Range Free Localization Schemes For Wireless Sensor Networks

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1 Rage Free Localizatio Schemes For Wireless Sesor Networks ASHOK KUMAR, VINAY KUMAR AND VINOD KAPOOR Departmet of Electroics ad Commuicatio Egieerig Natioal Istitute of Techology Hamirpur (HP) INDIA Abstract: - Wireless Sesor Networks (WSNs) are beig used for a large umber of locatio-depedet applicatios, where the measuremet data is meaigless without accurate locatio of its origi. I may of these applicatios, where coarse accuracy is sufficiet, rage free localizatio techiques are beig pursued as low cost alterative to the rage based localizatio techiques. Localizatio i WSNs is to determie the physical positio of a sesor ode based o the kow positios of other sesor odes havig a priori kowledge of their positio. I this paper, we preset rage free Adaptive Neural Fuzzy Iferece System (ANFIS) traied Sugeo weighted cetroid localizatio ad combied Mamdai-Sugeo fuzzy localizatio methods. I the proposed techiques the weights of achor odes are obtaied either through ANFIS traied Sugeo fuzzy iferece system or by combied Mamdai-Sugeo fuzzy iferece approach. We compared the proposed techiques, through extesive simulatio with simple cetroid, Mamdai ad Sugeo fuzzy methods. The simulatio results demostrate the effectiveess of proposed schemes. Key-Words: - Cetroid localizatio, Edge weight, Fuzzy logic system, Rage-free localizatio, Wireless sesor etworks. 1 Itroductio Wireless sesor etworks (WSNs) is a emergig ad promisig techology for ubiquitous sesig, moitorig ad cotrollig home, office, city ad eviromet for large umber of applicatios [1]. Recet advaces i wireless commuicatios ad electroics has eabled the developmet of low cost, low power ad multifuctioal sesors, which whe etworked wirelessly provide broad spectrum of applicatios i defese, habitat moitorig, target trackig ad traffic moitorig etc. [3, 5]. Localizatio (locatio estimatio) capability is essetial i most of the WSN applicatios, where the measuremet data is meaigless without the kowledge of precise locatio from where it is obtaied. WSN localizatio techiques are used to estimate the locatio of sesor odes i the etwork usig a priori kowledge of locatio of few specific sesor odes i the etwork, kow as achor odes. The achor odes ca obtai their locatio usig global positioig system (GPS), or by placemet of achor odes at poits with kow coordiates. I applicatios requirig global coordiate systems, the achors determie the locatio of sesor odes with referece to the global coordiate system ad the applicatio where a local coordiate system is sufficiet, the positio of sesors are referred to the local coordiate system of etwork. May localizatio algorithms have bee proposed i literature for locatio estimatio of sesor odes i WSNs. The localizatio algorithms ca be divided ito two categories: ragebased localizatio methods [7, 10] ad rage-free localizatio methods [4, 9, 12]. Rage-based localizatio is defied by protocols that use absolute poit-to-poit distace estimates (rage) or agle estimates for calculatig the locatio. These methods utilize the measuremet such as agle of arrival (AOA), time of arrival (TOA), time differece of arrival (TDOA) ad received sigal stregth (RSS) profilig. I cotrast, rage-free localizatio schemes make o assumptio about the availability or validity of such iformatio. Normally rage-based schemes have higher locatio accuracy tha the rage-free localizatio schemes, but are hardware itesive [2]. Because of the hardware limitatios of WSN devices, solutios i rage-free localizatio are beig pursued as a cost-effective alterative to more expesive rage-based approaches. Bulusu et al. proposed a simple ad ecoomic rage free localizatio techique, based o proximity based cetroid algorithm [9]. I this method the sesor ode localizes itself by calculatig the cetroid of positio of all the adjacet coectig achor odes. Though simple ad ecoomic, the localizatio estimates of this method are quite poor with large error i measuremet. To improve the performace of cetroid algorithm, a ehaced weighted cetroid algorithm was proposed by Kim ad Kwo [6]. The method provides good accuracy i localizatio. But the accuracy of localizatio depeds upo optimized selectio of edge weights, ISBN:

2 which are selected heuristically i the proposed method. Y.Sukhyu et al. used TSK fuzzy modelig for calculatig the weights of achor odes [15]. I this method, geetic algorithm is used to optimize the fuzzy membership fuctio to obtai optimized edge weight based o the received sigal stregrh (RSS). Y. Sukhyu et al. also proposed eural etwork approach for calculatio of weights for cetroid algorithm [14]. Both of these approaches provide acceptable accuracy i localizatio of sesor odes. But the computatioal complexity is quite high for resource costraied sesor odes. I this paper we propose rage free localizatio techiques for WSNs based o weighted cetroid algorithm. The weights of achor odes have bee obtaied based o the received sigal stregth iformatio (RSSI), which requires o additioal hardware i sesor ode as it is available as part of physical layer of sesor ode. I first proposed localizatio techique (ANFIS traied Sugeo weighted cetroid localizatio techique) fuzzy membership fuctios have bee optimized usig ANFIS traied Sugeo fuzzy iferece system to calculate the weight of achor odes ad further weighted cetroid method has bee used to obtai the localizatio iformatio of sesor ode. Secod proposal cosists of localizatio scheme (Combied Mamdai-Sugeo Fuzzy Approach for Localizatio), wherei the edge weight of achor ode is calculated by averagig the edge weights obtaied from Sugeo ad Mamdai fuzzy approach. Rest of this paper is orgaized as follows: I Sectio 2, cetroid localizatio, weighted cetroid localizatio ad fuzzy logic system is itroduced. I Sectio 3, proposed localizatio techiques are preseted. I Sectio 5, simulatio eviromet ad results are discussed. Fially, coclusios are dra Sectio 5. ecoomic, but the results shows that locatio estimatio error is very large i this method ad is uacceptable i case of applicatio requirig accurate localizatio of sesor odes. A improved versio of cetroid algorithm is weighted cetroid method [6]. I this method, locatio of sesor ode is calculated by edge weights of achor odes coected to the sesor ode, ad each sesor ode computes its positio by: w 1 X w X w 1 Y w Y ( X e st, Y e s t) =, i= 1 i= 1 (2) where is the edge weight of i th achor ode coected to the sesor ode. The edge weight is decided based upo the proximity of achor ode to the sesor ode. Performace of this approach highly depeds o the optimizatio of edge weights. 2.2 Fuzzy logic system Fuzzy iferece is the process of formulatig the mappig from a give iput to a output usig fuzzy logic. The mappig the provides a basis from which decisios ca be made, or patters discered. The process of fuzzy iferece ivolves the Membership Fuctios, Logical Operatios, ad If-The Rules. Fuzzy logic iferece system is very simple approach for desigig of edge weights for achor odes [14, 15]. A fuzzy logic system cosists of a fuzzifier, some fuzzy IF THEN rules, a fuzzy iferece egie ad a defuzzifier. Fuzzy iferece system ca be implemeted i two ways: Mamdai-type ad Sugeo-type. These two types of iferece systems vary somewhat i the way the outputs are determied. The mai differece betwee Mamdai ad Sugeo is that the Sugeo output membership fuctios are either liear or costat. 2 BACKGROUND 2.1 Cetroid localizatio Cetroid localizatio techique uses the kowledge of adjacet coected achor ode positio (X i, Y i ) to estimate the locatio of sesor ode [9]. I this method, the achor odes trasmit a beaco cotaiig their respective locatio iformatio. The sesor ode computes its positio as cetroid of the positios of all the adjacet coected achor odes to itself X X N Y Y N ( X est, Y est) =, (1) N N where (X est, Y est ) represets the estimated positio of the sesor ode ad N is the umber of adjacet coected achor odes to the sesor ode. The method of locatio estimatio usig cetroid algorithm is very simple ad 3 LOCALIZATION USING FLS This sectio describes the proposed localizatio methods i detail. We have cosidered two differet fuzzy systems (Sugeo ad Mamdai) for locatig the sesor odes. I oe of the proposed approaches, the edge weights obtaied i Sugeo fuzzy iferece method are optimized by usig ANFIS traiig to calculate the weight of achor odes ad further weighted cetroid method is used to obtai the localizatio iformatio of sesor ode. I case of Combied Mamdai-Sugeo Fuzzy Approach, the edge weight of achor ode is calculated by averagig the edge weight obtaied from Sugeo ad Mamdai fuzzy approach. Received sigal stregth iformatio (RSSI) of adjacet coected achor odes has bee used to obtai the weight of achor ode ad further to fid edge weights usig ISBN:

3 fuzzy modelig betwee the sesor ode ad achor odes. 3.1 Fidig the adjacet referece odes WSN cosists of a set of sesor odes deployed radomly i vast area to moitor the parameters of iterest. These odes ca be categorized as: achor odes ad ormal sesor odes. Achor odes are special type of odes embedded with GPS or other facility to obtai their positio with i the etwork. If feasible these odes ca also be placed maually at kow positios with i the etwork.. The positio of achor odes is assumed as (X 1, Y 1 ), (X 2, Y 2 ),,(X, Y ). Achor odes trasmit periodic beaco sigals cotaiig their respective positios with overlapped regio of coverage. Sesor odes are deployed i the sesig field, with radomly distributed positios. These sesor odes locate themselves with the help of beaco sigals, trasmitted by the achor odes. Each sesor ode collects the RSS iformatio of all coected adjacet achor odes through beaco sigal ad RSSI is used to obtai the edge weight of the achor ode for weighted cetroid localizatio. Followig assumptios have bee made: The achor odes kow their positios through GPS or by other meas such as pre-cofiguratio. TDM techique is used to avoid iterferece of beacos trasmitted by achor odes. The radio propagatio is perfectly spherical ad the trasmissio rages for all radios are idetical. 3.2 Calculatig the Edge Weights usig Fuzzy Iferece System Basic theory ad rules for edge weights calculatio The sesor ode collects the received sigal stregth idicatio (RSSI) values betwee the sesor ode ad achor odes. The edge weights of achor odes eed to be calculated for fidig sesor ode positio. I this paper, fuzzy systems with symmetrical trapezoidal membership fuctio for iput (RSSI) ad output (Weight) has bee used. The fuzzy model is composed of followig rules: Rule l: IF x is A l THEN y is B l The iput variable x is the RSS iformatio from achor ode ad takes a value i the iterval [0, RSS max ], where RSS max is the maximum RSS value. The output variable y is the edge weight of each achor ode for a give sesor ode ad takes a value i the iterval [0, W max ], where W max is the maximum weight. For modelig the fuzzy logic iferece system (FLS), if-the rules have to be cosidered, which follow the basic priciple that if a sesor ode seses high powered sigal from a achor ode, the achor ode is likely to be i close proximity to the give sesor ode ad hece is assiged high weight. Coversely, if a sesor ode is coected to a achor ode but seses low powered sigal, the achor ode is likely to be far from the give sesor ode ad hece is assiged a low weight. Cosequetly, we used the fuzzy rule bases ad tued the membership fuctios [14, 15] as sho Table 1. Table 1. Fuzzy logic rules for edge weight Rule IF: RSS is THEN: Weight is Rule 1 very low very low Rule 2 low low Rule 3 medium Medium Rule 4 high High Rule 5 very high very high Edge weight calculatio usig Mamdai fuzzy iferece system I this localizatio method, fuzzy logic system has bee modeled usig Mamdai fuzzy iferece system. The iput (RSS iformatio) ad output (Weight) space have bee decomposed ito five symmetrical trapezoidal membership fuctios amely: very low, low, medium, high, ad very high, as sho Fig. 1 ad 2. Fig. 1. Fuzzy membership fuctio of RSSI Fig. 2. Fuzzy membership fuctio of Weight Edge weight calculatio usig Sugeo fuzzy iferece system I this method, fuzzy system has bee modeled usig Sugeo method of fuzzy iferec [8], it is similar to the Mamdai method i may respects. The first two parts of the fuzzy iferece process, fuzzifyig the iputs ad applyig the fuzzy operator, are exactly the same. The mai differece betwee Mamdai ad Sugeo is that the Sugeo output membership fuctios are either liear or costat. I our approach we have cosidered lier output membership fuctio ad have decomposed ISBN:

4 the iput (RSS iformatio) ito five symmetrical trapezoidal membership fuctios amely: very low, low, medium, high, ad very high, as sho Fig. 3. The output has bee decomposed ito five liear symmetrical fuctios amely: very low, low, medium, high, ad very high. Sugeo systems do ot have the output membership fuctio plot. The defuzzificatio is cosidered to be weighted average. sesor ode ca be calculated by takig the average of coordiates as per the followig formula: X est-sug+xest-mam Y est-sug+yest-mam ( X est fial, Y est fial) =, (6) 2 2 Fig. 4. RSSI vs. Weight for Sugeo FIS Fig. 3. Fuzzy membership fuctio of RSSI 3.3 Weighted localizatio algorithm After calculatig edge weights usig Sugeo or Mamdai fuzzy iferece system, the weighted cetroid algorithm is used to estimate the sesor ode positio, usig the positio of adjacet coected achor odes (X 1, Y 1 ), (X 2, Y 2 ) (X, Y ) respectively. No achor sesor ode calculates its positio as per followig weighted cetroid formula [14, 15]: w 1 X w X w 1 Y w Y ( X est, Y est) =, i= 1 i= 1 (5) where is the umber of adjacet coected achor odes. 3.4 Proposed ANFIS traied Sugeo fuzzy system The acroym ANFIS derives its ame from adaptive euro-fuzzy iferece system. Usig a give iput/output data set, ANFIS ca costruct a fuzzy iferece system (FIS) whose membership fuctio parameters are tued (adjusted) usig either a back propagatio algorithm aloe or i combiatio with a least squares type method. This adjustmet allows our fuzzy systems to lear from the data we are modelig [16, 17]. I our approach, the Sugeo fuzzy iferece system is traied usig the ANFIS, which model the system to get the liear edge weight related to the RSS iformatio as sho Fig. 4. Fig Proposed CMS Localizatio Approach A combided Mamdai-Sugeo localizatio approach has bee proposed for calculatig the sesor ode positio. If the estimated ode coordiates for Sugeo ad Mamdai system are (X est-sug, Y est-sug ) ad (X est-mam, Y est-mam ) respectively, the the estimated positio of Fig. 6. ANFIS traied fuzzy membership fuctio of RSSI Fig. 5. RSSI vs. Weight for traied Sugeo fuzzy system 3.6 Performace evaluatio For evaluatig the proposed schemes ad comparig with existig methods, followig two performace idices have bee used: The distace betwee the estimated positio ad the actual positio of sesor ode, 2 2 = est a + est a (7) Locatio Error (X -X ) (Y -Y ) Where (X est, Y est ) is the estimated positio of sesor ode while (X a, Y a ) is the actual positio of sesor ode. The average distace betwee the estimated positio ad the actual positio of all sesor odes, 2 2 (Xest-X a) + (Yest-Y a) Average Locatio Error= (8) umber of sesor odes ISBN:

5 4 SIMULATION AND RESULTS MATLAB has bee used for performace evaluatio of the proposed schemes i the simulatio experimets with followig primary etwork parameters: 60 sesor odes with ukow positio ad 121 achor odes with kow positio are distributed radomly with i 10mX10m area. The trasmissio rage of all achor odes is assumed to be 8.94 m. A sesor ode is assumed to be i the proximity of adjacet achor odes if its distace from the achor ode is smaller tha the trasmissio rage. We used the followig RSS model [14]. α Rij= ( kdij ) + ( AWGN * Var) (9) Where R ij is the RSS value betwee the i th sesor ode ad the j th adjacet achor ode, k is a costat which takes ito accout carrier frequecy ad trasmitted power, d ij is the distace betwee the i th sesor ode ad the j th adjacet achor ode ad α is the atteuatio expoet. Here, we have used k = 50 ad α = -1 [14]. approach, D) ANFIS traied Sugeo fuzzy approach ad E) a combied Mamdai-Sugeo approach. Fig. 4 to 11 show the result of locatio estimatio ad localizatio error of these methods respectively. Table 2 shows the compariso of differet algorithms. Table 2. Compariso of Simulatio Results Approaches Max. error (cm.) Avg. error (cm.) Simple cetroid Mamdai FLI Sugeo fuzzy ANFIS traied Sugeo FLI Combied M- S FLI Fig. 9. Localizatio error for Sugeo FLI Fig. 7. Localizatio error for simple cetroid method Fig. 8. Localizatio error for Mamdai FLI I this paper, we have simulated three existig localizatio techiques for compariso with two proposed localizatio techiques. Same simulatio setup has bee used for all the techiques for compariso of results. Followig localizatio techiques have bee simulated : A) the simple cetroid approach, B) the Mamdai fuzzy approach, C) the Sugeo fuzzy Fig. 15. Locatio estimates of SNs i combied Mamdai- Sugeo FLI Fig. 10. Locatio error for ANFIS traied Sugeo FLI 5. CONCLUSION Give the iheret costrais of the sesor devices ad estimatio accuracy desired by locatio depedet applicatio, rage free localizatio are regarded as cost effective solutio for sesor ode localizatio. I this paper, two rage free localizatio methods have bee proposed for wireless sesor etworks (WSNs). The ISBN:

6 RSS iformatio betwee sesor odes ad its eighbor achor odes ca be used to estimate the positios without ay complicated hardware. Fuzzy logic system is the mai compoet of our proposed schemes. Fig. 11. Localizatio error for combied M-S FLI I the first scheme, we fid the edge weight of each achor odes usig ANFIS traied Sugeo fuzzy iferece. I secod approach, which is less computatio itesive, a combied Mamdai ad Sugeo fuzzy system is used to determie the edge weight of achor ode ad to ehace the locatio estimatio accuracy of sesor odes i the sesor etwork. The proposed approaches are simulated usig MATLAB. The results have bee compared with existig techiques through extesive simulatios. Simulatio results demostrate the effectiveess of the proposed schemes i compariso to the previous methods. Refereces: [1] Pal, Localizatio Algorithms i Wireless Sesor Networks: Curret Approaches ad Future Challeges, Network Protocols ad Algorithms 2 (1), (March 2010), pp [2] Savvides, C. Ha, ad M. B. Strivastava, Dyamic Fie-Graied Localizatio i Ad-hoc Networks of Sesors, Proceedigs of the seveth aual iteratioal coferece o Mobile computig ad etworkig (MobiCom 01), (Rome, Italy, July 2001), pp [3] Chog ad S. P. Kumar, Sesor Networks: Evolutio, Opportuities, ad Challeges, Proceedigs of the IEEE 91, 8 (August 2003), pp [4] Niculescu ad B. Nath, DV Based Positioig i Ad hoc Networks. Joural of Telecommuicatio Systems 22, 1 (Jauary 2003), pp [5] I.F. Akyildiz, W. Su, Y. Sakarasubramaiam, E. Cayirci, Wireless sesor etworks: a survey. Computer Networks (March 15, 2002), pp [6] Kim, S. Y., & Kwo, O. H., Locatio estimatio based o edge weights i wireless sesor etworks. Joural of Korea Iformatio ad Commuicatio Society 30, 10A (2005). [7] L. Girod ad D. Estri, Robust Rage Estimatio usig Acoustic ad Multimodal Sesig. Proceedigs of IROS 2001, 3 (October 2001), pp [8] M. Sugeo, A itroductory survey of fuzzy cotrol. Iformatio Scieces 36, 1-2 (July- August 1985), pp [9] N. Bulusu, J. Heidema ad D. Estri, GPS-less Low Cost Outdoor Localizatio for Very Small Devices. IEEE Persoal Commuicatio Magazie 7, 5 (October 2000), pp [10] P. Bahl ad V. N. Padmaabha, RADAR: A I- Buildig RF-Based User Locatio ad Trackig System. Proceedigs of the IEEE Ifocom 2000, 2 (March 26-30, 2000), pp [11] S. Meguerdichia, F. Koushafar, M. Potkojak, ad M.B. Srivastava, Coverage Problems i Wireless Ad-hoc Sesor Networks. Proceedigs of IEEE lfocom (April 22-26, 2001), pp [12] T. He, C. Huag, B.M. Blum, J. A. Stakovic, ad T. Abdelzaher, Rage-Free Localizatio Schemes for Large Scale Sesor Networks. Proceedigs of the ith aual iteratioal coferece o Mobile computig ad etworkig (MobiCom 2003), (Sa Diego, CA, USA, September 2003), pp [13] Y. B. KO ad N. H. Vaidya, Locatio-Aided Routig (LAR) Mobile Ad Hoc Networks. Proceedigs of the fourth aual iteratioal coferece o Mobile computig ad etworkig (MobiCom 98), Dallas, Texas, (October 1998), pp [14] Y. Sukhyu, L. Jaehu, ad Wooyog, A soft computig approach to localizatio i wireless sesor etworks. Expert Systems with Applicatios 36, 4 (May 2009), pp [15] Yu, S., Lee, J., Chug,W., & Kim, E., Cetroid localizatio method i wireless sesor etworks usig TSK fuzzy modelig. Iteratioal symposium o advaced itelliget systems, (Sokcho, Korea, September 2005), pp [16] Jag, J.-S.R., ANFIS: adaptive-etwork-based fuzzy iferece system, IEEE Trasactios o Systems, Ma ad Cyberetics, Vol. 23, No. 3, May/Ju 1993, pp [17] MATLAB /help/fuzzy Logic Toolbox ISBN: