Towards a cloud‑based automated surveillance system using wireless technologies

Towa ds a cloud‑based au oma ed su eillance sys em using wi eless echnologies Ja ie J. Salme ón‑Ga cía1 · Sjoe d an den D ies2 · Fe nando Díaz‑del‑Río1 · A u o Mo gado‑Es e ez3 · Jose Luis Se illano‑Ramos1 · M. J. G. an de Moleng a 2 addi ion, his p oo -o -concep claims ha many in e es - ing oppo uni ies and challenges a ise, o example, when mobile wa ch obo s and ixed came as would ac as a eam o ca ying ou complex collabo a i e su eillance s a egies. Keywo ds Su eillance · Wo ld modeling · Wi eless communica ion · Compu a ion o loading · RGBD came as · Cloud obo ics 1 In oduc ion Nowadays, he de elopmen o au oma ed su eillance sys- ems has become mo e han widesp ead [7]. The e a e innu- me able en i onmen s whe e hei use has become c i ical, such as ci ies, households and co po a e buildings, no only o secu i y, bu also o people loca ion o cus ome queue analy ic [38]. Consequen ly, he e is a need o p ocess, send and analyze he high olume o senso ial media p oduced by came as, ada s and o he senso s. This ask in ol es no only he communica ion in as uc u e bu also he neces- sa y mul imedia compu ing amewo k. Mo e p ecisely, in a u u e sma ci y he e would be innume able de ices (small senso s, compu e s, mobile obo s, amongs o he s) ha wo king oge he should pe ec ly c ea e an ex emely sa e en i onmen . Howe e , se e al cu ing-edge su eil- lance solu ions ely on e y expensi e sensing echnology, high pe o mance dedica ed ha dwa e and wi ed connec- i i y, and a e he e o e una o dable o many companies o scena ios. Hence, he ques ion o be add essed in his pape would be: un il wha ex en can all hese he e ogene- ous de ices collabo a e o pe o m a mul i ace ed ask-like su eillance using cu en low-cos a ailable echnologies? Abs ac Cloud Compu ing can b ing mul iple bene i s o Sma Ci ies. I pe mi s he easy c ea ion o cen alized knowledge bases, hus s aigh o wa dly enabling ha mul- iple embedded sys ems (such as senso o con ol de ices) can ha e a collabo a i e, sha ed in elligence. In addi ion o his, hanks o i s as compu ing powe , complex asks can be done o e low-spec de ices jus by o loading compu- a ion o he cloud, wi h he addi ional ad an age o sa - ing ene gy. In his wo k, cloud’s capabili ies a e exploi ed o implemen and es a cloud-based su eillance sys- em. Using a sha ed, 3D symbolic wo ld model, di e en de ices ha e a comple e knowledge o all he elemen s, people and in ude s in a ce ain open a ea o inside a build- ing. The implemen a ion o a olume ic, 3D, objec -o i- en ed, cloud-based wo ld model (including seman ic in o - ma ion) is no el as a as we know. Ve y simple de ices (o ange Pi) can send RGBD s eams (using kinec came as) o he cloud, whe e all he p ocessing is dis ibu ed and done hanks o i s inhe en scalabili y. A p oo -o -concep expe imen is done in his pape in a es ing lab wi h mul- iple came as connec ed o he cloud wi h 802.11ac wi e- less echnology. Ou esul s show ha his kind o su eil- lance sys em is possible cu en ly, and ha ends indica e ha i can be imp o ed a a sho e m o p oduce high pe o mance igilance sys em using low-speed de ices. In *Fe nando Díaz-del-Río [email p o ec ed] 1 Escuela Técnica Supe io de Ingenie ía In o má ica, Uni e sidad de Se illa, Se illa, Spain 2 Technische Uni e si ei Eindho en, Eindho en, The Ne he lands 3 Escuela Supe io de Ingenie ía, Uni e sidad de Cádiz, Cádiz, Spain A powe ul ool o ul ill his challenge is he inco po a- ion o Cloud Compu ing, because many o i s p ope ies can bene i such su eillance sys ems. Speci ically, he ol- lowing cha ac e is ics i pe ec ly: ––On-demand esou ce p o ision: The cloud is able o p o ide bo h compu ing and s o age esou ces. This is no mally done in he o m o he so-called con ain- e s. Fo ins ance, should a su eillance o “wa ch” obo equi e compu ing powe o pe o m a ce - ain ask, hen i would only ha e o spin up as much ins ances as equi ed o comple e i . Mos comme cial cloud pla o ms ( o example, Amazon EC2, Windows Azu e, Google App Engine, e c.) use a pay-as-you-go app oach. Ne e heless, his should be much cheape han all he capi al expendi u e equi ed o a clus e , no o men ion all he main enance cos s. ––Dynamic scalabili y: This p ope y ela es o he abil- i y o he cloud o dynamically adap o he use needs. A clea example is ha o a mul iple in usion. I , sud- denly, he numbe o eques s d ama ically inc ease due o his ansg ession, he se ice migh no be able o p ocess hem in a easonable ime. To add ess his, he cloud could inc ease he numbe o compu ing esou ces de o ed o his ask. Mo eo e , once he h ea has ceased and he numbe o eques s dec eases, he cloud could educe he amoun o compu ing esou ces. This ela es o he “u ili y” concep behind Cloud Com- pu ing. In ac , a g ea in e es exis s in applying and de eloping Cloud Compu ing amewo ks o he su eillance a ea [17, 26]. Howe e , many challenges s ill emain. Fo ins ance, he inco po a ion o he cloud pa adigm o ces de elop- e s o e hink hei so wa e a chi ec u es so, o ins ance, he dynamic scalabili y p ope y could be exploi ed (see Sec . 4). Ano he majo conce n is ha , as poin ed ou in [26], he cos o deploying a ideo su eillance sys em using cloud compu ing is no necessa ily less expensi e han implemen ing he ha dwa e locally. Fu he mo e, legal and p i acy issues mus be add essed when using comme - cial cloud pla o ms. An in e es ing app oach is he use o a p i a e cloud se - ing, so ha c i ical su eillance da a is always kep in he p i a e cloud [17]. This app oach also acili a es he in e- g a ion o embedded de ices ha can success ully pe o m collabo a i e asks such as localiza ion, mapping [30], a - ic-ligh s a us de ec ion [4] o heal hca e [10]. As a conse- quence, concep s such as “In e ne o Robo s” [13, 37] a e now possible. Fu he mo e, a p i a e cloud can be comple- men ed wi h comme cial cloud pla o ms when needed o op imize deploymen and main enance cos s, an app oach commonly known as hyb id cloud [17]. The e o e, using he cloud o a collabo a i e su eillance sys em is ano he in e es ing applica ion ha we add ess and e alua e in his wo k. In a g ea ex en , he answe o educe cos s and deploy mo e easily a igilance sys em ha in eg a es a numbe o embedded de ices comes om he use o p i a e/hyb id Cloud Compu ing and high bandwid h wi eless echnolo- gies. Mo eo e , he cloud is he mos sui able and cos - e ec i e candida e o wha is known as compu a ion o - loading. The idea is o ee an embedded de ice om hea y compu a ions, so ha an ex e nal pla o m would do hem and ge he esul s back. This is especially in e es ing as i allows he de ices o pe o m mo e complex asks, o e - coming hei ha dwa e limi a ions and hei inhe en di - icul upg ading. Fu he mo e, o loading CPU-in ensi e asks implies less powe consump ion in he de ices and obo s. Following his idea, building e y simple de ices ha ely on compu a ion o loading will se a end in o hcoming yea s [31]. On he o he hand, he use o high bandwid h wi eless echnologies is especially in e es ing o wo easons. Fi s , i pe mi s he use o mobile obo s o UAVs (Unmanned Ae ial Vehicles), b inging innume - able possibili ies in su eillance. Second, he new s anda d 802.11ac p omises bandwid hs o he o de o 1 Gbps [9]. Hence, e en ually, cu en bo lenecks o WiFi connec ions could be o e come. The e o e, he con enien combina ion o Cloud Compu ing, wi eless echnologies and he e oge- neous embedded de ices (depending on he budge ) could be a pe ec candida e o c ea ing a cos -e ec i e su eil- lance and in ude de ec ion solu ions. Besides, he e is a p oblem ha needs o be add essed o de ec in ude s: he co ec in e p e a ion and sha ing o in o ma ion o he en i onmen . In his sense, he lack o seman ic in o ma ion p o okes p oblems in his kind o asks, o ins ance [5, 11, 22]: ––Some ope a ions such as clea ing could be done mo e e icien ly i mo e in o ma ion o he s a ic en i onmen was known. ––Wi hou symbolic in o ma ion, i is e y di icul o he obo o make p edic ions based on he en i onmen . The sys em p esen ed he e is a i s p o o ype o a cloud- based su eillance sys em using a sha ed, 3D symbolic wo ld model; simple embedded de ices ha can o load compu a ion o he cloud; mobile de ices including “wa ch” obo s; high-bandwd h wi eless connec ions; and Kinec came as as he sensing echnology. The pape is o ganized as ollows. Fi s , nex sec ion p esen s he sys em model. In Sec . 3, a b ie e iew o ecen ad ances in he a ea is p esen ed. Then, in Sec . 4 all he main elemen s o he p o o ype (wo ld model, in o ma ion s o age, compu a- ion o loading) a e desc ibed. To p o e he e icacy o his i s p o o ype, some p elimina y expe imen al esul s a e included (especially ocused on communica ion and com- pu a ion pe o mance) in Sec . 5. Finally, u u e lines o wo k and conclusions a e p esen ed in Sec s. 6 and 7. 2 Sys em model In his pape , we p opose a amewo k o gi ing suppo o a su eillance sys em ia an Objec O ien ed Wo ld Model ha is dis ibu ed among he cloud and he es o de ices. We use a 3D olume ic, objec -o ien ed wo ld model, called En i onmen Desc ip o (ED), (de eloped by he “Con ol Sys ems Technology” in TU/e Uni e si y [5, 11]) ha allows he modules in he sys em o ake he con- ex o he en i onmen . Such a ully o loaded cloud-based implemen a ion o a olume ic, 3D, objec -o ien ed wo ld model (including seman ic in o ma ion) has ne e been ca ied ou as a as we know. Figu e 1 shows an applica ion scena io o he p oposed Cloud-based Wo ld Model pla o m. Su eillance asks can be sha ed by se e al ixed de ices wi h di e en ha dwa e con igu a ions and sensing echnologies: s e eo came as, RGBD came as, ange inde s, lase , amongs o he s. In addi ion, each de ice can be assigned o di e en asks. The common case would be ha o came a de ices ins alled in di e en places. Meanwhile, in addi ion o his, he e can be mobile “Wa ch obo s”, which na iga e he a ea o building looking o in ude s. Then, as hey all sha e he same wo ld model, i a came a de ec ed an in ude , hen all de ices would know hei exac loca ion. Consequen ly, “Wa ch obo s” would ans o m hei loca ion coo dina es o ela e hei own pos u e o ha o he in ude , wi h he aim o pu suing him/he . Figu e 2 shows a concep ual diag am o he p oposed Cloud-based Wo ld Model pla o m. In ou implemen a- ion, ask o ganiza ion is lexible enough so ha , depend- ing on he ha dwa e speci ica ions, some de ices can pe - o m all he compu a ions on hei own (Case 2) o o load compu a ions o he cloud (Case 1). Non-sensing de ices ha o m pa o he sys em such as domo ic con olle s can ecei e an o de and eac acco dingly, o ins ance, by locking a doo i an in ude we e ound (Case 3). Hence, using he cloud pa adigm as a na u al cen al in e ace o many de ices, an ex emely e icien and coope a i e su - eillance sys em can be de eloped. A de ailed explana- ion o he blocks o his Fig. 2 will be desc ibed in u he sec ions. I is wo h no ing ha in his kind o applica ions, conce ns such as esponse ime equi emen s o da a sa e y and p i acy may lead o an al e na i e pa adigm o Cloud Compu ing called Edge Compu ing [34]. This e m e e s o p ocessing he da a a he edge o he ne wo k, ha is, close o he “ hings”, in he IoT sense, o end de ices. As men ioned be o e, in ou expe imen s we will use a p i a e cloud which could be conside ed as a kind o edge-compu ing sys em. The eason is ha his way we a oid longe and mo e a iable delays and can ocus on implemen a ion issues such as compu a ion o - loading o en i onmen sensing and modeling. Howe e , along he pape we p e e o use he mo e gene al model and e minology o Cloud Compu ing. Fu he mo e, he ex ension o ou su eillance sys em o a la ge Sma Ci y would bene i om a mo e gene al Cloud Compu - ing pa adigm, o ins ance, ga he ing sensing da a om di e en buildings. 3 Rela ed wo k The e a e nume ous esea ch wo ks in he a ea o au o- ma ed su eillance. In ela ion wi h his wo k, he mos ele an can be a anged in hose wo ks ha use he cloud o in eg a ing senso in o ma ion, hose ha deal wi h he de ec ion algo i hms, hose ha es se e al senso de ices, and inally, o he s ha s udy ne wo k echnologies. The idea o using he cloud o in eg a ing mul iple sen- so in o ma ion, including su eillance in o ma ion, has also been explo ed in he li e a u e. Mo eo e , a new e m has been coined o de ine he applica ion o Cloud Com- pu ing (in e ms o ubiqui y, s o age, compu a ion, p i acy, scalabili y, amongs o he s) o his a ea: Video Su eillance as a Se ice [20, 29]. Fo ins ance, in [27] he au ho s deal wi h he p oblem o in eg a ing he e ogeneous senso in o - ma ion by c ea ing a da abase scheme able o in eg a e di - e en ypes o senso . Howe e , mos o he a ailable solu- ions ely on wi ed ne wo ks, such as [14, 15, 21, 36]. Only a ew wo ks ha e de eloped some aspec s o cloud-based su eillance pla o m using wi eless ne wo ks. Au ho s o [38] p esen ed Vigil, whe e some de ices wi h came as execu e ace ecogni ion algo i hms and send he esul s o he cloud. They also in oduce he concep o in a-clus e p ocessing, when he e is a se o came as wi h o e lap- ping isions. Compa ed wi h Vigil, he wo k p esen ed in his pape akes in o conside a ion he use o e y simple de ices hanks o compu a ion o loading. In [35] a Cloud Ga eway o il e ing he in o ma ion om mul iple senso s in Wi eless Senso Ne wo ks was implemen ed. This il e - ing is applied o a basic cloud su eillance sys em ob ain- ing an 83% accu acy. Mos o he cloud-based su eillance sys ems ha e he ollowing a chi ec u e: mul iple came as s eam o he cloud, which is esponsible o analyzing he oo age in sea ch o e en s. Only in he case o [14] some p e-p o- cessing is done be o e sending o he cloud. Compa ed o hese wo ks, his pape o e s a mo e lexible app oach, as he de ices can decide whe he o o load o no depend- ing on hei needs. Ano he impo an elemen o Cloud Compu ing is Quali y-o -Se ice (QoS) managemen . In his sense, in [3] a se ice QoS-adap i e con igu a ion amewo k o op imizing his ac o in su eillance sys ems is p oposed. Ano he s udy elemen co esponds o image analysis o de ec in ude s. Se e al algo i hms ha e been de ised, such as La en Seman ic Analysis (LSA), Ke nel-based Fig. 1 Example o applica ion in a building Online Anomaly De ec ion (KOAD) and Ke nel Es ima- ion-based Anomaly De ec ion (KEAD), Ke nel P incipal Componen Analysis (KPCA) and One-Class Neighbo Machine (OCNM) [2], using machine lea ning echniques as well. In [25], ins ead o egula ideo came as, omnidi ec- ional came as a e used, which pe mi he su eillance o a whole oom o a bigge open space wi h only one came a. In [12], he senso used is ha o Mic oso Kinec , wi h sa is ac o y esul s. In his sense, he sys em p esen ed he e also makes use o Kinec Senso s which, a a easonable p ice, p o ide a dep h channel, ex emely impo an o objec de ec ion and acking. As i can be seen, mos o he su eillance sys ems a ail- able in he li e a u e no mally use ixed came as. In his sense, one o he majo asse s o his wo k is he collabo- a ion o di e en he e ogeneous de ices (such as mobile “wa ch” obo s) hanks o he use o a sha ed wo ld model and he adap a ion o he cloud o each obo ’s needs. Mo eo e , he use o mobile obo s b ings some in e es - ing oppo uni ies and challenges o mo e complex su eil- lance s a egies and sys ems. The app oach o using an edge-compu ing pla o m and wi eless echnologies is becoming inc easingly a ac i e o esea che s on su eillance sys ems [6]. Fo ins ance, [16] shows ha an edge node can educe he eac ion ime be ween edge and senso s, and can imp o e ne wo k s a- bili y as well by uploading only necessa y da a. Also, [23] imp o es he op imal bandwid h dis ibu ion and alloca ion in a ideo su eillance sys em using up- o-da e wi eless echnologies. In he p esen pape , we ollow his app oach wi h he addi ional con ibu ions o implemen ing a 3D, objec -o ien ed, cloud-based wo ld model ha includes seman ic in o ma ion, as well as allowing he use o sim- ple embedded de ices and mobile “wa ch” obo s h ough compu a ion o loading. 4 Implemen a ion o he pla o m In his sec ion, he implemen a ion o he Cloud Wo ld Model based Su eillance Pla o m is de ailed. The imple- men a ion o he Su eillance Pla o m consis s o he ol- lowing pa s: –– De ini ion o he elemen s o Wo ld Model (Sec . 4.1) ––De ini ion and deploymen o a Wo ld Model S o age Sys em (Sec . 4.2) ––Implemen a ion o he Senso In eg a ion Module, esponsible o p ocessing he image s eams (Sec . 4.3). 4.1 Elemen s o he wo ld model Wo ld modeling is he p ocess o c ea ing a model o an en i onmen (wi h i s inhe en complexi y), which is a key ask o in elligen sys ems. The mo e accu a e he ep esen a ion o he en i onmen , he mo e success ully a ask will be pe o med. Hence, as s a ed in Sec . 1, add- ing seman ic in o ma ion o each elemen o he en i on- men implies a d ama ic inc ease in accu acy. In he imple- men a ion shown in his pape , he main elemen s o he wo ld model (simpli ying om [5, 11]) a e en i ies and ans o ms. En i ies: An en i y is an elemen o he wo ld. I has he ollowing p ope ies: Fig. 2 Concep ual diag am o he cloud-based su eillance sys em ––ID: Unique iden i ie o an en i y. No o he en i y can ha e he same iden i ie , e en i he en i y was emo ed. ––Type: Speci ies he ype o he en i y. This allows o make classi ica ions o c ea e subse s o en i ies o di - e en ac ions. Fo example, se e al ac ions can be done depending i he en i y is a pe son, a u ni u e o a wall. ––Pose: Es ablishes he posi ion and o ien a ion o he en i y in he wo ld model. These coo dina es a e abso- lu e. ––Shape o con ex hull: Adds olume ic in o ma ion o he en i y. This shape can ei he be eal o i ual. An en i y can ha e bo h. ––Measu emen s: In o ma ion ob ained by senso s ha is associa ed o an en i y in he wo ld model. These meas- u emen s hold he senso da a and he ime-s amp. T ans o ms: de ines he spa ial ela ionship be ween wo di e en en i ies in he wo ld model. Mul iple ans o - ma ions o each pa en –child pai can be s o ed o di - e en imes amps which esul s in a g aph o en i ies and ans o ms. 4.2 Cloud wo ld model s o age As s a ed in p e ious sec ions, a collabo a i ely buil wo ld model o e s se e al ad an ages. Howe e , he e a e some bo lenecks ha he cloud solu ion mus add ess. Fi s , i he numbe o obo s inc eases (and he e o e, he numbe o Da abase ead/w i e ope a ions), he cloud migh no be able o sa is y all que ies in a easonable ime. To make ma e s wo se, i he numbe o en i ies inc eases, he cloud esou ces assigned o he da abase may un ou . The e o e, one o he main objec i es o he cloud solu ion is o adap compu ing esou ces a un ime depending on he needs. To sa is y hese equi emen s, Dis ibu ed Da abase Managemen Sys ems (DDBMS) a e he pe ec candi- da es o s o ing all he in o ma ion, hanks o hei mas- si ely pa allel na u e, ease o use and po abili y. Among all he a ailable op ions, one o he bes DDBMS pa a- digms is ha o Big able. I s main ad an age is ha i uses a spa se, dis ibu ed and mul idimensional so ed map, using he ollowing da a o ganiza ion [8]: ( ow:s ing, column:s ing, ag:s ing, ime:in 64) → s ingHence, he e would only be one (o wo) big ables ha can be pe ec ly sca e ed among di e en con aine s in he cloud. The e o e, i he a o emen ioned bo lenecks occu , hen mo e con- aine s can be spun up a un ime. On he con a y, i he da abase is less loaded, hen he cloud can scale back, hus sa ing esou ces o o he asks. Ha ing in mind his DB model, he nex schema was designed o i wi h he needs o a su eillance sys em. The idea is no only o s o e he cu en in o ma ion abou en i ies, bu also hei his o y. In his sense, we use he concep o del a, ha is, a change in a ce ain en i y ( ollowing he so-called e en -d i en mechanism [17]). To cla i y he concep , an example ollows (depic ed in Fig. 3): suppose wo obo s A, B and an en i y E. Bo h obo s ha e synch onized hei local wo ld models a an ins an , inding ha he mos ecen change was done a −1 (“Las ime” in Fig. 3). The e o e, hey know all he in o ma ion abou E (shape, pose, ype, e c.). A +1 , B egis e s changes in he pose o E, and he e o e will upda e he wo ld model wi h his new in o ma ion. I no hing bu he pose has changed, e-sending he in o - ma ion abou he shape would be a was e o bandwid h. The e o e, B will only send he change ( ha is, he del a) o he pose. This same easoning can be applied o A, which is only in e es ed in he mos ecen changes (del- as) since da abase’s mos ecen ime-s amp −1 ( o sa e esou ces). Thanks o he use o imes amps, a obo can que y all he new del as since a gi en ime . Taking his in o accoun , he e will be a able o del as in he da abase wi h he ollowing columns: pose, ype, shape, con ex hull and dele ed (whe he he en i y was clea ed o no ). The in o ma ion will be s o ed in he JSON (Ja aSc ip Objec No a ion) o ma . The ow ID will co espond o he en i y’s ID. Among he a ailable implemen a ions o he Big able model, Hype able has been chosen because i is w i en on C++ and p omises highe pe o mance esul s com- pa ed o compe i o s such as HBase [1]. This is o g ea impo ance when wo king wi h obo s, and in some cases, nea eal- ime condi ions. The elemen s o a Hype able sys em ha we e con igu ed o ou sys em a e [19]: ange se e ( o handling he ead and w i e ope a ions in he able), Mas e node ( o c ea ing/dele ing ables), Th i B oke (in e ace be ween he da abase and he cli- en s) and FS B oke (no malized ilesys em in e ace). Figu e 4 on he le shows he implemen a ion o he wo ld model using Hype able ( he FS B oke is no shown, as i is being used in all he nodes). On he igh , an example o adap a ion when a bigge numbe o obo s demands mo e esou ces has been depic ed. In his case, mo e ins ances o Range se e s and Th i B oke s a e spin up by he cloud o sa is y his new load. 4.3 Senso in eg a ion modules The senso in eg a ion o loading implemen ed he e is a simpli ied e sion o [5, 11]. The wo k low is depic ed in Fig. 5. All modules a e implemen ed using he Robo - ics Ope a ing Sys em (ROS) F amewo k. ROS is an Open Fig. 3 Example o a del a upda e Fig. 4 Implemen a ion o he cloud Wo ld model using hype able Sou ce Robo ics So wa e F amewo k (using C++ and Py hon) ha allows he implemen a ion o ad anced dis- ibu ed a chi ec u es, e y simila o Mul i-Agen sys ems [18]. Thanks o he use o opics, se ices, pee - o-pee communica ions and comp ession echniques, ROS makes he o loading p ocess simple and scalable. Main p ocesses (nodes in ROS con en ion) co espond o hose modules o Fig. 5, while connec ing links o hese modules ha e been implemen ed using ROS opics and se ices. ––The di e en ope a ions done in he senso in eg a ion a e he ollowing:RGBD Masking: This p ocess will only use he D channel o he s eam. I is esponsible o de ec ing en i ies using ea u e ex ac ion (p oduc- ing 3D Poin Clouds), associa ion (compa ing wi h a ende ed wo ld model) and segmen a ion (c ea ing new en i ies om he unassocia ed da a) [5, 11]. Figu e 6 shows an example. This module is one o he mos com- pu a ionally demanding. In ou case, as we a e dealing wi h su eillance cases, his module will disca d hose en i ies whose size is no big enough o be a pe son. ––Senso usion: This p ocess ecei es he RGBD s eam and he a o emen ioned en i ies wi h mask measu es. The idea is o use he p e iously unused RGB channel and ex ac o he p ope ies om he en i ies. The e- o e, he module mus bu e he RGBD s eam un il i s ela ed masked measu emen a i es. Figu e 7, using he mask om Fig. 6, now ob ains an RGBD measu emen (which, o example, con ains colo in o ma ion). ––Pe cep ion: This p ocess ecei es he RGBD measu e- men s (c ea ed by he Senso Fusion module) and pe - o ms an analysis o disco e he ype o he en i y. This will be he esponsible module o de ec ing whe he an en i y is an in ude o no . As ou aim is demons a - ing he cloud wo king and easibili y, in he expe imen s p esen ed in his pape he sys em only de ec s whe he he en i y is wea ing o no an uni o m. I he pe son was no wea ing i , hen i would no be an au ho ized pe sonnel. On comme cial su eillance sys ems, i is ob ious ha a knowledge base (wi h empla es) mus be used. Fo he p e ious example, Fig. 8 analyzes he RGBD measu emen and concludes ha he en i y is an au ho ized pe son. The p esen ed pla o m is aimed o be as much lexible as possible. In his sense, a obo may no ha e enough p ocessing powe o do all he senso in eg a ion on-boa d bu su icien o do pa o his p ocessing. The e o e, he obo should be able o choose which pa s o he senso in eg a ion a e o loaded. To implemen his ea u e, he sys em was implemen ed using modules. Fo ins ance, one ins ance may only ha e he Pe cep ion module (and hus Fig. 5 Wo k low o he Senso In eg a ion model Fig. 6 Example o a mask measu emen only pe o m ecogni ion ope a ions), whe eas ano he node may ha e all modules. As a consequence, se e al o - loading op ions can be aken in o conside a ion. 5 P elimina y esul s These p elimina y es s a e aimed o show he e icacy and iabili y o no only ha ing a sha ed wo ld model, bu also o using wi eless echnologies. The es s a e di ided in o h ee ca ego ies: 1. Real expe imen in a es ing en i onmen . 2. Que ies in he wo ld model. 3. Senso in eg a ion o loading. All he es s we e done in a p i a e Cloud wi h 5 nodes (1 on -end node and 4 compu ing nodes). Each node has a AMD FX-8320 oc a-co e CPU (wi h i ual ex ensions enabled) and 8 GB o RAM. They a e all connec ed in e - nally using Gigabi E he ne bandwid h. Fo he cloud middlewa e, we decided o make use o Kube ne es + Docke ( ecen ly adop ed by majo cloud endo s, such as Google Con aine Engine o Amazon EC2 Con aine Engine) ins ead o adi ional VM-based p i a e clouds (such as Opens ack o Eucalyp us). One o he main easons behind his decision was o a oid he o e heads de i ed om he i ualiza ion o ha dwa e and ope a ing sys em laye s [33]. 5.1 P oo ‑o ‑concep expe imen A ew p elimina y expe imen s we e de ised o check whe he he whole sys em wo ks p ope ly. Fo his pu pose, only a ew se o objec s we e modeled o an indoo en i- onmen ; mainly ough shapes o he u ni u e and wo peo- ple (one in ude and one au ho ized pe son). In one o he o ices, wo came a de ices we e ins alled o de ec in ude s Fig. 7 Example o a RGBD measu emen Fig. 8 Example o pe cep ion