A SMART WIRELESS SENSOR NETWORK FOR STRUCTURAL DAMAGE DETECTION

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1 Intenational Symposium on Innovation & Sustainability of Stuctues in Civil Engineeing Xiamen Univesity, China, 2011 A SMART WIRELESS SENSOR NETWORK FOR STRUCTURAL DAMAGE DETECTION Ying Lei 1, Lijun Liu 1,2, Yongqiang Jiang 1, Yuliang Tang 3 and Yu Luo 3 1 Depatment of Civil Engineeing, Xiamen Univesity, Xiamen, , China 2 Depatment of Mechanical and Electical Engineeing, Xiamen Univesity, Xiamen, , China 3 Depatment of Communication Engineeing, Xiamen Univesity, Xiamen, , China Abstact: The eal vitue of a wieless senso netwok is the fact that it is a new sensing paadigm offeing distibuted data pocessing capacity, which can be used fo stuctual health monitoing. Fo this pupose, a smat wieless senso netwok is established fo autonomous stuctual damage detection in this pape. The designed wieless senso netwok has a two-level cluste-tee achitectue. Distibuted sensos ae gouped into clustes in which a cluste head is assigned to each cluste to coodinate the sensos in its cluste. Hadwae designs of the senso unit and the cluste head ae studied, especially the cluste head consists of a low powe DSP with stong computing capacity. Thus, the wieless senso netwok povides distibuted computation esouces at goup level. Then, an algoithm fo distibuted stuctual damage detection with limited input and output measuements is poposed. A lage size stuctue is decomposed into smalle substuctues based on substuctue appoach. Inte-connection effect between adjacent substuctues is consideed as unknown inputs to substuctues. Element level stuctual paametes and the unknown inputs in each substuctue ae identified by a two step Kalman estimato and least-squaes estimation appoach to save computational powe and stoages. Finally, the poposed algoithm is embedded into the designed wieless senso netwok, which gants the netwok with smat chaacteistics. The smat senso netwok is expeimentally veified by application to detect stuctual damage of a multi-stoy building. It is shown that it is effective fo autonomous detection of stuctual damage. Keywods: smat senso netwok, wieless senso, zigbee, netwok topology, hadwae design, distibute computing, Kalman estimato, stuctual damage detection 1 INTRODUCTION With the developments of wieless communication and senso netwok, some innovative wieless senso netwoks have been established in ecent yeas fo stuctual health monitoing without the extensive lengths of wies in the tetheed systems (Spence,2004; Lynch and Loh,2006; Lynch,2007 ). Howeve, it is still difficult to tansmit all the measued data in eal time to the cental data acquisition station using wieless sensing netwok fo lage-size stuctues due to the limitation of bandwidth in wieless communication (Lynch,2007). Moeove, it s also too powe consuming to send the temendous amount of ecoded data to the cental station because potable battey is the only powe souce fo each senso node. Peseving battey enegy of wieless senso netwok is of majo concen (Gao, Spence J and Ruzi-Sandoval,2006; Glase and Li etc,2007). A main design challenge in senso netwoks is enegy efficiency to polong the netwok opeable lifetime. Since most of the enegy is spent fo adio communication, the eal vitue of wieless senso netwok is the fact that wieless sensos ae a new sensing paadigm offeing distibuted data pocessing (Pata and Roy,etc,2010; Ababneh,2010). One of the efficient stategies is to embed some data pocessing and analysis algoithms in the wieless senso s micopocesso. The embedded algoithms enable the wieless senso netwok to autonomously analyze data, which gants the netwok with smat chaacteistics. So fa, some smat netwoks have been established (Lynch,2007; Gao, Spence J,2006; Lynch,Sundaaajan,2003; Zimmeman,2008; Lei and Shen, 2010), but most of data pocessing and analysis ae designed at senso level, which is not suitable fo autonomous detection of stuctual damage in lage size

2 stuctues. On the othe hand, many complex senso netwoks equie deploying a lage numbe of sensos. Hieachical clusteing is geneally consideed as an efficient way to facilitate the management and opeation of lage-scale netwoks and minimize the total enegy consumption fo polonged lifetime (Gu,2010). In this pape, a new type of hieachical wieless senso netwok is established. The senso netwok has a two-level cluste-tee achitectue with Zigbee communication potocol built on IEEE wieless communication standad (IEEE Standad,2003; Chipcon Inc). The distibuted sensos ae gouped into clustes, in which a cluste head is assigned to each cluste to contol the communication in the allocated cluste. A cluste head not only seves as a oute of the netwok messages but also possesses computational capabilities with the data collected fom the senso nodes in the cluste. Hadwae of the senso unit and the cluste head ae studied, especially the cluste head consists of a low powe DSP with stong computing capacity. Theefoe, the senso netwok povides paallel computation esouces at goup level, which is a paticulaly useful featue fo the implementation of computational methodologies fo stuctual health monitoing and damage detection of lage-size stuctues. It is an impotant but challenging task to detect stuctual damage in lage-size stuctue as stuctual damage is an intinsically local phenomenon. Vaious stuctual damage detection techniques have been poposed while appoaches based on system identification (SI) have eceived geat attention (Meie, Havaanek,etc,2009; Chang,2009). It is staightfowad to identify stuctual damage based on tacking the changes in the identified values of stuctual dynamic paametes at element level, e.g., the degading of element stiffness paametes. Howeve, as an invese poblem, damage detection by the conventional SI appoaches is challenging. It is highly desiable to deploy as few sensos as possible, so it s essential to exploe efficient algoithms which can detect stuctual damage utilizing only a limited numbe of measued esponses of stuctues subject to some unknown (unmeasued) excitation inputs. Extended Kalman filte (EKF) has been studied and shown to be useful fo stuctual identification with limited measuements of stuctual esponse outputs (Hoshiya and Saito,1984; Saito and Takei,1998; Yang and Lin,etc,2006), but the taditional EKF appoaches equie that all excitation inputs ae measued o available. Moeove, in the extended state vecto, both stuctual esponse state vecto and stuctual paametes ae included. The state vecto and the paametic vecto ae estimated simultaneously, which may lead to divegent behavio fo a lage numbe of unknown paametes (Yang and Lin,etc,2006). Moeove, such estimation equies lage computation effot and stoages, which can hadly be implemented by the mico-pocessos in the wieless senso netwok. To emove these dawbacks of the cuent EKF appoaches, a two-step Kalman estimato appoach, which is not available in the pevious liteatue, is poposed in this pape. In the fist step, stuctual esponse state vecto is consideed as an implicit function of the stuctual paametes, and the paametic vecto is estimated diectly by the Kalman estimato. In the second step, stuctual state vecto is updated by applying the Kalman estimato again. Thus, the numbes of unknown paametes to be estimated in each step ae geatly educed. Then, unknown extenal excitations ae estimated via least-squaes estimation. So, the poposed algoithm can identify stuctual paametes and unknown excitation in a sequential manne, which simplifies the identification poblem and both educe the computational effot and stoages compaed with othe existing wok. Fo the identification of a lage numbe of unknown paametes in lage size stuctual systems, its computational effots incease temendously. Consequently, substuctual identification appoaches ae used, in which a lage size stuctue is decomposed into smalle size substuctues with fewe unknown paametes (Koh and Hong, etc, 2003; Tee and Koh, 2005; Lei and Wu, 2009). The poposed stuctual damage detection algoithm is extended to detect local damage of lage size stuctues based on substuctue appoach. Inte-connection effect between adjacent substuctues is consideed as the unknown inputs to substuctues at substuctue intefaces (Lei and Wu, 2009). Element level stuctual paametes and the unknown inputs to the substuctue ae identified by the above two step Kalman estimato and the least squaes estimation appoach. In this pape, the poposed algoithm enables distibuted identification of local damage in lage stuctues with limited input and output measuements. Based on the advantages of the designed wieless senso netwok and the poposed distibuted stuctual damage decoction algoithm, autonomous detection of stuctual local damage in lage-size stuctues can be conducted by the implementation of the damage detection algoithm into the clustes of the wieless senso netwok, which gants the wieless senso netwok with smat chaacteistics. In this pape, the established smat senso netwok is expeimentally veified by application to detect local damage in a multi-stoy fame in lab to demonstate its pefomance in autonomous detection of stuctual damage.

3 2 THE WIRELESS SENSOR NETWORK Recently, a hieachical wieless senso netwok has been designed by the authos. Powe consumption analysis and pimay expeiment tests on the accuacy of data acquisition, time synchonization of measuement data and othe capabilities of the wieless senso units validate that the designed wieless senso netwok possesses favoable pefomances of data collection, tansmission and distibuted computation (Lei and Lai,2011; Lei, Lai and Liu,2011) Senso Netwok Topology Many senso netwoks equie deploying a lage numbe of sensos. Hieachical clusteing is geneally consideed as an efficient and scalable way to facilitate the management and opeation of such lage-scale netwoks and minimize the total enegy consumption fo polonged lifetime. In this pape, a two-level cluste-tee netwok topology is poposed fo the wieless senso netwok as shown in Fig.1. A lage-size stuctue can be divided into substuctues. The distibuted senso units deployed in a substuctue ae gouped into a cluste. A cluste head is assigned to each cluste to coodinate the sensos in its cluste and to collect data fom them duing monitoing. Communication between the distibuted senso units with thei coesponding cluste head foms the lowe tie and the netwok of cluste heads foms the uppe tie. To povide fault-toleance to the netwok in a case when a cluste head goes out of sevice, which could happen, fo example, duing an exteme event such as an eathquake, a backup cluste head is designed fo each cluste head, so the whole senso netwok can still wok with the backup cluste head though the backup outs as shown in Fig.1(Jiang and Zhou,2009). nth substuctue Backup Cluste Head Cluste Head Cente Senso Routing Backup outing Fig. 1 A two-level cluste-tee wieless netwok topology A cluste head not only seves as a oute of the netwok messages but also possesses computational capabilities with the data collected fom the senso nodes in the cluste. This netwok topology povides paallel computation between the substuctues, which is a useful featue fo the implementation of computational methodologies fo stuctual health monitoing and damage detection of lage-size stuctues. Finally, a cente node combines the function of a cluste head with additional computational capabilities that can be used fo the final decision of stuctual damage detection Hadwae Design Fig.2(a) shows the oveall hadwae design of the wieless senso unit. The senso unit mainly consists of six functional modules: 1) senso inteface; 2) signal conditioning; 3) senso signal digitization; 4) computational coe; 5) wieless communication; 6) battey management. To package the selected hadwae components into a compact wieless senso pototype, a two-laye pinted cicuit boad was designed and fabicated. As shown in Fig 2(b), all electical components ae suface mounted to the pinted cicuit boad.

4 (a) Hadwae stuctue the wieless senso unit. (b) Pototype of the senso unit Fig. 2 Wieless senso unit in the senso netwok In this design, The Chipcon CC2430 is selected as the wieless tansceive, which is a tue System-on-Chip (SoC) solution specifically tailoed fo IEEE and ZigBee applications. In the computation coe, a low-cost, low-powe 8-bit Atmel AVR micocontolle (ATmega 128) is selected fo the senso unit. The micocontolle, togethe with cetain intenal and extenal memoies, povides the capability of onboad data inteogation at the senso level [Lynch and Loh,2006; Lynch 2007; Atmel Copoation,2004]. The hadwae stuctue of a cluste head in the designed wieless senso netwok is simila to that of a senso unit except that its computational coe is eplaced by a TMS320C5409 digital signal pocesso (DSP) as shown in Fig. 3(a). TMS320VC5509 has a stong data pocessing capacity of 400 Million Instuctions Pe Second (MIPS) and a 512Kbyte RAM with a powe consumption of 100mW. Thus, it povides the capability of inteogating the lage amount of data collected fom the sensing nodes in the cluste with low powe consumption. This design wieless senso netwok povides paallel computation esouces at goup level, which is a unique featue compaed with othe senso netwoks. Fig. 3(b) shows the pototypes of a cluste.

5 Fig. 3 Hadwae stuctue of a wieless cluste head 3 ALGORITHM OF DISTRIBUTED STRUCTURAL DAMAGE DETECTION In pactical stuctual damage detection, it is often impossible to deploy many sensos to accuately measue all excitation inputs and all output esponses of stuctues. Theefoe, it is essential to develop an efficient technique which can detect stuctual local damage utilizing only a limited numbe of measued esponses of stuctues subject to some unmeasued excitation inputs. Fo a lage-size stuctue, which involves a lage numbe of Degees-of-feedom (DOFs), it is easonable to apply substuctue appoach to educe the computational budens and the difficulty in obtaining easonably accuate esults of stuctual damage detection (Yang and Lin,2006; Koh and Hong, 2003; Tee and Koh,2005). The equation of motion of a substuctue can be extacted fom the equation of motion of a lage size stuctue to yield as: x (t) x(t) u u M x (t) + [ C C s ] [ s ] (t) (t) s(t) + K K s(t) = B f + B f x x whee subscipt denotes intenal DOFs of the substuctue concened, subscipt s denotes inteface DOFs, x, x and x ae vectos of displacements, velocity and acceleation esponse of the coesponding stuctue, espectively; M, C and K ae mass, damping, and stiffness matices of the coesponding stuctues, u espectively; f () t is a measued extenal excitation vecto, f (t) is an unmeasued extenal excitation u u vecto, and B and B ae the influence matices associated with f () t and f (t), espectively. Usually, mass of a stuctue can be estimated with accuacy based on its geomety and mateial infomation. Fo simplicity, it can be assumed that mass matix is a diagonal matix. (1) By teating the inteconnection effects as unknown inputs to the substuctue, the above equation can be e-aanged as : u * M x (t) +C x (t) + K x (t) = B f (t) + B f (t) + B f (t) (2) whee f (t) is the unknown input vecto at the substuctue inteface, * associated with the unknown inputs f * t, and * B is the influence matix * * B f (t) = C x (t) K x (t) (3) s s s s

6 Intoducing a state vecto X = x x T, one can tansfom Eq.(2) into a state equation, i.e., x = X (,,, ) (t) (t) (t) (t) (t) g X, f f f M B f + B f + B f C x K x 1 u * (4) in which denotes the paametic vecto of the substuctue Some sensos ae deployed on the substuctue to measue the esponse signals. Usually acceleation signals ae measued and the obsevation vecto of the focused substuctue can be expessed in the discetized fom as: y[k] = D x[k] + v[k] = h X[k], θ[k], f[k], f [k], f [k] + v [k] (5) in which v [k] is the measued noise vecto. Extended Kalman Filte (EKF) has been shown to be useful fo stuctual identification with limited esponse outputs (Hoshiya and Saito1984; Saito and Takei,1998; Yang and Lin,2006), but in the EKF appoach, the augmented state vecto includes the unknown stuctual paametes. Stuctual state vecto and the paametic vecto ae estimated simultaneously. Such estimation equies lage computation effot and stoages, which can hadly be implemented even by the DSPs of a cluste head in the designed wieless senso netwok. In this pape, a two-step Kalman estimato appoach is poposed. In the fist step, stuctual esponse state vecto is consideed as an implicit function of the unknown stuctual paametes. Then the discetized obsevation can be e-witten as: y [k] = h X ( θ [k]), θ [k], f [k], f [k], f [k] + v [k] (6) Let θ ˆ [k k-1] be the estimated value of θ [k] and X ˆ [k k-1] be the estimated value of X [k] at time t=(k-1)dt. Since hx ( θ[k]), θ[k], f[k], f [k], f [k], which is a nonlinea function of unknown paametic vecto Taylo expansion, i.e. h whee θ, it can be lineaized aound θ ˆ [k k-1] though ˆ ˆ X ( θ[k]), θ[k], f[k], f [k], f [k] = h X[ k k - 1], θ[ k k - 1], f[k], f [k], f [k] + H ˆ [k] θ[k] θ[k - 1] H [k] is deived based on the chain ule of patial diffeentiation as: H [k] = H [k] + H [k] X [k] (8),θ, X,θ h h X H [k] = ; H [k] = ; X [k],θ, X,θ θ ˆ ˆ X ˆ ˆ θ X ˆ = X[ k k-1], θ= θ[ k k-1] X= X[ k k -1], θ= θ [ k k -1] θ= θ[ k k-1] (9) Then, the ecusive solution fo the paametic vecto can be estimated based on Kalman estimato as: ˆ ˆ ˆ ˆ θ[k+1k] = θ[k k-1] + K [k] y[k] h X[k k-1], θ[k k-1], f[k], f [k], f [k] (10) in which K [k] is the Kalman gain matix fo θ given by: (7)

7 T -1 K P H H P H R T [k] = [k] [k] [k] [k] [k] + [k] (11) and P [k] is given by = P [k] I K [k] H[k] P [k - 1] (12) In the second step, the ecusive solution fo the stuctual state vecto is deived based on the Kalman estimato as: ˆ ˆ ˆ X[ k +1 k] X [ k +1 k] + K [k] [k] [ k k - 1], [ k k - 1], [k], [k], [k] X y h X θ f f f (13) whee (k+1)δt ˆ ( ˆ X[k+1k]= X[k k-1]+ g X,, f, f, f ) dt (14) kδt K X [k] is the Kalman gain matix fo state vecto Diffeentiation both sides of Eq.(4) with espect to Then, X [18]. θ, one can deive the equation fo X,θ as: ( ),,θ = g,θ θ, ( ) g( X, f, f, f ) X = X X, (15) X (k+1)δt = +, ( ), ˆ, θ[k+1k] X, θ[k k-1] dt kδt g X X θ (16), θ Howeve, since f and f ae unknown inputs to the substuctue concened, it s impossible to obtain ecusive solution by the classical extended Kalman estimato alone. Fo the geneal case that measuements (sensos) ae not available at the DOFs at the substuctue inteface, * the unknown input f at time t=(k+1) t can be estimated based on its expession in Eq.(2), i.e., ˆ * Bf [k+1 k] = Cˆ [k+1 k] x ˆ [k+1 k] Kˆ [k+1 k] xˆ [k+1 k] s s s s (17) in which f ˆ * [k+1k] is the estimation of f * [k +1] given the estimated values of extended state vecto in diffeent substuctue. With the estimated value of the unknown input f ˆ * [k+1k], the unknown extenal excitations f ˆ * [k+1k] can be estimated by the least squae estimation. So, the poposed algoithm can identify stuctual paametes and unknown excitation in a sequential manne, which simplifies the identification poblem and educe both computational effot and stoages compaed with othe existing wok. 4 STRUCTURAL DAMAGE DETCTION BY THE SMART SENSOR NETWORK

8 The designed hieachical senso netwok has a two-level cluste-tee achitectue. The distibuted sensos ae gouped into clustes, in which a cluste head assigned to each cluste not only seves as a oute of the netwok messages but also possesses computational capabilities with the data collected fom the sensing nodes in the cluste, especially the cluste head consists of a low powe DSP with stong computing capacity. Theefoe, the senso netwok povides paallel computation esouces at goup level, which is a paticulaly useful featue fo the implementation of computational methodologies fo stuctual health monitoing and damage detection of lage-size stuctues. The poposed stuctual damage detection algoithm can detect local damage of lage size stuctues based on substuctue appoach. Element level stuctual paametes and the unknown inputs in the substuctue ae identified by a two step Kalman estimato and the least squaes estimation appoach. The algoithm enables distibuted identification of local damage in each substuctue of lage stuctues with less computational effot and stoage compaed with othe existing algoithms. Based on the unique advantages of the damage decoction algoithm and the wieless senso netwok, autonomous detection of stuctual local damage can be conducted by implementation of the damage detection algoithm on the wieless senso netwok. The poposed algoithm fo distibuted detection of stuctual damage is coded in C language and embedded into the cluste heads in the wieless senso netwok, which gants the wieless senso netwok with smat chaacteistics. 4.1 Expeimental Validation To assess the pefomance of the smat wieless sensing netwok fo stuctual damage detection, detecting stuctual damage of an eight-stoy shea type building in lab is selected as an expeimental example, as shown by Fig.4. Fig. 4 Expeimental study with an eight-stoy building in lab Fig.5 Stuctual Damage detection with smat senso netwok

9 The stuctual model behaves as a lumped m ass shea stuctue. The building is excited by a magnetic shake, which induces unmeasued white noise inputs to the building at the 3d stoy level. Six light PCB acceleometes ae installed at the 1st, 3d, 4th, 5th 7th and 8th floos to measued the acceleation esponses of at the coesponding floo levels. Stuctual damage is simulated by eplacing the flexible columns with ones with fewe thicknesses, which esults in the eduction of coesponding stoy stiffness k i (i=1, 2,.8). In the expeiment, stuctual damage is assumed to occu in the 5th stoy which leads to the eduction of k 5. In the substuctue appoach, the building is divided into two substuctues with floos 1-4 being the 1st substuctue and floos 5th-8th being the second one as shown as shown in Fig. 5. Topology of the wieless senso netwok is also shown by Fig. 5. In each substuctue, the thee senso units ae gouped into a cluste. One of the senso unit (SU) in each substuctue is assigned as the cluste head (CH) to each cluste to collect data fom them duing vibation. Each CH is implemented with the algoithm fo stuctual damage detection. Theefoe, each CH not only seves as a oute of the netwok messages but also can detect stuctual damage in diffeent substuctues concuently with paallel computing. Then, the identified stuctual dynamic paametes of each substuctue ae sent by each cluste head (CH) to the cental seve whee a final decision of stuctual damage detection is made by compaing the identified stoy stiffness with those of undamaged stuctues. Fig.6(a) shows the esults of the identified stoy stiffness paametes sent to the PC seve fo the undamaged building while the identified values stoy stiffness paametes fo the damaged building ae shown on the cental seve in Fig. 6(b). By compaing these two identification esults shown on the PC seve in Figs.6 (a)-6(b), it is clealy shown that the poposed technique can autonomously detect and localize the stuctual damage based on the degading of identified values of element stiffness paametes of k 5. (a): Identification esults of stoy stiffness of the undamaged building (b): Identification esults of stoy stiffness of the damaged building Figue 6: Identification esults of stoy stiffness of the building

10 5. CONCLUSIONS In this pape, a smat wieless senso netwok is established fo autonomous stuctual damage detection. The designed hieachical wieless senso netwok has a two-level cluste-tee achitectue. The distibuted sensos ae gouped into a cluste, in which a cluste head consists of a low powe DSP with stong computing capacity. Thus, the senso netwok povides paallel computation esouces at goup level, which is a paticulaly useful featue fo the implementation of computational methodologies fo stuctual health monitoing and damage detection of lage-size stuctues. An algoithm fo distibuted stuctual damage detection with limited input and output measuements is poposed. The algoithm is based on a two step Kalman estimato and can identify stuctual paametes and unknown excitation in a sequential manne, which simplifies the identification poblem and educes both computational effot and stoages compaed with othe existing algoithms. A smat wieless senso netwok fo autonomous detection of stuctual local damage is implemented by embedding the poposed stuctual damage detection algoithm into the cluste heads in the wieless senso netwok. Lab expeiment of detecting local damage in a multi-stoy shea building shows that smat senso netwok is effective fo autonomous detection of stuctual damage with limited input and outputs measuements. Moe eseaches on the applications of the established smat senso netwok fo autonomous stuctual damage of othe lage-size stuctues in complex configuations ae needed to futhe validate the pefomances of the smat senso netwok. ACKNOWLEDGMENTS This eseach has been patially suppoted by the National Natual Science Foundation of China (NSFC) though Gant No and by China National High Technology Reseach and Development Pogam 2007AA04Z420. REFERENCES Ababneh. N.(2010). Pefomance Evaluation of a Topology Contol Algoithm fo Wieless Senso Netwoks. Intenational Jounal of Distibuted Senso Netwoks, Aticle ID: Atmel Copoation.(2004). Atmel 8-bit AVR Micocontolle with 128k Bytes In-System Pogammable Flash. Atmel Copoation, San Jose, CA. Chang. F.K. (ed.)(2007),(2009). Poceedings of the 6th and 7th Intenational Wokshops on Stuctual Health Monitoing, Stanfod Univesity. Stanfod, CA, CRC Pess, New Yok. Chipcon Inc. Gao.Y., Spence J. B.F. and Ruzi-Sandoval. M.E.(2006). Distibuted computing stategy fo stuctual health monitoing. Stuctual Contol Health Monitoing..13, Glase. S.D., Li. H., Wang. L.M., Ou.J.P. and Lynch.J.P. (2007). Senso technology innovation fo the advancement of stuctual health monitoing: a stategic pogam of US-China eseach fo the next decade. Smat Stuctues and Systems, 3:2, Gu.Y., Wu.Q.S. and Rao.N.S.(2010). Optimizing Cluste Heads fo Enegy Efficiency in Lage-Scale Heteogeneous Wieless Senso Netwoks. Intenational Jounal of Distibuted Senso Netwoks, Aticle ID: Hoshiya. M. and Saito. E.(1984). Stuctual identification by extended Kalman filte. Jounal of Engineeing Mechanics (ASCE).110:12, IEEE Compute Society. IEEE Standad , The Institute of Electical and Electonics Enginees, Inc. 3 Pak Avenue, New Yok, NY , USA, Jiang. N., Zhou. R.G. and Ding. Q.L.(2009). Dynamics of Wieless Senso Netwoks. Intenational Jounal of Distibuted Senso Netwoks.. 5:6, Koh.C.G., Hong.B. and Liaw.C.Y.(2003). Substuctual and pogessive stuctual identification method. Engineeing Stuctues, 25,

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