Predictive model-aided filtering scheme of data-collection in WSN

The Journal of China Universities of Posts and Telecommunications April 2011, 18(2): 17–24 www.sciencedirect.com/science/journal/10058885

http://www.jcupt.com

Predictive model-aided filtering scheme of data-collection in WSN HUANG Ru1 ( ), ZHANG Zai-chen2, XU Guang-hui3 1. School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China 2. State Key Laboratory of Mobile Communications, Southeast University, Nanjing 210096, China 3. Institute of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, China

Abstract

The paper proposes a prediction-mode-based filtering mechanism (PMF) to solve the problems of transmission energy wasting caused by time-redundant data in wireless sensor networks (WSN), according to the characteristic of spatio-temporal correlations on sampling series in data-collection. Prior works have suggested several approaches to decrease energy cost during data transmission process via data aggregation tree structure. Distinguish from those methods in above researches, our proposed scheme mainly focus on reducing the temporal redundant degree in event-source to achieve energy-saving effect via self-adaptive filtering structure. The framework of PMF for energy-efficient collection is composed of prediction module for mining the change law of time domain, self-learning module for updating model, and driving module for controlling data filtering operation. Combined with the design of error driving rule and threshold distributing rule, which is the middleware in the above filtering mechanism, the quantity of transmission load in networks can be greatly inhibited on the premise of quality of service (QoS) assurance and energy consumption can be reduced consequently. Finally, the experimental results show that the performance of PMF can significantly outperform some classical data-collection algorithms on energy-saving effect and self-adaptability. Keywords WSN, data-collection, filtering mechanism, energy-saving

1

Introduction 

The traditional architecture in WSN consists of a large number of sensor nodes deployed over an area of interest to sample data, process them locally and send the results to a data-collection point, i.e., base station (BS). Data collection [1–2] is a critical operation in WSN for extracting useful information from the operating environment. Energy-efficient data collection in WSN has been studied in many articles. In Ref. [3], the authors try to compute a minimum spanning tree over sensor network to minimize the total energy expended in a round of communication. Modulation scaling [4] is integrated into the weighted fair queuing scheduling policy and the problem of balancing the energy dissipation along a multi-hop communication path is studied. Data aggregation techniques [5–9] can be introduced into cluster-based approaches in WSN, which is defined as the process of Received date: 06-03-2010 Corresponding author: HUANG Ru, E-mail: [email protected] DOI: 10.1016/S1005-8885(10)60040-4

aggregating the data from multiple sensors to eliminate redundant transmission. Some efficient algorithms are proposed by the following works with the aim to construct routing trees in aggregation way. The authors of Ref. [10] present an ant colony based method to solve the problem of constructing an aggregation tree for a group of source nodes within WSN. However, the construction of the appropriate tree depends heavily on the nodes’ deployment and depletes an amount of important power. In Ref. [11], the authors propose an adaptive data aggregation scheme for clustered WSN, where the temporal and spatial aggregation degrees are determined by the current scheme state according to the observed reliability, but the scheme neglected the importance of scalability in such kind of networks. The central work of Ref. [12] adopts the mobile agents (MAs) technologies for an energy-efficiency data collection. However, the size of the mobile agent message is too large to waste an important part of the reduced power, when sent on the networks. According to the distributed nature of data generation in WSN, Distributed source coding has been well-studied and

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introduced into data-compression technologies [13–14]. However, those results are non-constructive and applicable only to a few specific distributions. The above researches emphasize on the idea that the underlying structure of the network can be abstracted as a data aggregation tree. The functions of tree are executed by aggregating and collecting information from multiple sources route to BS. Presented solutions mainly achieve energy-saving effect during data transmission process. However, in most environmental monitoring applications, the data generated by sensors is highly correlated both in time and space, which causes rich spatio-temporal redundancy degree [15] in data-collection. Different from the above mentioned methods, predictive mechanism on time series model is adopted in this paper to reveal the variation law in time dimension and mine the implication of time-redundancy. The addressed schemes mainly focus on reducing the temporal redundant degree in event-source to achieve energy-saving effect, that is to say, redundant-data filtering operation of PMF, run at the source at data-collection structure, can be performed before the course of data transmission. Paper organization: the background of related works is discussed in Sect. 1. The system model of prediction-based filtering scheme in data-collection is presented in Sect. 2. In Sect. 3, PMF for energy-saving problem in WSN is described in details. Simulation results are shown in Sect. 4. Finally, concluding remarks are made in Sect. 5.

2

System model

WSN can be regarded as an undirected weighted connectivity-graph G( N , E, C) , where N and E represent the set of nodes and links in G, respectively, while C denotes the set of corresponding cost in E. Definitions of corresponding symbols in the design of system model are shown at Table 1. At the beginning stage of basic data-collection, BS firstly initiates query request for data gathering. After initialization, the major task in this stage is to construct and modify predictive model based on historic information. Cluster head (CH) receives the sampling sequence from cluster’s members in the hierarchical clustering structure, i.e., gb  G,(b 1,2,...) , realizes the parameter estimation for time series

predictive model, then returns the results to its members. Finally, each member sets up the identical model replica on itself, respectively. The above series of operations could be modeled as the following steps: CH collects the sampling sequence from cluster with given interval, which is used as

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training-data entering into queue b for model-building. CH estimates the stability characteristic of training-data according to parameter rule [16–17] and executes the smooth pre-processing. Then CH selects obeyed order to modify the model parameters by calculating the partial autocorrelation function. The corresponding l-step predictive model i.e. , auto regressive moving average model (ARMA) [18] and the Green function can be obtained as follows:

I0

Xˆ j  l

p

1  ¦Iv

p

q

v 1

y 1

 ¦Iv Xˆ j  l  v  ¦M yH j  l  y

(1)

½ °  M k ); kı1¾ °¿

(2)

v 1

G0 1 Gj

¦ (I G k

k

j k

Table 1 Symbol definition Symbol ci

j

erf h erfi

j

Definition Energy consumed by ni  1 at moment j  T . The maximal error range based on QoS Precision interval allocated to ni  ȃ at moment j  T .

oij

Transmission when oij

T gb

Time series, i.e., T

The bth cluster in WSN with head CH b , CHb  gb  G

gb

The number of nodes in gb

Xj

Sampling at moment j  T

Xˆ j  m ni

1 , otherwise oij

The predictive value of X j with step length m Node belongs to WSN, ni  gb  ȃ  G

Iv , v 0,1, 2, ...

Autoregressive coefficient of ARMA

M y , y 1, 2, ...

Moving average coefficient of ARMA

Gj

Green function

Hl

Prediction error at time step l

Hh

Threshold of prediction error for QoS Variance yield Packet sent by ni  gb

var pi

0.

{ j, j  1, "}

Vl

Variance of prediction error

zh z

Threshold of total abnormity times Counter for abnormity times

Furthermore, the mentioned predictive model can be modified according to new observed series and the process of model adjustment is shown as follows: m ½ Xˆ j  l  m Xˆ l  m  ¦ Gl  k H j  l  k ° k 1 ° ° (3) H j  l  k X j  l  k  Xˆ j  l  k 1; k  [1, m ] ¾ ° l  m 1 l 1 var(H j  l  m ) ¦ G 2j V l2  ¦ G 2j V l2 ; lı1, m ! 0 ° °¿ j 0 j 0 From the modification of prediction equation shown in Eq. (3), we can see that the (l  m) step prediction variance is decreased by Gl2V l2 compared with the original variance,

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HUANG Ru, et al. / Predictive model-aided filtering scheme of data-collection in WSN

that means, to modify the predictive value using the new sampling series while repeated calculation is not necessary. As a result, the algorithmic complexity is reduced accordingly. Based on the predictive results, Eq. (4) gives mathematical programming (MP) of filtering mechanism with constant precision threshold, where N ( pi ) denotes the quantity of total transmission packets sent by in ni  g b  G . Therefore, the objective function shows the goal to minimize the amounts of transmission packets, which is subjected to constraint condition of data consistency. ½ ­° ½° min ® ¦ N ( pi ) ¾ °° °¯ ni gb °¿ (4) ¾ ° ˆ s.t. X j  X j İerf h °¿ In the filtering mechanism with constant precision, each member in cluster is allocated with the same precision range and the transmission operation is triggered only when the prediction-error exceeds threshold erf h . The range of precision threshold is in inverse proportion to energy cost, which should enlighten the way to further reduce energy consumption by distributing the personalized precision range to ni  g b according to its own energy status (as seen in Eq. (5)). Therefore, adjustable precision range can be exploited to reduce the total energy cost in cluster. In the application of environment monitoring in WSN, extracting the mean value of sampling data is usually treated as the data fusion mode, and the cluster as a whole can be regarded as an abstracted node under the premise of QoS. ½ min ¦ N ( pi ) ° ni gb ° j ˆ ­ X j  X j İerf i ° ° °° j °§ ¦ erfi · (5) ¾ ° ¸İerf ° s.t. ®¨¨ i h ¸ ° °© g b ¹ ° ° j °Threshold allocation :6 erf i ° °¿ ¯

^

s.t.

¦ erf i

gb

19

j i

İerf h  ni  gb  N , j  T

(7)

The objective function Eq. (6) indicates that the prediction mechanism can achieve energy-saving effect by reducing the total number of data transmission in gb . Because the accumulated transmission times of ni  gb should be in inverse proportion to assigned precision interval erf i j .

3 The design of prediction-based filtering structure in data-collection mechanism In the self-adapting data filtering stage, the same bidirectional predictive model is introduced into both CH and its members. The core in energy-saving mechanism is the self-adapting filtering framework (as seen in Fig. 1), which is mainly composed of predictive module (PM), self-learning module (LM), trigger module (TM), threshold allocation rule 31 and error driving rule * 1 . The execution step of filtering framework is as follows: firstly, predictive module constructs the ARMA ( p, q ) based on input series to execute prediction function, subjected to rule 31 ; then, the deviation from threshold and error range is extracted to act as input signal of trigger module to generate results driving corresponding node’s operation. We describe the design and operation of the filtering system, whose structure based on our approach, is shown in Fig. 1.

`

The constraint condition in Eq. (5) embodies the precision requirement for the cluster as a whole, subjected to specific threshold allocation rule, and gb denotes the number of nodes in gb . MP for energy optimization in inter-cluster data collection is shown as follows: ­° ½° min ®¦ ¦ cij oij ¾ ; cij  C (6) ¯° jT ni gb ¿°

Fig. 1

Prediction-based filter structure

The pseudo-code of LM algorithm based on system model is shown as follows: Step 1 Initialization b m0; // clearance of the data memory Step 2 model modification based on self-learning Learn (data stream) for j 1: t read X j and enqueue it into b // sensing-data entering into queue ( p, q) m ACF(b ), PACF(b )

E

// model and order selection

(I1 ,...,I p , M1 ,..., Mq ) m Para(b )

G j m Compute( G )

j

¦ (I G k

jk

// parameter estimation

 Mk )

// calculation of

k 1

the Green’s function var(l ) m Predict( E , l )

// execution of

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the l-step prediction b(') m CompVal(b ) the variance

3.1

// calculation of

The design of threshold allocation and error driving rule

According to the analysis on energy optimization scheme in inter-cluster data collection shown as Eqs. (6) and (7), the objective for energy optimization can be converted into Eq. (8) and precision threshold allocation rule 31 is defined as Eq. (9). ­° c j °½ min ®¦ ¦ i j ¾ ¯° jT ni gb erf i ¿°

decrease energy consumption. In the practical application of data-collection, the abnormal values obtained by sensors can be caused by various reasons. Sometimes, it does not mean that the predictive model missmatches the distribution of environment data, it is unreasonable that the transmission should be triggered only by single-time abnormity. Therefore, threshold of cumulative abnormity times is set as zh to reduce the probability of maloperation, the transmission can be executed only when zızh . Time

(8)

­ ¦ erf i j ° i erf h ° g b ° (9) s.t.31 : ® c jgb c1j ° j j 2 °erf1 " : erf gb (erf j ) 2 " : 1 erf gjb °¯ Rule 31 in TM can indirectly control the transmission





performed by ni  gb  G . And the precision threshold erf h could be constant value or adjustable one based on QoS. The MP for energy-saving effect is discussed as follows: 31 is performed in the later stage of data-collection to produce the self-adaptive filtering threshold. Driven by 31 , filtering mechanism can further optimize energy-saving effect, which is the specific case of data-fusion. ni  g b  G makes use of historical sampling series { X j } to deduce Xˆ j 1 according to the predictive model in

window

which

i

oi

¦o

( 11)

z

i

(10)

i

PMF should run on both CH and its members. The pseudo-code of TM algorithm is shown as follows: Step 1 Each member is allocated with corresponding threshold of predictive precision based on 31 . Step 2

To execute algorithm of TM on cluster members.

member_update(UPDATE actual_update) {

UPDATE predicted_update; predicted_update=predict(); // to calculate predictive values If ( erf i jıerf h ) // the predictive error erfi j

exceeds the threshold erf h z ; If ( zızh )

{

// cumulative frequency

{ If ( erfi j  H h ) {

// to distinguish the modification of model and the

occurrence of monitored event send_update_signal(1);

// to send the trigger signal for

model modification to CH Learn(actual_update);

// to start up the execution of

module LM } else {

TM triggers the operations of LM and modifies the predictive model; when erf i j  [H h , f] and cumulative abnormity times have been exceeded, the monitored events occur, and then data transmission is started up. The above QoS-based predictive mechanism, in essence, reflects the data-filtering operation via reducing the transmitted data volume to

{ X j } jj  m (m size(W j )) ,

­°1; X j  Xˆ j ! erf i j , j  [1,size(W j )] ® °¯0; otherwise

The concrete operating process of * 1 based on error

CH, which embodies the design idea that energy-saving effect is improved by reducing the temporal-redundancy information in WSN on the premise of QoS, which can be abstracted as threshold H h of filter; when erfi j  [erf h , H h ) ,

Wj

corresponds to time series is used to intercept the infinite flow data and ¦ oi denotes the times of abnormity in W j .

PM, and then performs adaptive filtering-operations based on rule * 1 . range is illustrated as follows˖ If the following requirements of predictive precision are met, corresponding operations are executed. When erf i j  [0,erf h ) , members do not need send sampling series to

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Output(actual_update); // to perform transmission } z

} } else

0;

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HUANG Ru, et al. / Predictive model-aided filtering scheme of data-collection in WSN

21

tbr  tbsıtth , where tbs denotes the sending-moment of CH b

{ send_update_signal(0); If ( tıtth )

// no need for modifying model

from CHb .

{ send_alive_signal(1);

// node is alive

} } }

// end

Step 3 CH has received the triggering signal from members Step 4

and tbr is the moment at which BS has received packets

Operation algorithm of TM is performed on CH

Head_update(UPDATE actual_update,SIGNAL beacon) { If (send_alive_signal = =1)

// symbol of alive node

{ If (send_update_signal()= =0)

// node is alive and no need

for modifying the model {

^ ¦ N (p )`

½ ° ° (12) ¾ ­°tbs  tbrıt th ° s.t. ® j ˆ °¯ X j  X j İerfi ¿° If BS does not receive any information from CHb in t, the min

ni gb

i

actual sampling series should be substituted by the calculated predictive values; otherwise, BS decodes codes sent by CHs for data-reconstruction. The energy-saving essence of proposed scheme is using the predictive calculation substitute the actual transmission operation to greatly reduce the amount of communication loads in network.

4 Simulation and results

actual_update=predict(); // to substitute the actual data to the predictive result } else { Learn(actual_update);

// to update the predictive model

} else { Delete();

// to delete the dead node from the

member’s list } } }

// end

3.2 Data reconstruction based on predictive mode

CH and its members own the duplicate copy of predictive model. If CH does not receive the next frame sampling series during the predetermined blocking time, it means the precision requirement is met. And the data reconstruction is implemented by CH to substitute the calculated predictive results for the actual sampling series on the premise of meeting QoS. As a result, the frequency of transmission is greatly reduced. To prevent the misjudgment caused by the death of nodes, ni  g b periodically broadcasts beacon to CHb in order to show that it is alive. If CHb does not receive beacon during specified time interval tth , ni is considered dead. The same bidirectional predictive model is introduced into both BS and CH. BS makes judgment that whether the requirement of temporal-consistency is met, i.e.,

The goal for filtering mechanism in WSN is to achieve energy-saving effect by reducing the redundant loads in network, which is the trade off between predication-precision and energy-efficiency. In the simulator platform NS2, WSN can be randomly formed in a 150 m×150 m planar topology with adjustable density and BS is located at (0,0). The monitored data-collection is used in application scenario of environmental thematic monitoring and events occur in area with diagonal coordinate [(120,90), (150,60)]. It is assumed that the occurrence of events obey the Poisson distribution with the limited duration time and tunable event radius. The setting of experiment parameters are shown in Table 2 Table 2 Experiment parameters setting Parameters Region scale Medium access control Queue mode Type of antenna Radio transmission Packet length Bandwidth Cost of TX/RX Precision range Network radius Range of CR Time duration Number of nodes Poisson distribution Initial energy of node Communication range Time step Threshold of abnormity times Allowable error range

Configuration 150 m×150 m IEEE 802.11 Droptail/PrQueue Omni antenna Bidirection with no error 64 B 250 kbit/s 660 mW/395 mW [1.0 , 2.0] 150 m [5 m,30 m] [0.2 s,500 s] 400 [0, 3.0] 0.5 J 30 m 0.05 s 18 [0.01,0.14]

Given the range of QoS-oriented predictive error, the simulation results are obtained from 500 rounds test and the size of data packet is set as 100 B. Continuous 3 000

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The Journal of China Universities of Posts and Telecommunications

observation samplings are randomly chosen with the sampling interval as 8 min, the sampling series contain trend component, periodic component and the stable component. Querying requirement launched by BS is as follows˖ {SELECT ID nodei WHERE temp25qC DURATION 30 min

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introduced into PMF on the basis of bidirectional mode, in which identical predictive model is run on both BS and CH at the same time.

err0.2qC CONFIDENCE 95%}

Set the model-modification threshold as H h

0.15qC and

the performance evaluation index contains average relative error (ARE), average absolute error (AAE) and data send rate (DSR), where both ARE and AAE represent the precision of predictive algorithm; DSR denotes the proportion of data volume being transmitted to total samplings volume, which reflects the relation between the prediction precision and transmission cost. The corresponding indices are defined as follows: k X  Xˆ

¦

RARE

RDSR

j

Xj

j 1

¦

(13)

; k 1, 2,...

k k

RAAE

j

X j  Xˆ j

j 1

(14)

k





1 k ª ¦ G | X j  Xˆ j |! erf h   z ! zh º¼ k j1 ¬

(15)

where G (<) is the Boolean sign function. Table 3 presents the comparison in prediction-precision between PMF and some classical algorithms, i.e., dual prediction [19], prediction-based monitoring (PREMON) [20]. The simulation results show that PMF is more precise than the other two methods, it can further improve prediction precision by adopting the distribution rule and introducing the self-learning module to modify the predictive model.

Fig. 2

Energy consumption among various prediction methods

The relation between error-threshold and transmission rate contrasted in different network sizes ( N i 1.2 N i 1 , N1 172, i 1,2,3)

is shown in Fig. 3, where the

transmission rate is defined as the ratio of the amount of sent-data to the total amount of sampling series. From the simulation result, we can see that the transmission rate gradually decreases with the increase of error-threshold, which results in the transmission cost being greatly reduced. When the error-threshold is increased to the specific value, i.e., erf hı4.5% , the change on network size will make few contribution to decrease energy consumption, that is to say, the network’s scale effect is weaken. The reason is that enlarging the allowable error range will inhibit the transmission operation executed by most nodes.

Table 3 Comparison of algorithm precision Threshold 0.04 0.08 0.12

Precision ( u103 ) Algorithm DSR ARE PMF 298.814 0.512 Dual prediction 301.672 0.933 PREMON 319.469 1.024 PMF 113.740 1.092 Dual prediction 117.463 1.467 PREMON 123.251 1.932 PMF 97.400 2.695 Dual prediction 102.220 3.015 PREMON 109.172 5.995

AAE 13.86 14.02 17.73 30.74 34.69 37.81 51.13 59.25 64.74

Fig. 2 shows that when introducing the predictivemode-filtering in data-collection mechanism, the energy cost is gradually decreased with the increase of the predictive precision. Compared with other algorithms, among which the Naive does not adopt prediction scheme, energy consumption of PMF is the least because reasonable error-range could be

Fig. 3

Permissible error vs. transmission rate

From Fig. 4, we can see that the normalized average energy cost is greatly reduced by adopting the filtering mechanism in data-collection. All amounts of data should be transmitted when the permissible precision constraint is set as zero, which means filtering effect does not exist. When adopting the

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HUANG Ru, et al. / Predictive model-aided filtering scheme of data-collection in WSN

time-field filtering mechanism, the average energy cost of node tends to decrease with the increase of accuracy interval and the simulation result shows that the energy cost could be further decreased by adopting the rule 31 to distribute tunable precision threshold, compared with the adoption of constant threshold. Because the precision threshold can be adaptively allocated according to the energy status, which helps to effectively balance the network energy cost.

Fig. 4 Allowable error bounds vs. average energy cost of each node with different metrics

Fig. 5 compares energy-saving effect of filtering scheme in different predictive precision ranges. Define energy consumption ratio as E1 / E2 , where E1 and E2 are corresponding to the energy cost with and without predictive mechanism, respectively. The simulation result denotes E1  E2 , which is caused by the reason that the total energy consumption could be reduced by adopting the filtering mechanism. Furthermore, we can see from Fig. 5 that the value of ratio changes with the variations in precision range and reaches the peak value at range [1.71, 1.74]. The above simulation result shows that the optimal energy-saving effect can be achieved by rationally setting precision range, which is set by threshold allocation rule 31 .

Fig. 5 Network energy consumption ratio vs. prediction accuracy

5

23

Conclusions

In this paper, the characteristic of temporal correlation on time series data is explored to design novel energy-saving filtering mechanism in data-collection of WSN, subject to the constraint in application level performance. By capturing the characteristic of temporal correlation on time series, the addressed prediction-mode-based filtering mechanism contributes to solve the energy-waste problems caused by the transmission of time-redundant data. The operation process of filtering mechanism can construct and modify the predictive model via self-learning on sampling series to accurately capture the changing law of time domain. In the design of filtering system, allocation rule on prediction accuracy threshold and prediction-error-driven rule are introduced, tunable prediction threshold is set according to node energy status, and internal information included in the data variation patterns is precisely obtained according to prediction error bound, so as to further improve the filtering effect for time redundancy data. The experiment results show that the proposed energy-saving filtering mechanism can effectively reduce the energy cost of data collection mechanism by mining the temporal redundancy and associability. Acknowledgements The work was supported by the National Natural Science Foundation of China (60802005), the Science Foundation for the Excellent Youth Scholars at East China University of Science and Technology (YH0157127), the Undergraduate Innovational Experimentation Program in ECUST (X1033).

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(Editor: WANG Xu-ying)