Dynamic Resource Discovery based on Preference and Movement Pattern Similarity for Large-Scale Social Internet-of-Things

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1 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 Dynac Resource Dscovery based on Preference and Moveent Pattern Slarty for Large-Scale Socal Internet-of-Thngs Zhyuan L, Rulong Chen, Lu Lu, Meber, IEEE and Geyong Mn, Meber, IEEE Abstract Gven the wde range deloyent of dsconnected delay-tolerant socal Internet-of-Thngs (SIoT), effcent resource dscovery reans a fundaental challenge for large-scale SIoT. The exstng search echanss over the SIoT do not consder reference slarty and are desgned n Cartesan coordnates wthout suffcent consderaton of real-world network deloyent envronents. In ths aer, we roose a novel resource dscovery echans n a 3-densonal Cartesan coordnate syste wth the a of enhancng the search effcency over the SIoT. Our schee s based on both of reference and oveent attern slarty to acheve hgher search effcency and to reduce the syste overheads of SIoT. Sulaton exerents have been conducted to evaluate ths new schee n a large-scale SIoT envronent. The sulaton results show that our roosed schee outerfors the state-of-the-art resource dscovery schees n ters of search effcency and average delay. Index Ters socal Internet of thngs, resource dscovery, cosne slarty, reference, oveent attern. I. INTRODUCTION HE ntegraton of socal networkng concets nto the TInternet-of-Thngs (IoT) has led to a burgeonng toc of research, so called Socal Internet of Thngs (SIoT) aradg, accordng to whch the sart obects are caable of establshng socal relatonshs n an autonoous way wth resect to ther owners. The benefts are those of rovng scalablty n resource/servce dscovery when the SIoT s ade of a large nuber of heterogeneous nodes, slarly to what haens wth socal networks aong huans [1]. SIoT s turnng out to be a successful aradg for eer-to-eer councatons, beng Manuscrt receved Arl 15, Ths work was suorted n art by the Natonal Natural Scence Foundaton of Chna under Grant No , the Proect Funded by Chna Postdoctoral Scence Foundaton No. 2015M570469, the Natural Scence Foundaton of Jangsu Provnce under Grant No.BK and the Senor Professonal Scentfc Research Foundaton of Jangsu Unversty under Grant No.12JDG049. Z.Y. L and R.L. Chen are wth the Deartent of Internet of Thngs, School of Couter Scence and Telecouncatons Engneerng, Jangsu Unversty, Zhenang, Chna (e-al: lzhyuan@us.edu.cn). L. Lu s wth the Deartent of Coutng and Matheatcs, Unversty of Derby, Derby, UK and the Deartent of Internet of Thngs, School of Couter Scence and Telecouncatons Engneerng, Jangsu Unversty, Zhenang, Chna (e-al: l.lu@derby.ac.uk). G. Mn s wth the Deartent of Matheatcs and Couter Scence, Unversty of Exeter, Exeter, UK (e-al: g.n@exeter.ac.uk). drven by the followng roertes: Sart obects belongng to the sae county due to ther owners often have coon nterests. Sart obects carred by ther owners wth coon nterests usually exhbt slarty n ther oveents and behavor atterns. Sart obects carred by ther owners wth coon nterests tend to eet each other frequently. However, leveragng the above entoned socal roertes for desgnng an effcent resource dscovery echans over SIoT s an acute ssue n the context of resource sharng n SIoT. The exstng resource dscovery echanss n SIoT can be classfed nto three categores: socal connectvty-based ethods, oveent attern-based ethods and reference slarty-based ethods. The socal connectvty-based ethods [2-9] utlze the long-ter socal tes exstng between the nodes for buldng countes, n such a way that those havng frequent encounters n the ast wll belong to the sae county. Once the countes are bult, resource sharng s acheved aong the nodes wthn the countes by socal-connectvty. The oveent attern-based ethods [10-13] utlze the traectores of the oble users to buld the countes, n such a way that nodes exhbtng slar oveent and behavour atterns wll belong to the sae county. Both the above two strateges are beneftted wth low transsson delay but also have slar dsadvantages, such as anageent overhead and low search effcency snce both the two aroaches do not consder reference slarty. The reference slarty-based ethods [14-15] can show better search effcency, whch utlzes the nterests and shared resources of the oble users to for the countes wth nodes havng slar references. However, the dsadvantages of those aroaches can be attrbuted to ts toology satch and hgher traffc cost, snce reference slarty-based ethods do not consder the nforaton regardng the geograhcal locaton of the oble nodes. In order to overcoe the drawbacks of the resource dscovery echanss entoned above, ths aer rooses a novel Resource Dscovery echans based on the Preference and Moveent attern slarty (RDPM). Frstly, we extract the references of the nodes fro ther rofle table and resources, and also oveent attern fro ther traectores usng the AGNES clusterng ethod [16]. Then the cosne slarty of the references and oveent attern of the nodes

2 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 2 s generated for the urose of buldng sub-county n a 3-densonal Cartesan coordnate syste n order to rove the effcency of resource dscovery. Next, vrtual global countes are fored by utlzng the slarty found aong sub-countes, ultately to rove the searchng erforance for the resources. Fnally, we desgn a resource dscovery algorth that can dynacally adust the search radus n order to balance the erforance and councaton overhead. The reander of the aer s structured as follows: Secton II ntroduces the related works. Secton III resents the network odel. Our roosed resource dscovery algorth based on reference and oveent attern slarty s elaborated n Secton IV. The exerental evaluaton and erforance results are resented n Secton V. Fnally, Secton VI concludes ths aer. II. RELATED WORK In ths secton, the exstng resource dscovery echanss are analyzed by classfyng the nto three categores socal connectvty-based ethods, oveent attern-based ethods and reference slarty-based ethods. A. Socal connectvty-based ethods L and Wu [4] roosed a Moble county-based Pub/Sub schee (MOPS) to roote the content-based servce by utlzng the long-ter neghborng relatonsh between the nodes. In ths aroach, the node county s bult n a dstrbutve fashon based on the encounter frequences of the nodes. A contact duraton aware fraework was roosed [5] to odel the content dssenaton rocess for MSNs n order to reduce the content dssenaton delay. ICast [6] adots the behavor-aware echans whch regularly extracts weak tes,.e. nodes havng rare encounters, to ensure rearkable erforance results n content delvery. By analyzng the tght-couled relatonsh between huan and oortunstc connecton of sart thngs (e.g., oble hones, vehcles), [7] roosed oortunstc Internet of Thngs. It enables nforaton sharng and dssenaton wthn/aong oortunstc countes that are fored wth the oveent and oortunstc contact nature of huan. [8] roosed a county detecton schee based on frend relatonsh to solve the roble n ntegrated IoT and Socal Network (SN) archtecture. Zheng and Wang [9] roosed a grah-based socal aware algorth n whch cellular lnks and D2D lnks are establshed accordng to socal tes and socal contrbutons of users for effcent ult-fle dssenaton. However, such ethods are subected to lower effcences n ther resource dscovery echanss snce they do not consder reference slarty. B. Moveent attern-based ethods Huan traectores often show a hgher degree of teoral and satal regularty, wth each ndvdual beng characterzed by a te ndeendent characterstc length scale and also a sgnfcant robablty of returnng to a few hghly frequented locatons [10], whch tends oble users to exhbt slar oveent and behavor atterns. Such oveent atterns of a grou of nodes can be extracted by recordng the node oveents. A oveent attern-aware otal routng for socal delay tolerant networks was roosed n the works of [11], where both a local search schee and a tabu-search schee are utlzed n fndng the otal set. The tabu-search based routng shows the ablty to gude the evaluaton of relay node sets nto otcal node sets. [12] analyzed frstly the socal relaton between oble-aware nodes, and then ned the actvty rules of nodes and the county roerty and guded to select the oble-aware nodes n target regons, so as to rove the dscovery effcency of nodes n obectve regons, and ncrease the success rato of servce dscovery. An effcent roactve servce dscovery rotocol was roosed [13], whch can leverage both the socal behavor and the eole oblty. Sulaton results show that the rotocol acheves ncreased effcency n dscoverng servces. Such oveent attern-based ethods can enoy lower resource dscovery delay and transsson delay than the socal connectvty-based ethods. However, they do not fully utlze the reference of users, and hence the success rate of the resource dscovery s stll low. C. Preference slarty-based ethods An adatve content sharng rotocol to facltate the leentaton of MSNs over eer-to-eer networks was roosed [14] for reference drven councaton. The roosed rotocol takes nto account the nforaton about user s nterests, reference based content storng and forwardng, and host oblty n a dsconnected delay tolerant MANET, whch can rove the erforance of content dssenaton. A P2P content-based fle sharng syste, naely SPOON, was roosed [15] for dsconnected MANETs. The syste uses an nterest extracton algorth to derve a node s nterests fro ts fles for content-based fle searchng. For effcent fle searchng, SPOON grous coon-nterest nodes that frequently eet each other, as countes. It exlots the node oblty for the urose of categorzng the nodes nto two tyes, n such a way that the nodes wth ore frequent encounters wth the county ebers are categorzed as stable nodes and the nodes frequently vstng other countes are categorzed as hghly oble nodes. Stable nodes are generally assgned as county coordnates for ntra-county searchng and the hghly oble nodes are assgned as county abassadors for nter-county searchng. In general, the success rate of resource dscovery of the reference slarty-based ethods s better than two other ethods. However, t ncurs toology satch, whch leads to hgher councaton overhead snce they lack ultdensonal context-aware nforaton n ther resource sharng echanss. III. NETWORK MODEL The archtecture of the roosed RDPM odel can be classfed nto three layers as shown n Fg. 1. Each node reresents a counterart of a hyscal sart devce at the hyscal layer. Frstly, the hyscal area s dvded nto square unts of equal area. In each square unt, the nodes belongng to the sae sub-county have slar references and show consstent oveent atterns. Then, slar sub-countes are erged nto a global vrtual county. For nstance,

3 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 3 hundreds of football fans are watchng a World Cu soccer tournaent n an oen-ar stadu. In the stadu, they sontaneously for a football sub-county. Meanwhle, fans outsde the stadu for another football sub-county by watchng TV n ubs or at hoe. But all of the together for the football global vrtual county. The sub-countes together for the global vrtual county and also have ther corresondng local characterstcs. For exale, n the sub-county network layer, node 2 has two neghbors, as node 1 and node 3, resectvely. Furtherore, consderng the global county network layer, node 1 and node 3 are the neghbors of node 2. Ths strategy greatly reduces the traffc overheads ncurred by the toology satch and roves the effcency of resource dscovery. Then, Forula (2) s used to calculate the slarty aong the resource u and the resource v. s( u, v) w w u v wu wv 1 1. where s the total nuber of coon keywords, wu and w v reresent the weghts of the th coon keyword n the two resources, resectvely. In a slar way, a global slarty atrx could be obtaned by coarng the slartes of all the resources. The eleent of the slarty atrx s s ( u,v ), and then they are sorted n a descendng order to generate a resource slarty lst. Forula (3) s used to choose the to r hgher slarty resources. r s( u,v) 1 arg n,0 1, 0 k s( u,v) 1 r r k Fg. 1. Three-layer network structure of RDPM. IV. THE DESIGN OF RDPM A. Preference Extracton Users wth coon references tend to on the sae county, and ther shared resources or servces are often slar. Hence, we extract the nterests and references of the nodes usng ther rofle lsts and shared resources or servces. 1) Resource or servce Reresentaton The roertes of the resources or servces are ult-densonal, and the assocaton roertes of the nodes are generally weak. The dfferences n the assocaton roertes can only be attrbuted to the occurrence frequency of the keywords. For resource R, ts reference vector s defned as R ( k, w);( k, w );...;( k, w ) n n where k refers to the th keyword and w s the weght of the th keyword. They are sorted by weghts n descendng order. 2) Preference Reresentaton The shared resources are requred to be clustered n order to be reresentatve rather than a sngle entty. For exale, the coon resource vectors are extracted fro R u and R v, utlzng the coon keywords and corresondng weghts. where k s the length of the resource slarty lst and ρ s a user-secfc araeter to deterne to r resources wth hgher slarty to reresent the nterests of a oble user. The araeter could be set accordng to the secfc user s requreent. Generally seakng, the hgher ρ, the hgher r. When ρ = 1, all the resources wll be selected (r=k). Whle ρ = 0, none of the resources wll be selected (r=0). Next, the reference vector should be re-calculated for the to r hgher slarty resources to reresent the nterests of a oble user, as shown below w w / r. k r 1 k R R ( k, w ), 0. n k k where wr denotes the weght of the keyword k n the resource R, and R reresents the reference vector of the node n. n B. Moveent Pattern Analyss and Extracton 1) Current locaton The current locaton nforaton of nodes can be obtaned va the Global Poston Syste (GPS). The current locaton of a node s reresented by the vector lx (, y, z, t ), where ( x, y, z ) s three-denson coordnates of the node and t reresents the testa that the node stays at the oston ( x, y, z ). 2) Stay regon The stay regon A s a hyscal sace where a user stayed over a te threshold t 0. Suose that l( x, y, z, t ) s the

4 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 4 node s oston at te t, and l ( x, y, z, t ) s the node s oston at te t, then the soourn te of a user s T, and the value T s equal to ( t t ). When T s larger than the threshold value t and the dstance between the oston l and the oston l s saller than a threshold value r, we conclude that the oble users are n the sae stay regon. The shae of the regon A s a shere whose radus s r and the center s ( x, y, z ). 3) Moveent attern reresentaton In order to analyze and extract the oveent atterns of a node, the oton traectores of the nodes are requred ncludng the locaton, the start te and the end te. Forula (6) s used to reresent the oton traectory of a node. As shown n Fg. 2, the oton traectory of node and node s the sae fro te t a to te t b. Ths shows the slarty of oveent attern. V (, ) T ( A1( x1, y1, z1, lst( tstart, tend), 1),...., A ( x, y, z, lst( t, t ), )) n n n n start end n where t start reresents the start te when a node oves nto the regon A, and tend reresents the end te when the corresondng node oves out of the regon A. The lst s an array storng ultle te records. The weght value n shere A s calculated usng (7). 1 0 ( lst ( ). tend lst ( ). tstart )) t. where s the length of a lst, and t total reresents the total te. C. County Constructon 1) Sub-county constructon The references are cobned wth the oveent attern of the oble users n order to buld a sub-county. 1 Preference slarty Forula (8) s used to calculate the reference slarty between node and node. S total wk wk k wk wk k1 k1 S. (8) where s the total nuber of coon keywords of the reference vector S, and wk and w k reresent the weghts of the k th coon keyword of the reference vector and S. 2 Moveent attern slarty S Fg. 2. Overla soourn regon at the sae erod. Equaton (9) s used to calculate the oveent attern slarty S between node and node. S n n V n n (, ) 4n r. where V (, ) s the overla sace between the volue of the soourn regon A of the node and the volue of the soourn regon A of node at the sae te erod, both the weght values of the soourn regons resectvely. 3 Constructon rocedure Algorth 1. Constructon of the sub-county and A and are A, Inut: reference and oveent attern vectors of a node N and ts neghbors Outut: sub-county eber lst Begn for each N nb of N do N. brocaststo N nb t N nb.couters S. t f S nb. > T s nb N nb.add N To CoLst N nb.sendonessageto N

5 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 5 f accetedessage s onessage N.addN nb To CoLst Return subcolst End Forula (10) s used to calculate the total slarty between node and node. t S S S, 1. t If S s greater than a redefned thresholdt s, node ons the sub-county of node. In the ntal rocess of sub-countes constructon, node wll broadcast the Hello essage to ts neghbors, and the neghbor nodes use Forula (10) to coute ther total slarty wth node. The neghborng node s added to the sub county when the total slarty value S t s larger than the redefned threshold value. To reduce the overhead, each node saves the to k neghbors. The seudo code for the constructon of the sub-county s shown n Algorth 1. Regardng T s n Algorth 1, T s s drectly related to the sze of each sub-county. T s should be set accordng to the slarty between county ebers and the sze of each sub-county. Generally seakng, the hgher T s, the saller the sub-county. When T s =0, there should be only one sub-county where ts sze s equal to the global county. When T s =1, each sub-county wll be the node tself. After the successful constructon of the sub-county, the anageent node calculates the central locaton vector of the county whch s shown below n n n ( x x, y y, z z ). k k k n k1 n k1 n k1 where n s the nuber of ebers n the sub-county. 2) Node role assgnent and sub-county erger In each sub-county, there are three tyes of nodes, ncludng adnstrators, abassadors and ordnary nodes. An Adnstrator lays the anagng role n a sub-county, and antans the ndexes of all resources belongng to the sub-county. Hence a stable and relable node s chosen as the adnstrator n a sub-county. The adnstrator has two ortant tasks: One s to hel ordnary nodes to search resources wthn or out of the sub-county, and the other s to erodcally collect the nforaton of the ordnary users n the sub-county and to extract the nterest vector usng the AGNES clusterng ethod. The rary resonsblty of an abassador s to brdge the sub-countes n order to for the global vrtual county. To ths end, the abassador regularly oves n and out of the sub-countes. Then, we gve a rocedure of sub-county erger. Ste 1: County reference vectors extracton Adnstrator erodcally collects the reference vectors nforaton of the nodes n the sub-county, and calculates the s(, ) value usng Forula (2), where and reresent any two nodes n the sae sub-county, resectvely. Then the sae clusterng ethod wth the reference extracton s leveraged to get the reference vector of sub-county R. Analogously, we can get the reference vector of global county by calculatng the slarty between the sub-county reference vectors. Ste 2: Sub-county erger The oblty of the abassadors s utlzed to carry the reference vectors of the sub-countes n order to erge slar sub-countes. When an abassador oves nto another sub-county, t frstly calculates the slarty of the nterest vector between the two sub-countes. If ths slarty s larger than the threshold value, the two sub-countes are then erged to for a bgger vrtual county. Ideally, all the slar sub-countes wth coon nterests wll be assocated together to construct a global county. It s noted that the reference vector for the global county s also need to be calculated. D. Message Routng Message routng and data forwardng on the two densonal lane wll reduce the effcency of resource dscovery and transsson. As a result, a greedy algorth s used to rove the effcency of resource dscovery n the three densonal Cartesan coordnate syste. Fg. 3 s a dagra of essage routng. The source node S lans to construct a ath SD to the target node D. After a node B suddenly oves out of the councaton range of node S, ths node needs to calculate ts angle wth nodes EFand, G. As shown n Fg.3, 1 s the angle between SD and SE, 2 s the angle between SD and SF, 3 s the angle between SD and SG. Now, node S chooses node E as the next ho snce 1 beng the nu forwardng angle. After that, node C s also chosen as the next ho. To ensure quck essage forwardng, the su value 1 k 0 should be nu, where, s the th forwardng node. c k

6 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 6 Y S F 2 1 E 3 Fg. 3. Dagra of essage routng. E. Resource Dscovery Process Algorth 2. Search wthn the sub-countes Inut: the request vector of a node Outut: resource holder lst Begn f S QC, < T Q then N.sendQueryTo N Ad else N.rank N nb By S, Z QNb 4 B N C G D X 1) Searchng wthn the sub-county The requester calculates the slarty between the query vector and ts sub-county nterest vector. If ths slarty s lower than the threshold value, the query essage s sent out of the local sub-county. Otherwse, the query essage s sent to the to k neghbors wth hgher slarty. The neghbor contnues to forward ths query essage to ts neghbors f t lacks the requested resource. TTL (te-to-lve) s used to control the search deth and avod excessve overhead. The TTL value decreases one when the essage s forwarded every te. The resource dscovery wll eventually sto when the TTL value becoes zero. Algorth 2 shows the seudo code of the resource dscovery rocess wthn a sub-county. 2) Searchng wthn the global countes The Fg.4 llustrates an exale of searchng wthn the global countes. If the requested resource s not found wthn a sub-county 1-1, the query essage wll be forwarded to ts slar neghborng sub-countes 1-2, 1-3, and 1-4 by the abassador node 1, the abassador node 2 and the abassador node 3 resectvely. And the search wthn the sub-county s recalled agan. The search wthn the global county stos when ether the TTL value of the query essage becoes zero or the requested resources s dscovered. for each N nb of N do N.sendQueryTo N nb wth essage routng Count++ f(n nb ==resource holder) then Fg. 4. Searchng wthn the global countes The searchng rocess wthn the global countes s descrbed n Algorth 3. Algorth 3. Searchng wthn the global countes Return N nb else f (Query.hos<MaxHo) then N.sendQueryTo N next Query.hos++ f Count>k then break f(searchresults == null) then N.sendQueryTo N Ad End Inut: the request vector of Outut: resource holder lst Begn N N Ad. rankc sub ByCentralLocaton for each C sub of N Ad do N Ad. sendqueryto C sub wth essage routng Count++ Searchng wthn sub-countes() f Count>k then break

7 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 7 f(searchresults == null) then N.sendQueryTo N Ad End 3) Global searchng over socal Internet of Thngs Fg. 5 llustrates an exale of global searchng over the socal IoT. End V. PERFORMANCE EVALUATION A. Sulaton Envronent and Paraeters Settng The oortunstc network envronent (ONE) [17] has been used as the sulaton latfor n order to evaluate the erforances of our schee. As shown n Fg. 6, the oble socal actvtes of the users over the SIoT are based on the caus envronent of Jangsu Unversty n Chna. Fg. 5. Global searchng over the socal Internet of Thngs. If the search wthn a global county s not successful, the global search over the socal IoT s trggered. The slarty between the query vector and other vrtual countes ncludng vrtual county 2, vrtual county 3 and vrtual county 4 are calculated, resectvely. And then, the query essage s quckly forwarded to the vrtual county wth hgher slarty through the essage routng. Now, searchng wthn the sub-countes of the corresondng global county s trggered agan. The global searchng rocess over socal Internet of Thngs s descrbed n Algorth 4. Algorth 4. Global searchng over socal Internet of Thngs Inut: the request vector of Outut: resource holder lst Begn P N Ad. rankc vruby S, QC N for each C vru of N Ad do N Ad. sendqueryto C vru wth essage routng Count++ Searchng wthn the global countes() f Count>k then break; Fg. 6. The geograhc a. 100 oble users have been classfed nto four nterest grous and one oveent grou, such that each grou corses 20 users. The four nterest grous nclude the checal nterest grou, the electrcal nterest grou, the scentfc research nsttute s nterest grou and the ebedded syste nterest grou, accordngly. The oveent grou conssts of rando walk users along the roads, as shown n Fg. 6. The resonsblty of the rando walk users s to hel the eber nodes n the nterest grous to carry and forward the essage and data. The corresondng araeter confguraton nforaton s shown n Table 1. TABLE I. SIMULATION PARAMETERS Paraeters Descrton Default values MoveentModel.world Sze( 2 ) Sulaton scenaro area Scenaro.endTe(hour) Sulaton te 12 Scenaro.nrofHosts Nuber of nodes 100 Scenaro.nrofHostGrous Nuber of grous 5 BtInterface.tye BtInterface.transt Seed(Kbs/s) BtInterface.transt Range() Grou.oveentModel Councaton tye nterface Bandwdth 250 Transsson range 10~20 Moveent odel of nodes wthn a grou Moblty Seed(/s) Walkng seed of nodes 0.5~1.5 SleBroadcast Interface ShortestPathMa BasedMoveent Message.Sze(k) Sze of essage 500~1024

8 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 8 Message. Interval(s) Message nterval 1~2 Message.TTL(n) T s T Q The te to lve value for the forwarded essage The threshold value for choosng the to r hgher slarty resources The threshold value for constructon of the sub-county The threshold value for resource query wthn the sub-countes The weght wth reference slarty araeter. The weght wth oveent attern slarty araeter. Our roosed schee RDPM s coared wth the two related ethods - geograhc locaton-based schee (LOC) [12] and the reference slarty-based schee (SPOON) [15] n ters of the success rate of resource dscovery and the average delay. The success rate of resource dscovery s couted as the rato of the nuber of successful queres to the total nuber of the queres. The average delay s the average te used for successful queres. B. Success Rate of Resource Dscovery (b) Intercounty search effcency. (a) Intracounty search effcency. (c) Global search effcency. Fg. 7. Search effcency. We evaluate the search effcency of the three schees based on the ntra-county search, the nter-county search and the global search. Fg. 7(a) dects the ntra-county search effcency coarsons, Fg. 7(b) shows the nter-county search effcency coarsons, and Fg. 7(c) shows the global search effcency coarsons. The LOC schee leverages the slartes of the oveent atterns aong oble users for the urose of constructng countes to search the desred resources. However, ths schee does not exlot the content slarty of the resources, whch akes t less effcent than both the RDPM schee and the SPOON schee. The SPOON schee selects the oble users wth hgher reference slarty over the overlay socal networks n order to construct a county but t does not consder the hyscal councaton dstance between the users. Thus, the success rate of resource dscovery of the SPOON schee s sgnfcantly lower than that of the RDPM schee. The RDPM schee cobnes the advantages of the SPOON wth LOC schee, and hence, t can aarently rove the effcency of resource dscovery.

9 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 9 C. Average Delay Fg. 8. Average delay. Fg. 8 shows the coarson of the three schees n ters of the average delay. Sulaton results show that the average delay of the SPOON s larger aong the three. Ths s because the query essage s forwarded to the node wth the hghest reference slarty n the SPOON schee. However, f the otal forwardng node s out of the councaton range of the requestng node, the transsson of the query essage wll ncur addtonal watng te n oortunstc wreless ad-hoc networks, whch results n hgher average delay. The resource dscovery of the LOC schee can only suort forwardng n roxate SIoT. Hence, the average delay of the LOC schee s lower than that of the SPOON schee. Nevertheless, the LOC schee does not utlze the reference slarty of the oble users. And thus, the resource dscovery of the LOC schee ncurs longer delay te than our roosed schee. Our schee leverages the reference slarty of the oble users to construct nterest countes n roxate SIoT. As shown n Fg. 8, the average delay of the RDPM schee s lower than the other two schees. VI. CONCLUSION AND FUTURE WORK Ths aer rooses a resource dscovery algorth based on reference and oveent attern slarty n dsconnected and delay-tolerant socal Internet of Thngs. Our roosed schee s based on reference and oveent attern slarty as well as ncororates the 3-denson geograhcal locaton awareness to acheve the hgher search effcency and reduce the syste overheads for socal Internet of Thngs. Our algorth has been sulated and evaluated n an oortunstc socal Internet of Thngs envronent. Sulaton results show that our roosed schee outerfors the state-of-the-art resource dscovery schees n ters of the search effcency and the average delay. To further rove the effcency of our resource dscovery algorth and to reduce the wat te of the nodes n oble socal Internet of Thngs, we lan to utlze the characterstc of day-to-day huan socal behavor to desgn an effectve behavor redcton odel to solve the technologcal roble as our future work. ACKNOWLEDGEMENT Ths work was artally suorted by the Natonal Natural Scence Foundaton of Chna under Grant No , the Natural Scence Foundaton of Jangsu Provnce under Grant No.BK and the Senor Professonal Scentfc Research Foundaton of Jangsu Unversty under Grant No.12JDG049. REFERENCES [1] L. Atzora, A. Ierab, G. Morabtoc, M. Ntt, The socal Internet of thngs (SIoT) when socal networks eet the nternet of thngs: concet, archtecture and network, Couter Networks, vol. 56, no.16, , Nov [2] S.M. Allen, M.J. Chorley, G.B. Colobo, E. Jaho, M. Karalooulos, I. Stavrakaks, et al, Exlotng user slarty and socal lnks for cro-blog forwardng n oble oortunstc networks, Pervasve and Moble Coutng, vol. 11, Arl 2014, , Arl [3] S.H. Chen, G.J. Wang, and W.J. Ja June. Cluster-grou based trusted coung for oble socal networks usng lct socal behavoral grah. Future Generaton Couter Systes. [Onlne]. Avalable:htt:// [4] F. L and J. Wu, MOPS: Provdng content-based servce n dsruton-tolerant networks, Proc. the 29th IEEE Int. Conf. on Dstrbuted Coutng Syste(ICDC 09), IEEE Press, Jun. 2009, [5] Y. L, Q. L, D.P. Jn, S. L, H. Pan and L.G. Zeng, Contact duraton aware evaluaton for content dssenaton delay n oble socal network, Wreless Councatons & Moble Coutng, vol. 15, no.3, , Feb [6] E. Pagan, L. Valero and GP. Ross Weak socal tes rove content delvery n behavor-aware oortunstc networks, Ad Hoc Networks, vol. 25, no.pb, , Feb [7] B. Guo, Z. Yu, X. Zhou, and D.Zhang, Oortunstc IoT: Exlorng the socal sde of the nternet of thngs, Proc. the 16th IEEE Int. Conf. on Couter Suorted Cooeratve Work n Desgn (CSCWD 12), IEEE Press, May 2012, [8] S. Msra, R. Barthwal, M. S. Obadat, County detecton n an ntegrated Internet of thngs and socal network archtecture, Proc. the 54th IEEE Global Councatons Conf. (GLOBECOM 12), IEEE Press, Dec 2012, [9] Z.J. Zheng, T.Y. Wang, L.Y. Song, Z. Han and J.J. Wu, Socal-aware ult-fle dssnaton n devce-to-devce overlay networks, Proc. of the 33rd IEEE Int. Conf. on INFOCOM, Couter Councatons Workshos, IEEE Press, Ar. 2014, [10] J. An, X. Gu, W. Zhang, J. Jang, Nodes Socal Relatons Cognton for Moblty-Aware n the Internet of Thngs, Proc. of the 4th IEEE Int. Conf. Internet Thngs Cyber, Phys. Soc. Cout., Thngs/CPSCo, IEEE Press, Oct. 2011, [11] M. C. Gonzales, C. A. Hdalog and A. L. Barabas, Understandng ndvdual huan oblty atterns, Nature, vol. 453, , Jun [12] L. You, J.B. L, C.J. We and L.J. Hu, MPAR: A oveent attern-aware otal routng for socal delay tolerant networks, Ad Hoc Networks, vol.24, no. PA, Jan. 2015, [13] M. Grola, P. Barsocch and S. Chessa, A socal-based servce dscovery rotocol for oble Ad Hoc networks Proc. 12th Annual Int. Conf. on Ad Hoc Networkng Worksho (MED-HOC-NET, 2013), [14] B. Quresh, G.Y. Mn and D. Kouvatsos, An adatve content sharng rotocol for P2P oble socal networks, Proc. of the 24th IEEE Int. Conf. on Advanced Inforaton Networkng and Alcatons Workshos, Ar 2010, [15] K. Chen, H.Y. Shen and H.B. Zhang, Leveragng socal networks for P2P content-based fle sharng n dsconnected MANETs, IEEE Transactons on Moble Coutng, vol. 13, , Feb [16] L. Kaufan and P.Rousseeuw, Fndng grous n data: an ntroducton to cluster analyss, John Wley and Sons, [17] K. Ar, O. Jrg and K. Teeu, The one sulator for DTN rotocol evaluaton, Proc. of the 2nd Int. Conf. on Sulaton Tools and Technques(ICST 09), Mar 2009.

10 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 10 Zhyuan L s an assocate rofessor n the School of Couter Scence and Telecouncaton Engneerng at Jangsu Unversty. He receved hs Ph.D. degree n nforaton and councaton engneerng fro Nanng Unversty of Posts and Telecouncatons, Nanng, n Deceber He s a ebersh of Chna Couter Federaton and ACM. Hs research nterests nclude socal Internet of Thngs, oble eer-to-eer coutng, vehcle networks, and oble cloud coutng. Rulong Chen s a graduate research student n the School of Couter Scence and Telecouncaton Engneerng at Jangsu Unversty. He s a student ebersh of Chna Couter Federaton and ACM. Hs research nterests nclude oble socal networks and resource sharng. Lu Lu s the Professor of Dstrbuted Coutng at the School of Coutng and Matheatcs n the Unversty of Derby and adunct rofessor n the School of Couter Scence and Councaton Engneerng at Jangsu Unversty. Prof. Lu receved hs Ph.D. degree fro Unversty of Surrey (funded by DIF DTC) and M.S. n Data Councaton Systes fro Brunel Unversty. He s the Fellow of Brtsh Couter Socety and Meber of IEEE. Dr. Lu s research nterests are n areas of cloud coutng, servce-orented coutng, eer-to-eer coutng, vrtual coutng and syste of systes engneerng. Geyong Mn s the Professor and Drector of Hgh Perforance Coutng and Networkng (HPCN) Research Grou at the Unversty of Exeter, UK. He receved the PhD degree n Coutng Scence fro the Unversty of Glasgow, UK, n 2003, and the B.Sc. degree n Couter Scence fro Huazhong Unversty of Scence and Technology, Chna, n He oned the Unversty of Bradford as a Lecturer n 2002, becae a Senor Lecturer n 2005 and a Reader n 2007, and was rooted to a Professor n Couter Scence n Hs an research nterests nclude Next-Generaton Internet, Analytcal Modellng, Cloud Coutng, and Bg Data.