=b---\left(3\right)$

Wherein, X⁽¹⁾Being Accumulating generation queue, a, b is parameter respectively.

Order matrix $A=\left(\begin{array}{c}{x}^{\left(0\right)}\left(2\right)\\ x^{\left(0\right)}\left(3\right)\\ ...\\ x^{\left(0\right)}\left(t\right)\end{array}\right),B=\left(\begin{array}{cc}-0.5\left(x^{\left(1\right)}\right(1)+x^{\left(1\right)}\left(2\right))& 1\\ -0.5\left(x^{\left(1\right)}\right(2)+x^{\left(1\right)}\left(3\right))& 1\\ \mathrm{...}& \\ -0.5\left(x^{\left(1\right)}\right(t-1)+x^{\left(1\right)}\left(t\right))& 1\end{array}\right).$ Can in the hope of parameter by method of least square $\left(\begin{array}{c}\hat{a}\\ \hat{b}\end{array}\right)={\left(B^{T}B\right)}^{-1}{B}^{T}A.$ Wherein, x⁽⁰⁾I () is the initial data in formula (1), x⁽¹⁾I () is the Accumulating generation data in formula (2).

Therefore the Accumulating generation result of t+1 can be obtained, ${\hat{x}}^{\left(1\right)}(t+1)=e^{-\widehat{\mathrm{\α}}t}\left(x^{\left(1\right)}\right(1)-\frac{6}{\widehat{\mathrm{\α}}})+\frac{\hat{b}}{\widehat{\mathrm{\α}}}.$ WhereinIt is parameter, thus can obtain the predictive value in t+1 moment:

The difference of predictive value and actual value $\mathrm{\Δ}(t+1)={\hat{x}}^{\left(0\right)}(t+1)-x^{\left(0\right)}(t+1).$ WhereinIt is the predictive value in t+1 moment, x⁽⁰⁾(t+1) it is the actual value in t+1 moment.

Making �� is model modification threshold value, and �� is event detection threshold value.

When �� (t+1), < during ��, then it is assumed that predictive value is identical with observation, member node transmits data unlike sink, and continues the prediction in next cycle.

As �� < �� (t+1), < during ��, before being considered as, model is unavailable, the data collected is passed to sink, then Renewal model.

As �� (t+1) > �� time, then it is assumed that having abnormal accident to occur, now member node needs exceptional value to be sent to sink node at once.

Second step: spatial coherence model is set up.

When there being anomalous event to occur, all detect that abnormal node will calculate the priority oneself becoming leader, choose that the highest node of priority as leader.

Leader's electoral machinery is as follows: from node by catabiotic angle, and the energy of node consumption should be relevant with the distance of nodal point separation sink, and the more remote consumed energy of distance is more many, and the priority electing leader as just should be more little. From the angle of dump energy, dump energy is more few, and the probability of node premature death is more big, and the priority electing leader as should be more little. In order to obtain the scheme of a relative compromise, it is proposed that a priority voter model, model describes as follows:

$\mathrm{Pr}iority={\mathrm{\&alpha;E}}_i+\mathrm{\&beta;}\frac{1}{S_i}---\left(4\right)$

Here �� and �� is weight coefficient, it is possible to be the coefficient different value of distribution according to different importance rates, but alpha+beta=1. E_iAnd S_iIt is the distance of the dump energy of node i and node i distance sink node respectively.

For convenience, it is assumed that anomalous event generation area is circular (being also applied for irregular figure). Detect centered by the node of event starting most, set up several grids that the length of side is c, make all grids can cover whole event region. Coordinate (x according to Centroid_e,y_e) and node self coordinate (x_i,y_i) and grid length of side c can obtain the grid position at each node place in event area:

When $\frac{(x_n-x_e)}{\frac{c}{2}}>1$ Time,

When $\frac{(x_n-x_e)}{\frac{c}{2}}<-1$ Time,

Otherwise, x_c=0.

Wherein, x_cFor the abscissa of node place grid, x_nFor the abscissa of node, x_eFor the abscissa of event, c is the grid length of side. In like manner can obtain the vertical coordinate y of node place grid_c��

Then the node of each relevant range being carried out vote and enumerate a bunch head, the model of election is identical with formula (4), and wherein, �� and �� is weight coefficient, it is possible to be the coefficient different value of distribution according to different importance rates, but alpha+beta=1. E_iAnd S_iIt is the dump energy of node i and the distance of node i distance leader respectively.

5th step: data are transmitted.

Member node sends the data to a bunch head, and the data collected are sent to leader by bunch head, and the data collected are sent to sink node by leader.

In data transmission and repeating process, it is proposed that the data transmission method of a near-optimization. Owing to node energy consumption great majority concentrate in the process that node sends data. And the energy consumption model that node sends data the most frequently used at present is:

E_s(k, d)=e_elec��k+��_amp��k��d^r(5)

Wherein, E_s(k, d) for the consumption of energy, k represents the bit value of transmission data, and d represents the distance between often jumping, e_elecAnd ��_ampBeing the relevant parameter of power amplifier, r is parameter.

Can be seen that the defeated distance of every jump set is more remote, energy expenditure is more big, and the gross energy of whole system consumption should be each forward node transmits the catabiotic summation of data. N is needed to jump if the spacing of two nodes is D transmits data, then, when this n jumping transmission range is identical, the gross energy consumed should be minimum, if the distance d=D/n of n hopscotch. Then once transmitting the system gross energy consumed is:

Wherein, E_AlwaysFor system total energy consumption, k represents the bit value of transmission data, and D represents the distance of origin-to-destination, and d represents the distance between often jumping, e_elecAnd ��_ampBeing the relevant parameter of power amplifier, r is parameter.

In order to obtain E_AlwaysMinima, to d derivation and to make derivative be 0. Then:

-D��e_elec��kd^-2+(r-1)D����_amp��kd^r-2=0 (7)

Wherein, E_AlwaysFor system total energy consumption, k represents the bit value of transmission data, and D represents the distance of origin-to-destination, and d represents the distance between often jumping, e_elecAnd ��_ampBeing the relevant parameter of power amplifier, r is parameter.

Make r=2. Solve:

$d=\sqrt{\frac{e_{elec}}{{\mathrm{\&epsiv;}}_{amp}}}---\left(8\right)$

When the transmission range of the node transmitted on path is all close to d, the gross energy that whole transmission path consumes is minimum. So spatial coherence data transfer path selects the distance of the about d of each node-node transmission.

The principle of the invention and beneficial effect: labor is based on the pluses and minuses of the EAST algorithm of temporal correlation, and for deficiency in collecting data precision and energy consumption of EAST algorithm, it is proposed that innovatory algorithm. Make full use of, by gray model, the environmental data that node collects to be modeled, when detecting that anomalous event occurs, set up based on grid bunch, choosing of leader cluster node and leader, consider dump energy and distance factor, finally utilize theoretical optimal path transmission data. Compared to EAST algorithm, it is proposed to GM_EAST algorithm all increase significantly in data precision and energy efficiency.

From two aspects, experimental result is analyzed, is first data precision, next to that energy efficiency. Using the data set of theIntelBerkeleyResearchLabin2004, the data message extracting three days from data set at random is tested. Meanwhile, by 400 node random placements in the spatial dimension of 300m*300m, the generation of simulation anomalous event, and modified hydrothermal process and original EAST algorithm are compared, experimental result shows, no matter from precision or energy consumption, algorithm performance all promotes to some extent compared to EAST algorithm. Experiment parameter is as shown in table 1.

Table 1

Parameter	Value
		Network size	(-150,-150)�C(150,150)
Number of nodes	400
		Sink node coordinate	(-150,-150)
Primary power	2J
		Event center	(75,75)
Event radius	75m
		c	30m
eelec	50nJ/bit
		��amp	10pJ/bit/m2
Bag size	500bit

With regard to data precision aspect, the gray scale algorithm of use need set t initial value algorithm just can be made constantly to run, here t setting more big, gray level model precision is more big, but the amount of calculation that excessive t value can make node is excessive, thus causing unnecessary energy expenditure. In order to obtain suitable t value, make t value between 3 to 9, respectively in ��=0.1 to ��=1 time carry out emulation experiment, for each t value, obtain precision on data set, then obtain the rate of increase of precision, determine optimum t value. From Table 2, it can be seen that when ��=0.1, when 0.3,0.4,0.5,0.6,0.8,0.9, when t takes 5, data precision rate of increase takes maximum. When ��=1 and 0.7, when t takes 6, data precision rate of increase takes maximum. When ��=0.2, when t takes 4, data precision rate of increase takes maximum. Considering data precision rate of increase and operand, the optimal value taking t is 5. Simulations below contrast experiment all gives tacit consent to t in gray model and takes 5.

Table 2

t\��	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9	1
											3	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
4	0.0763	0.0922	0.0144	0.0158	-0.0321	0.0047	-0.0099	-0.0202	0.0056	0.0285
											5	0.0810	0.0720	0.0967	0.0944	0.0929	0.0734	0.0322	0.0712	0.0607	0.0303
6	0.02089	0.0423	0.0355	0.0372	0.0310	0.0586	0.0489	0.0548	0.0534	0.0624
											7	0.0066	0.0027	0.0149	0.0205	0.0229	0.0153	0.0353	0.0292	0.0283	0.0262
8	-0.0098	0.0103	0.0057	0.0102	0.0192	0.0179	0.0207	0.0146	0.0141	0.0137
											9	-0.0120	-0.0027	0.0026	0.0046	0.0002	0.0068	-0.0076	0.0017	0.0048	0.0138

For the comparison of data precision, Experimental comparison, as in figure 2 it is shown, owing to method introduces gray model, employ one's time to the best advantage relevant information, thus reducing mean square error, improves data precision. Network lifecycle Experimental comparison results, according to Fig. 3, it can be seen that during dead equal number node, and the time of algorithm experience is longer, and this is owing to the models of priority proposed has considered dump energy and transmission range. Can be seen that according to Fig. 4 the method energy consumption of the present invention is lower, because taking the transmission method of an approximate optimal path, thus reduced by system energy consumption as far as possible.

Claims (3)

1. the EAST innovatory algorithm based on Grey Model, it is characterised in that following steps:

The first step, sets up the temporal correlation model based on gray model;

First, collect some historical datas, be respectively stored in member node queue Qm and sink node queue Qs, it is assumed that these historical datas are X⁽⁰⁾:

X⁽⁰⁾=(x⁽⁰⁾(1),x⁽⁰⁾(2),x⁽⁰⁾(3)...x⁽⁰⁾(t))(1)

x⁽⁰⁾(i), i=1,2,3 ... t represents the initial data in i moment, t sets up the number of initial data needed for model;

Then initial data queue is carried out Accumulating generation and obtain queue X⁽¹⁾:

X⁽¹⁾=(x⁽¹⁾(1),x⁽¹⁾(2),x⁽¹⁾(3)...x⁽¹⁾(t))(2)

Wherein:It is the result of Accumulating generation, x⁽⁰⁾I () is the initial data in i moment in formula (1);

Then show that single argument single order gray model is:

$\frac{{\mathrm{dX}}^{\left(1\right)}}{dt}+{\mathrm{aX}}^{\left(1\right)}=b---\left(3\right)$

Wherein, X⁽¹⁾It is Accumulating generation queue, a, b is parameter respectively;

Order matrix $A=\left(\begin{array}{c}{x}^{\left(0\right)}\left(2\right)\\ x^{\left(0\right)}\left(3\right)\\ ...\\ x^{\left(0\right)}\left(t\right)\end{array}\right),B=\left(\begin{array}{c}\begin{array}{cc}-0.5\left(x^{\left(1\right)}\right(1)+x^{\left(1\right)}(2\left)\right)& 1\end{array}\\ \begin{array}{cc}-0.5\left(x^{\left(1\right)}\right(2)+x^{\left(1\right)}(3\left)\right)& 1\end{array}\\ ...\\ \begin{array}{cc}-0.5\left(x^{\left(1\right)}\right(t-1)+x^{\left(1\right)}(t\left)\right)& 1\end{array}\end{array}\right);$ Parameter is tried to achieve by method of least square $\left(\begin{array}{c}\hat{a}\\ \hat{b}\end{array}\right)={\left(B^{T}B\right)}^{-1}{B}^{T}A;$ Wherein, x⁽⁰⁾I () is the initial data in formula (1), x⁽¹⁾I () is the Accumulating generation data in formula (2);

Obtain the Accumulating generation result of t+1, ${\hat{x}}^{\left(1\right)}(t+1)=e^{-\hat{a}t}\left(x^{\left(1\right)}\right(1)-\frac{\hat{b}}{\hat{a}})+\frac{\hat{b}}{\hat{a}};$ Wherein It is parameter, thus obtains the predictive value in t+1 moment:

The difference of predictive value and actual value $\mathrm{\&Delta;}(t+1)={\hat{x}}^{\left(0\right)}(t+1)-x^{\left(0\right)}(t+1);$ WhereinIt is the predictive value in t+1 moment, x⁽⁰⁾(t+1) it is the actual value in t+1 moment;

Making �� is model modification threshold value, and �� is event detection threshold value;

When �� (t+1), < during ��, member node does not transmit data to sink, and continues the prediction in next cycle;

When the data collected < during ��, are passed to sink, then Renewal model by �� < �� (t+1);

As �� (t+1) > �� time, now member node needs exceptional value to be sent to sink node at once;

Second step, spatial coherence model is set up; 3rd step, data are transmitted.

2. the EAST innovatory algorithm based on Grey Model according to claim 1, it is characterised in that described spatial coherence method for establishing model is as follows:

When there being anomalous event to occur, all detecting that abnormal node will calculate the priority oneself becoming leader, choose that the highest node of priority as leader, priority P riority describes as follows:

$\mathrm{Pr}iority={\mathrm{\&alpha;E}}_i+\mathrm{\&beta;}\frac{1}{S_i}---\left(4\right)$

Wherein, �� and �� is weight coefficient, is the coefficient different value of distribution according to different importance rates, but alpha+beta=1; E_iAnd S_iIt is the distance of the dump energy of node i and node i distance sink node respectively;

Assume that anomalous event generation area is for circle; Detect centered by the node of event starting most, set up several grids that the length of side is c, make all grids cover whole event region; Coordinate (x according to Centroid_e,y_e) and node self coordinate (x_i,y_i) and grid length of side c obtain the grid position at each node place in event area:

When $\frac{(x_n-x_e)}{\frac{c}{2}}>1$ Time,

When $\frac{(x_n-x_e)}{\frac{c}{2}}<-1$ Time,

Otherwise, x_c=0;

Wherein, x_cFor the abscissa of node place grid, x_nFor the abscissa of node, x_eFor the abscissa of event, c is the grid length of side; In like manner obtain the vertical coordinate y of node place grid_c;

Then the node of each relevant range being carried out vote and enumerate a bunch head, the model of election is identical with formula (4), and wherein, �� and �� is weight coefficient, is the coefficient different value of distribution according to different importance rates, but alpha+beta=1; E_iAnd S_iIt is the dump energy of node i and the distance of node i distance leader respectively;

3rd step, data are transmitted.

3. the EAST innovatory algorithm based on Grey Model according to claim 1 and 2, it is characterised in that data transmission method is as follows:

Member node sends the data to a bunch head, and the data collected are sent to leader by bunch head, and the data collected are sent to sink node by leader;

Bunch head sends the data to leader and leader is sent in the process of sink node, finds the Along ent on transmission path, and making adjacent 2 distances is all d, finds apart from the nearest node of these Along ents as forward node;

$d=\sqrt{\frac{e_{elec}}{{\mathrm{\&epsiv;}}_{amp}}}---\left(5\right)$

Wherein, e_elecAnd ��_ampIt it is the relevant parameter of power amplifier.