Cellular Traffic Offloading through Opportunistic Communications Based on Human Mobility

Transcription

1 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 9, NO. 3, Mar Copyrght c 2015 KSII Cellular Traffc Offloadng through Opportunstc Communcatons Based on Human Moblty Zhgang L 1, Yan Sh 1, Shanzh Chen 2,3 and Jngwen Zhao 4 1 State Key Laboratory of Networkng and Swtchng Technology, Beng Unversty of Posts and Telecommuncatons, Beng Chna [e-mal: zhgang.l.mal@gmal.com, shyan@bupt.edu.cn] 2 State Key Laboratory of Wreless Moble Communcatons, Chna Academy of Telecommuncatons Technology, Beng Chna 3 Datang Telecom Technology & Industry Group Beng Chna [e-mal: chensz@datanggroup.cn] 4 Internatonal school, Beng Unversty of Posts and Telecommuncatons, Beng Chna [e-mal: @bupt.edu.cn] *Correspondng author: Yan Sh Receved December 11, 2014; revsed February 7, 2015; accepted February 27, 2015; publshed March 31, 2015 Abstract The rapd ncrease of smart moble devces and moble applcatons has led to explosve growth of data traffc n cellular network. Offloadng data traffc becomes one of the most urgent techncal problems. Recent work has proposed to explot opportunstc communcatons to offload cellular traffc for moble data dssemnaton servces, especally for acceptng large delayed data. The basc dea s to delver the data to only part of subscrbers (called target-nodes) va the cellular network, and allow target-nodes to dssemnate the data through opportunstc communcatons. Human moblty shows temporal and spatal characterstcs and predctablty, whch can be used as effectve gudance effcent opportunstc communcaton. Therefore, based on the regularty of human moblty we propose NodeRank algorthm whch uses the encounter characterstcs between nodes to choose target nodes. Dfferent from the exstng work whch only usng encounter frequency, NodeRank algorthm combned the contact tme and nter-contact tme meanwhle to ensure ntegrty and avalablty of message delvery. The smulaton results based on real-world moblty traces show the performance advantages of NodeRank n offloadng effcency and network redundant copes. Keywords: Cellular traffc offloadng, human moblty, opportunstc communcatons Ths work s partally funded by the Natonal Scence Foundaton Proects (Grant No ) and the Natonal Scence Fund for Dstngushed Young Scholars (Grant No ) n Chna. ISSN :

2 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 9, NO. 3, March Introducton In the past several years, the explosve ncrease of ntellgent moble devces such as smartphones, tablets, and laptops, especally the ncreasng popularty of varous applcatons for smartphones has led to the rapd growth of data traffc n cellular network. Global moble data traffc wll ncrease nearly 11-fold between 2013 and Moble data traffc wll grow at a compound annual growth rate (CAGR) of 61 percent from 2013 to 2018, reachng 15.9 exabytes per month by 2018 [1]. The unpredctable contnuous growth of data traffc wll brng great challenge to the current cellular network. How to reduce the congeston of network, mprove the qualty of servce for customers and reduce the cost become the most urgent problems to solve at present. The moble data dssemnaton n moble networks may nclude multmeda newspapers, weather forecasts, move tralers, moble socal applcatons, shoppng advertsements, etc. All these nformaton s generated by content servce provders. If everyone gets nformaton from cellular network nfrastructure lke Fg. 1(a), the load of the cellular network nfrastructure wll be hgh. In order to save the bandwdth of cellular network nfrastructure, beneft from the delay-tolerant nature of non-real-tme applcatons, these servce provders through the base staton may delver nformaton to only a small fracton of selected users (.e. target-nodes, lke the N 1, N 2, N 3 n Fg. 1(b)), then the target-nodes can further dssemnate the nformaton to subscrbed users when ther moble phones are n proxmty and can communcate opportunstcally usng W-F or Bluetooth technology as shown n Fg. 1(b) to reduce cellular data traffc. The servce provder wll send the nformaton to N 4 drectly through cellular networks, f he or she fals to receve the nformaton before ther delvery deadlne. We can translate ths obectve nto maxmzng the expected number of users that can receve the delvered nformaton through opportunstc communcatons. Fg. 1. (a) The nfrastructure-only mode; (b) the opportunstc communcaton mode The exstng works selectng the target-nodes that merely have the hghest probablty to encounter other users as the ntal sources, but they do not consder the message s fully delvered n the meetng tme, also dd not consder the messagng success s exceed the tme can be tolerated. Meanwhle, the message dssemnaton through target-nodes on encounter spread wll cause a huge amount of copes. In addton, they all utlze the hstorcal traectory nformaton to calculate the sum of the encounter frequency between nodes, therefore they can

3 874 L et al.: Cellular Traffc Offloadng through Opportunstc Communcatons Based on Human Moblty not avod the stuaton that hstorcal data couldn t reflect the current movng rule of human moblty. We ntroduce NodeRank algorthm based on the regularty of human moblty ranks the mportance of a node n the contact graph. The exstng research results show that the moblty of human follows a certan spatal-temporal regulaton and there s a potental predctablty. So to take advantage the rule of human moblty can gude the opportunstc communcatons. NodeRank s nspred by the PageRank algorthm [2] used n Google s search engne to measure the relatve mportance of a Web page wthn a set of pages. Smlar to the PageRank, NodeRank dentfes the most popular nodes to dssemnate the message, gven that popular nodes are more lkely to meet other nodes n the networks. We study the target-nodes selecton problem as the frst step towards bootstrappng the cellular traffc offloadng for nformaton delvery n cellular networks. Our frst contrbuton s that we proposed NodeRank algorthm to choose target-nodes and dssemnate message. The NodeRank utlze the characterstcs of human moblty whch combned wth the encounter frequency, contact tme and nter-contact tme. Our second contrbuton s to explot the regularty of human moblty and apply the target-nodes dentfed by usng moblty regularty to nformaton delvery n the future. In the algorthm, we choose the target-nodes n consderaton of the contact tme and nter-contact tme that to ensure the ntegrty and avalablty of message delvery. Our thrd contrbuton s usng the NodeRank algorthm whch combnes the centralzaton and dstrbuton. Ths algorthm chooses the target-nodes not only n consderaton of the hstorcal nformaton of human moblty but also the present state of human moblty. Wth the hstorcal data, we can ensure the practcablty of predcton towards human moblty and wth the present nformaton, we can avod the stuaton that hstorcal data couldn t reflect the current movng rule of human moblty. Ths paper uses the real-world data sets that are based on the encounter of Bluetooth to evaluate the effect of data traffc offloadng. The trace-based evaluaton shows our method offloads almost 75% data traffc compared wth the tradtonal method wthout offloadng data, although we also take the ntegrty and avalablty of message delvery nto consderaton by addng contact tme and nter-contact tme. Smlarly, the offloadng effcency has mproved up to 6% compared wth Greedy algorthm, and the network redundant copes also have reduced almost 20%. The result of turns out that our method can well balance the success rate of message delvery and the network copy redundances. The rest of ths paper s organzed as followng: Secton 2 revews related work on cellular traffc offloadng. The NodeRank algorthm and target-nodes selecton are presented n secton 3. In Secton 4, we evaluate the NodeRank algorthm performance through real-world moblty trace, and then conclude ths paper. 2. Related Work 2.1 Cellular Traffc Offloadng Techncal Analyss To allevate the data overloadng of cellular networks and mprove the user experence, most moble operators have ntroduced and started to mplement moble data offloadng strategy [3]. To the best of our knowledge, we analyze and summarze the exstng moble data offloadng solutons. Base staton: The tradtonal approach of scalng network capacty wth nstallng more base statons per area s always avalable, but not cost-effectve and vable consderng the pace at whch the demand for data servces s ncreasng. Moreover, from the perspectve

4 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 9, NO. 3, March of energy savng, ths wll greatly mprove the entre network energy consumpton. W-F: Due to degradaton of cellular servces n overloaded areas, an ncreasng number of users are already usng W-F to access Internet servces for better experence. W-F s attractve because t allows data traffc to be shfted from expensve lcensed bands to free unlcensed bands (2.4GHz and 5GHz). Nevertheless, the operator loses vsblty (and hence control) of ts subscrbers whenever they are on the W-F network. Meanwhle, the operator s unable to delver any subscrbed content, leadng to potental loss of revenue. Femtocells: Femtocell s a small cellular base staton typcally desgned for ndoor use (e.g. n a home or offce). It connects to the servce provder s network va broadband (e.g. dgtal subscrber lne, DSL), especally n areas where access would otherwse be lmted or unavalable. Although femtocells can provde a hghly effectve method of easng the traffc carred by a macrocellular network, the careful plannng s requred as they operate n costly (lcensed) and lmted spectrum bands. Spread spectrum: The development of spread spectrum technology mproves the utlzaton of the frequency spectrum to a great extent and at the same tme reduce the mpact that comes from the co-channel nterference, whch greatly mproves the capacty of the cellular network and can provde users wth hgher qualty of the servce. However, the spectrum resources are always lmted and therefore the optmum utlzaton s restrcted. Devce to Devce: D2D communcaton technology [4] can mprove the utlzaton of the spectrum resources and the network capacty, allevate the burden of the network, reduce the consumpton of the battery on the moble termnals and ncrease the bt rate by obtanng the frequency resources and transmsson power. However, when ths technology sharng wreless resources wth the cellular network, there wll be some nterference. Dfferent from the opportunstc communcatons that D2D s needed durng the communcaton under the control of the macro-cellular base staton, means that need money cost. There are also some methods such as Meda optmzaton solutons, IP Flow Moblty and so on. Compared to the above methods, we manly focus on offloadng the cellular traffc from the perspectve of system and network to optmze the qualty of servce, whch s the research method, cellular data offloadng va opportunstc communcaton, of ths paper. Opportunstc communcatons s a promsng soluton to partally solve cellular traffc overloaded, because there s scarcely any monetary cost for t, and does not exst the shortcomngs of the above methods. We wll dscuss the related work n the next secton. 2.2 Cellular Traffc Offloadng Through Opportunstc Communcatons Usng opportunstc communcaton to offload cellular traffc for moble content dssemnaton applcatons s a novel and nterestng dea and t has drawn great attenton of the researchers. Opportunstc communcaton [5-6] s a technque that allows users wthout permanent connecton to exchange nformaton usng low-cost proxmty-based connecton, such as W-F or Bluetooth, when they encounter opportunstcally. Although usng opportunstc communcaton to offload cellular traffc can sgnfcantly reduce the cost of communcaton, but t can only be appled to non-real-tme messages that have hgh delay-tolerant characterstc and sometmes the messages wll be unreachable for few nodes. Meanwhle, t wll also generate more copy of the message. The work [7] studes the traectory of 100, 000 anonymzed moble phone users whose poston are tracked for a sx month perod and the result shows that the moblty of human follows the characterstcs of the perodc and return the locaton of hgh frequency to vst.

5 876 L et al.: Cellular Traffc Offloadng through Opportunstc Communcatons Based on Human Moblty follows a certan knd of regularty. The work [8] ndcates that there s a potental 93% average predctablty n user moblty, an exceptonally hgh value rooted n the nherent regularty of human behavor. Consequently, to take advantage the rule of human moblty can gude the opportunstc communcatons. Nelson et al. proposed encounter-based routng [9] to encounter communcaton opportunty. Some studes [10-11] explot uncast routng to enable a source to delver the obect to multple destnatons n an opportunstc network. All the above studes consder an envronment where the source nodes are gven. However, our work dffers n that t focuses on how to choose a set of sources to dssemnate the nformaton to all subscrbers before the nformaton deadlne. The work [12] selects target-nodes to delver the message by usng the Greedy algorthm on the bass of the encounter probablty of the users hstorcal traectory and then dssemnate the message to all the subscrbed users. However, the selecton of the target-nodes only consders the encounter frequency, at the same tme the message dssemnaton through target-nodes on encounter spread wll cause a huge amount of copes. Based on the study [12], the work [13] evaluates the amount of data exchanged from encounterng to separatng through Bluetooth between two phones. It shows that we need to consder the ntegrty of message delvery, however, the factor does not be appled n the algorthm. However, the factor does not be appled n the algorthm. The work [14] selected the target-nodes based communty. Smlar to the above mentoned works, t only uses the encounter frequency and the hstorcal traectory nformaton to calculate the sum of the encounter frequency between nodes. On the bass of the node whch has the maxmum encounter frequency and by usng the breadth frst search algorthm, t wll traverse the detecton communty. Then t chooses the target-nodes whose number equals to the number of the communty and use the target-nodes to dssemnate nformaton. Compared wth the above studes, we add the contact tme when we select the target-nodes, thereby guaranteeng the ntegrty of the data transmsson. At the same tme, we added to the contact tme also better accord wth the process of data transmsson n opportunstc communcaton. In addton, we add the nter-contact tme n NodeRank algorthm to ensure the avalablty of data delvery and receved good results. In addton all the above works explot the hstorcal traectory nformaton to predct the present mportant nodes and the encounter of nodes for message dssemnaton. Usng the global hstorcal encounter nformaton between nodes to establsh contact graph for selectng target-nodes, although n reference [11] they observe that the hstorcal data (one week or month) s enough to detect users that keep ther mportance, snce the random of human moblty, for example a long dstance travel or a travellng on offcal busness, the node wll dsappear from the zone where t supposed to appear. So we adopt the combnaton of hstorcal and present moble nformaton to choose the nodes and predct the encounter of the nodes, thereby dssemnatng the nformaton. That s our NodeRank algorthm whch combnes the dstrbuted and the centralzed. We wll dscuss t n detal n next secton. The work [15] offloads the cellular traffc through opportunstc communcaton. It manly proposes a Subscrbe-and-Send archtecture whch conssts of an applcaton and an opportunstc routng protocol. Compared wth spreadng message by choosng target-nodes based on the analyss of the regularty of the human moblty, ths archtecture manly emphaszes on the content dstrbuton mechansm wth base staton nstead of dssemnatng messages wth the analyss of users encounter regularty, whch s dfferent from usng opportunstc communcaton to offload cellular traffc for moble content dssemnaton applcatons.

6 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 9, NO. 3, March Algorthm Descrpton and Target Nodes Selecton Our focus here s on offloadng cellular data traffc through opportunstc communcatons, especally n metropoltan areas wth hgh populaton densty. The content servce provders who can tolerate large delay through the base staton send the message to the target-nodes selected accordng to NodeRank algorthm and then the target-nodes can further dssemnate the message to all subscrbed users as large as possble. 3.1 NodeRank Algorthm NodeRank algorthm s nspred by the most famous web page rankng employed by Google to rank web pages. PageRank measures the relatve mportance of a page wthn a web graph. Motvated by the success of ths algorthm, we propose to apply a smlar algorthm whch we call NodeRank to rank the nodes n a contact graph. The man dea s that nodes wth a hgher NodeRank value wll generally be more mportant nodes to dssemnate the message, gven that popular nodes are more lkely to meet other nodes n the networks. Before gong nto the detals of our NodeRank algorthm, we revst the propertes of the PageRank algorthm. PageRank performs a random walk on the World Wde Web graph, where the nodes are pages and the edges are lnks between pages. PageRank gves the probablty dstrbuton used to represent the lkelhood that a person randomly clckng on lnks wll arrve at any partcular page. PageRank s gven by the followng equaton: 1 d PR p PR p d (1) n L p p M p Where p 1, p 2,, p n are the pages, M( p ) s the set of pages that lnk to p, Lp ( ) s the number of outbound lnks on page p, n s the total number of pages, and d (0 < d 1) s a dampng factor whch s defned as the probablty, at any step, that the person wll contnue clckng on lnks nstead of openng another random page. Varous studes have tested dfferent dampng factors, but t s generally assumed that the dampng factor wll be set around 0.85 [2]. Frstly, we wll ntroduce ntal NodeRank algorthm namely NodeRank. It uses the same dea as PageRank algorthm to represent the mportance of a node. In the same way web pages are lnked, we buld a contact graph between nodes when they contact wth each other. As we all know PageRank calculates page rankng value through drected graph whch bult by the ctatons between pages, nevertheless n most cases the mportant pages have connecton wth each other, so that s a bdrectonal lnk between A and B. For the same reason, the encounter of nodes s equvalent ths specfc bdrectonal lnk. Our NodeRank algorthm was based on the characterstc of the bdrectonal lnk to buld the contact graph and to compute the mportance of nodes. So we denote such a contact graph Gc ( Nc, Ec ) wth a node set N c and an edge set E c. An edge ( N, N ) Ec there s a contact between nodes N and N. Consequently, the NodeRank value s gven by the followng equaton: 1 d NR( N ) NR N d (2) n L( N ) N E ( N) Where N 1, N 2,..., Nn are the nodes (users), EN ( ) s the set of nodes that encounter to N, LN ( ) s the number of nodes encounter to N.

7 878 L et al.: Cellular Traffc Offloadng through Opportunstc Communcatons Based on Human Moblty In NodeRank algorthm we use only the encounter frequency between nodes to rank the mportant of nodes. Therefore, we choose the mportant nodes that have hgh rankng value to dssemnate message through opportunstc communcaton wth the am of offloadng traffc because they have more communcaton opportuntes. But n the actual message transfer, except for encounter frequency havng an mpact on communcaton opportunty of message, the contact tme and nter-contact tme are also should be consdered. The contact tme between two nodes wll affect the ntegrty of the delvery of the message. That s to say, f a message needs 10 seconds to be fully delvered, however, the contact tme between two nodes s only 5 seconds, thus the message transfer s not fully completed. Smlarly, f the nter-contact tme between two nodes s so long, for example over 12 hours or even longer, that exceed the maxmum tolerate tme then even the message s successfully delvered t s meanngless. Therefore, we ntroduce NodeRank algorthm based on NodeRank whch takes contact tme and nter-contact tme as weght of the edge to ensure the ntegrty and avalablty of message delvery. The weghted NodeRank value s gven by Eq (3). 1 d NR( N ) ( N, N) NR N d (3) n L( N ) N E ( N) In Eq (3), ( N, N) s the weght of the edge between node N and N, whch ntegrates contact tme ( ) and nter-contact tme ( ) between the two nodes. ( N, N ) s ct calculated as follows: ct ( N, N ) ( N, N ) ( N, N ) (4) ct ct From Eq (2) and Eq(3) we can fnd that the three ndexes encounter frequency, contact tme and nter-contact tme have close relatons to the lnk between two nodes, n other words, the encounter of nodes. However, the three ndexes are ndependent wth each other n terms of computng relatonshps. Wth regard to encounter frequency, the more encounter wth other nodes, the hgher the rankng value wll be. Combne wth the above analyss on the ntegrty and avalablty of message delvery, we can know that the rankng value ncreased wth the ncrease of contact tme and decreased wth the ncrease of nter-contact tme. The work [7] studes the opposte drecton walkng consderng the moderate human walkng speed s around 1 m/s and the contact duraton of two moble phones s about 20 seconds. Apart from the tme used for detectng and connectng the devces,the average number of transferred bytes s KB and the average duraton of data transfer s seconds whch means wthn 29.8s the transferred bytes s 1MB has been hgher than that of most of message. So we use 29.8s as the segmentaton of contact tme to ndcate the ntegrty level of the data transmsson. As for the nter-contact tme, we defne 12h as the nterval. Ths s because f the nter-contact tme s bgger than ths value there wll be excessve delay and the message wll become meanngless. The technques we descrbed thus far are sutable for centralzed mplementatons where the contact graph s known a-pror. Clearly ths may not be feasble n the moble wreless envronment we are consderng. We explot the combnaton method of dstrbuted and centralzed to select the target-nodes n the next secton. 3.2 Target Nodes Selecton for Message Dssemnaton The combnaton of dstrbuted and centralzed of NodeRank s shown n Algorthm 1. In ths

8 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 9, NO. 3, March method, whenever two nodes meet each other, they exchange two peces of nformaton: () ther NodeRank values ncluded the value of the day and week; and () the number of contact nodes they have. Then, the two nodes update ther NodeRank values usng the formula gven n Eq.3. Implctly, ths algorthm explots the moblty and contact behavor of the nodes snce the NodeRank value s updated every tme the nodes meet. Note that n ths algorthm frequently seen nodes update ther NodeRank more often. In fact, the rank of the node whch meets wth more other nodes wll ncrease rapdly. Algorthm Combnaton NodeRank Requre L n NR n NR n day 0 week whlen sncontact wth n n f n E n then end f day day send NR n L n send NR n Ln receve NR n L n send NR n L n week update NR n update NR n Eq(3) day whlembuffer n do week f n subscrber m and NR n max NR n end f end whle Delvered m n week week and day end whle Note that ths combned NodeRank algorthm, where the day and week are respectvely the rankng value from the past days and the past weeks contnung untl the current tme, s manly n response to the dsappearance of nodes n the regon as a result of sudden long-dstance travel. Ths s because the rankng value of a week s relatvely stable and can predct the mportance of nodes well, but t fals to predct the unexpected events of nodes. In the contrary, the rankng value of a day can reflect the mportance of the nodes of the current tme better, thus the combnaton wll predct the mportance of nodes more accurately and then dssemnate the messages. Here max NRn week and day combnes the rankng value of a day and a week, however, t takes the week rankng value as the frst ndcator and dvdes t nto hgh, medum and low levels. When the week rankng values are at the same level, we choose the target-nodes to dssemnate message based on the day rankng values. Once the target-nodes have been chosen, the target-nodes wll contnue dssemnate messages to subscrbed users. There s one problem we could not gnore whch s the strategy of message dssemnaton, because t must offer the best trade-off between cost (number of message replcas) and rate of successful message delvery. Ths s because end-to-end paths between two communcatng nodes are rarely avalable n opportunstc networks [16]. In such stuatons, the nodes mght stll copy and delver messages to nodes that are more lkely to

9 880 L et al.: Cellular Traffc Offloadng through Opportunstc Communcatons Based on Human Moblty meet the destnaton. There are also several exstng works [17-18] for nformaton dssemnaton n wreless networks. They all dedcated to reduce the number of cost whle ncreasng the success rate, and were confrmed wth good results. Here we adopt the Hgh Rank sole One (HRO) method n whch we choose the contnuous dssemnaton nodes on the bass of NodeRank algorthm to ensure the success rate of message dssemnaton. By choosng the node whch has the bggest NodeRank value among the meetng nodes to contnuously dssemnate message, we can reduce the number of message replcas effectvely. Snce n ths stuaton the message delvery stll uses the way of opportunstc communcaton, there wll be a small quantty of message replcas. Meanwhle, because of property of wthout permanent connecton of opportunstc communcaton, sometmes the message s unreachable for few nodes. At ths tme we stll need to send messages through the base staton. However, the result of our smulaton turns out that our method can well balance the success rate of message delvery and the network copy redundances. 4.1 Moblty Traces 4. Performance Evaluaton We now evaluate the performance of NodeRank based opportunstc offloadng usng the real-world moblty traces from the Realty Mnng proect [19]. The Realty Mnng trace was collected usng Noka 6600 smartphones carred by 94 users from the MIT Meda Laboratory and Sloan Busness School, from to The nformaton n ths trace ncludes call logs, neghborng Bluetooth devces, assocated cell-tower IDs, etc. For the traffc offloadng effcency analyses n ths paper, we used a subset of the moblty traces data whch are Bluetooth data collected from October, November and December, 2004, snce compared wth other months these three months data are complete enough to support the research of ths paper. Moreover, Bluetooth data we used are dfferent from cell tower data whch thnk two moble users are n contact of each other f ther phones are assocated wth the same cell tower. The reason why we choose Bluetooth data s that there s a good evdence to prove that the two users have a close connect f a user scannng another va Bluetooth. What s more, when forwardng message, we use Bluetooth or W-F as a way of nformaton dssemnaton whch s close connect compared wth under the cell tower. Furthermore, n the envronment where the data coverng areas are relatvely small, f we consder that under the same cell tower s equvalent to encounter, then the contact graph establshed accordng to ths rule conssts of many strongly connected domans, whch wll enlarge the scope of message dssemnaton greatly and get the naccurate success rate of transmsson. From the secton 3 we have already known that we dssemnate messages by choosng nodes accordng to the rank value whch combnes two tme perod and the NodeRank algorthm whch consders the ndex of contact tme and nter-contact tme s weghted. Next we wll compare the related performance of the algorthms, snce NodeRank and Greedy algorthm selects target nodes all only based on encounter frequency, so when we compare the NodeRank and Greedy algorthm, also add NodeRank algorthm to compare. In Table 1 we use the three algorthms n dfferent tme perod to calculate the mportant nodes. From the Table we can see that the node sets are dfferent from each other, where the day s lasted 6 hours startng from 6 December, :00PM and the week s lasted 7 days begnnng from 1 December, 2004.

10 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 9, NO. 3, March Table 1. The top 6 most actve users for dfferent algorthm Algorthm No. 1 No. 2 No. 3 No. 4 No. 5 No. 6 Greedy(day) NodeRank(day) NodeRank(day) Greedy(week) NodeRank(week) NodeRank(week) Smulaton Results The performance of cellular traffc offloadng s determned by two conflctng factors: () the success rate of message delvery; and () the cost nduced by the dssemnaton mechansm,.e. the number of message replcas n the system. In the followng, we assess the NodeRank performance wth regard to these two performance ndcators. In the meantme, we wll compare the performance of Greedy, NodeRank and NodeRank usng the real-world traces n ths secton. Besdes, n the experment, we assume that all the users are subscrbers. Before analyzng the offload effcency, we frstly consder the number of target-nodes selected to dssemnate messages wth the am of gettng better offload effcency. As we known n above works have verfed select the sze of target-nodes s a NP-Completeness problem. In ths paper, we utlze the real-world traces analyzes the problem of the sze of target-nodes. We select the number of target-nodes s from 2 to 30, and respectvely select 1 hour, 2 hours and 6 hours as message dssemnate deadlne. In Fg. 2 we can see the smulaton results show that when usng 5 target-nodes ganed the best offloadng effect n ths data. Fg. 2. Percentage of offloadng for dfferent sze of target-nodes Next, let us analyze the offload effcency. We can see from Fg. 3 that the offload effcency n unweghted NodeRank algorthm s better than the weghted NodeRank. Ths s due to we takng contact tme and nter-contact tme nto account n Nodebank algorthm that affected the delvery rate to some degree, but t ensures the ntegrty and avalablty of the message delvered. It s easy to understand that when usng the NodeRank algorthm, some of the messages faled to fully complete transmsson, and some messages become meanngless once exceedng tolerant tme. However, the NodeRank algorthm whch takes two ndexes nto consderaton stll can offload almost 75% data traffc n cellular network and the offloadng effcency has mproved up to 6% than by Greedy algorthm. When the delay of message s less than 6h, t s obvous that NodeRank and NodeRank are sgnfcantly better than the offload effcency of Greedy

11 882 L et al.: Cellular Traffc Offloadng through Opportunstc Communcatons Based on Human Moblty algorthm. Along wth the growth of the delay tme, the NodeRank stll mantans a hgh offload effcency, NodeRank and Greedy converge to a consstent offload performance, whch s not dffcult to explan: when the ncrease of delay goes to a certan extent, nodes wll encounter more and more users, even ones wthn the whole regon, whch corresponds wth the phenomenon that the message spread to less users when t ust begns to delay. Another queston s about the value of the dampng factor d, we are able to fnd out that the offload effcency acheves the best value when d s around 0.85, n agreement wth the PageRank algorthm [2]. Fg. 3. Percentage of offloadng comparson of NodeRank, NodeRank and Greedy algorthms We verfed the offload effcency of NodeRank algorthm and got the deal result. However, t wll cause the waste of a great deal of termnal resources and make the moble phone users unwllng to cooperate f there are a large number of redundant copes. There are many papers whch study how to establsh effectve ncentve schemes to prompt people forwardng messages, because t s not the man research content of ths paper, so we wll not do too much dscusson n ths paper. From Fg. 4 we can see that snce the Greedy algorthm dssemnatng messages through encounter dssemnaton, t has the maxmum amount of copes. It s easy to see when comparng wth NodeRank, the number of copes generated by NodeRank s lesser. That s because, frstly, the dssemnaton method NodeRank and NodeRank adopted whch selects the nodes that have hgher rank value to keep on dssemnatng messages when encounterng a lot of nodes s dfferent from Greedy algorthm. Secondly, the reason why NodeRank algorthm has few copes s that ths algorthm s weghted, therefore the success rate decreases resultng n less number of copes. Fg. 4. The number of copy comparson of NodeRank, NodeRank and Greedy algorthms

12 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 9, NO. 3, March Then we extract 10 days a month randomly from October, November and December, every day we wll start from 12:00 PM contnung 12 hours of delay tme, through 10 randomly-selected experments, the average offload effcency obtaned s shown as Fg. 5. We can see from the fgure that NodeRank stll has the best offload effcency, and NodeRank s slghtly better than the Greedy algorthm, but because of the use of a longer delay tme of 12 hours, so n November 2014 Greedy Algorthm has almost the same offload effcency as NodeRank. Fg. 6 s that we use the same method as offload effcency to calculate the copy quantty, we can fnd out from the fgure the result stll matches the prevous copy smulaton analyss. Fg. 5. Month average offloadng effcency comparson of NodeRank, NodeRank and Greedy algorthms Fg. 6. Month average copes comparson of NodeRank, NodeRank and Greedy algorthms 5. Concluson In ths paper, we study the way to offload cellular network traffc through opportunstc communcatons. We propose NodeRank algorthm based on the regularty of human moblty to choose the target-nodes and dssemnate nformaton. NodeRank algorthm uses the encounter frequency characterstcs to rank the mportant nodes, meanwhle we add contact tme and nter-contact tme to ensure the ntegrty and avalablty of message delvery to a certan extent. So from the perspectve of the effect of data offloadng, our mprovement s lmted. Ths paper uses the real-world moblty traces for smulaton experment to show that

13 884 L et al.: Cellular Traffc Offloadng through Opportunstc Communcatons Based on Human Moblty compared wth the tradtonal method wthout traffc offloadng our method can offload almost 75% data traffc n cellular network. Smlarly, the offloadng effcency has mproved up to 6% than that by encounter frequency, and the network redundant copes also have reduced almost 20%. References [1] Csco, Csco vsual networkng ndex: Global moble data traffc forecast update, Artcle (CrossRef Lnk) [2] S. Brn, L. Page, The anatomy of a large-scale hypertextual web search engne, Comput. Netw. ISDN Syst., vol. 30, pp , Artcle (CrossRef Lnk) [3] A. Aaz, H. Aghvam, M. Aman, A survey on moble data offloadng: techncal and busness perspectves, Wreless Communcatons of IEEE, vol. 20, no.2, pp , Artcle (CrossRef Lnk) [4] Km S. Y, Lm C. H, Cho C. H, Performance Analyss of a Dense Devce to Devce Network, KSII Transactons on Internet and Informaton Systems, vol. 8, no. 9, pp , [5] A. Chantreau, P. Hu, J. Crowcroft, C. Dot, R. Gass, J. Scott, Pocket swtched networks: Real-world moblty and ts consequences for opportunstc forwardng, Unversty of Cambrdge, Computer Lab, Tech. Rep, Artcle (CrossRef Lnk) [6] M. Motan, V. Srnvasan, P. S. Nuggehall, Peoplenet: engneerng a wreless vrtual socal network, n Proc. of ACM MobCom, Artcle (CrossRef Lnk) [7] M. C. Gonzalez, C. A. Hdalgo, A.-L. Barabas, Understandng ndvdual human moblty patterns, Nature, vol. 453, pp , Artcle (CrossRef Lnk) [8] Song C, Qu Z, Blumm N, et al. Lmts of predctablty n human moblty, Scence, vol. 327, no. 5968, pp , Artcle (CrossRef Lnk) [9] S. Nelson, M. Bakht, R. Kravets, Encounter-based routng n DTNs, IEEE INFOCOM, Artcle (CrossRef Lnk) [10] U. Lee, S. Y. Oh, K.-W. Lee, M. Gerla, RelayCast: Scalable multcast routng n delay tolerant networks, n Proc. of IEEE ICNP, Artcle (CrossRef Lnk) [11] W. Gao, Q. L, B. Zhao, G. Cao, Multcastng n delay tolerant networks: A socal network perspectve, n Proc. of ACM MobHoc, Artcle (CrossRef Lnk) [12] B. Han, P. Hu, M. Marathe, G. Pe, A. Srnvasan, A. Vullkant, Cellular traffc offloadng through opportunstc communcatons: A case study, n Proc. of ACM Chants, [13] B. Han, P. Hu, V. Kumar, M. Marathe, J. Shao, A. Srnvasan, Moble Data Offloadng through Opportunstc Communcatons and Socal Partcpaton, Moble Computng of IEEE Trans, vol. 11, no. 5, pp , Artcle (CrossRef Lnk) [14] Chuang Y J, Ln K C J. Cellular traffc offloadng through communty-based opportunstc dssemnaton, n Proc. of Wreless Communcatons and Networkng Conference (WCNC), IEEE, pp , Artcle (CrossRef Lnk) [15] LU Xaofeng, HUI Pan, Petro Lo, Offloadng Moble Data from Cellular Networks Through Peer-to-Peer WF Communcaton: A Subscrbe-and-Send Archtecture, Chna Communcatons, vol. 10, no. 6, pp , Artcle (CrossRef Lnk) [16] A. Mtbaa, M. May, M. Ammar, C. Dot, PeopleRank: Socal Opportunstc Forwardng, n Proc. of INFOCOM, Artcle (CrossRef Lnk) [17] C. Lndemann, O. P. Waldhorst, Modelng Epdemc Informaton Dssemnaton on Moble Devces wth Fnte Buffers, n Proc. of SIGMETRICS 2005, pp , [18] M. Papadopoul, H. Schulzrnne, Effects of power conservaton, wreless coverage and cooperaton on data dssemnaton among moble devces, In Proc. of MOBIHOC 2001, pp , Artcle (CrossRef Lnk) [19] N. Eagle, A. Pentland, D. Lazer, Inferrng Socal Network Structure usng Moble Phone Data, n Proc. of the Natonal Academy of Scences, vol. 106, no. 36, pp , Artcle (CrossRef Lnk)

14 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 9, NO. 3, March Zhgang L receved the B.Sc. degree from Huanghe Scence and Technology College n 2006 and the M.Sc. degrees from Behang Unversty n He s currently workng toward the Ph.D. degree wth State Key Laboratory of Networkng and Swtchng Technology, Beng Unversty of Posts and Telecommuncatons (BUPT), Beng, Chna. Hs current research nterests nclude moble computng and cellular network. Yan Sh receved her Ph.D. degree from Beng Unversty of Posts and Telecommuncatons (BUPT) n She s currently a research staff of the State Key Laboratory of Networkng and Swtchng Technology. Her current research nterests nclude network archtecture evoluton, protocol desgn and performance optmzaton of future networks and moble computng, especally moblty management technology. Shanzh Chen receved hs Ph.D. degree from Beng Unversty of Posts and Telecommuncatons (BUPT), Chna, n He oned Datang Telecom Technology & Industry Group n 1994, and has been servng as CTO snce He was a member of the steerng expert group on nformaton technology of the 863 Program of Chna from1999 to He s the vce drector of State Key Laboratory of Wreless Moble Communcatons, and the board member of Semconductor Manufacturng Internatonal Corporaton (SMIC). He has made great contrbutons to TD-SCDMA 3G ndustralzaton and TD-LTE-advanced 4G standardzaton. He receved State Scence and Technology Progress Award of Chna n 2001 and Hs current research nterests nclude wreless moble communcaton, IoT and emergency communcaton. Jngwen Zhao s currently pursung her B.E n Beng Unversty of Posts and Telecommuncatons. Her maor s e-commerce Engneerng wth Law.