An Algorithm of Mobile Robot Node Location Based on Wireless Sensor Network

Transcription

1 A Algorithm of Mobile Robot Node Locatio Based o Wireless Sesor Network Peg A Nigbo Uiversity of Techology, Zhejiag, Chia eirxvrp2269@26.com Abstract I the wireless sesor etwork, there is a cosistet oe-to-oe match betwee the iformatio collected by the ode ad the locatio of the ode. Therefore, it attempts to determie the locatio of ukow odes for wireless sesor etworks. At preset, there are may kids of ode localizatio methods. Because of the distace error, hardware level, applicatio eviromet ad applicatio costs ad other factors, the positioig accuracy of various ode positioig methods is ot i complete accord. The objective fuctio is established ad algorithm simulatio experimets are carried out to make a mobile root ode localizatio. The experimettal results showed that the proposed algorithm ca achieve higher localizatio precisio i fewer odes. I additio, the localizatio algorithm was compared with the classical localizatio algorithm. I coclusio, it is verified that the localizatio algorithm proposed i this paper has higher localizatio accuracy tha the traditioal classical localizatio algorithm whe the umber of odes is larger tha a certai umber. Keywords wireless sesor, mobile robot ode locatio, algorithm optimizatio Itroductio The geeral coditios of the mobile robot workig eviromet is complex, ad some areas are ot eve suitable for huma to stay, so i order to esure that the robot ca successfully complete the task, the operator eeds to cotrol the real-time locatio of their work []. Therefore, the mobile robot eeds to be tracked ad positioed durig operatio. Wireless sesor etworks have the fuctio of mutual positioig betwee odes, ad mobile robots ca oly be tracked by the correspodig positioig service, ad the complete the task [3]. Therefore, it is very importat i practice to apply the localizatio fuctio of wireless sesor etworks to the localizatio of mobile robots [6]. The process of sesor ode locatio is to idetify the positio of the ode to be located (kow as the ukow ode) accordig to a certai positioig mechaism by usig a few kow locatio beaco odes (also kow as achor odes) [8]. Therefore, after the ukow ode is correctly positioed, it ca locate the specific locatio of the evet that the sesor ode moitors. Thus, the accurate positioig of the se- 4

2 sor odes is the primary coditio for the moitorig of the locatio iformatio service [2]. The odes are placed o the mobile robot, they costitute a mobile wireless sesor etwork odes, so the localizatio problem of the mobile robot ca be trasformed ito the problem of locatig the odes o the robot. I the wireless sesor etwork, the geeral structure is show i figure. O the oe had, the odes participatig i the work are limited i the eergy supply due to the coditio, ad because of this reaso, its fuctio will be greatly reduced [4]. O the other had, because of the low cost of ode productio, a large umber of odes have to be ivolved to complete the task. This poses a very high requiremet for the localizatio techology ad localizatio algorithm of ukow odes. Therefore, it is ecessary to develop a scietific ad effective localizatio algorithm for mobile robots based o the iheret characteristics of low eergy cosumptio, low productio cost, isufficiet storage ad commuicatio capacity, ad computatioal capability of the mobile robot [5]. Global Iteret Maagemet ode Sik odes Wireless AP Eergy supply module Sesor module Processor module Wireless commuicatio module Fig.. The ode structure ad system structure of the sesor etwork 2 State of the art I 949, the US Navy lauched ati-submarie soar moitorig system research program. I 960s, durig the Vietam War, whe the target was movig i the viciity of the sesor, the soud ad vibratio of the target will be detected by the tropical tree. Tropical trees will immediately sed the data to the strategic commad ceter by radio waves. Through the use of related techologies, the commad ceter itegrates iformatio data to get the vehicle's directio, locatio, umber ad other importat iformatio. The, the decisio maker decided to take the ext step, such as commad fighter bombig. ijoe Vol. 3, No. 5, 207 5

3 I 978, the US Defese Advaced Research ad Plaig Agecy (DARPA) fuded the Caregie Mello Uiversity's Distributed Sesor Network Workig Group to study the techology of distributed wireless sesor etworks. I 990, DARPA lauched a low-power Wireless Itegrated Microsesors (LWIM) program, ad desiged CMOS itegrated micro-system techology miiaturized low-power wireless sesor. Later, i 993, it lauched a wireless itegrated etwork sesor (referred to as WINS) research program. The program maily studies the microelectromechaical system sesor commuicatio circuit, etworkig protocol ad processig architecture. I 998, uder the support of DARPA ad other istitutios, the maistream of the Uited States lauched a smart dust program. The program eables highly itegrated computig devices with sesig ad commuicatio. There is a cubic millimeter size of the micro-sesor. Compared with some developed coutries, the research of wireless sesor etworks i Chia is relatively late, but the developmet is very rapid. The research o wireless sesor etworks maily focuses o miiaturizatio ad itelligetizatio of sesor odes, topology cotrol, etwork protocols (icludig routig protocols ad MAC protocols), ode locatio ad target trackig, data fusio, Qo S protectio ad reliability desig, ad etwork security ad so o. Most of the domestic research still stays at the simulatio level. Due to the fact that the actual eviromet is complex ad ucertai, there is still a big gap from the practical applicatio. I summary, most of the above-metioed positioig algorithms require expesive equipmet to support the operatio of the algorithm, ad it is critical to the stability of the eviromet. Therefore, i this paper, we proposed a localizatio method for mobile robot odes, ad carried out the simulatio experimet. I the simulatio experimet, after selectig the objective fuctio, the algorithm is optimized by the rakig method. The experimetal results show that the proposed algorithm ca achieve higher localizatio precisio i fewer odes. I this paper, the evirometal parameters, amely the path loss factor k, are calculated, ad the calculated value of k is simulated ad aalyzed by usig the optimized localizatio algorithm. At last, this paper compared the localizatio algorithm with the classical localizatio algorithm. 3 Evirometal parameters I the existig ragig algorithm, the ragig algorithm based o sigal stregth (RSSI) is oe of the most commo localizatio algorithms i wireless sesor etwork [7]. It does ot rely o the assistace of additioal expesive equipmet, oly uses the sigal stregth of the received wireless sigal to judge, ad the calculates the relative distace betwee the trasmitter ad receiver. Because of the ifluece of the trasmissio medium, such as the electromagetic wave, the sigal propagatio model ad parameters of differet media eviromet are also differet. If the fixed sigal propagatio model ad parameters are used to calculate the sigal stregth ad distace betwee the receiver ad trasmitter, the result is certaily uscietific. Therefore, this paper presets a method to calibrate the evirometal parameters, ad carries o the parameter calibratio experimet. 6

4 I the calculatio, we eed to determie the two parameter values: eviromet parameter k ad ode parameter P + G, that is, the path atteuatio factor, the trasmissio power P of the ode ad the atea receptio gai G of the ode (with respect to the ode, the relative chage is ot large), but K chages with the chage of the exteral eviromet, ad has a great ifluece o the measuremet results, so it eeds to be calibrated i time. I the case where the differet receivers are away from the source distace d, the RSSI values are tested by the followig equatios: $ PL( d) PL( d0) + 0k & log( d / d0) # PL( d0) k & log( d0) + 0k & log( f ) " RSSI ( P + G) % PL( d) There are measuremet positios, dd, d2,..., d, respectively, the followig equatios ca be obtaied: $ & k(0 % log( d / d ) + 0 % log( d ) + 0 % log( f )) RSSI & k(0 % log( d2 / d0) + 0 % log( d0) + 0 % log( f )) RSSI # " & k(0 % log( d / d0) + 0 % log( d0) + 0 % log( f )) RSSI Amog them: & ( 0 ' log(d /d )( 0 ' log(d )( 0 ' log(f) # $ 0 0 $ ( 0 ' log(d /d )( 0 ' log(d )( 0 ' log(f) A $ $ $ $ ( 0 ' log(d /d )( 0 ' log(d )( 0 ' log(f % 0 0 " & RSSI # $ $ RSSI B $ $ $ % RSSI " $ P + G X [ $, k] T " T X ( A A) A B From the above calculatio process, we ca calculate the trasmitter power P ad the atea gai G of the ode ad the path atteuatio factor k, so as to realize the calibratio of the parameter k. # 0 ijoe Vol. 3, No. 5, 207 7

5 The value of the evirometal parameter k is ofte estimated empirically. The actual eviromet parameters ad ukow odes i the simulatio process ca be set maually. This sectio uses the followig process for simulatio: Fig. 2. Parameter calibratio schematic I the Figure 2, we first select the eviromet parameter k i a real eviromet, ad the set the ukow ode locatio ad combie the actual eviromet parameters with the above formula to geerate a large umber of beaco odes' positio ad sigal stregth iformatio. By usig the geerated beaco odes to carry out calibratio calculatio, the calibratio results are combied with the beaco ode iformatio to calculate the localizatio error of ukow odes; by usig the actual eviromet parameter ad the geerated beaco ode iformatio to calculate the localizatio error of the ode; by usig the estimated evirometal parameter ad the geerated beaco ode iformatio to calculate the positioig error of the ukow ode. At last, we compare ad aalyze the positioig errors of the three calculatios. I order to make the simulatio results more scietific, several sets of beaco odes are geerated i the simulatio. The distributio of the odes is similar to that i Figure 3, where the red poit is the ukow ode ad the beaco ode is scattered i the tested area as far as possible. At the same time, each group of data will be calculated for differet beaco odes, ad the the average value ad the maximum value are calculated for the same set of data. 8

6 set the actual evirometal parameters estimate the evirometal parameters geerate a beaco ode calibratio calculatio calculate the ode positio error calculate the ode positio error calculate the ode positio error comparative aalysis Fig. 3. Simulatio process 4 Positioig algorithm priciple Referrig to Figure 3, i the three-dimesioal coordiate system, the surface of the graph is the theoretical distributio of the sigal stregth with distace uder the eviromet where the ukow ode is located, amog them, the X-axis ad Y-axis are used to measure the distace from the beaco ode to the ukow ode, ad the Z-axis is used to measure the received sigal stregth at the locatio of the beaco ode. I the Figure 4, p, p2, p3 p are the actual positios of the beaco odes i the three-dimesioal coordiate system, ad L, L2, L3 L is the offset of the beaco ode relative to the actual sigal stregth. Sice the shape of the surface i the figure is related to the path atteuatio factor k ad the coordiates of the ukow ode, so we ca costruct the appropriate objective fuctio to fid the coordiates of k ad ukow odes. As ca be see from the Figure 4, if the surface is offset a short distace i the directio perpedicular to the z-axis, the offset of each beaco ode must ijoe Vol. 3, No. 5, 207 9

7 be chaged. It is ot difficult to see that whe the surface shape ad surface locatio closer to the ukow ode sigal stregth of the theoretical surface, the correspodig calculated k ad the coordiates of the ukow ode is more accurate. The wireless sesor etwork positioig priciple that proposed i this paper ca be described as follows, assumig there are (> 3) sesor beaco odes, ad the positio coordiates of each beaco ode are kow, the sigal stregth RSSI ( of i ode to ukow ode i beaco ode is kow. Accordig to the logarithmic ormal distributio path loss model of the sigal, the objective fuctio is established. By fidig the optimal solutio of the objective fuctio, the coordiates (x, y) of the ukow ode ad the path atteuatio factor k are determied. 5 Establish the objective fuctio Accordig to the priciple of localizatio, we ca costruct the appropriate objective fuctio, ad get the coordiates of the ukow ode ad the path atteuatio factor k by solvig the optimal solutio of the objective fuctio. The we discuss how to costruct the objective fuctio. I order to miimize the z-distace of a ode to a surface i a model, that is, 2 Z ( RSSI(,( Z( RSSI( ) or Z( RSSI( is miimum, if there are odes i total, the objective fuctio ca be costructed as: that is, Z( " RSSI( Z( " RSSI( Z( " RSSI( 2 P + G " " 0k lg( f ) " 0k lg( d ) " RSSI( ( P + G " " 0k lg( f ) " 0k lg( d ) " RSSI( ) P + G " " 0k lg( f ) " 0k lg( d ) " RSSI ( Aother objective fuctio is to fid the algebraic sum of odes (Z (-RSSI (), ad the fid the absolute value of the result: i i i 2 0

8 ( Z( " RSSI( ) ( P + G " " 0 # k lg( f ) " 0k lg( d i ) " RSSI( ) 6 Algorithm simulatio experimet I order to aalyze the localizatio effect of each objective fuctio whe the ukow ode is located, it is ecessary to use the simulatio software to compare the calibratio results of each objective fuctio, so as to choose more scietific objective fuctio for i-depth aalysis. 6. Simulatio process The ode localizatio algorithm that proposed i this paper is realized by M laguage programmig o matlab200b simulatio platform. I the simulatio eviromet, the ukow odes ad the beaco odes are distributed i a plae area of 5m * 5m. Steps ca be see i figure 4. Geerate a beaco ode: First of all, to determie the parameters of the simulatio model, i matlab usig three-dimesioal drawig commad to draw the model surface. Sice the locatio iformatio ad the sigal stregth of the beaco ode are kow durig the simulatio, so the theoretical value of the beaco ode ca be take o the model. The resultig beaco odes cosist of x, y coordiates ad the sigal stregth at that poit, each ode produces a set of data. I the simulatio, 50 sets of data are take at oe time. geerate a beaco ode data is added to radom oise data filterig determie the optimal iitial value optimizatio calculatio result drawig Fig. 4. Simulatio process ijoe Vol. 3, No. 5, 207

9 Data is added to radom oise: After the theoretical sigal stregth of the beaco ode is obtaied i the previous step, the Gaussia radom oise of the sigal oise is added to the data. The mea value of the oise sigal is 0 ad the stadard deviatio is the iitial value. The theoretical value of the sigal ad the algebraic sum of the oise sigal costitute the actual sigal stregth of the beaco ode. Data filterig: Due to the large umber of data, there are some idividual beaco odes too far from the ukow ode, the actual ukow odes caot receive the sigals. I this case, if the data are take ito the ext step, the calculated positioig error is ot large, which is ot coducive to improve the positioig accuracy. Therefore, it is ecessary to optimize the data before the screeig. Determie the optimal iitial value: Usig the optimizatio fuctio of matlab to optimize the calculatio, the iitial value of the determiatio will affect the accuracy of the results. For this reaso, other localizatio methods are used to carry out the iitial positioig before the optimizatio calculatio. I this paper, we use the maximum likelihood method to determie the iitial value, that is, first select some data i the data set, use the maximum likelihood method to calculate a rough ukow ode, ad the use the optimizatio fuctio i matlab to optimize the calculatio. Optimizatio calculatios: We ivoke the preparatio of the target fuctio file, use the optimizatio fuctio i matlab to optimize the calculatio, ad obtaied the positio coordiates of the ukow ode ad the value of the path loss factor k, ad compared with the set values i the iitializatio to calculate the errors. Result drawig: Usig the matlab drawig commad output the error compariso chart. 6.2 The selectio of objective fuctio Accordig to the simulatio flow ad the objective fuctio i the last chapter, the umber of ukow odes is 5i (i ~ 0). Whe the values of k are 2, 3, 4, we calculate 50 sets of data ad get the simulatio results (Figure 5). Figure 5 shows that uder the coditio of the objective fuctio is the sum of the absolute values, the sums of squares, the sum of square root, ad the absolute value after summatio, the simulatio results of select differet Gaussia radom variables ad differet path loss factors. From the average error ad maximum error, the average error curve ad the maximum error curve of the absolute value of the objective fuctio are compared with the error curve of the other objective fuctio, the variatio rage ad the tred of the error idicate that the fuctio correspodig to the absolute value after summatio is ot the best objective fuctio. Therefore, it will be rouded out i the follow-up simulatio study. I the remaiig objective fuctio, the performace of the sum of square ad the sum of absolute values is good, ad the performace of the sum of square root is i geeral. Therefore, the follow-up study will focus o the sum of the squares ad the sum of absolute values as the objective fuctio. 2

10 K2,the radom variable is.5 K4,the radom variable is 4 Fig. 5. Average error of ode positio As ca be see from Figure 6, the ode locatio method adopted i this paper ca effectively locate the odes. Whe the umber of odes is small, the curve appears iflectio poit. Whe the umber of odes gradually icreased, the curve becomes smooth ad slow. This idicates that the further icrease of the umber of beaco odes has a weak ifluece o the positioig accuracy. ijoe Vol. 3, No. 5, 207 3

11 K2,the radom variable is.5 K4, the radom variable is 2 Fig. 6. Maximum error of ode positio Coclusio: The sum of absolute values ad the sum of square is most suitable for the objective fuctio. 6.3 The calculatio of k values Accordig to the simulatio flow of Figure 8, we ca calculate the ode coordiates, at the same time, calculate the path loss factor K. Sice the actual rage of k is 4

12 2 to 5, so i the optimizatio calculatio, we first determie the optimal value of the iitial calculatio, while give the chage iterval of k is 2 to 5. As with the ukow ode coordiates, we would like to kow what is the relatioship betwee the selectio of the objective fuctio ad the calculated value of k. Therefore, the followig simulatio experimet is to explore whe the selectio of differet objective fuctio, the variatio of the calculated value of K with the icrease of ode umber. K2, the radom variable is K4,the radom variable is.5 Fig. 7. The mea value of the calculated value of k ijoe Vol. 3, No. 5, 207 5

13 I the Figure 7, the maximum value of the calculated value of k is mostly above the true value. The sum of square root i the objective fuctio fluctuates too violetly with respect to the sum of square ad the sum of absolute values. Therefore, whe select the objective fuctio, the sum of square root is removed. While the sum of the absolute values ad the sum of square are relatively stable, ad the fluctuatio rage is smaller ad better tha the other objective fuctio i the maximum value of the calculated value of k, so it is appropriate as the objective fuctio. Coclusio: The sum of the absolute values ad the sum of the squares i the objective fuctio are suitable as the optimal objective fuctio, which is beeficial to the improvemet of the path atteuatio factor. 7 Coclusio I this paper, we proposed a localizatio method for mobile robot odes, ad carried out the simulatio experimet. I the simulatio experimet, after selectig the objective fuctio, the algorithm is optimized by the rakig method. The experimetal results show that the proposed algorithm ca achieve higher localizatio precisio i fewer odes. I this paper, the evirometal parameters, amely the path loss factor k, are calculated, ad the calculated value of k is simulated ad aalyzed by usig the optimized localizatio algorithm. At last, this paper compared the localizatio algorithm with the classical localizatio algorithm. The simulatio results show that the localizatio algorithm proposed i this paper has higher localizatio accuracy tha the traditioal classical localizatio algorithm whe the umber of odes is larger tha a certai umber. 8 Refereces [] Deshpade, N., Grat, E., & Hederso, T. C. (204). Target localizatio ad autoomous avigatio usig wireless sesor etworks A pseudogradiet algorithm approach. IEEE Systems Joural, 8(), [2] Hu, Y., Dig, Y., Hao, K., Re, L., & Ha, H. (204). A immue orthogoal learig particle swarm optimisatio algorithm for routig recovery of wireless sesor etworks with mobile sik. Iteratioal Joural of Systems Sciece, 45(3), [3] Mahboubi, H., Moezzi, K., Aghdam, A. G., Sayrafia-Pour, K., & Marbukh, V. (204). Distributed deploymet algorithms for improved coverage i a etwork of wireless mobile sesors. IEEE Trasactios o Idustrial Iformatics, 0(), [4] Rawat, P., Sigh, K. D., Chaouchi, H., & Boi, J. M. (204). Wireless sesor etworks: a survey o recet developmets ad potetial syergies. The Joural of supercomputig, 68(), [5] Sara, G. S., & Sridhara, D. (204). Routig i mobile wireless sesor etwork: A survey. Telecommuicatio Systems, 57(),

14 [6] Tua, G., Gugor, V. C., & Gulez, K. (204). A autoomous wireless sesor etwork deploymet system usig mobile robots for huma existece detectio i case of disasters. Ad Hoc Networks, 3, [7] Youis, M., Seturk, I. F., Akkaya, K., Lee, S., & Seel, F. (204). Topology maagemet techiques for toleratig ode failures i wireless sesor etworks: A survey. Computer Networks, 58, [8] Zhu, C., Shu, L., Hara, T., Wag, L., Nishio, S., & Yag, L. T. (204). A survey o commuicatio ad data maagemet issues i mobile sesor etworks. Wireless Commuicatios ad Mobile Computig, 4(), Author Peg A is Associate Professor at Nigbo Uiversity of Techology, Zhejiag, Chia (eirxvrp2269@26.com). Article submitted 05 April 207. Published as resubmitted by the author 5 May 207. ijoe Vol. 3, No. 5, 207 7