Efficient Data Forwarding in Mobile Social Networks with Diverse Connectivity Characteristics

Transcription

1 Efficient Data Forwarding in Mobile Social Network with Divere Connectivity Characteritic Xiaomei Zhang and Guohong Cao Department of Computer Science and Engineering The Pennylvania State Univerity, Univerity Park, PA, Abtract Mobile Social Network (MSN) with divere connectivity characteritic i a combination of opportunitic network and mobile ad hoc network. Since the major difficulty of data forwarding i the opportunitic part, technique deigned for opportunitic network are commonly ued to forward data in MSN. However, thi may not be the bet olution ince they do not conider the ubiquitou exitence of Tranient Connected Component (), where node inide a can reach each other by multi-hop wirele communication. In thi paper, we firt identify the exitence of and analyze their propertie baed on five real trace. Then, we propoe -aware data forwarding trategie which exploit the pecial characteritic of to increae the contact opportunitie and then improve the performance of data forwarding. Tracedriven imulation how that our -aware data forwarding trategie outperform exiting data forwarding trategie in term of data delivery ratio and network overhead. I. INTRODUCTION In Mobile Social Network (MSN), human-carried mobile device opportunitically form wirele peer-to-peer connection with each other in the abence of the network infratructure []. Due to the unpredictable human mobility, it i hard to maintain end-to-end connection. A a reult, carry-and-forward [2][3] i ued, where mobile node phyically carry the data, and forward the data when contacting a node with higher forwarding capability. Although mot reearch aume that MSN are highly pare, a cloe crutiny on mot current MSN trace reveal that the connectivity inide a network i divere, and there are ubiquitou exitence of Tranient Connected Component (). Inide, node have tranient contact with each other and form a connected component. For example, tudent in a claroom have tranient connection with each other, and vehicle on highway form platoon and have tranient connection inide a platoon [4]. An MSN with divere connectivity characteritic i a combination of opportunitic network and mobile ad hoc network (MANET). Inide a, a MANET i formed where node can reach each other by multi-hop wirele communication. Outide of, node contact each other opportunitically through carry-and-forward. Since the Thi work wa upported in part by Network Science CTA under grant W9NF C B A (a) D E Figure. The left figure how a of five node. The right table how their forwarding metric. A line between two node mean that they are within communication ditance. major difficulty of data forwarding i the opportunitic part, technique deigned for opportunitic network are commonly ued to forward data in MSN. However, thi may not be the bet olution ince they do not conider the divere connectivity characteritic of MSN. For example, Figure (a) how a of five node and Figure (b) how their forwarding metric which repreent the capability of forwarding data to other node (e.g., node centrality [5]). Node A ha the data which will be forwarded to the detination through carry-and-forward. Baed on exiting technique, the data hould be forwarded to a contacted node with higher forwarding metric. In thi example, node A ha two poible contact: B and E (contact are repreented by line). Since node B ha lower forwarding metric (2.) than A (3.), B will not get the data. E ha higher forwarding metric (4.5) than A, and thu A forward the data to E. After E receive the data, it contact D, which ha higher forwarding metric (5.8), will get the data. D will not forward the data to it contact B which ha a lower forwarding metric. Although C ha a much higher forwarding metric (7.) and much higher chance of reaching the detination, the data will not be forwarded toc ince it i not the contact of D (i.e., no line between them). However, ince C and A are within the ame, it i better for them to exchange their forwarding metric through multihop wirele communication, and then it will be poible for C to forward the data to the detination. In thi paper, we addre the problem of data forwarding in MSN with divere connectivity characteritic by propoing two -aware data forwarding trategie. Since a (b)

2 i a MANET, it i treated a one component and data carrier in thi are elected for opportunitic data forwarding. More pecifically, the paper ha two contribution. ) We identify the exitence of and analyze their propertie baed on five trace. We find that there are ignificant number of in MSN, and the ditribution of ize and node degree follow exponential ditribution. By treating multi-hop wirele communication inide a indirect contact, through theoretical analye, we how that the contact opportunitie can be ignificantly increaed in all trace. 2) We firt propoe a -aware data forwarding trategy to exploit to improve the performance of data forwarding in MSN. In our olution, node inide a exchange their forwarding metric through multi-hop wirele communication, and the node with the highet forwarding metric i elected to get a replicated data copy. Although the -aware data forwarding trategy can increae the data delivery ratio, it increae the data copie in the network. To addre thi problem, we enhance the -aware data forwarding by electing an optimal et of node in the to avoid overlap in their contact and maximize the data forwarding opportunity with a mall number of node. Trace-driven imulation how that our aware data forwarding trategie outperform exiting data forwarding trategie with le network overhead. The ret of the paper i organized a follow. In Section II, we identify the propertie of baed on five trace. Section III preent our -aware data forwarding trategie. In Section IV, we evaluate the performance of the aware data forwarding trategie. Section V review related work, and Section VI conclude the paper. II. TRACE-BASED ANALYSIS In thi ection, we identify the exitence of and analyze their propertie baed on five realitic MSN trace. A. Trace We tudy the propertie of baed on five trace: Social Evolution [6], Friend & Family [7], Reality Mining [8], Infocom [9] and UCSD []. Thee trace record contact among uer carrying different kind of mobile device. The firt three trace are collected by the MIT Reality group baed on Bluetooth on martphone. Among them, Social Evolution record the contact among tudent in an undergraduate dormitory. Friend & Family record the contact among member of a young-family reidential community. Reality Mining track the contact between individual in reearch lab. The trace of Infocom i collected in a conference environment by recording the contact between conference attendant carrying imote. In thee four trace, mobile device periodically detect their peer via Bluetooth interface. A contact i recorded when two mobile device move into the detection range of each other. The UCSD trace i collected at a campu cale, where the device are WiFi enabled PDA. Thee device earch for nearby WiFi Acce Point (AP), and a contact i detected when two device detect the ame AP. The detail of thee five trace are hown in Table. B. Propertie of the Contact Graph Baed on the collected trace, we can draw contact graph which conit of mobile device (node) and their contact (edge), with which we tudy the tranient contact tatu between node. A contact graph can be drawn at each time point, i.e., there i an edge between two node if they are contacting each other at that time point. Here, a contact graph i drawn every minute in the trace. The extracted contact graph have ome intereting propertie, and one of them i related to the ditribution of the node degree. The Complementary Cumulative Ditribution Function (CCDF) of the node degree P(K > k), which repreent the probability that a node ha more than k contact with other node, follow exponential ditribution for k : P(K > k) = e k k k where k i the exponential contant. By recognizing that e k k = when k =, we have the following: P(K > k) = e k k k () The plot of CCDF P(K > k) i hown in Figure 2. The CCDF curve i drawn in a emi-log plot (log cale on y axi), where the exponential ditribution i a traight line. The Leat Square approach [] i applied to fit the degree ditribution with the exponential ditribution. A we can ee in Figure 2, the degree ditribution can be well approximated by exponential ditribution in mot trace. Since the contact graph atify exponential degree ditribution, mot node have low degree. Thi i becaue the contact graph i partitioned into connected component, where node are only connected to ome node in the ame connected component, o that mot node have low degree. For example, in the trace of Friend & Family, people in the reidence community form mall connected component when they tay with their familie. In the trace of Reality Mining, tudent form connected component when they are in the ame laboratory. Since the connected component only appear temporarily, they are referred to a Tranient Connected Component (). C. Propertie of We tudy the propertie of in thi ubection. The firt property i related to Giant Connected Component (GCC), which i the larget connected component of the

3 Table I TRACE SUMMARY Trace Social Evolution Friend & Family Reality Mining Infocom UCSD Device Cell Phone Cell Phone Cell Phone imote WiFi Network type Bluetooth Bluetooth Bluetooth Bluetooth WiFi Number of device Number of contact 2,84 47,774 78,98 26,84 94,68 Start Date Duration(day) Granularity(ec) P(K>k) 2 3 P(K>k) 2 3 P(K>k) 2 3 P(K>k) 2 3 P(K>k) Degree ditribution k (a) Social Evolution (k =.7) Figure 2. 4 Degree ditribution k (b) Friend & Family (k =.4) 4 Degree ditribution k (c) Reality Mining (k =.55) 4 Degree ditribution k (d) Infocom (k = 4.3) The ditribution of node degree can be approximated by exponential ditribution. k i the exponential contant. 4 Degree ditribution k (e) UCSD (k = 2.36) contact graph. The GCC i commonly ued to repreent the overall connectivity of the network [2], and the property of the GCC i cloely related to the degree ditribution [3][4][5]. Baed on [5], in a random graph with exponential degree ditribution, if the exponential contant k i larger than, there exit a GCC with ize that cale linearly with the ize of graph. With the increae of k, the ize of the GCC alo increae. Since k in all trace i larger than a hown in Figure 2, we can expect that there are large GCC inide thee contact graph. Proportion of node in larget evolu family mining infocom ucd Figure 3 ue boxplot to how the ize of the larget a a proportion of the network ize, where the larget i detected every minute. Here, the network ize i the number of node that are in contact with ome node in the trace. Other node may turn off their device or contact node that are not included in the trace, and thee node are not included in our analyi. The reult in Figure 3 are conitent with our concluion that a larger exponential contant k lead to a larger GCC. For example, the Family & Friend trace ha the mallet GCC, becaue k i only lightly larger than. For the trace of Infocom, k i larger than 4 and thu mot node belong to the GCC. Even though the ize of GCC implie the overall connectivity of the network, it i not enough to characterize the complete tructure. Therefore, we are alo intereted in the ditribution of the ize. Figure 4 plot the ditribution of the ize. Similar to the degree ditribution, the ize ditribution alo follow exponential ditribution. The CCDF of the ize P(S > ) i exponential for Figure 3. The boxplot of the larget ize a a proportion of the network ize. The middle line inide the box repreent the median. The lower and upper edge of the box repreent the 25th and 75th percentile, repectively. 2: P(S > ) = { e 2 where i the exponential contant. In the Infocom trace, it only follow exponential ditribution when the ize i mall, i.e., in the range of [2,]. Thi i due to the fact that the network alway conit of a GCC which include mot of the node in the trace, and then the remaining node only form mall. D. Increaing Contact with Inide a, node can communicate with each other through multi-hop wirele communication, which i much fater than carry-and-forward. Therefore, a long a two (2)

4 P(S>) 2 3 P(S>) 2 3 P(S>) 2 3 P(S>) 2 P(S>) ize ditribution (a) Social Evolution ( = 5.25) Figure 4. 4 ize ditribution (b) Friend & Family ( =.52) 4 ize ditribution (c) Reality Mining ( = 3.3) 3 ize ditribution (d) Infocom ( = 2.86) 4 ize ditribution (e) UCSD ( = 4.9) The ditribution of ize can be well approximated by exponential ditribution for mot of the trace. i the exponential contant. node are within a, they have -contact, which can be direct contact or indirect contact. Direct contact are contact between two node within one-hop communication ditance, and indirect contact are contact between node within the ame but multi-hop away. Next, we analyze whether and how contact opportunitie are increaed by conidering -contact. We find that the increae of contact opportunitie i related to the ditribution of node degree and ize. Specifically, we have the following theorem: Theorem. For a contact graph, the contact opportunitie can be increaed by conidering -contact, if the following two condition are atified: ) The ditribution of node degree and the ditribution of ize both follow exponential ditribution with exponential contant k and repectively. (Equation () and (2)) 2) k < ˆk, where ˆk i a function of : ˆk = log 2 (e +( e ) 3 ) 3e +( e ) 3 (3) The proof of Theorem can be found in Appendix A. In the proof, we alo compute the ratio of -contact to direct contact, which can be determined by two exponential contant k and : m t = 2( e k ) e +( e ) 3 e +( e ) 3 (4) where m t i the number of -contact and i the number of direct contact. With Equation (4), we can etimate the increaed contact opportunitie a long a the node degree and ize follow exponential ditribution. Table II lit k, and the value of ˆk in term of in four trace. (Since ize ditribution for Infocom trace doe not follow exponential ditribution, we do not include it in Table II.) We alo did experiment to verify if the etimated value mt m in Equation (4) (i.e., Et. t ) i conitent with the actual value (i.e., Act. mt ) and the reult are hown in Table II. A can be een, k i maller than ˆk in all trace, which indicate that the contact opportunitie are increaed in all thee trace. We alo find that the error of the etimated mt compared to the actual value i very mall, which prove the accuracy of our etimation. Thu, with the ditribution of node degree and ize, we can accurately etimate the increae of contact opportunitie. We alo did experiment on the Infocom trace and find the m t i 8.86, which indicate that the contact opportunitie are alo ignificantly increaed in the Infocom trace. Table II -CONTACTS & DIRECT CONTACTS Trace k ˆk Et. m t Act. Social Evolution Friend & Family Reality Mining UCSD E. Duration of -contact m t Becaue are formed when node have direct contact with each other, the duration of the -contact i maller than the duration of direct contact. The comparion between the duration of -contact and direct contact in five trace are hown in Figure 5. A hown in the figure, even though -contact have maller duration than direct contact, the median duration for -contact are till everal minute. Therefore, by conidering both direct and indirect contact inide, contact opportunitie are increaed and the contact duration are till pretty long. Thi property can be exploited to deign more efficient data forwarding trategie, a hown in the next ection. III. -AWARE DATA FORWARDING STRATEGIES Data forwarding in MSN i difficult due to the opportunitic nature of the network. Being aware of the exitence of, we propoe better data forwarding trategie by utilizing the contact opportunitie in. Inide a, node can reach each other by multi-hop wirele communication, which will ignificantly increae the contact opportunitie and increae the chance of forwarding data to the detination. In thi ection, we firt preent our

5 CDF.6.4 CDF.6.4 CDF.6.4 CDF.6.4 CDF Direct Contact Contact min minhour day week Contact Duration (a) Social Evolution.2 Direct Contact Contact min minhour day week Contact Duration (b) Friend & Family Figure 5..2 Direct Contact Contact min minhour day week Contact Duration (c) Reality Mining The duration of direct contact and -contact..2 Direct Contact Contact min minhour day week Contact Duration (d) Infocom.2 Direct Contact Contact min minhour day week Contact Duration (e) UCSD -aware data forwarding trategy and then improve it performance with an enhanced verion. A. -Aware Data Forwarding Strategy The objective of the -aware data forwarding trategy i to utilize to improve the performance of data forwarding in MSN. ) Identifying the : To utilize, the firt tep i to identify the. A node can detect the node in the current by broadcating inide the, and each node receiving the broadcat end an acknowledgement. In order to know what data item are being forwarded in the current, the node receiving the broadcat meage replie with an acknowledgement that carrie the information about the data item in it buffer. By collecting acknowledgement from other node in the, the original ender can identify the node and the forwarded data in it current. 2) Forwarding Strategy: For each data item created in the network, we intend to forward it from the ource to the detination within the time contraint T. In the proce of forwarding, data can be replicated and forwarded to other node in accordance with pecific trategie. The forwarding i ucceful a long a one data copy of thi data item arrive at the detination. Strategie deigned for opportunitic network are commonly ued to forward data in MSN. Thee data forwarding trategie are normally baed on ocial-aware forwarding metric uch a centrality [3][6][5], which quantify mobile node capability of contacting other in the network. When a node carrying data contact another node, the data packet i forwarded baed on Compare-and-Forward [3][2]; i.e., the data carrier forward the data if and only if the contacted node ha a higher centrality and doe not have thi data. The original data carrier alo keep a copy after forwarding the data. In our -aware data forwarding trategy, a imilar method i ued. However, the data forwarding deciion are not limited to thee two contacted node, but among all node in the. A can be formed by merging two exiting or by adding new node to exiting. For example, node A and B are originally from two different before they contact. After A contact B, the two are merged to a new. Algorithm -aware Data Forwarding Strategy when two Node A,B Contact : M A the et of node in A original 2: M B the et of node in B original 3: if M A = M B then 4: Do NOTHING /*A,B are already in the ame */ 5: ele /*Forwarding deciion inide the will be made centrally at a command node C, which i choen from A,B.*/ 6: if M A M B then 7: C B 8: ele 9: C A : end if /*C will identify the node inide the new and the data item carried by them, and make the forwarding deciion.*/ : M M A MB 2: D the et of unique data item in the new 3: for each data ite D do 4: if M include d detination then 5: d i forwarded to d detination 6: Go to next data item 7: end if 8: H d node with the highet centrality 9: if H d ha the data then 2: Do NOTHING and go to the next data item 2: ele 22: d i forwarded to H d 23: end if 24: end for 25: end if When two node contact, they firt check if they are already in the ame before the contact. They detect their original by uing broadcat meage decribed in Section III-A. A long a one of them receive acknowledgement from the other one, they are already within the ame. If the detected are different for the two node, thee two are merged when they contact. After a new i formed, a imple compare-andforward trategy i ued to forward data to node with higher centrality. However, thi trategy create many data copie. To reduce redundancy, data i only forwarded to the node with the highet centrality, and then at mot one additional data copy i created in a.

6 B (a) A C A B C A B C D E F G Figure 6. The left figure how a of three node. The right table how their contact probabilitie with all other node in the network. The network ha 7 node. 3) The Algorithm: The whole proce of the -aware data forwarding trategy i outlined in Algorithm. When two node contact, they firt check if they belong to the ame (Line 3 5). If o, the data packet ha already been forwarded in the. If not, a new i formed, and the data item will be forwarded inide the new. The forwarding deciion inide thi i made centrally at a node, denoted a command node, which i choen from the two node in contact. The node that ha more node in it original will be the command node (Line 6 ). Then, the command node check all the node and their carried data item in the new and make the forwarding deciion (Line 24). Specifically, for each data item that exit in the, if it detination i inide thi, it i directly forwarded to the detination (Line 4 7). Otherwie, the command node check if the node with the highet centrality ha the data. If the node ha the data, nothing need to be done. Otherwie, one data copy i created and forwarded to the node with the highet centrality (Line 8 23). B. -aware Data Forwarding Strategy ) Motivation: In the -aware data forwarding trategy, the node with the highet centrality in the get one data copy and the original data carrier alo keep their data copie. However, thi may not be the bet option in many cae. For example, Figure 6 (a) how a of three node and Figure 6 (b) lit each node contact probability with other in the network. If the centrality metric i baed on the Cumulative Contacting Probability (CCP) [5], the CCP of node A i = 3.8, the CCP of node B i = 3.3, and the CCP of node C i = 3.3. If C originally carrie the data, A will receive one data copy ince it ha the highet centrality. However, node A and C have imilar contact probabilitie to other node B, D, E, F and G; i.e., they both have high contact probabilitie with B, D and E, and low contact probabilitie with F and G. Thu, keeping data copie at A and C may not help too much ince their contact capabilitie to other node have a large overlap. (b) To deal with thi problem, it i better to chooe A and B a data carrier, becaue B ha high contact probabilitie to F and G, which i complement to A. Even though B and C have the ame CCP centrality, uing A and B a data carrier i better than uing A and C. Thu, we propoe an enhanced -aware data forwarding trategy, where data carrier are elected to maximize the data forwarding opportunity. Different from the previou -aware data forwarding trategy, which elect the highet centrality node a the data carrier, we elect a et of node a data carrier which can maximize the data forwarding opportunity. 2) Set Centrality: We firt define the concept of et centrality to quantify the data forwarding capability of a et of node. Given that a data item i created at time, and expire at time T, the et centrality of a node et S at time t i defined a follow: Definition. The et centrality of a node et S at time t < T i defined a the ummation of their overall probability to contact each of the remaining node in N \S before time T. C S (t) = ( p ji (T t))) (5) i N\S( j S where N i the et of node in the network, and p ji (T t) i the probability that node j will contact node i within time T t. Aume ymmetric contact between node i and j, and then we have p ij (T t) = p ji (T t). Since the inter-contact time between node i and node j ha been experimentally validated in [5] to follow an exponential ditribution with rate parameter λ ij, the probability that the two node will contact before T can be calculated a: p ij (T t) = p ji (T t) = e λij(t t) (6) 3) Selecting the Optimal Set of Data Carrier: We aume that there are k data copie carried by node in a (denoted a M), where k < M. Our objective i to chooe an optimal node et S of ize k with the highet et centrality, where S M. Node in the optimal node et S will be the new data carrier for the k data copie. The detection of the optimal node et S can be formalized a an optimization problem: max ( x i )( ( x j p ji (T t))) (7) i N j N.t. x i {,}, i M (8) x i =, i N \M (9) x i = k () i M where x i {,} indicate whether the node i i elected to the optimal node et S. Formula (7) maximize the et centrality of the elected node. Since S i elected

7 Algorithm 2 -aware Data Forwarding Strategy when two Node A,B Contact : M A the et of node in A original 2: M B the et of node in B original 3: if M A = M B then 4: Do NOTHING /*A,B are already in the ame */ 5: ele /* Forwarding deciion inide the will be made centrally at a command node C, which i choen from A,B.*/ 6: if M A M B then 7: C B 8: ele 9: C A : end if /*C will identify the node inide the new and the data item carried by them, and make the forwarding deciion.*/ : M M A MB 2: D the et of unique data item in the new 3: for each data ite D do 4: if M include d detination then 5: d i forwarded to d detination 6: Go to next data item 7: end if 8: k d number of copie of d in M 9: H d node with the highet centrality 2: if H d doe not have d then 2: k d k d + /*Add one extra data copy*/ 22: end if 23: S the optimal et of k d node /*Decide S a dicued in Section III-B3.*/ /*Node in S will be new carrie of data ite.*/ 24: Data are forwarded from old carrier to node in S 25: end for 26: end if from M, x i {,} for node inide M, and x i = for node outide of M a depicted in (8) and (9) repectively. Formula () indicate that the number of elected node i k. The optimization problem can be eaily olved uing dynamic programming imilar to the knapack problem [7]. With dynamic programming, the optimization problem can be olved with time complexity O( M ). 4) Forwarding Strategy: The enhanced -aware data forwarding trategy i preented in Algorithm 2. Baed on the original -aware data forwarding trategy, the enhanced -aware data forwarding trategy add an extra tep that reditribute data copie inide a (Line 8 24). Similar to the original trategy, one extra data copy i created if the node with the highet centrality doe not have the data (Line 8 22). Then, all data copie are reditributed in the, to make ure they are carried by a et of node with the optimal forwarding capability. Specifically, the command node firt compute an optimal node et with the highet et centrality uing the method in Section III-B3 (Line 23). Afterward, the data copie are forwarded from the original data carrie to the node in the optimal node et (Line 24). IV. PERFORMANCE EVALUATIONS In thi ection, we evaluate the performance of the aware data forwarding trategie by comparing them with exiting data forwarding trategie. A. Strategie in Comparion In -aware data forwarding trategie ( and Enhanced ), the centrality metric i baed on the Cumulative Contacting Probability (CCP) [5]. We compare it with the following four forwarding trategie: Compare-and-Forward: When a node carrying data contact a node without data, the data carrier forward the data if the contacted node ha a higher forwarding metric (CCP). Thi trategy ha been utilized in [3][2][8]. [9]: Upon contact, the data carrier forward data to the contacted node if it doe not have the data. Thi method ha the bet data delivery ratio but the highet network overhead, which i ued a the upper bound. : The data ource doe not forward data to any other node until it contact with the detination. Thi trategy ha the wort delivery ratio but the lowet network overhead, which i ued a the lower bound. R3 [2]: R3 elect a fixed number of forwarding path which together achieve the minimum expected delay. For each path, one data copy i created and then forwarded along the path. B. Simulation Setup We evaluate all the data forwarding trategie baed on the five trace lited in Table I. For each trace, the firt half i ued for warm up, baed on which we determine the centrality metric, and the econd half i ued to evaluate the performance. For each data item, the ource and detination are picked randomly, and the data generation time i randomly choen in the daytime, ince node activity remain low at night and high at daytime. The experiment i repeated time for tatitical convergence. C. Simulation Reult Figure 7 and Figure 8 compare -aware data forwarding trategie ( and ) and other exiting trategie. Figure 7 how data delivery ratio under different time contraint, where delivery ratio i the proportion of data item uccefully delivered to the detination before data expire. Figure 8 how the network overhead under different time contraint, where the overhead i meaured by the average number of data copie created in the network for each data item. Generally peaking, -aware data forwarding trategie have the bet performance by achieving comparable delivery ratio with with much lower network overhead. In the following, we dicu the

8 Delivery Ratio (a) Social Evolution Delivery Ratio (b) Friend & Family Figure 7. Delivery Ratio (c) Reality Mining Delivery Ratio (d) Infocom Comparion baed on data delivery ratio under different time contraint. Delivery Ratio (e) UCSD Overhead 5 5 Overhead 5 5 Overhead Overhead Overhead (a) Social Evolution Figure (b) Friend & Family (c) Reality Mining (d) Infocom Comparion baed on the number of data copie (overhead) created under different time contraint (e) UCSD imulation reult in detail by firt comparing our aware data forwarding trategie with Compare-and-Forward and R3, and then comparing thee two -aware data forwarding trategie. ) Comparion with Compare-and-Forward: A hown in Figure 7 and Figure 8, both and have better performance than Compare-and-Forward, with higher data delivery ratio and lower network overhead. Specifically, the -aware data forwarding trategie achieve % 4% higher delivery ratio than Compare-and-Forward in all five trace. Thi i becaue by utilizing the contact opportunitie inide, node have higher probabilitie to forward data to the detination. Moreover, the aware data forwarding trategie conume 5% 5% le network overhead than Compare-and-Forward. The decreae in network overhead i becaue there i at mot one data copy created when a new i formed. However, in Compare-and-Forward, data copie can be created upon every contact. Thee reult demontrate the effectivene of -aware data forwarding trategie when compared with trategie deigned for opportunitic network. 2) Comparion with R3: To compare with R3 [2], we et the number of data copie (path) to be 5 (R3-5copie) in R3. A hown in Figure 7, the data delivery ratio for R3 with 5 copie i about 5% le than that of and. We alo tet R3 with copie and 2 copie, and find that the delivery ratio doe not increae much a the number of data copie increae. Thi obervation i conitent with the reult in [2], which alo found that the performance doe not increae much a the number of data copie i larger. The reaon for R3 low delivery ratio i that R3 i baed on ource routing, and ource routing i extremely time-conuming when utilized in network with opportunitic feature. MSN with divere connectivity characteritic have the opportunitic feature, becaue node contact opportunitically outide of. 3) Comparion between the -aware data forwarding trategie: From Figure 7 and Figure 8, we can alo ee that conume le overhead than but achieve imilar delivery ratio. Thi i becaue, in the enhanced trategy, data copie can be forwarded from the original data carrier to a et of node with the highet et centrality; i.e., more effective node are elected a data carrier. By chooing a mall number of effective node to carry data, the enhanced trategy require le data carrier than the original trategy. Therefore, the number of data copie can be decreaed with. V. RELATED WORK Data forwarding algorithm deigned for opportunitic network are commonly ued to forward data in MSN, and mot of them are baed on [9], where data i flooded upon contact with other node. Later olution attempt to reduce the number of data copie created by, and thee trategie are known a controlled flooding [2]. For example, Compare-and-Forward i commonly ued to control the data copie created, and the data carrier only forward data to another node with higher forwarding metric. The forwarding metric meaure node capability of forwarding data to the detination. In ome reearch, the forwarding metric are determined baed on node contact probability with the detination, uch a PROPHET [2] and MaxProp [22]. By further aggregating the network to ocial graph [23], ocial propertie of mobile node are analyzed.

9 Baed on thi, node centrality [6][8][24] or community baed olution[25][26][27][28] are ued a forwarding metric. However, thee trategie are deigned for opportunitic network, and they are not the bet olution for MSN with divere connectivity characteritic, ince they neglect the multi-hop communication opportunitie inide. Phe-Neau et al. conidered the multi-hop communication opportunitie around a node vicinity in [29]. However, in their approach, multi-hop communication opportunitie around a node are only utilized when the detination of the data i within the node vicinity, which i baically a WAIT trategy and then it doe not contain mechanim to make the data reach more node and get cloer to the detination. The work by Tie et al. [2] conidered data forwarding in network with divere connectivity characteritic; however, their olution i different from our and they do not conider the effect of. They identified packet replication to be the key difference between protocol deigned for wellconnected network and parely-connected network, and deigned a routing protocol called R3, which determine the number of data copie to be created baed on the predicted delay along network path. R3 i baed on ource routing; i.e., data copie are forwarded along the pre-determined forwarding path. However, protocol baed on ource routing are not uitable for network with opportunitic feature, becaue it i extremely time-conuming to forward data along the pre-determined path. Moreover, R3 doe not conider the effect of on data forwarding, which i the key contribution of our work. Other exiting algorithm try to modify well-known MANET protocol to make them more adaptive to network with divere connectivity characteritic. For example, Raffelberger et al. [3] integrated tore-and-forward to MANET protocol. In MANET protocol, a data item i dropped when the routing table doe not contain an entry for the detination. With tore-and-forward, data i buffered until a route to the detination can be found uing MANET protocol. However, their olution lack a mechanim to chooe effective data carrier to deliver data to detination. There exit ome work on analyzing. For example, [3] demontrated the ize of the giant connected component change over time. [3][4][5] proved that the property of connected component i cloely related with the ditribution of node degree. However, they did not examine the detailed tructure of uing real trace, and did not conider how to ue them to increae the contact opportunitie and improve the performance of data forwarding, which i the focu of our work. VI. CONCLUSIONS In thi paper, we deigned efficient data forwarding - trategie for MSN with divere connectivity characteritic, by exploiting the exitence of. We firt identified the exitence of and analyzed their propertie baed on five trace. By treating multi-hop wirele communication inide a indirect contact, through theoretical analye, we howed that the contact opportunitie can be ignificantly increaed in all trace. Baed on thi obervation, we deigned a -aware data forwarding trategy to improve the performance of data forwarding in MSN. Then, we enhanced the -aware data forwarding by electing an optimal et of node in the to avoid overlap in their contact and maximize the data forwarding opportunity with a mall number of node. Trace-driven imulation howed that our -aware data forwarding trategie outperform exiting data forwarding trategie with le network overhead. REFERENCES [] S. Ioannidi, A. Chaintreau, and L. Maoulié, Optimal and calable ditribution of content update over a mobile ocial network, in IEEE INFOCOM, 29. [2] A. Lindgren, A. Doria, and O. 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