Self-Orienting Wireless Multimedia Sensor Networks for Maximizing Multimedia Coverage

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1 Self-Orentng Wreless Multmeda Sensor Networks for Maxmzng Multmeda Coverage Nurcan Tezcan Wenye Wang Department of Electrcal and Computer Engneerng North Carolna State Unversty Emal: Abstract The performance of a wreless multmeda sensor network (WMSN) s tghtly coupled wth the pose of ndvdual multmeda sensors. In partcular, orentaton of an ndvdual multmeda sensor (drecton of ts sensng unt) s of great mportance for the sensor network applcatons n order to capture the entre mage of the feld. In ths paper, we study the problem of self-orentaton n a wreless multmeda sensor network, that s fndng the most benefcal pose of multmeda sensors to maxmze multmeda coverage wth occluson-free vewponts. We frst propose a dstrbuted algorthm to detect a node s multmeda coverage and then determne ts orentaton, whle mnmzng the effect of occlusons and total overlappng regons n the sensng feld. Our approach enables multmeda sensor nodes to compute ther drectonal coverage, provsonng self-confgurable sensor orentatons n an effcent way. Smulatons show that usng dstrbuted messagng and self-orentaton havng occluson-free vewponts sgnfcantly ncrease the multmeda coverage. I. INTRODUCTION As more sophstcated sensng electroncs are manufactured cheaper everyday, the nature of the nformaton to be hauled by wreless sensor networks (WSNs) change. We are now able to capture audo-vsual nformaton from the envronment usng low-cost, low-resoluton cameras embedded to the sensor nodes. The need for usng such multmeda sensors s usually drven by the necessty of provdng comprehensve nformaton pertanng to a specfc regon of nterest. From the perspectve of sensor networkng, consderable works are present for omndrectonal coverage problem [2], [3], whch am to cover a plane by arrangng crcles on the plane. A common lmtaton of these exstng protocols [2], [3] s that the collected nformaton on phenomena (e.g., temperature, concentraton of a substance, lght ntensty, pressure, humdty, etc.) are assumed to come from a omndrectonal sensng. However, multmeda sensors, (.e., lowresoluton cameras, mcrophones, etc.) have the unque feature of capturng drecton-senstve multmeda content. Especally, vdeo sensors can only capture useful mages when there s lne of sght (LOS) between the event and tself [1]. Hence, coverage models developed for tradtonal wreless sensor networks are not suffcent for deployment plannng of a multmeda sensor network. Fndng the most favorable orentaton for multmeda sensors for maxmzng multmeda coverage s an mportant and challengng problem. Frst, WMSNs are composed of large number of nterconnected low-cost sensor nodes havng batteryoperated, low energy consumpton multmeda sensors, e.g., smart camera, low-resoluton magng sensors [1]. Second, 0 Ths work s supported n part by Natonal Scence Foundaton under Award ECCS multmeda coverage s hghly occluded by any obstacle n the envronment (e.g., trees, buldngs, lakes, etc.). In such WMSNs havng large number of nodes, nherent dsadvantages due to physcal obstacles can be turned nto a mult-modalty advantage, wth the flexblty to adjust orentatons of the multmeda sensors attached to the wreless nodes. There have been several works on vson plannng whch take the object geometry nformaton as an nput from a database, as well as mods of the camera and the lens to determne camera poses and settngs [8]. Therefore, orentaton of multmeda sensors can be performed on ste once the multmeda sensors have been deployed. However, such methods need accurate feld nformaton database before deployment and are mostly appled to a small number of multmeda devces. Due to external effects or applcaton-specfc queres n WMSNs, multmeda nodes may need to change/re-orent ther pose over tme. In WMSNs, nodes may fal due to battery outage or external effects whch should be handled by a dynamc update of the poses whch can be performed va local nformaton exchange among sensors. In ths paper, we present a dstrbuted algorthm that fnds the most benefcal orentatons for the sensors used n a WMSN. We specfcally consder () mnmzng the effects of occluson n the envronment and () mprovng the cumulatve qualty of the nformaton sensed from the regon of nterest. Usng the proposed algorthm, each node dscovers ts neghbors and examne possble overlappng sensng regons as well as the obstacles n the envronment. Nodes use only the local nformaton and communcaton overhead s ncurred only between neghborng nodes. Each sensor node then determnes the most benefcal orentaton for ts multmeda sensor so that the entre mage of a feld can be constructed usng low-resoluton snapshots from multple sensors. Our approach enables multmeda sensor nodes to montor ther coverage performance, provsonng self-confgurable sensor orentatons. The remander of the paper s organzed as follows. We summarze the challenges on multmeda converge and defne multmeda coverage problem n Secton II, and propose a new dstrbuted algorthm for multmeda coverage calculaton n Secton III. Performance evaluaton s dscussed n Secton IV, and Secton V concludes the paper. II. MULTIMEDIA COVERAGE AND SELF-ORIENTATION As audo-vsual sensors take ther places on wreless nodes, omndrectonal sensng range assumpton loses ground sgnfcantly snce a typcal audo or vdeo sensor has a sectoral percepton and effected by surroundng obstacles heavly. Multmeda sensors, such as cameras, are powerful mult-dmensonal /08/$ IEEE 2206

2 (a) (b) Fg. 1. Illustraton of two dmensonal feld of vew (FoV) of a multmeda sensor node, where α s the vertcal angle to the boundary edge of FoV, Θ s the FOV vertex angle, and R s s the maxmum multmeda sensng range. sensors that can capture a drectonal vew, usually called Feld of Vew (FoV). The most commonly used low-resoluton camera module s equpped wth a lens provdng a 45 o FoV [8]. In ths work, we assume sensors nodes have a fxed lenses provdng feld of vew wth angle Θ, and they can only pan to adjust ther FoV. We use the term camera sensors for smplcty to represent wreless multmeda sensors ncludng vdeo and audo sensors havng drectonal vew. We also assume that each node s equpped to learn ts locaton nformaton va any lghtweght localzaton technque for wreless sensor networks [4]. A sensor s called self-orentng, f t s capable of adjustng ts pose at the pont of deployment (low-cost multmeda sensors [6] that are capable of pannng). In ths context, term feld of vew refers to the drectonal vew of a multmeda sensor and assumed to be an sosceles trangle (two-dmensonal approxmaton). A feld of vew of a sensor s s denoted by Λ Θ, where the parameter Θ s the vertex angle of the sosceles trangle. We defned vsble FoV, denoted by vλ Θ,asaFoVofa sensor node s whch s vsble to the sensor tself,.e., has not been blocked by any obstructon wthn FoV, obs j n A, f obs j Λ Θ =, then Λ Θ vλ Θ, where obs j s an obstacle n the sensng feld. The contrary of vfov s the occluded FoV such that, obs j n A, fobs j Λ Θ, then Λ Θ oλ Θ. The vsble FoV s referred to as overlappng FoV f t ntersects wth any of the neghborng sensor s vsble FoV, s j C,f there exsts vλ Θ vλ Θj j, then Λ Θ xλ Θ. The FoV dsk assocated wth a sensor defnes the set of all possble FoVs. For smplcty, we assume that the orentaton of all sensors can be anywhere n between [0 0, ]; thus, FoV dsk s a crcular dsk havng a radus of R s,.e., the maxmum dstance to capture wth a gven resoluton. III. A DISTRIBUTED SOLUTION TO MULTIMEDIA SENSORS SELF-ORIENTATION In ths secton, we wll explan the detals of the selforentaton algorthm for a multmeda sensor network. Our algorthm start exchangng messages between neghbors to collect ths neghborhood nformaton. All sensors broadcast a HELLO MSG ndcatng ther unque sensor IDs and ther locaton coordnates. We assume that statonary sensors havng dentcal FoV ranges are located n the sensng feld. Intal messagng ensures that every sensor s aware of ts neghbors and ther locatons. The rest of algorthm has two major phases: () dstrbuted FoV detecton and () self-orentaton algorthm. Next, we walk through each phase n detal. Fg. 2. An example showng the permeter test for sensor s 1. A. Dstrbuted FoV Detecton Dstrbuted FoV detecton uses three consecutve tests to detect sensor s maxmum vsble FoVs. The frst test, namely permeter test, checks the exstence of a vsble FoV wthn [0 0, ]. If a sensor fals to fnd a vsble FoV durng the permeter-test, t moves to the second test called neghbordstance test whch examnes the dstance wth FoV neghbors. Fnally, obstacle-dstance test, s performed f the sensor fals from the neghbor-dstance test whch compares the occluded FoVs to fnd the largest vsble FoV. Here, we explan these three tests n detal as follows: 1) Permeter Test: In permeter-test, each sensor scans ts FoV dsk permeter to determne whether a vsble FoV (whch can not be captured by any other FoV neghbor) exsts n ts FoV dsk. The reason s that FoV dsk permeter can effectvely show occlusons and possble overlappng regons. The ntersecton ponts of any tangent touchng an exstng obstacle on the permeter can be used to determne the sze of occluson. For example n Fg. 2, FoV dsk of sensor s 1 s llustrated. There are two obstacles nsde ts FoV dsk whch are close enough to s 1 that may result n occluson. The ntersectons of the tangents on the permeter are shown wth ponts F and G for the frst obstacle (obs1); H and A for the second obstacle (obs2). Therefore, a sensor s can determne that f there exsts a Λ Θ where Λ Θ Λ FOG =0or Λ Θ Λ HOA =0then Λ Θ s a vsble FoV and we refer arcs FG (counter clock-wse) and ĤA as occluded arcs on the FoV dsk of s 1. permeter test () { for each {obs k n FoV dsk} compute the occluded arc of obs k for each {s j C } compute the overlapped arc of s j scan permeter (0 0,360 0 ) for any arc P m P n f {( P m P n ) > Θ} and {vsble( P m P n )==TRUE} and {overlapped ( P m P n )==FALSE} return PASS; else {return FAIL;} Fg. 3. Pseudo code of permeter test. Permeter-test not only fnds the vsble FoV but also helps 2207

3 to determne non-overlappng FoVs n a FoV dsk. In ths step, sensors do not know the orentatons of ther FoV neghbors. However, they can determne possble overlappng FoVs nsde ther FoV dsks. Smlar to occluded arcs, each sensor fnds possble overlappng arcs on ts permeter usng the locaton nformaton receved from ts neghbors. To do ths, the ntersecton ponts of the arcs are determned and the permeter s scanned as llustrated n Fg. 2. For example, sensor s 1 has an overlappng arc BD and ĈE. By examnng each FoV neghbor and obstacles, a sensor decdes whether occluded and overlapped arcs enclose ts permeter from 0 0 to [5]. If there s a vλ Θ wth Θ Θ such that xλ Θ does not exst, we refer that permeter-test s passed. Ths means that the sensor has a vsble FoV whch has not been captured by any other sensor n any orentaton. Snce our goal s to maxmze the vsble FoV n the total sensng regon, sensors whch pass the permeter-test wll adjust ther pose. On the other hand, sensors that do not pass the permetertest contnue the FoV detecton wth the neghbor-dstance test, whch wll be explaned n the followng subsecton. Fg. 5. An example showng the neghbor-dstance test for sensor s 1. neghbor dstance test () { scan permeter (0 0,360 0 ) for any arc P m P n f {occluded( P m P n )==FALSE} and { ( P m P n ) > Θ} f {xλ P mp n exsts wth FoV neghbor s j } fnd the dstance d(, j) return PASS; store s j wth max d(, j); else { return FAIL; }} Fg. 4. Pseudo code of neghbor-dstance test. Fg. 6. s 1. An example showng the obstacle-dstance test condton for sensor 2) Neghbor-Dstance Test: Passng the parameter test mples that a sensor has vsble FoV, whch can not be covered by ts neghbors n any orentaton (non-overlapped n any case). In neghbor-dstance test, however, we examne whether a sensor has vsble FoV whch mght be overlapped. If a sensor has a vλ Θ wth an angle Θ Θ n ts permeter, t s assumed to pass the neghbor-dstance test, otherwse t moves to obstacledstance test. Sensors that pass the neghbor-dstance test then fnd the largest vsble FoV based on neghbor s dstances. Even though the fnal orentatons of the neghbors are not known, FoV neghbors mght have overlappng FoVs. In ths case, sensors need to fnd the smallest overlappng FoV by scannng vsble arcs and calculatng the dstances between each neghbor. A closer neghbor mples a larger overlappng FoV. In Fg. 5, FoV dsk of sensor s 1 and ts neghbors are shown. Snce permeter of s 1 s enclosed by an occluded arc FH and overlappng arcs FA, BC, DE, and ĜA, sensor s 1 fals the permeter-test. However, t passes the neghbor-dstance test, snce arc ĤF s vsble whch s greater than Θ, the FoV angle of the camera sensors whch s assumed to be fxed. Among the neghbors s 2, s 3, s 4 and s 5, sensor s 2 has the largest dstance to s 1, denoted by d(1, 2), ndcatng smallest possble overlappng FoV, shown as dark shaded areas nsde the FoV dsk. 3) Obstacle-Dstance Test: Fnally n obstacle-dstance test, sensors wth no vfov are examned. Fg. 6 shows an example sensor s 1 surrounded by four obstacles. Snce there s no vsble arc n the permeter greater than Θ, the fnal orentaton of sensor s 1 wll not have a vsble FoV. However, by fndng the dstances between the obstacles and the sensor node, occluded FoV can be mnmzed by keepng the vsble FoV maxmzed. Smlar to neghbor-test, a closer obstacle means a larger occluded FoV. In such condtons, a sensor scans the permeter n order to fnd the most benefcal arc Θ, to maxmze the vsble FoV. Note that the permeter of FoV dsk may not be fullyoccluded or fully-overlapped. For example, In Fg. 6, arc FA and DE are vsble and non-overlapped arcs, but smaller than Θ. In such cases, these small segments can be ncluded to the FoV. In Fg. 6, the FoV of sensor s 1 s shown n shaded regon whch ncludes the arc ĈD and occluded regons wth larger dstance from obstacles. Note that, n our algorthm, multmeda sensors can update ther neghbor lst and orentatons perodcally by takng the advantage of local nformaton exchange. Thus, all tests are performed usng up-to-date FoV neghbors and ther orentaton decsons. B. Dstrbuted FoV Detecton-Based Heurstc Algorthm for Self-Orentaton Under the pan-capablty assumpton, multmeda sensors wll determne ther pose for self-orentng by usng ther local nformaton. The dmensons and the locatons of the obstacles are assumed to be known by sensors before self-orentaton. We do not consder the multmeda sensors as 2208

4 obstacles wth respect to the other multmeda sensors due to ther small sze. Usng the tests presented n Secton III-A, we propose a heurstc algorthm as follows: STEP 1: Sensors send HELLO MSG that ndcates the locaton of the sensors. For self-orentaton, sensors must buld a lst of FoV neghbors that are close enough to have an overlappng FoV. A receved HELLO MSG s then used to update the neghbor lsts. Note that, we assume that the maxmum sensng range, R s, s equal or smaller than the transmsson range of the multmeda sensors. STEP 2: After exchangng HELLO MSG, each sensor has an upto-date FoV neghbor lst wth ther locatons and pror-known obstacle locatons. Next step s performng the permeter test. As we explaned n Secton III-A.1, permeter test checks f a sensor s has a vsble FoV, vλ α, whch can not be captured by any other FoV neghbor n a FoV dsk. Thus, when permeter test s passed, the sensor s can self-orent to vλ α and fnalze the self-orentng algorthm. On the other hand, sensors falng the permeter test wll contnue the algorthm wth the neghbordstance test. In partcular, permeter test shows the exstence of at least one vfov that can not be observed by others n any orentaton. However, there may be more than than one vsble FoVs that result n passng permeter test. Then sensors change ther pose to the most benefcal vfov. Here, the term benefcal corresponds to havng smallest pannng angle to a self-orentng multmeda sensor. Therefore, a sensor selects a vλ α wth a vertcal angle of α to the boundary such that α α 0 s the smallest among all possble vfov s, where α 0 s the current vertcal angle. After changng the pose, a sensor should advertse ts decson to all ts neghbors wth a POSE ADV MSG and fnalze the self-orentng procedure. Then, sensors that have faled n the permeter test update ther neghbor lst based on the POSE ADV MSGs they receved. If a sensor receves a POSE ADV MSG from a FoV neghbor, t updates ts neghbor lst by addng the pose of ts neghbor for the next steps. STEP 3: In step 3, sensors nvoke neghbor-dstance test to fnd a occluson-free FoV. By passng the neghbor dstance test, a sensor determnes the exstence of a vsble FoV n the FoV dsk. From the vsble FoVs, t selects the pose toward the FoV neghbor s d wth maxmum dstance usng canddate pose selecton procedure and sends ts canddate pose by a CANDIDATE ACK MSG to the neghbor s d. Ths message ndcates the canddate pose of the sensor to ts neghbors. Snce sensor nodes perform the self-orentng smultaneously, sensors then receve CANDIDATE ACK MSG from ther neghbors who have passed the neghbor-dstance test, thus replyng wth a ACK POSE MSG f no xf ov occurs. Whenever a sensor receves ACK POSE MSG, t ndcates that the sensor can select ths pose safely and fnalze the self-orentng procedure. Otherwse, a sensor should repeat the step 5 wth the second mnmum dstance neghbor. STEP 4: Fnally, sensors that faled from permeter and neghbor dstance test perform the last test, obstacle-dstance test. Snce they have faled from the prevous tests, no vsble FoV exsts n ther FoV dsk. Sensors wll select an occluded FoV wth maxmum coverage; that s, the pose toward the obstacle wth maxmum dstance smlar to the neghbor-dstance test. If there s a vsble FoV wth an angle smaller than Θ, the fnal pose wll be selected from the small vfov ncludng occluded FoV to maxmze the vsble regon that the sensor wll capture. At the end of ths algorthm, each sensor selects ts pose and self-orents to maxmze total vsble FoVs on the sensng feld. Next, we show our smulaton results for dfferent scenaros. IV. PERFORMANCE EVALUATION We have used Ns-2 smulator [7] for the performance evaluaton of our algorthms. Smulatons have been performed for randomly placed sensor nodes n a rectangular two-dmensonal terran. All sensor nodes have been confgured wth an FoV vertex angle Θ=60 0, and an R s of 30m. A sensng feld spannng an area of 250 x 250m 2 has been used on whch the number of sensors were vared to study the system performance from sparse to dense deployments. In the basc scenaro, 50 statc multmeda sensor nodes are deployed wth self-orentaton capabltes. In our smulatons, we consder total coverage and messagng overhead as the two key metrcs to evaluate the performance of our self-orentng algorthms. We assume that global access to obstacle locatons on a calbrated coordnate system s avalable for the sensors. Total vsble FoV s calculated n a btmap fashon usng bns (.e. 1mX1m bns for each pont) on the 250m x 250m feld. Bns that fall nto a sensor s trangular FoV are tested for lne of sght (LOS) vew (.e., lne segment from the bn correspondng to the FoV pont to the camera sensor should not ntersect wth any obstacle on the feld). The effect of self-orentaton on coverage: In Fg. 7, a sensng feld wth several obstacles (represented by black rectangular areas) and 50 multmeda sensors wth 30m range s shown. Each multmeda sensor s llustrated wth a small damond and ts vfov s shown wth a dark shaded area. The FoVs for the network n Fg. 7 (a) are randomly determned, whereas n Fg. 7 (b) and (c) usng the proposed self-orentng algorthm. In Fg. 7 (a), an experment outcome wth random orentaton s llustrated, resultng 21.09% overall coverage of the feld. Although sensors had the capablty to exchange nformaton regardng ther neghbors and obstacles, due to the lack of proper coordnaton, several sensors went overlappng. Mostly occluded FoVs are serous waste of resources. However, n Fg. 7 (b), sensors were programmed to determne ther FoV dsk, scan ther coverage neghbors, obstacles and communcate wth ther neghbors to decde on the optmal pose. We observed that by usng our approach n a 50 node network, a coverage Nodes Obstacles Multmeda coverage gan % % % % % % % TABLE I MULTIMEDIA COVERAGE RATIOS. 2209

5 (a) Random orentaton, N=50, 4 random obstacles. (b) Self-orentaton algorthm, N=50, 4 random obstacles wth same deployments as (a). (c) Self-orentaton algorthm, N=50, 8 random obstacles. Fg. 7. Multmeda coverage. rato of 29.88% could be acheved, whch s very close to the maxmum possble coverage wth 50 sensors of 30m range on ths feld. Smlarly, n Fg. 7 (c), smlar sensors deployed on to the feld havng 8 obstacles. We observed an average of 30.50% coverage rato that slghtly better than the scenaro havng 4 obstacles. Note that, n our experments we do not target to reach full coverage but ncrease the total covered area restrcted by gven number of nodes that have lmted multmeda ranges (corresponds to low-resoluton). A set of resultant coverage gan (%) of self-orentng algorthms are also gven n Table I for dfferent scenaros. Here, coverage gan s defned as the ncrease (n %) when self-orntaton algorthm s used compared to random orntaton n the same deployment. The results are the average of fve teratons of each test. The effect of self-orentaton on overlappng area: Selforentng algorthm not only determnes occluson-free vewponts for sensors but also avods overlappng FoVs usng neghbor-dstance test, as explaned n Secton III-A.2. For example, n Fg. 8, coverage rato gans up to 41% were obtaned by usng self-orentaton. Preventng overlappng FoVs contrbuted 12% of the the total ncrease n coverage. In Fg. 8 (a), we show the rato of overlappng FoV when self-orentaton algorthm s used. We observe that ncrease n the number of nodes causes dramatc ncrease n the total overlappng area. Self-orentaton results n at most 9% overlappng area, whereas random orentaton results n overlappng areas up to 29%. The overhead of self-orentaton algorthm: For the frst test Rato of total overlappng area (%) Self orentng Alg. Self orentng Alg. n hgh Occluson Random orentng Random orentng n hgh occluson Number of multmeda nodes (a) Overlappng FoV rato. Messagng Overhead (%) R = 30 m s R = 60 m s Number of Nodes (b) Messagng overhead. we present, multmeda sensors wth a 30m range on a feld of 250 x 250m are used. In Fg. 8 (b), we show the rato of total number of messages used by the self-orentng algorthm to the total number of control messages, ncludng routng. As we explaned n Secton III-B, our algorthm uses O(n) messages whch s 6% of all control messages on average when N =50. The rato ncreases only up to 35% of total control traffc when N = 200 and R s =60m, ndcatng a very dense network wth hgh degree of connectvty. V. CONCLUSION In ths paper, we proposed a self-orentng algorthm for multmeda wreless sensor networks n order to maxmum feld occluson and to attan occluson-free coverage. We fnd that () the proposed algorthm uses local nformaton; that s, communcaton overhead s ncurred only between neghborng nodes wth a complexty of O(N), () the proposed algorthm s a fully dstrbuted, whch can operate after ntal deployment and update the orentaton of multmeda sensors on the fly, () the proposed algorthm can support prortzed or accurate observaton that requre more than multple nputs from more than one sensor node, and (v) coverage s ncreased even sparse networks by usng self-orentaton nstead of random orentatons, for arbtrary obstacles n the sensor feld. REFERENCES [1] I. F. Akyldz, T. Meloda, and K. R. Chowdhury. A Survey on Wreless Multmeda Sensor Networks. Computer Networks, [2] M. Carde, M. Tha, and W. Wu. Energy-effcent Target Coverage n Wreless Sensor Networks. In Proc. of IEEE Infocom, Mam, Forda, USA, March [3] H. Gupta, S. R. Das, and Q. Gu. Connected Sensor Cover: Self- Organzaton of Sensor Networks for Effcent Query Executon. In Proc. of ACM Mobhoc, Annapols, Maryland, USA, June [4] T. He, C. Huang, B. M. Blum, and J. A. Abdelzaher. Range-free Localzaton Schemes for Large Scale Sensor Networks. In In Proc. of ACM Mobcom, pages 81 95, San Dego, Calforna, USA, September [5] M.-F. Huang and Y.-C. Tseng. The Coverage Problem n a Wreless Sensor Network. In Proc. of ACM WSNA, San Dego, CA, USA, September [6] M. Ncolescu and G. Medon. Electronc Pan-Tlt-Zoom: A Soluton for Intellgent Room Systems. [7] Ns [8] K. A. Tarabans, R. Y. Tsa, and A. Kaul. Computng Occluson-free Vewponts. IEEE Transactons on Pattern Analyss and Machne Intellgence, 2: , March Fg. 8. Performance of self-orentaton algorthm. 2210