How to Select Measurement Points in Access Point Localization

Transcription

1 Proceeding of the International MultiConference of Engineer and Computer Scientit 205 Vol II, IMECS 205, March 8-20, 205, Hong Kong How to Select Meaurement Point in Acce Point Localization Xiaoling Yang, Bing Chen, and Fengyu Gao Abtract In Acce Point(AP) Localization, a lot of manual effort are required to acquire neceary information of the AP to be localized for precie localization, but little reearch ha been devoted to the problem on how to elect appropriate meaurement point for lower cot and with high accuracy. Thi paper preent an approach to optimize the election of meaurement point. The idea i that the next meaurement point i determined baed on real-time meaurement, and it i located at the interection of the coverage area of AP, whoe location are roughly etimated by the previouly meaurement information, o a to detect a many AP a poible at each meaurement point. Simulation reult how that the propoed algorithm can reduce the number of neceary meaurement point and improve accuracy. Index Term Acce Point(AP) localization, meaurement point election, information collection T I. INTRODUCTION he localization of mobile device in wirele environment ha attracted much attention[], but they are often etimated with prior knowledge of the location of the acce point(ap). When there i limited prior knowledge, for example, anonymou environment, however, the localizing of acce point i required. Beide, AP localization i alo important for wirele network management and locating unauthorized AP[2]. Recently, everal attempt have been made for acce point(ap) localization. Han et al. [3] conidered the trend of receive ignal trength(rss) by comparing value of RSS in different meaurement point. Koo and Cha [4] conidered the relative poition of each AP and calculated real poition of AP later uing multidimenional caling (MDS) technique. Subramanian et al. [5] ued directional antenna to etimate the direction of the AP and calculated the poition of AP by k-mean method. Seung [6] propoed a modified verion of the Hata-Okumara model to calculate the ditance information inferred from the meaured ignal trength. Thee approache have a common feature: AP localization include two phae: firt, maive information hould be collected at many meaurement point, and then a certain localization method i ued to etimate the location of AP. Generally, the firt phae i a time-conuming proce and require extenive manual effort. Typically, everal hour or Manucript received November 6, 204. Xiaoling Yang, Bing Chen and Fengyu Gao are with the Department of Computer Science and Technology, Nanjing Univerity of Aeronautic and Atronautic, P.R. China (correponding author to provide phone: , yxl_9009@63.com; cb_china@nuaa.edu.cn; feng32@63.com). even more time i required to collect uch an amount of data when the area conidered i very large. However, the focu in current literature about AP localization i the localization method, and little reearch ha been devoted to clearly etimate how many meaurement point are needed and where to meaure in a given workpace to improve accuracy and reduce the meaurement cot. Thi paper conider the problem on how to elect uitable meaurement point to collect enough information for AP localization, we conider online AP localization, that to ay, the location of AP i etimated while collecting information. The cloet reearch to our objective i Zhang work in [7], they tried to locate an AP by guiding the next meaurement point baed on the current information, but only one AP i located each time and it i inappropriate for localizing multiple AP. In thi work, we propoe an approach that aim at getting a balance between overall accuracy and manual effort. To reduce manual effort, there hould be a many AP a poible to be detected in each meaurement point. We etimate the location of AP baed on previou meaurement and elect ome point at the interection of the coverage area of the AP a the next meaurement point. Thi approach can enure the validity of meaurement point for AP localization. To the bet of our knowledge, there have been no previou attempt to develop a method for the election of meaurement point in multiple AP localization. Simulation reult how that the propoed algorithm can reduce the number of meaurement point and improve localization accuracy. II. METRIC OF MANUAL EFFORTS IN AP LOCALIZATION To collect enough information, meaurement hould be taken in many point, the number of which i indicated a N, and l i the total length of the path from the firt point to the lat. For each meaurement point, an RSS ample (a et of RSS value) i collected. A RSS value vary noticeably due to interference and environment condition, everal conecutive RSS meaurement need to be collected for each ample. We aume t S i the time needed to get an RSS ample, and v i the peed of the worker. The total time t i formulated a follow: t N t l / v () P Meaurement point hould be elected in order to achieve a reaonable coverage of the AP. On one hand, increaing the number of meaurement point generally lead to better accuracy; on the other hand, it require more effort. However, the effectivene of a election pattern highly depend on the detailed workpace condition, i.e., the number and poition S P IMECS 205

2 Proceeding of the International MultiConference of Engineer and Computer Scientit 205 Vol II, IMECS 205, March 8-20, 205, Hong Kong of AP to be localized. Unfortunately, there i not any related work, epecially for multiple AP. Therefore, the objective of thi work i to elect a reaonable number of meaurement point while getting enough accuracy. III. SELECTION OF MEASUREMENT POINTS The workflow of the propoed approach i hown in Fig.. Firt, a point i randomly elected a the firt meaurement point, and the poition of AP are etimated according to the collected meaurement information. If there i no AP whoe poition ha been etimated, chooe the next point according to the current meaurement point (detail available in 3., NextRandom). Or ele, we would check if all the AP are found; if not, we would chooe the next point according to either the interection area or the extended area (detail available in 3.2, Next). We keep moving on to the next point until all the AP are found. The function and variable are explained a follow: Random(): elect a point randomly; MeaureAt(Pt): get the meaurement information at point Pt; DetectedAP: the number of AP whoe poition have been etimated; NextRandom(): randomly generate a point baed on the current meaurement point and all the previou meaurement point; FoundAP: the number of AP which have been found, the total number of AP to be localized i N; Next(): elect a point at the interection area of the coverage area of predicted AP; if there i no available point at the interection area, elect the point at the extended area. N Y FindPt (FindPt, Pt) = Next() N START Pt = Random() MeaureAt(Pt) DetectedAP = 0 N FoundAP = N Y END Fig.. Workflow of the propoed approach Y Pt = NextRandom() Some notation in the approach are decribed a follow: AllMPtLit: the lit of point where have meaured. EtiApLocDict: it i repreented by tuple <ApName, ApLoc>, ApName mean the name of AP and ApLoc mean the etimated poition of the correponding AP. AllMApInfoNum: It i repreented by tuple <ApName, Number>, ApName mean the name of AP, Number mean the amount of meaurement information of the correponding AP. It i ued to ae the priority of Ap, the le information, the higher the priority of the AP. miniditance: it i repreented by integer, it mean the minimum ditance between meaurement point. ExperimentArea: it mean the area of experiment and i repreented by a rectangle. A. Cae : NextRandom Given the current meaurement point (denoted by tmpt) and the initial extended ditance (denoted by ExtendDit), the next meaurement point i calculated in detail by Algorithm. CalcPtFromPt( ) return a point according to angle and ditance away from an original point. IFarFromAllMPt( ) repreent whether the point i far away from all the previou meaurement point; if it i, return true, or ele, return fale. Algorithm : NextRandom() Require: Point tmpt null {the current meaurement point} Require: Integer ExtendDit 0 {the ditance from TempMPt} Require: Integer AngleNum 0 {the number of angle to be calculated in each ExtendDit}. {calculate the longet ditance that could be extended} 2. Integer MaxDit = max( tmpt, P,, tmpt, P4 ), where P P4 are the four point in ExperimentArea and tmpt, P mean the ditance between tmpt and P. 3. Integer Num = 0 4. While ExtendDit <= MaxDit do 5. If Num > AngleNum do 6. Num = 0, ExtendDit = ExtendDit + miniditance 7. Ele do 8. Integer angle = Randomly generate an angle in [0, 360) 9. Point Pt = CalcPtFromPt(tMPt, ExtendDit, angle) 0. If IFarFromAllMPt(Pt) do. Return Pt {the next meaurement point i found} 2. End If 3. Num = Num + 4. End If 5. End While B. Cae 2: Next Given the current meaurement point (denoted by tmpt), the next meaurement point i calculated in detail by Algorithm 2. Some function are detailed a follow: GetPrioSortApLit( ): get an AP lit in decending order of priority according to the amount of information, the le information, the higher the priority of the AP. CalcInterSection( ): calculate the interection rectangle according to the given AP lit and their repective etimated location. The calculation about interection rectangle of two AP coverage area i hown in Fig. 2. repreent the coverage area of AP, which i denoted by circle determined by their poition and tranmit power. The interection area i denoted by P, P 2, P 3 and P 4 a hown in Fig. 2, and they can be calculated eaily. Meanwhile, in order to implify, the interection area of two AP coverage area i repreented by a rectangle approximately, which i repreented by a black rectangle in Fig. 2. x, x 2, y and y 2 can be calculated by the following formula(2). Thu, the correponding rectangle i calculated. x min( P. x, P2. x, P3. x, P4. x) x2 max( P. x, P2. x, P3. x, P4. x) y min( P. y, P2. y, P3. y, P4. y) y2 max( P. y, P2. y, P3. y, P4. y) where Pi. x and Pi. y mean the value in x axi and y axi (2) IMECS 205

3 Proceeding of the International MultiConference of Engineer and Computer Scientit 205 Vol II, IMECS 205, March 8-20, 205, Hong Kong y Coverage area of an AP Interection rectangle Of and 2 y 2 P P 4 P y P 2 x x2 Fig. 2. Calculation of interection rectangle of two AP x Interection rectangle Of thee three AP 3 Interection rectangle Of 2 and 3 Fig. 3. Calculation of interection rectangle of three AP Coverage area of an AP Interection rectangle N- N Rectangle Rectangle 2 Rectangle 3 Rectangle M many temporary rectangle The Final Rectangle Fig. 4. Calculation of interection rectangle of four AP coverage area of an AP Fig. 5. Calculation of interection rectangle according to AP lit valid area of a meaurement point The extended area valid range of a meaurement point The interection area Meaured point Valid candidate point Invalid candidate point Meaured point Valid candidate point Invalid candidate point Fig. 6. Example about valid candidate point in interection area The calculation of interection rectangle of multiple AP i hown in Fig. 3 and Fig. 4, which repreent the number of AP i odd and even, repectively. When calculating the interection rectangle according to the given AP lit, if the number of AP lited i only, the interection rectangle i the bounding rectangle of the coverage area of AP. Or ele, the interection rectangle of multiple AP i calculated a hown in Fig. 5, for example, if there are N AP in the lit, firt, elect two AP equentially and calculate the interection rectangle of thee two AP. If N i odd, the lat AP (N) i calculated with the prior AP (N-). Then, M rectangle are obtained, M = (N-)/2 +. Thu, the interection rectangle of M rectangle i calculated. Finally, the final interection rectangle i calculated. CalcPtByRect( ): calculate the next point according to the given rectangle. Fig. 6 how an example. The rectangle box with olid line repreent the interection rectangle of AP. Firt, the interection rectangle i divided into many cell according to miniditance, and the center point of each Fig. 7. Example about valid candidate point in extended rectangle cell i choen a the candidate point, which form a et of point, indicated by Q, then another et of point, indicated by P, can be calculated by formula (3): P { pt IFarFromAllMPt ( pt), pt Q} (3) They are the valid point in Fig. 6. If P i empty, thi indicate that the next meaure point cannot be calculated according to interection rectangle, or ele, the next meaurement point can be calculated by formula (4): NextMeaurePt arg min tmpt, pt, pt P (4) where tmpt, pt mean the di tan ce between thee two point Group( ): calculate the combination that chooe a given number AP from a given AP lit. For example, if the AP lit i {0,, 2, 3} and number i 3, all the combination in order are a below: { {0,, 2}, {0,, 3}, {0, 2, 3}, {, 2, 3} }. GetRectByAp( ): given a point and ditance, obtain the rounding rectangle of a circle, which i determined by point a center point and ditance a radiu. IMECS 205

4 Proceeding of the International MultiConference of Engineer and Computer Scientit 205 Vol II, IMECS 205, March 8-20, 205, Hong Kong CalcPtFromExtendRect( ):given a rectangle, the next point i calculated by extending each ide a miniditance each time. Fig. 7 how an example. All the valid candidate point form a et of point, indicated by P. If P i not empty, the point nearet the current meaurement point from P i choen a the next meaurement point. Or ele, thi indicate that the next meaurement point cannot be found. Algorithm 2: Next() Require: Point tmpt null {the current meaurement point}. {get the orted AP lit} 2. thisortedaplit = GetPrioSortApLit(AllMApInfoNum) 3. Integer ApNum = Count(EtiAPLocDict) 4. ApLit = null {the AP lit to be calculated} 5. Rectangle Rect = null {the interection rectangle calculated according to ApLit} 6. Point Pt = null {the temporary point} 7. While ApNum > 0 8. If Count(AllMApInfoNum) = 0 9. {there i no information to ae the priority of AP} 0. ApLit = chooe ApNum AP from EtiApLocDict in order. If CalcInterSection(ApLit) and Pt = CalcPtByRect(Rect) 2. FindPt = true, Return Pt { find the next meaurement point } 3. Ele do 4. ApNum 5. End If 6. Ele 7. { get all the combination in order in thisortedaplit } 8. For ApLit in Group(thiSortedApLit, ApNum) 9. If CalcInterSection(ApLit) and Pt = CalcPtByRect(Rect) 20. FindPt = true, Return Pt {find the next meaurement point} 2. End If 22. End For 23. ApNum 24. End If 25. End While 26. {there i no next meaurement point in the interection area, the extended area would be calculated} 27. {Get the etimated location of the AP which ha the highet priority} 28. Point ApLoc = GetHighetPrioApLoc() 29. {Get the rounding rectangle of the coverage area of the AP who ha the highet priority} 30. Rectangle OriginRect = GetRectByAp(ApLoc) 3. While OriginRect < ExperimentArea 32. If Pt = CalcPtFromExtendRect(OriginRect) 33. FindPt = true, Return Pt {find the next meaurement point } 34. End If 35. End While 36. {there i no next meaurement point in the ExperimentArea} 37. FindPt = fale IV. EXPERIMENT A. Simulation Platform and Configuration We evaluated the performance of the propoed approach with our own imulation platform. DriveByLoc[5] i choen a the AP localization method, which record the direction of AP in each meaurement point with a directional antenna and ue k-mean algorithm to etimate the location of AP. We imulate the directional antenna and meaure information per 30 degree. The AP to be localized are put randomly in the experiment area. Since the effectivene of a election pattern about meaurement point highly depend on the detailed workpace condition, i.e., the number and poition of AP to be localized, thu, we vary the number and poition of AP. The number of AP i, 5, 0 and 20. Five AP layout are randomly generated under each etting, and each experiment i repeated ten time. Since the tranmit power of each AP may be different, we varied P 0 from 0 to 20 dbm. And conidering the real wirele communication environment between the AP and receiver, we varied RSS ditortion, which i affected by hadow fading, multi-path and mall fading effect. Fig. 8 how an example of imulation topology. Table I how ome global parameter. Fig. 8. Example of imulation topology TABLE I GLOBAL PARAMETERS IN SIMULATION Parameter Name Value ExperimentArea 300 m * 80 m miniditance 20 m Number of AP to be localized, 5,0,20 Number of AP layout each number of AP (time) 5 Repetition of experiment each AP layout (time) 0 Poible value of P 0 (dbm) 0, 5, 0, 5, 20 Aumed P 0 (dbm) 5 Reception enitivity (dbm) -90 RSSI ditortion 0-50% B. Simulation Reult t i almot 20. v i the In thi ection, we evaluate the performance of the propoed algorithm. Since mot approache about AP localization have not conidered neither multiple AP localization nor the election of meaurement point, the propoed approach cannot be compared with exiting method. We compare InterArea we propoed with Random. In Random, like the cae (NextRandom), the next meaurement point i calculated by randomly electing an angle from the current meaurement point. A for the manual effort in formula, t i related to three factor: the number of meaurement angle, the repetition of each meaurement and the time taken in each meaurement. The number of meaurement angle i 2, the repetition of each meaurement i 0 and the time taken in each meaurement i almot econd, thu ame with whether InterArea or Random, and the value i et to be m/. l i related to N and the location of meaurement point. The performance comparion i made between InterArea we propoed and Random. Firt, we conider the relationhip between the number of meaurement point and manual effort t, a hown in Fig. 9. It can be een that the cot with InterArea and Random ha little difference, the maximum difference i almot 500 econd, and it i mainly related to l. It would be maller when with greater t. Thu, the cot can be evaluated by the number of meaurement point roughly. p IMECS 205

5 Proceeding of the International MultiConference of Engineer and Computer Scientit 205 Vol II, IMECS 205, March 8-20, 205, Hong Kong Fig. 9. Manual effort with InterArea and Random Fig. 0. CDF of error ditance with RSS ditortion 0-0% Fig.. CDF of error ditance with RSS ditortion 0-20% Fig. 2. CDF of error ditance with RSS ditortion 0-50% Fig. 3. Relationhip between meaurement point and error ditance of individual AP when only one AP need be localized Fig. 4. Relationhip between meaurement point and error ditance of individual AP when more than one AP need to be localized Next, we conider the effect of the number of meaurement point and the RSS ditortion on the Cumulative Ditribution Function (CDF) of error ditance, a hown in Fig. 0-2, wherein, 70-InterArea mean that it i the reult with a total of 70 meaurement point and the next meaurement point i calculated with InterArea. Fig. 0 how the CDF of error ditance when the RSS ditortion range from 0 to 0% and the number of meaurement point i from 30 to 70, the reult how that when the total number of meaurement point i 70, the localization accuracy (with probability of 90%) i 5 m with InterArea and 20 m with Random. With the decreae of the number of meaurement point, the accuracy decreae both with InterArea and Random. When the number of meaurement point i down to 50, the method can localize AP (with probability of 90%) within 5 m with InterArea and about 30 IMECS 205

6 Proceeding of the International MultiConference of Engineer and Computer Scientit 205 Vol II, IMECS 205, March 8-20, 205, Hong Kong m with Random. And in cae of 30 meaurement point, the accuracy (with probability of 90%) i 25 m with InterArea and 40 m with Random. To prove our propoed algorithm, we change the ditortion to 0 to 20%, a hown in Fig.. When there i a total of 70 meaurement point, the localization accuracy (with probability of 90%) i 5 m with InterArea and 30 m with Random. When the total number i down to 50, the accuracy (with probability of 90%) i about 20 m with InterArea, but i reduced to about 40 m with Random. In cae of 30 meaurement point, the accuracy (with probability of 90%) i 30 m with InterArea and more than 50 m with Random. Further, we change the ditortion to 0 to 50%, the reult i hown in Fig. 2. It can be een that, the accuracy (with probability of 90%) reduce to 25 m with InterArea and about 55 m with Random in cae of a total of 70 meaurement point. And the fewer the total of meaurement point, the lower the accuracy. It can be hown that, the accuracy with InterArea i higher than with Random. Next, we analyze the relationhip between the individual AP localization error ditance and the number of meaurement point, a hown in Fig. 3 and Fig. 4, where, Fig. 3 how the relationhip in cae of only one AP to be localized, and Fig. 4 how the relationhip when there are many AP to be localized. To facilitate a clear expreion, ix tet are choen from ten tet in the imulation. It can be een that the change of error for individual AP may be divided into three tage: firt, the error ditance may change lowly or jitter, then gradually decreae, and finally reache a mooth minimum value. In the econd tage, the error ditance gradually decreae, but it may remain unchanged in certain period, which may be longer when there are many AP to be localized, a can be een from the comparion of Fig. 3 and Fig. 4. The reaon i that when there are many AP to be localized, the location of the next meaurement point i determined according to the priority of AP; the higher the priority of AP, the more likely the next meaurement point i biaed toward the AP, which may lead to a lower poibility of detecting other AP in the next meaurement point, and the error of other AP may remain unchanged. However, if there i only one AP to be localized, all the meaurement point are calculated for etimating the location of thi AP, reulting in a horter period. Thu, the more the AP to be localized, the longer the error remain unchanged for an individual AP. point i calculated according to the current meaurement point, or baed on the interection or extending area of coverage area of AP. Simulation reult how that AP localization with InterArea we have propoed, compared with Random, enure the validity of meaurement point for localization and reduce the total number of the meaurement point and improve localization accuracy. And it i adequate both for ingle and multiple AP localization. While thi approach o far i evaluated by imulation, it can be extended to the actual environment, then an environment map hould be imported into the ytem and the detailed environment hould be conidered when calculating the next meaurement point. We will conider thi in the future. We expect that better localization would be got with the propoed approach. ACKNOWLEDGMENT The author thank the reviewer for their comment which helped improve the paper. REFERENCES [] A. Kuhki, K.N. Platanioti, and A.N. Venetanopoulo, Intelligent dynamic radio tracking in indoor wirele local area network, Mobile Computing, IEEE Tranaction on, 200, 9(3): [2] T.M. Le, R.P. Liu, and M. Hedley, Rogue acce point detection and localization, Peronal Indoor and Mobile Radio Communication (PIMRC), 202 IEEE 23rd International Sympoium on. IEEE, 202: [3] D. Han, D.G. Anderen, M. Kaminky, K. Papagiannaki, S. Sehan, Acce point localization uing local ignal trength gradient, Paive and Active Network Meaurement. Springer Berlin Heidelberg, 2009: [4] J. Koo and H. Cha, Unupervied Locating of wifi acce point uing martphone, Sytem, Man, and Cybernetic, Part C: Application and Review, IEEE Tranaction on, 202, 42(6): [5] A.P. Subramanian, P. Dehpande, J. Gaojgao J, and S.R. Da, Drive-by localization of roadide WiFi network, INFOCOM The 27th Conference on Computer Communication. IEEE, [6] S. Nam, Localization of acce point baed on ignal trength meaured by a mobile uer node, IEEE Communication letter. 8(8) [7] Z. Zhang, X. Zhou, W. Zhang, Y. Zhang, G. Wang, B.Y. Zhao, H. Zheng, I am the antenna: accurate outdoor ap location uing martphone, Proceeding of the 7th annual international conference on Mobile computing and networking. ACM, 20: V. CONCLUSION Mot approache about AP localization focu on the localization method and the election of meaurement point attract little attention, however, in fact, the number and location of meaurement point are cloely related to the localization accuracy. In thi paper, we have propoed a new method for optimizing the election of meaurement point in AP localization. The main idea i that, the next meaurement point i determined by the interection of the coverage area of AP, whoe location are roughly etimated by the previouly meaured information, o that a many AP a poible are detected at each meaurement point. To do thi, we divide the election of the next meaurement point into two cae: the IMECS 205