Research Article VSMURF: A Novel Sliding Window Cleaning Algorithm for RFID Networks

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1 Hndaw Journal of Sensors Volume 2017, Artcle ID , 11 pages Research Artcle VSMURF: A Novel Sldng Wndow Cleanng Algorthm for RFID Networks He Xu, 1,2 Wewe Shen, 1,2 Peng L, 1,2 Danele Sgandurra, 3 and Ruchuan Wang 1,2 1 School of Computer Scence, Nanjng Unversty of Posts and Telecommuncatons, Nanjng , Chna 2 Jangsu Hgh Technology Research Key Laboratory for Wreless Sensor Networks, Nanjng , Chna 3 Informaton Securty Group, Royal Holloway, Unversty of London, Surrey TW200EX, UK Correspondence should be addressed to He Xu; xuhe@njupt.edu.cn Receved 9 February 2017; Revsed 24 May 2017; Accepted 22 June 2017; Publshed 27 July 2017 Academc Edtor: Nashwa El-Bendary Copyrght 2017 He Xu et al. Ths s an open access artcle dstrbuted under the Creatve Commons Attrbuton Lcense, whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted. Rado Frequency Identfcaton (RFID) s one of the key technologes of the Internet of Thngs (IoT) and s used n many areas, such as moble payments, publc transportaton, smart lock, and envronment protecton. However, the performance of RFID equpment can be easly affected by the surroundng envronment, such as electronc productons and metal applances. These can mpose an mpact on the RF sgnal, whch makes the collecton of RFID data unrelable. Usually, the unrelablty of RFID source data ncludes threeaspects:falsenegatves,falsepostves,anddrtydata.falsenegatvesarethekeyproblem,astheprobabltyoffalsepostves and drty data occurrence s relatvely small.ths paper proposes a novel sldng wndow cleanng algorthm called VSMURF, whch s based on the tradtonal SMURF algorthm whch combnes the dynamc change of tags and the value analyss of confdence. Expermental results show that VSMURF algorthm performs better n most condtons and when the tag s speed s low or hgh. In partcular, f the velocty parameter s set to 2 m/epoch, our proposed VSMURF algorthm performs better than SMURF. The results also show that VSMURF algorthm has better performance than other algorthms n solvng the problem of false negatves for RFID networks. 1. Introducton The Internet of Thngs (IoT) [1] s defned as the set of objects that can communcate over the Internet. Cloud computng s an Internet-based computng servce that can provde task allocaton [2] and secure data servces [3] to IoT devces on demand [4]. Ths has led IoT to be a global technology used n many felds, such as ubqutous ctes [5]. Wth the development of these systems, data process technology has attracted many researchers nterest. As an example, to meet the secure requrements of ubqutous ctes, Shen et al. have proposed a sharng framework based on attrbute-based cryptography to support dynamc operatons for urban data [5]. Rado Frequency Identfcaton (RFID) s consdered one of the key technologes to realze IoT and s wdely used n many areas, such as moble payments, publc transportaton, health montorng, envronment protecton, and smart cty [6, 7]. More and more bg data are generated by those types of RFID and Cloud-based systems, whch make the spatal-temporal data management a current hot topc [8]. RFID uses rado frequency sgnals through wreless communcatons to acheve noncontact transmsson of nformaton and to perform automatc dentfcaton of objects attached wth RFID tags. The advantage les n the fact that the RFID tags and readers can perform dentfcaton wthout physcal contact. RFID system can be dvded nto the followng three components: readers, tags, and back-end computer system. The reader and tag communcate through an antenna. To ths end, the reader frstly emts electronc sgnals through an antenna, and then the tag emts dentfcaton nformaton of nternalstorageafterrecevngthesgnal.secondly,thereader receves and dentfes the nformaton sent back from the tag va an antenna. Fnally, the reader sends the dentfcaton results to the computer system [9]. The workng prncple of RFID system s shown n Fgure 1. In early RFID applcatons, the reader s drectly connected to the applcaton, and RFID data wll be processed as logcal data by the applcaton. Ths data processng

2 2 Journal of Sensors Reader Computer system Antenna Fgure 1: Workng prncple of RFID system. approach meets the needs of earler RFID systems, but the system s desgn s complex and the effcency s low. There s an mportant problem wth these legacy systems; n partcular reusablty s dffcult. To reduce the complexty and meet the requrements of the rapd development of RFID technology, RFID mddleware [10] systems were ntroduced. Mddleware system works as a separate level whch can perform some of the data processng and sheld the reader hardware and the upper applcaton system. In these scenaros, the program s scalablty and applcablty are greatly mproved. RFID mddleware can be used to clean, flter, and format the data collected by the readers and transfer the processed data to the back-end applcatons [11]. Ths paper manly studes the data preprocessng method, whch s also referred to as data cleanng method n RFID mddleware system. Tradtonal data cleanng technology [12] s manly applednthreefelds:(1) data warehouse (DW or DWH); (2) Data Dscovery or Knowledge Dscovery n Data (KDD) [13]; (3) Total Data Qualty Management (TDQM) [14]. In these areas, data cleanng s an ntegral and essental part for data processng. However, tradtonal data cleanng technology s only sutable for relatonal database or data warehouse technology.therfiddataflowsdfferentfromthetradtonal relatonal database and the data stream generated n data warehouse. The nteracton between the reader and the tag n the RFID system determnes the followng characterstcs of RFID data [15, 16]: the source data has a smple structure that holds the characterstcs of flow, batch, magnanmty, temporal, dynamc change, correlaton, and unrelablty. The way n whch the data s generated by the RFID devce determnes that the source data s often serously unrelable [17, 18]. The man reasons for unrelablty of data n RFID system are as follows [19]: Tag (1) False postves: tags datawhchshouldnotberecognzed by the reader for some reason (nose, electromagnetc nterference, and the lke) have been read. (2) False negatves: tags data that should be recognzed by the reader have not been correctly read. (3) Drty data: reader detects the tag that exsts n ts readng range, but the data whch s read by the reader contans errors. Falsepostveanddrtydataarefortuty,andtheprobablty of ther occurrence s relatvely small. Instead, the false negatve phenomenon s more common and s the man reason of causng unrelablty n RFID system. Therefore, n order to mprove the qualty of data and ensure that the upper applcaton works effectvely, RFID source data cleanng operaton s needed. Prevous researches amed at RFID data cleanng technology have done extensve and n-depth study and proposed some classc data cleanng algorthms. Ths paper proposes a novel data cleanng algorthm (named as VSMURF), whch s based on the tradtonal SMURF algorthm n RFID networks, whch s amed at reducng the number of false negatves. The paper s organzed as follows: Secton 2 descrbes the related work and the SMURF algorthm. Secton 3 presents the proposed VSMURF algorthm. Secton 4 descrbes smulaton and expermental results. Fnally, Secton 5 summarzes the paper. 2. Related Work and SMURF Algorthm In ths secton we descrbe the related work on data cleanng technologyandrecapthesmurfalgorthm Data Cleanng Technology. Data cleanng [20] refers to a process that can detect and correct the dentfable error n data fles. Some classcal data cleanng algorthms exst n the lterature, whch wll be ntroduced n ths secton. The data cleanng method based on sldng wndow s a typcal and commonly used method. For example, Ba et al. proposed a method based on a fxed sldng wndow [21], where the wndow s fxed and moves forward over tme. The cleanng process s shown n Fgure 2. Here, the raw data s generated by reader. Jeffery et al. proposed a scalable data cleanng framework [22] based on Extensble receptor Stream Processng (ESP), whch s a declaratve query processng tool that s easly ppelned to deploy the confguraton to each recpent. The framework s capable of cleanng data from varous features of dfferent readers. ESP ppelned structure s processed nto the followng fve consecutve programmable stages: Pont, Smooth, Merge, Arbtrate, and Vrtualze. Gonzalez et al. proposed a dynamc data cleanng method based on Dynamc Bayesan Networks (DBNs) [23], whch dynamcally adjusts the trustworthness state (whch s the probablty of tag exstence). Song of Laonng Unversty proposed a three-ter structurekalmanflter-basedrfidcleanng(kfbc)[24] to address the problem of false negatve n RFID system. As shownnfgure3,thssakalmanflterupdateprocess, whch conssts of tme update and measurement update, and t s an autoregressve process SMURF Algorthm. Here, we frstly recall the basc concepts of the SMURF algorthm [25], and then we descrbe the algorthm n detal. Interrogaton Cycle. Itsannquryandanswer-response process between a reader and a tag, whch s the basc reader s protocol that tres to detect all tags by the reader.

3 Journal of Sensors 3 Real data Raw data Sldng wndow Cleanng result Fgure 2: Data cleanng method based on fxed sldng wndow. Table 1: Lst of tags. Tme update (predct) Measure update (correct) TagID Responses Tmestamp :05: :05: :05:10 wndow sze (W )ateachepochareallthesameandcanbe calculated usng the formula: Fgure 3: The updatng process of Kalman flter. Readng Cycle.Itsasetofmultplenterrogatoncycles.Its alsonamedasepoch,thevalueofwhchsfrom0.2seconds to 0.25 seconds [25]. In each epoch, the reader keeps the recordng of how many and whch tags are dentfed. Read Rate p,t.itstheprobabltyoftag to be read by reader at t epoch, whch s calculated as p,t = Responses N t, (1) where N t s the number of nterrogaton cycles at t epoch, and Responses s the number of responses of tag at t epoch n the reader s readng range. Sldng Wndow Sze W. The value denotes w epochs n whch tags can be dentfed by readers. It can be expressed by W = (t w,t]. S. It denotes the subset of tags observed by the reader durng that epoch, whch can be expressed by S W.Inthegeneral case,tsassumedthattag s seen n a subset of all the epochs n W. The SMUR algorthm works as follows. In each epoch, the reader detects all tags n ts readng range and stores the nformaton of tags nto the tag lst, ncludng TagID, Responses,and Tmestamp, as shown n an example n Table 1. Thelstcanberepresentedbyatrple(TagID,Responses, and Tmestamp), and all those peces of nformaton are transmtted to the reader s clent at regular ntervals. Each epoch s treated as an ndependent Bernoull experment. In detal, the probabltes of tag appearng n the p avg =( 1 S ) p,t. (2) t S In addton, the followng two formulas are the most mportant ones n the SMURF algorthm: S W p avg W 1 p avg ln ( 1 ), (3) δ 2 W p avg (1 p avg ), (4) where δ denotes confdence n formula (3). Confdence methodcanbeusedtoprocessandanalyzevarousdata, such as mage reconstructon. In the lterature [26], authors use the confdence method to remove the nose from mage accordng to the confdence of the depth to compute the range map. Expermental results show that the confdence method obtans good effectveness n data processng. In SMURF algorthm, confdence s used to meet the ntegrty requrements of the data and ensure that the tag s successfully read n the sldng wndow W accordng to formula (3). Formula (4) s obtaned by the central lmt theorem, whch s the control condton for settng the current sldng wndow sze and the condtons of the tag s dynamc change. A reasonable sldng wndow sze needs to balance the ntegrty requrements wth the tag dynamc changes. Determnng whether to output data s decded accordng to the data n the wndow n SMURF algorthm. In addton, determnng howtoadjustthewndowszesdoneaccordngtothe requrements of ntegrty and dynamc detecton. Therefore, SMURF s a type of sngle tag s data cleanng algorthm. SMURF algorthm s composed of the followng steps. Step 1. The ntal settng of W s 1. T s the tag s pool and W s calculated accordng to formula (3).

4 4 Journal of Sensors Step 2. If W >W, adjust the wndow sze accordng to the followng formula: W max {mn {W +2,W },1}. (5) Step 3. If W W, the dynamc change state of tag s detected accordng to formula (4). If the tag s state s changed, the wndow sze needs to be adjusted accordng to the followng formula: W max {mn { W 2,W },1}. (6) 2.3.LmtatonsofSMURFAlgorthm. In the RFID mddleware system, the sldng wndow flterng method s the key technology to reduce false negatves. However, there are some shortcomngs n SMURF algorthm: frstly, the sze of sldng wndow s dffcult to set n the case of dynamc movement oftags.ifthewndowssettoolarge,twllgeneratefalse postvesntheprocessoffllngdata;andfthewndowsset too small, t wll not be able to completely fll the data whch results n false negatve. The selecton of the optmal value of sldng wndow sze drectly affects the effectveness of the algorthm. In addton, the effect of ths cleanng algorthm sremarkableonlywhentherfiddatastreamsnandeal condton, whch means that the RFID tags move at a unform speed. However, ths stuaton does not happen n some condtons, for example, when the external envronmental factors change (such as the tag moves n or out at the reader s detecton range rapdly). In these cases, the performance of the cleanng effect s greatly reduced. In addton, SMURF algorthm s confdence s just based on emprcal evaluaton; that s, t s not the result of a specfc analyss. If the tag quckly leaves the reader s detecton range, the value δ has a great mpact on the results [20]. Specfcally, formula (3) shows how the sldng wndow sze (W ) srelatedtothetwoparametersp avg and δ. In fact, W s nversely proportonal to p avg,whchmeansthat the performance of the SMURF algorthm decreases rapdly when the tag moves out or nto the reader s detecton range. Assumng a set of raw data streams (0.5, 0.6, 0.6, 0.6, 0.5, 0.8, 0.8, 0.0, 0.4, and 0.8), the ntal wndow sze (W )s set to 5 epochs, where each epoch contans 10 nterrogaton cycles, and δ s set to Hence, after calculaton, p avg s Because the sldng wndow sze (W )s5epochs, the processng tag has been moved out from the reader s detecton range n the thrd epoch of the second wndow. In the end, the tag s stll n reader s detecton range accordng to SMURF algorthm, whch results n false postves n RFID network. Therefore, SMURF algorthm fals under ths condton. For these reasons, we have desgned a novel algorthm to take nto account the lmtatons of SMURF, whch s descrbed n the next secton. 3. VSMURF: A Novel Sldng Wndow Cleanng Algorthm for RFID Networks Based upon the prevous analyss, we propose a novel algorthm to address SMURF algorthm s lmtatons, whch s called VSMURF. In the followng, we frstly descrbe the dynamc detecton mechansm of tags and analyss of confdence. Then, we detal our adaptve sldng wndow cleanng algorthm The Dynamc Detecton Mechansm of Tags. Assume that the current wndow sze of tag s w = (t w,t], w s dvded nto two parts, where the frst part s denoted as w 1 = (t w,t w /2] and the second part s denoted as w 2 =[t w /2, t], and the bnomal dstrbuton samples are, respectvely, denoted as S 1 and S 2. The dynamc detecton mechansm of a tag s movement s dvded nto three parts. Mechansm 1. In order to determne when the tag leaves the reader s detecton range, the algorthm ntroduces the sldng wndow w 2 ( S 2 )basedonformula(4).theleastsquares Method [27] s used to ft the slope of the curve to determne whether the tag s movng out. If the slope s negatve, the tag s movng out from the detecton range. In addton, our proposed algorthm reduces two epochs of sldng wndow sze. The purpose of ths s to reduce the occurrence of false negatves. Mechansm 2. Thesldngwndowwllbereducedbyhalff the tag s movng out and cannot be detected n the sldng wndow w 2 ( S 2 =0). Mechansm 3. The sldng wndow sze s ncreased f w, whch s calculated accordng to formula (3), s larger than the current wndow w (w > w )andtheactualnumber of readngs s greater than the expected number of readngs ( S >w p avg ). The above three mechansms are used to adaptvely adjust the sze of the tag s sldng wndow. The tag s dynamc detectonstllusesthecentrallmttheorem(clt)[28].in the ntal state, all tags whch need to be detected are set to 1 epoch and the wndow sze s adjusted to 3 epochs. The goal s to balance the effcency of the proposal algorthm and reduce false postves. Smlar to the SMURF algorthm, the proposed VSMURF algorthm outputs the results at the mddle of wndow and sldes once at each epoch Confdence δ Analyss. In the SMURF algorthm, the formula (1 p avg ) w < δ s the ntegrty requrement to ensure that the RFID data stream s completely covered, but t does not cover the partcular value of δ that should be selected. The SMURF algorthm only provdes an emprcal value. When the value of δ s less than 0.5, t has a lttle effect n the sldng wndow sze [25]. In fact, the SMURF algorthm cleans the data sgnfcantly when the tag moves slowly and does not move n and out frequently n the reader s detecton range. But when the tag moves quckly, the algorthm s error rate becomes hgh. The reason s that the sldng wndow sze becomes larger when the confdence δ s smaller, whch results n an ncrease n the number of false postve readngs and a correspondng ncrease n readng error rate.

5 Journal of Sensors 5 Reader Step 2. Detect whether readng cycle (epochs) ends, and f t ends, then the algorthm ends. Step 3. Calculate the mnmum value that satsfes the ntegrty requrement accordng to formula (3) and determne whether the tag s removed by usng least squares. Tag 2R Fgure 4: Reader detecton range. In order to solve the above problems, ths paper takes nto account the followng factors that affect the cleanng results: the reader s detecton range (R), readng frequency (f), the tag s speed (V), and confdence (δ). All these factors dynamcally affect the sldng wndow sze s adjustment. The maxmum number of epochs s determned by 2R/(V/f) = 2Rf/V when the tag passes through the center of the crcle (as shown n Fgure 4). When the tag s very close to the antenna of the reader readng range, the number of readngs s F = R f/v.to mprove the effcency of data cleanng, VSMURF algorthm dynamcally adjusts the parameter δ (δ =p avg /F). Therefore, we obtan the followng formula: (1 p avg ) W <δ= pavg F = pavg V Rf. (7) By applyng the natural logarthm on both sdes of the equaton, we get Ths s because Fnally, we obtan W ln (1 p avg ) < ln ( pavg V Rf ). (8) ln (1 x) x, x (0, 1). (9) W ln (Rf/pavg p avg V). (10) Thus, the ntegrty requrement s adjusted to formula (10). The maxmum threshold υ of V canensurethattheerrorrate s less than 10% [20] Adaptve Sldng Wndow Cleanng Algorthm VSMURF. VSMRUF s an mproved sngle tag s sldng wndow cleanng algorthm based on SMURF algorthm and s based on the adjustment process of tag at each epoch. The algorthm s composed of seven steps, whch are descrbed n detal n the followng. Step 1. Intalze the reader s detecton range (R), readng frequency (f), confdence (δ), and threshold of tag s maxmum speedandsetthentalwndowto1epoch.whenv>υ, δ s ncreased approprately (δ < 0.25). Otherwse, δ s reduced approprately. Step 4. If the tag s movng out and S 2 = 0, the sldng wndow sze s adjusted to the half accordng to Mechansm 2. Then adjust the wndow sze accordng to formula (11) and enter the next epoch: W max {mn { W 2,W },3}. (11) Step 5. Check whether the wndow s modfed accordng to formula(4).ifthewndowtransforms,adjustthewndowsze accordng to formula (12) and enter the next epoch: W max {mn {W 2,W },3}. (12) Step 6. If the current wndow sze does not meet the ntegrty requrement (W >W ) and the wndow does not convert ( S >W p avg ), accordng to Mechansm 3 and to formula (13) ncrease the wndow sze and then enter the next epoch: W mn {W +2,W }. (13) Step 7. If the current wndow satsfes the ntegrty requrement (W W ) and the wndow transforms ( S W p avg ), the wndow sze wll not change. Fgure 5 shows the data process flow of VSMURF algorthm. The pseudocode of the VSMURF algorthm s gven n Pseudocode Smulaton and Expermental Results 4.1.Reader sdetectonmodelandevaluatonmechansm. In our experments, the detecton range of the reader s dvded nto the major detecton range and the mnor detecton range [29] (as shown n Fgure 6). The reader detecton model has the followng characterstcs: (1) The reader has a hgh detecton probablty and the readng rate s hgher than 95% n the major detecton range, whch s near to the reader. (2) The area that extends from the end of the major detecton range to the end of the reader s detecton range s called mnor detecton range. Here, the readng rate s lnearly reduced to 0. The model uses the followng parameters to capture the behavor of readers under dfferent condtons: () Detecton range: the dstance between the reader and the boundary of the reader () Percentage of major detecton range (MajorPercentage): the major detecton range of the reader accountng for the percentage of the entre detecton range

6 6 Journal of Sensors Start Intalzaton (R, f, w, etc.) Yes Tag s velocty > v No Approprate to ncrease confdence Approprate to reduce confdence Is epoch endng? No Epoch - 1 Yes Calculate wndow sze by formula (3) End Is the tag movng out va Mechansm 1? & S 2 =0? Yes The wndow s halved va Mechansm 2. Then formula (11) was used to adjust wndow sze. No Does the tag transform? Yes No Adjust wndow sze va Mechansm 3 and use formula (13) to ncrease the wndow sze. Adjust the wndow sze accordng to formula (12). Fgure 5: Data process flow of VSMURF algorthm. () Major readng rate (MajorReadRate): readngrateof tags n the major detecton range (v) Error rate: error data dvded by the total number of data In addton, evaluaton mechansm of RFID data cleanng algorthm manly uses the average error rate (AvgErrors), the average false postve readngs (AvgPostves), and the average false negatve readngs (AvgNegatves). They are calculated as follows: AvgErrors j (FalsePostves j + FalseNegatves j ), NumEpochs = NumEpochs

7 Journal of Sensors 7 Input: T = set of all observed tag IDs δ = requred completeness confdence υ = the maxmum threshold of tag s speed R = the radus of reader s detecton range f = the frequency of reader s detecton Output: t =setofallpresenttagids Intalze: T, W 1, δ = 0.1 Whle (getnextepoch( )) do For ( n T) do processwndow(w ) p,t, p avg f((v > υ) (V < 0.25)) δ+ = 0.05 Else δ = 0.05 End f If(tagExst( S )) Output End f W, S, V completewndowsze(p avg,δ) tagleavng movedetecton(p,t,s ) If(tagleaVng ( S 2 =0)) W max {mn {W /2, W },3} Else f(detecttranston( S, W,p avg )) W max {mn {W 2,W },3} Else f((w >W ) ( S >W p avg )) W mn {W +2,W } Else W mn {W,W } End f End for End whle Pseudocode 1: Adaptve sldng wndow cleanng algorthm. MajorReadRate Probablty of detecton Hgh Low Major detecton range Near Mnor detecton range MajorPercentage Tag movement range Fgure 6: Reader detecton model. AvgPostves = NumEpochs j (FalsePostves j ), NumEpochs Detecton range Far AvgNegatves = NumEpochs j (FalseNegatves j ), NumEpochs Dstance from reader (14) where NumEpochs s the number of readng cycles; FalsePostves and FalseNegatves are the numbers of false postve readngs and false negatve readngs, respectvely. The evaluaton mechansm ncludes two types of errors, whch can effectvely evaluate the performance of varous algorthms. The readng rate p sdefnedasnthefollowngformula: { 1 1 p h D, D (0,r p (D) = r 1 ), 2 p { l D p l r 2, D (r { r 1 r 2 r 1 r 1,r 2 ), 2 (15) where D s the dstance between the tag and the reader, r 1 and r 2 are the reader s major and mnor detecton range, and p h and p l are the readng rates of the major and mnor detecton range, respectvely Settngs of Smulaton. Ths secton descrbes the expermental hardware and software envronment used n all the smulatons Hardware Envronment. The hardware s composed of the followng equpment: ()PCsHPIntelCore2Duonotebook.Thespeedof CPU s 2.33 GHz and the memory s 2 G. () RFID Reader s Speedway R420 reader. () Tag s EPC G2 UHF RFID tag. Fgure 7 shows the RFID reader, reader s antenna, and tags used n our experments Software Envronment. The software envronment ncludes the followng: () Operatng system: Wndows 7 () Programmng language: Java () Programmng envronment: Java JDK 1.8.0_40; Eclpse IDE for Java Developers Luna Servce Release 2 (4.4.2) VSMURF algorthm belongs to the sngle tag cleanng algorthm; hence, n the experments, only one reader s used and the number of tags s set to 25. The expermental modelofvsmurfalgorthmsshownnfgure8.atsome pont, when the tag moves nto the reader s detecton range, p s calculated on the current coordnate pont. The value of p s determned by the dstance between the tag and the reader usng formula (15). In addton, the system generates arandomnumbern (the value of s n between 0 and 1), where n s used to determne whether the tag s nformaton s generated. If p n,thetagsread;fp<n, then the tag s false negatve readng Results and Dscusson. In ths secton we descrbe the experments that we have performed.

8 8 Journal of Sensors (a) RFID reader (b) Reader s antenna (c) Tag Fgure 7: RFID reader (a), reader s antenna (b), and tag (c). Table 2: Experment 1 parameter settng. Parameters Value Detecton range 20 m MajorPercentage Varable (0, 1) MajorReadRate 0.8 Number of tags 25 Speed 2 m/epoch Readng cycles 1000 Experment 1. 25tagsareselected,whereeachtagrandomly changes ts state, the MajorPercentage s changed from 0 to 1, and the readng cycles are set as 1000 epochs. We adopt and compare the followng three algorthms: fxed wndow cleanng algorthm, SMURF cleanng algorthm, and VSMURF algorthm. The average error rate per epoch of above algorthms s compared. The experment parameters arelstedntable2. The results are shown n Fgure 9. In detal, when MajorPercentage s 0 (such as n a very nosy envronment), the large wndow performs better, whch shows about 4 errors per epoch. As MajorPercentage ncreases, the relablty of the orgnal data and the accuracy of each algorthm are mproved. The small wndow performs better when MajorPercentage reaches 1. For example, the major detecton range covers the entre detecton range of the reader. In ths experment, the VSMURF cleanng algorthm has a notceable effcency mprovement compared wth the Table 3: The executon tme of SMURF and VSMURF algorthm. Algorthm SMURF VSMURF Executon tme s s SMURF algorthm and has the lowest error rate of the whole detecton range at each epoch. In order to smulate realstc condtons n terms of tag and reader behavor, we have mplemented the SMURF and VSMURF algorthm usng R420 RFID readers. In the experments, we have collected 12 sets of real data. Due to the number of readng cycle, whch s set to 1000, the tme of data cycles s equal In the experments, each set of data needs to record the executon tme. The tme result s shown n Table 3. From Table 3 and Fgure 9, we can see that when the tag speed s large and the tag s moved out of the reader detecton range frequently, the executon tme of VSMURF s slghtly hgher than that of the SMURF algorthm because the VSMURF algorthm adds the confdence judgment. However, the average error rate of VSMURF s lower than that of SMURF algorthm. In addton, the overhead of VSMURF algorthm wth respect to tradtonal fxed wndow and SMURF algorthm s mnmal because our VSMURF algorthm only utlzes smple mathematcal operatons. Experment tags are selected, and the tag s speed s changed randomly, the MajorPercentage s kept at 0.5,

9 Journal of Sensors 9 Tag Reader 2R Table 4: Experment 2 parameter settngs. Parameters Value Detecton range 20 m Major range 10 m MajorReadRate 0.8 Number of tags 25 Speed Varable Readng cycles Tag Tag y Routng A Routng B x The average number of errors r 2 r 1 Major detecton range Mnor detecton range Fgure 8: The expermental model of VSMURF algorthm Speed of tags (M/epoch) RAW Statc 2 Statc 10 SMURF VSMURF Fgure 10: Results of average number of errors of compared algorthms when tags speed changes. Errors per epoch RAW Statc 2 Statc 10 Percentage of major detecton range SMURF VSMURF Fgure 9: Results of average errors per epoch of compared algorthms (MajorPercentage s changed from 0 to 1). MajorReadRate s 0.8, and the number of readng cycles s 1000 epochs. The fxed wndow cleanng algorthm, SMURF algorthm, and VSMURF algorthm are compared n ths experment. The average error rate of each algorthm s compared at dfferent speeds. The parameter settngs are shown n Table 4. AsshownnTable5andFgure10,allalgorthmswork well when the tags speed s less than 0.5, because the tag s n a stable envronment at ths speed. Statc 10, whch selects the large wndow to elmnate more false negatve readngs, obtans the best performance when the tag s statc, and the number of errors per epoch s less than 1. Wth the tag s speed ncrease, the average number of errors for statc 10 starts to ncrease and the effcency begns to declne. Ths s because the large wndow causes more false postve readngs. In addton, the small wndow statc 2 cannot completely make up for false negatve readng errors. However, t wll not cause a lot of false postve readngs, and the overall performance s better. SMURF performs well when the tag s speed s small. As the speed ncreases, that s, the tag moves n or out the detecton range frequently, the effcency of the algorthm s serously degraded. VSMURF algorthm works better n most condtons and, regardless of the fact that a tag s at

10 10 Journal of Sensors Table 5: Comparson of average errors n dfferent speeds. Speed Rawerrors Statc2 Statc10 SMURF VSMURF loworhghspeed,theaveragenumberoferrorssthelowest n all the compared algorthms, as confrmed by the results reported n Table 5 and Fgure Concluson Ths paper has presented a new sldng wndow cleanng algorthm VSMURF, whch s based on the tag s dynamc property andconfdence.thepapersbasedontheobservatonthat the SMURF algorthm performs well only when the tag s speed s slow. However, f the tag s speed s ncreasng and the tag moves nto or out the detecton range frequently, the effcency of the SMURF algorthm declnes dramatcally. To address ths lmtaton, we have ntroduced VSMURF algorthm and we have shown that t performs better n most condtons and whether the tag s speed s low or hgh. In partcular, f the velocty parameter s set to 2 m/epoch, whch represents a typcal stuaton, our proposed VSMURF performs better than SMURF. The same results apply when the tag s movng fast. The expermental results show that theproposedmethodsbettersutedthanothersmlar algorthms n reducng the problem of false negatves. As a future work, we ntend to mplement VSMURF also n RFID mddleware system. Conflcts of Interest The authors declare that there are no conflcts of nterest regardng the publcaton of ths artcle. Acknowledgments Ths work s fnancally supported by Jangsu Government Scholarshp for Overseas Studes, the Natonal Natural Scence Foundaton of Chna (no , no , no , no , and no ), the Natural Scence Foundaton of Jangsu Provnce (no. BK , no. BK ), Jangsu Natural Scence Foundaton for Excellent Young Scholar (BK ), Scentfc & TechnologcalSupportProjectofJangsuProvnce(nos.BE , BE , and BE ), Chna Postdoctoral Scence Foundaton (no. 2014M551636, no. 2014M561696), Jangsu Planned Projects for Postdoctoral Research Funds (no B, no B), Postgraduate Research and Practce Innovaton Program of Jangsu Provnce (SJLX15_0381, SJLX16_0326, and KYCX17_0798), Project of Jangsu Hgh Technology Research Key Laboratory for Wreless Sensor Networks (WSNLBZY201509), and NUPTSF (Grants no. NY214060, no. NY214061). References [1] T. T. Mulan and V. P. Pngle, Internet of Thngs, Internatonal Research Journal of Multdscplnary Studes,vol.2,no.1,pp.1 4, [2] Y. Kong, M. Zhang, and D. Ye, A belef propagaton-based method for task allocaton n open and dynamc cloud envronments, Knowledge-Based Systems, vol. 115, pp , [3] J. Shen, J. Shen, X. Chen, X. Huang, and W. Suslo, An effcent publc audtng protocol wth novel dynamc structure for cloud data, IEEE Transactons on Informaton Forenscs and Securty. [4] M. Díaz, C. Martín, and B. Rubo, State-of-the-art, challenges, and open ssues n the ntegraton of Internet of thngs and cloud computng, Journal of Network and Computer Applcatons,vol.67,pp ,2016. [5]J.Shen,D.Lu,J.Shen,Q.Lu,andX.Sun, Asecurecloudasssted urban data sharng framework for ubqutous-ctes, Pervasve and Moble Computng. [6] S. F. Wamba, A. Anand, and L. Carter, RFID applcatons, ssues, methods and theory: a revew of the AIS basket of TOP journals, Proceda Technology,vol.9,pp ,2013. [7]J.Zhang,G.Tan,A.Marndra,A.Sunny,andA.Zhao, A revew of passve RFID tag antenna-based sensors and systems for structural health montorng applcatons, Sensors, vol. 17, no. 2, artcle 265, [8] Q.Lu,W.Ca,J.Shen,Z.Fu,X.Lu,andN.Lnge, Aspeculatve approach to spatal-temporal effcency wth mult-objectve optmzaton n a heterogeneous cloud envronment, Securty and Communcaton Networks, vol.9,no.17,pp , [9]C.Zhao,C.Wu,J.Chaetal., Decomposton-basedmultobjectve frefly algorthm for RFID network plannng wth uncertanty, Appled Soft Computng,vol.55,pp ,2017. [10] R. Derakhshan, M. F. Orlowska, and X. L, RFID data management: challenges and opportuntes, n Proceedngs of the IEEE Internatonal Conference on RFID, pp , Grapevne, Tex, USA, March [11] J. Zhu, J. Huang, D. He, Y. Leng, and S. Xao, The desgn of RFID mddleware data flterng based on coal mne safety, n Proceedngs of the Second Internatonal Conference on Communcatons, Sgnal Processng, and Systems (CSPS), vol.246of Lecture Notes n Electrcal Engneerng, pp ,Sprnger, Tanjn, Chna, [12] B. Gao, The Research of Algorthm for Uncertan RFID Data Cleansng [Master s, thess], Nanjng Unversty of Informaton Scence and Technology, Nanjng, Chna, 2015.

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