fairness-related challenges in mobile opportunistic networking

Computer Networks 57 (2013) 228–242

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Computer Networks

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Fairness-related challenges in mobile opportunistic networking

Abderrahmen Mtibaa ⇑, Khaled A. HarrasSchool of Computer Science, Carnegie Mellon University, Doha, Qatar

a r t i c l e i n f o

Article history:Received 28 November 2011Received in revised form 24 May 2012Accepted 29 August 2012Available online 21 September 2012

Keywords:FairnessMobile opportunistic networksSocial networksRank-based opportunistic forwardingFairness efficiency trade-off

1389-1286/$ - see front matter � 2012 Elsevier B.Vhttp://dx.doi.org/10.1016/j.comnet.2012.08.019

⇑ Corresponding author. Tel.: +974 3315 9831.E-mail addresses: [email protected] (A. Mtibaa)

(K.A. Harras).

a b s t r a c t

The fundamental challenge in opportunistic networking, regardless of the application, iswhen and how to forward a message. Rank-based forwarding techniques currently repre-sent one of the most promising methods for addressing this message forwarding challenge.While these techniques have demonstrated great efficiency in performance, they do notaddress the rising concern of fairness amongst various nodes in the network. Higher rankednodes typically carry the largest burden in delivering messages, which creates a highpotential of dissatisfaction amongst them. In this paper, we adopt a real-trace drivenapproach to study and analyze the trade-offs between efficiency, cost, and fairness ofrank-based forwarding techniques in mobile opportunistic networks.

Our work comprises three major contributions. First, we quantitatively analyze thetrade-off between fair and efficient environments. Second, we demonstrate how fairnesscoupled with efficiency can be achieved based on real mobility traces. Third, we proposeFOG, a real-time distributed framework to ensure efficiency–fairness trade-off using localinformation. Our framework, FOG, enables state-of-the-art rank-based opportunistic for-warding algorithms to ensure a better fairness–efficiency trade-off while maintaining alow overhead. Within FOG, we implement two real-time distributed fairness algorithms;Proximity Fairness Algorithm (PFA), and Message Context Fairness Algorithm (MCFA).Our data-driven experiments and analysis show that mobile opportunistic communicationbetween users may fail with the absence of fairness in participating high-ranked nodes,and an absolute fair treatment of all users yields inefficient communication performance.Finally our analysis shows that FOG-based algorithms ensure relative equality in the distri-bution of resource usage among neighbor nodes while keeping the success rate and costperformance near optimal.

� 2012 Elsevier B.V. All rights reserved.

1. Introduction

Mobile opportunistic networks interconnect nodes withheterogeneous contact rates, unpredictable mobility, andlimited resources. These mobile nodes communicaterelying on both the transport of messages as well asmulti-hop forwarding. Current forwarding techniques[3,28,19,21,9] are generally designed to efficiently selectthe most likely relay nodes to deliver a message to its des-tination. Within those techniques, rank-based forwarding

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[5,7,8] represents one of the most promising methods foraddressing this message forwarding challenge. This meth-od differs in the type of information used (e.g., informationacquired during contacts [7,8], or social interaction be-tween users [5,26,25]), as well as how it is used to ranknodes in the network. A node with a lower rank will for-ward messages to nodes with higher ranks. This solution,however, creates a high potential of dissatisfaction amonghigh ranked nodes that carry a heavier burden compared toothers.

Ensuring fairness in mobile opportunistic networks is acrucial goal if rank-based forwarding algorithms are to beadopted in the future. In such networks, nodes can behaveselfishly and try to only maximize their own utility

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A. Mtibaa, K.A. Harras / Computer Networks 57 (2013) 228–242 229

without considering the global system-wide performance.Previous studies have considered absolute fair allocationof user resources [1,18,20]. They have shown that absolutefairness results in higher end-to-end delivery delays. Theydid not, however, investigate trade-offs between the threemetrics in mobile opportunistic networks: fairness, end-to-end delay, and cost.

Throughout this paper, we adopt a real-trace driven ap-proach to quantitatively analyze those trade-offs, and pro-pose a real-time distributed framework for fairness inopportunistic networking. Our initial conference versioncompromises three major contributions [23]. First, wequantitatively analyze the impact of an absolute fair andefficient environment on the overall network performance.We show that in an unfair environment, selecting prefer-ential or popular relay nodes in forwarding decisions isefficient and yields enhanced forwarding performance.We also show that absolute balancing across nodes causessignificant end-to-end delay and severe performance deg-radation. Second, we study the trade-off between fairness,cost, and efficiency in opportunistic forwarding. Relying onour experimental datasets, we consider an offline approachto construct forwarding paths while ensuring both fairnessand efficiency, and keeping the cost near optimal. Finally,we adopt a distributed real-time approach in order to effi-ciently discover these paths while utilizing only local infor-mation. We call our distributed real-time framework FOG(Fairness-based Opportunistic networkinG framework).

This journal paper adds the following contributions be-yond the conference version [23]:

� We address fairness in opportunistic forwarding moregenerally. While [23] focuses mainly on a trade-offbetween fairness and success delivery ratio, this paperextends that work to quantitatively study differenttrade-offs between fairness, cost and success rate. Costmetric is addressed after identifying the best perform-ing algorithms with respect to the other two metrics.We look then at the cost within those nominal values.We investigate fairness issues of social-based forward-ing algorithms such as PeopleRank [25] and Simbet [5]and contact-based forwarding such as Frequency (FR)[8] and Last Contact (LC) [7].� We propose two real-time distributed algorithms to

maximize the overall user satisfaction; Proximity Fair-ness Algorithm (PFA) and Message Context FairnessAlgorithm (MCFA). While the main advantage of PFAis its simplicity which allows any rank-based forward-ing algorithm to ensure minimum fairness withoutusing additional network information, MCFA uses localinformation to make better forwarding decisions. MCFAenables cost performance aware message delivery inmobile opportunistic networks taking into account thenumber of message replicas in the network and usersatisfaction of each message. We model the satisfactiona node derives from sending and receiving messages asa utility function. MCFA uses such utility in order toavoid overwhelming the popular nodes with many mes-sages when other nodes can deliver them to theirs des-tination in a bounded delay.

� Our real-trace analysis includes additional results thatrely on a modified San Francisco [27] dataset that we syn-thesize. We show that FOG uses the two fairness algo-rithms PFA and MCFA to ensure relative equality in thedistribution of resource usage among neighbor nodeswhile maintaining a high delivery success rate, and costperformance close to optimal. While the simplicity isconsidered as one of PFA algorithm strengths, we showthat MCFA uses fewer message replicas compared toPFA and achieves a better success delivery ratio.

The remainder of this paper is organized as follows. Sec-tion 2 provides a brief overview of rank-based forwardingtechniques and the experimental datasets used throughoutthe paper in our analysis. Section 3 discusses the fairnessvs. efficiency vs. cost trade-offs. In Section 4 we adopt anoffline approach to construct forwarding paths whileensuring both fairness and efficiency. In Section 5, we pro-pose a real-time distributed framework for fairness inopportunistic networking, and implement two distributedalgorithms that enable existing rank-based forwardingalgorithms to be both fair and efficient. We then presentthe related work in the field of fair and efficient mobileopportunistic forwarding in Section 6. Finally, Section 7concludes the paper.

2. Background and datasets

In this section we provide a brief overview on rank-basedforwarding algorithms, and present the experimental data-sets used in our analysis to improve the fairness of these for-warding algorithms. We also briefly describe our evaluationmethodology used throughout the whole paper.

2.1. Rank-based forwarding algorithms

Rank-based forwarding techniques represent one of themost promising methods for message forwarding in oppor-tunistic networks. They differ in the type of informationused, as well as how it is used, in order to rank nodes inthe network. We distinguish between two type of rank-based forwarding techniques; contact-based ranking tech-niques, where information learned during contact, are usedto rank nodes, and social-based ranking techniques, wheresocial interactions between users are used to rank the nodes.

2.1.1. Social-based ranking algorithmsThere are three well-known social-based ranking tech-

niques that differ in the type of social metric used:

� Degree-based forwarding: Consists of forwarding mes-sages to socially well connected nodes. Paths are thenconstructed according to a non-decreasing social nodedegree rule (more details in [22]).

� Centrality-based forwarding: The main idea behind thistechnique is that central nodes in social graphs aremore likely to socialize with other people and thereforemore likely to forward messages (more details in[5,22]).

230 A. Mtibaa, K.A. Harras / Computer Networks 57 (2013) 228–242

� PeopleRank: This is a distributed algorithm which ranksnodes in the social graph similar to what PageRank [2]does for web pages – i.e., it measures the relative‘‘importance’’ of a node in the social graph (more detailsin [25]).

2.1.2. Contact-based ranking algorithmsIn the following, we present two of the most well-

known contact-based ranking algorithms in the literature.As the name indicates, they use locally available contactinformation to rank nodes and decide whether to forwarda message when two nodes meet. These two algorithmsare:

� Last_Contact (LC): Node i forwards messages to node j if jhas contacted any other node more recently than node i[7].

� Frequency (FR): Node i forwards the message to node j ifj has more total contacts than node i [8].

2.2. Experimental datasets

Our analysis is based on two datasets collected inconference environments [22,4]. In addition to humanmobility information, our datasets contain social relation-ships between the experimentalists. A summary of thecorresponding parameters is provided in Table 1.

2.2.1. CoNext07This dataset [22] contains mobility information and

information about the social relationship between theparticipants during the ACM CoNext 2007 conference.During the experiment, the social networking applicationindicated when a contact, or a contact of a contact, waswithin Bluetooth range/neighborhood. This connectionneighborhood was then displayed on the user’s devicewhich in turn could add new connections or delete existingconnections based on the physical interaction consequentto the application notification.

2.2.2. Infocom06This dataset [4] was collected with 78 participants dur-

ing the IEEE Infocom 2006 conference. Experimentalistswere asked to carry an experimental device (called iMote)with them at all times. These devices were logging all con-tacts between participating devices. Questionnaires weregiven to participants to fill theirs nationalities, languages,

Table 1Characteristics of our experimental datasets.

CoNext07 Infocom06

Duration 3 days 3 daysMobility patterns Bluetooth contacts Bluetooth contactsSocial patterns MobiClique app. [22] Facebook# Connected nodes 27 47# Edges 115 219Average degree 9.5 9.3Social diameter 4 4Median inter-contact 10 min 15 minMedian contact time 240 s 150 s

countries, cities, academic affiliations and topic of inter-ests. Based on the forms filled by the experimentalists(e.g., fist name, last name, interests, nationalities,affiliation, etc.), we consider, in this paper, a social graphbased on users Facebook friendship graph (obtained offlineand connecting all the experimentalists). Note that we didnot use the implicit social information (i.e., based oninterests and affiliation) which is publicly available inCRAWDAD.1

2.3. Evaluation methodology

In mobile opportunistic networks, we are generallyinterested in delivering data among a set of N mobile wire-less nodes. Communication between two nodes is estab-lished when they are within radio range of each other.Data is forwarded from source to destination using theseopportunistic contacts. We model the evolution of contactsin the network by a time varying graph GðtÞ ¼ ðV ; EðtÞÞwith N ¼ jV j. We assume that the network starts at timet0 and ends at time T (T can be infinite). We call this tem-poral network the contact graph. Each GðtÞ describes thecontacts between nodes existing at time t. Such a time-varying graph model can be obtained from a mobility/con-tact trace or from a mobility model along with knowledgeof radio properties (e.g., radio range).

We evaluate the performance of forwarding algorithmsrelying on the two previously described data sets; CoN-ext07 and Infocom06. In our evaluation, used for dataanalysis throughout the whole paper, we compute thesequence of optimal paths found between any source anddestination in the data set. From the sequence ofdelay-optimal paths we deduce the delay obtained by theoptimal path at all time. We uniformly combine all theobservations of a trace among all sources, destinations,and for every starting time (the time in seconds whenthe message M was generated by the source node S). In thispaper, we assume that contacts between any two nodesare long enough to transfer a message successfully. Wepresent this aggregated sample of observations via itsempirical CDF. The detailed computation process couldbe found in [4]. Compared with previous generalizedDijkstra’s algorithm, this algorithm directly computesrepresentation of paths for all starting times.

3. Fairness vs. efficiency vs. cost

Our discussion of trade-offs between efficiency, fairness,and cost highly depends on the definition of those threemetrics. In this section, we first define these terms. Wethen quantitatively motivate the need to investigate thesetrade-offs using real experimental traces.

3.1. Definitions

In this paper, we let ‘‘efficiency’’ denote the successfuldelivery ratio of a given forwarding algorithm within tseconds. Higher efficiency means shorter end-to-end delay

1 http://crawdad.cs.dartmouth.edu/meta.php?name=cambridge/haggle.

http://crawdad.cs.dartmouth.edu/meta.php?name=cambridge/haggle


and more successful message delivery in the network; highsuccess rate within a short delay.

We aim to have fairness act as a means to provide userswith incentives to collaborate in mobile opportunisticcommunication – i.e., encourage them to contribute to for-warding messages and remaining longer in the network.This perception to fairness is localized to neighbors; ensur-ing fairness within node vicinity even if distant nodes incurless cost. We therefore define ‘‘fairness’’ as the relativeequality in the distribution of resource usage among neigh-boring nodes in the network – i.e., a forwarding algorithmis ‘‘fair’’ if the capacity assignment of a given node n in thenetwork is equivalent to those of n’s neighbors.

Cost, in addition to efficiency, is a very importantevaluation metric in opportunistic networks. Most mobiledevices have limited storage and energy supplies. Store-carry-forward algorithms heavily consume these re-sources, by sending, receiving, storing messages, and byperforming computation. We define the ‘‘cost’’ of a for-warding algorithm as the fraction of contacts involved inthe forwarding process as compared to flooding.

3.2. Trade-offs

We show in Fig. 1 a summary of previous studies on thefields of fairness, cost and efficiency in mobile opportunisticnetworks. Researchers give more attention to cost–fairness,and cost–efficiency trade-offs, while the trade-off betweenefficiency and fairness remains largely unexplored. Withinthe proposed solutions, rank-based forwarding representsone of the most promising methods for addressing thecost–efficiency trade-off. This solution, however, creates ahigh potential of dissatisfaction among high ranked nodesthat carry a heavier burden compared to others. Therefore,rank-based forwarding does not address the rising concernof fairness amongst various nodes in the network. An abso-lute fair treatment of users, however, causes significantend-to-end delay and message delivery performance deg-radation [1,18,20]. Consequently, there is no technique todate that ensures both fairness and efficiency. It is thereforeimportant to consider whether there exists a tradeoff

u

Fig. 1. Fairness–efficiency–cost trade-offs in mobile opportunistic communicatiodifficult solutions and the unwanted solutions. Our goal is to fall under the desi

relation between fairness and efficiency. In this paper, weanswer the following question in Section 4: Can we fall inthe desired trade-off region of Fig. 1?

3.3. Towards absolute efficiency

As discussed earlier, efficiency deals with high potentialof dissatisfaction among popular nodes. Let us assume thatdissatisfied nodes decide to boycott the forwarding pro-cess. We show the impact that these popular nodes haveon network performance when they refrain from messageforwarding; popular node may be relevant but not criti-cally needed for a network.

Methodology: We use a technique, which we call ‘‘noderemoval’’, to exclude nodes from the forwarding process.We study the impact of excluding popular nodes on theoverall forwarding performance by applying this techniqueto the mobility traces we have. We also studied the impactof removing the unpopular nodes. From the sequence ofdelay-optimal paths in our datasets, we deduce the delayobtained by the optimal path at all time. We uniformlycombine all the observations of a trace among all sources,destinations, and for every starting time (the time in sec-onds when the message m was generated by the sourcenode S). We present this aggregated sample of observa-tions via its empirical CDF. We plot the success rate of for-warding algorithms normalized by the success rate offlooding within a given message delivery delay. This tech-nique is a relevant metric used to analyze forwarding algo-rithms and is discussed with more details in [4].

Fig. 2 plots the empirical CDF of the normalized deliverysuccess rate of the PeopleRank forwarding algorithm (anexample of an efficient rank-based forwarding algorithmaccording to [25]). We have observed similar results forLC and FR algorithms. Fig. 2a shows the impact of exclud-ing the most popular nodes on the successful delivery rate;10% popular removal consists of removing the 10% mostpopular nodes from the original dataset (here, popularityrank is given by the PeopleRank values [25]). We show thatmobile communication between users may fail with theabsence of fairness, where excluding the most popular

n. Plain gray area and the big-dotted area group respectively the extremelyred trade-off zones.


nodes from the forwarding process causes significant per-formance regress; if only 10% of the most popular nodesboycott the forwarding process, PeopleRank’s success rateperformance degrades by roughly 20% within a 10 mintimescale.

However, PeopleRank performance shows insignificantregress when we exclude 10% of the most unpopularnodes; only 1.5% less within a 10 min timescale. This resultis consistent with the fact that unpopular nodes do notcontribute much to the forwarding process as the popularnodes do. Therefore, it is indeed shown that popular nodesin the network are more suitable than others to deliver agiven message to its destination.

In Fig. 2b, we plot the distribution of the successfuldelivery rate of PeopleRank as a function of different pop-ular node removal percentages. As expected, excludingpopular nodes from the forwarding process deterioratesthe success rate, especially for small timescales. The prob-ability to reach a destination within 10 min drops from96% to 83% when 5% of the popular nodes are excluded.Moreover, the success rate regress is more severe whenthe most popular nodes are removed; while we show a13% regress (from 96% to 83% within 10 min timescale)when only 5% of the most popular nodes are excluded, thisregress is only 3% when we exclude 5% of the remaining90% popular nodes (i.e., we compare 10% and 15% removalnodes curves).

Fig. 2. PeopleRank performance with different no

Fig. 2c shows that excluding unpopular nodes from theforwarding process does not seem to greatly impact thesuccess rate of PeopleRank. At times, this removal mayeven increase the performance when we remove only 5%of the most unpopular nodes. In contrast, removing themost unpopular nodes may, indeed, subtract high end-to-end delays from the overall distribution (i.e., high delaysare mainly caused by high waiting times to reach infre-quently seen destinations).

As a summary, we observe that popular nodes in thenetwork are more suitable than others to deliver a givenmessage to its destination; selecting preferential relays inforwarding decisions gives better forwarding performance.It is indeed important to further satisfy popular nodes. Pro-viding fairness is therefore crucial since the unfair treat-ment of users is considered as an disincentive toparticipation in the communication process.

3.4. Towards absolute fairness

While fairness is our goal, absolute fairness amongst allnodes is not. In this section, we discuss the impact of abso-lute fairness on the overall network performance. Let usassume an absolute fair allocation of resources acrossnodes in the network. We perform an offline study of thepath availability in our datasets, while each node forwardsthe same number of messages compared to all other nodes

de removal techniques (Infocom data set).


in the network. We ensure fairness by balancing costacross nodes. We call this offline fair path establishmenttechnique, the FAIR algorithm. FAIR provides, by definition,a uniform fair allocation of user resources among nodes inthe network.

Fig. 3a compares two contact-based ranking algorithms(i.e., LC and FR) to the FAIR algorithm using the Infocom06dataset. We show how an absolute fair allocation of re-sources leads to significant performance regression. Theprobability of success within 10 min decreases from 72%to 43% for Last Contact algorithm and from 60% to 43%for Frequency algorithm. The reason behind this regressionis that an absolute fair balancing of cost across nodescauses significant end-to-end delay and success rate per-formance degradation.

PeopleRank [25] uses a simplistic technique in order toincrease fairness across nodes. We study the impact ofsuch techniques on the PeopleRank performance. People-Rank allows us to tune the amount of the social informa-tion used through a damping factor d. Mtibaa et al. [25]shows that social forwarding schemes are effective onlyfor very accurate social information. The damping factoris then used to compensate the mismatch of the socialinteraction between users and their mobility patterns.

Fig. 3. Impact of fairness on th

Consequently, the more fairness we want, the closer to 0we will chose the damping factor.

In Fig. 3b, we plot the number of forwarded messagesper PeopleRank node (nodes are ranked from the unpopu-lar to the most popular node in the network) with regardsto the damping factor d. This result is normalized by thenumber of forwarded messages if we run an epidemic for-warding algorithm. In addition, we indicate the normalizedsuccess rate (within 10 min) regarding each damping fac-tor d in brackets in Fig. 3b’s labels. We observe that themore unfair PeopleRank is (higher values of d), the betterperformance it can achieve; while PeopleRank algorithmis given the best distribution of resources among the nodes(less than 25% difference between the highest and the low-est used nodes in CoNext07 dataset when d ¼ 0:4), itachieves only 75% of success rate performance within10 min timescale. However, PeopleRank running withd ¼ 0:9 gives 23% more success rate performance, and animportant resources usage disproportion among the nodes(more than 55% difference). Similar results have been ob-tained in Fig. 3c for the Infocom06 dataset. To summarize,an absolute fair balancing of cost across nodes causes sig-nificant end-to-end delay and success rate performancedegradation.

e network performance.


4. Can we be both efficient and fair?

In this section we quantitatively identify the trade-offrelationship between fairness and efficiency. We first pro-vide some intuition regarding the desired fairness we seekwhile introducing our satisfaction index metric. We thendiscuss an offline approach to verify, based on our experi-mental datasets, the availability of paths that would satisfyboth efficiency and fairness.

4.1. Desired fairness and satisfaction index

In a mobile opportunistic network, we can roughly di-vide nodes into three different categories with respect totheir popularity ranking; popular, semi-popular and unpop-ular nodes (as shown in Fig. 4). While ranking nodes in thenetwork may be considered to efficiently forward mes-sages in DTN, it creates a high potential of dissatisfactionamongst nodes since they forward more messages com-pared to others (as shown by the solid line in Fig. 4). Onthe other hand, an absolute fair allocation of resourcesyields poor forwarding performance (dashed line inFig. 4). Inspired by the previous results shown in Fig. 2,we believe that the desired fairness is different from anabsolute fair distribution of resources (optimal fairness).It is indeed important to increase the satisfaction of popu-lar nodes by reducing their cost. Unpopular nodes, how-ever, are unlikely impactful if they were involved in theforwarding process. Therefore, our goal is to further satisfypopular nodes by moving from a situation where popularnodes carry the largest burden in delivering messages toa ‘‘fair distribution’’ of this burden among popular andsemi-popular nodes as shown by the red dotted line inFig. 4.

To reach this desired situation, we need a metric bywhich we can assess the level of fairness amongst thenodes. We define a satisfaction index (SI) as the differencebetween the message load distribution given by the for-warding process (current fairness) and uniform distribu-tion among nodes (desired fairness). Our goal is toconstruct paths between any source and destination thatverify the condition in the following equation:

Fig. 4. Node categories with respe

9C P 0; 8i 2 1 . . . n;

SIðNiÞ ¼ loadðNiÞ �Xn

j¼1

loadðNjÞP �C; ð1Þ

where Ni represents a node i in the network, n is a totalnumber of nodes, loadðNiÞ is the current load at node Ni,and C is a positive constant integer. The goal is then to ver-ify if there exists a relatively small positive integer C, suchthat we can construct paths between any source–destina-tion pair and satisfy Eq. (1).

4.2. Availability of fair and efficient paths

We consider an offline approach to construct forward-ing paths that ensures both fairness and efficiency basedon global network information. In order to distribute theburden among semi-popular and popular nodes, wechange the common forwarding rule used by most rank-based forwarding algorithms: node A decides whether toforward a message m when it meets node B according tothis inequality: rankðBÞ > rankðAÞ. This rule will excludesemi-popular nodes if m was generated by a high rankednode. We then apply the following rule: rankðBÞ >rankðAÞ � e, where e is a positive number, or a linearfunction. In this section, our goal is not to give a ‘‘magic’’number or function of e since this largely depends on themobility characteristic present in the dataset. However,we discuss the fairness–efficiency tradeoff feasibility in agiven dataset.

Fig. 5 plots the normalized number of forwardedmessages per node with respect to different values of e.We show that when e ¼ 0:2, PeopleRank ensures a betterdistribution of the burden among popular and semi-popu-lar nodes compared to the original forwarding rule (whene ¼ 0). On the other hand, the distribution of the numberof forwarded messages by unpopular nodes seems ratherinsensitive to the variation of e; it is indeed unlikely tomeet these unpopular nodes, and the message will bedelegated directly to popular and semi-popular nodes.

We now consider e to be a function where increasing thenodes’ rank r linearly increases e value. We denote eða; bÞ asa function of the rank r, given by: eða; bÞ ¼ a � r þ b. Theabove forwarding rule can then be given by:

ct of their popularity rank.


rankðBÞ > ð1� aÞ � rankðAÞ þ b: ð2Þ

In Fig. 6, we test different values of a and b, and plot thesatisfaction index SI with respect to PeopleRank ranking.We show that the new rule given by Eq. (2) ensures a bet-ter distribution of the burden among the nodes andachieves a comparable success rate performance (only 1%or 2% less than optimal success rate performance). Thenew forwarding rule provides a fairly good satisfaction in-dex performance as described in Eq. (1) using a relativelysmall C constant (C ¼ 8 for eð0:3;�0:1Þ, and C ¼ 13 foreð0:2;0Þ).

To summarize, our offline data driven approach showsthat fair and efficient paths may exist in the network. How-ever, it does not indicate whether theses paths can befound efficiently by a distributed algorithm.

Fig. 6. Satisfaction factor with respect of different values of a and b(Infocom06 data set).

5. A real-time distributed framework for fairness-basedforwarding

We now propose a real-time distributed frameworkthat maximizes the overall user satisfaction using existingstate-of-the-art rank-based forwarding algorithms; it helpsrank-based forwarding algorithms utilize potential nodesto forward messages while satisfying the fairness propertygiven by Eq. (1). We call our systematic framework,Fairness-based Opportunistic networkinG (FOG). FOG is aframework that enables state-of-the-art rank-basedopportunistic forwarding algorithms to ensure a betterfairness–efficiency trade-off while maintaining a low over-head. FOG implements different opportunistic fairnessalgorithms and selects the suitable one with respect togiven network properties. We introduce two real-timedistributed algorithms; Proximity Fairness Algorithm(PFA), and Message Context Fairness Algorithm (MCFA).

5.1. Proximity Fairness Algorithm (PFA)

In mobile opportunistic networks, resource limitationsmay prevent us from delivering all messages. A new

Fig. 5. Load distribution with respect to different e values (Infocom06data set).

challenge arises: how to customize message forwarding,in order to bring maximum satisfaction to the end users.

We introduce a Proximity Fairness Algorithm (PFA)shown in Algorithm 1 implemented within FOG that oper-ates as follow: whenever two nodes are within close prox-imity of each other, they separately run an update process.They first update their relative maximum burden max andrelative minimum burden min per unit time, where a bur-den denotes the number of messages a node can carry.Afterwards, they decide whether or not to forward a mes-sage m if the following conditions are verified: (i) theencounter node Nj is the destination node of the messagem or (ii) Nj is higher ranked compared to Ni and its burden(burdenðNjÞ) does not exceed the relative average. Finally,the receiver of the message should update its actual bur-den value.

Algorithm 1. PFA (Ni).

1: max 02: burden 03: time systemtime4: min time5: while True do6: max maxðmax; burdenÞ7: min minðmin; burdenÞ8: while Ni in contact with Nj do9: updateðmax;minÞ10: if RankðNjÞP RankðNiÞ AND

burdenðNjÞ < ðmaxþminÞ=2 OR Nj ¼ destinationðmÞthen

11: Forwardðm;NjÞ12: end if13: if receivingðm;NiÞ14: burden ¼ ðburdenþ 1Þ=DT15: end if16: end while17: end while


Simplicity is considered as PFA’s strength. We note that,while the offline technique uses different parameters tomeasure the satisfaction index SI among all nodes, PFA asa fully distributed algorithm that relies on local informa-tion at the node to estimate the distribution of the burdenamong neighboring nodes. We believe that nodes are satis-fied by comparing themselves to their neighbors andacquaintances and require only relative equality amongsttheir neighbors in the network.

5.2. Message Context Fairness Algorithm (MCFA)

By limiting the maximum number of transferred mes-sages per node, PFA balances the traffic among the nodes,but does not prevent a popular node from being unavail-able (i.e., overloaded) when its help is needed; most popu-lar nodes will always be the most utilized until theybecome overloaded and stop collaborating in the forward-ing process. It is, therefore, important to better balance thetraffic among the nodes over time.

We propose a new algorithm that maximizes theoverall satisfaction among all users over time. The ideais to utilize the most popular nodes for forwarding ifand only if other nodes estimate that they definitelyneed them. For this purpose, we associate such needwith the number of message copies in the network,and propose a new distributed Message Context FairnessAlgorithm (MCFA) to maximize overall user satisfaction.MCFA enables cost performance aware message deliverywhile taking into account the number of message repli-cas in the networks by computing the utility functionto model the user satisfaction derived from sendingand receiving each message.

5.2.1. Estimating the number of replicasWhile the excessive replication of a given message in-

creases the message delivery ratio [32], it causes a heavynetwork load. Therefore, the number of message replicasin the network can be used as an estimation of the likeli-hood of a given message to reach its final destination with-in a time period Dt.

Previous work have estimated the average delay andthe number of replicas in the network as a function ofthe number of nodes and node mobility patterns [34,31].While these analyses provide important results, theirassumptions on knowing the exact total number of nodesand their mobility patterns remain unrealistic for mobileopportunistic networks.

In our work, we use only information provided to anode such as: nh, the number of hops traveled by a givenmessage m; nc , the number of copies of m the node itselfhas replicated over time, and ttlt , and a time-to-live fieldof each packet that represent the remaining time beforeerasing the packet. We note that ttlt are computed locallywithout any need for global clock synchronization.

Next, we assume that nr the total number of messagereplicas in the system at a given time t exponentiallydecrease when the number of hops increase. Since, rank-based forwarding algorithms tend to always forward the

messages to higher ranked nodes, after forwarding amessages across a large number of hops, the number offorwarding opportunities significantly decreases. Weestimate this number using an exponential decay e�knhðmÞ

with a constant rate k equal to 1.

nrðt;mÞ ¼ nTTLmax�ttlt ðmÞ

nh ðmÞ�e�knh ðmÞ

c � dt; ð3Þ

where dt denotes the time within which nc replicas of thesame message m were sent by the given node (i.e., the nodeestimating nrðt;mÞ). We validate our nr estimation using aCoNext07 dataset; the difference between nr and the realvalues does not exceed 8% among the all nodes throughoutthe duration of the experiment.

5.2.2. MCFA overviewIn order to improve fairness in opportunistic networks,

we revisit the satisfaction a node derives from sendingand receiving messages. We model such satisfaction as autility function. Such utility quantifies ‘‘the need’’ to for-ward to the most popular nodes instead of semi-popularones. The idea is to avoid overwhelming the most popularnode with many messages when other nodes can deliverthem to their destination in a bounded delay. We defineUðm;Ni;NjÞ the utility a node Ni derives from sending amessage m to another node Nj as shown in the followingequation:

Uðm;Ni;NjÞ ¼f ðbjÞ

RANKðNjÞ � nrðt;mÞ; ð4Þ

where bj denotes the actual buffer size at node j. f esti-mates the remaining buffer size as the difference betweenbj and the maximum buffer size max (as defined in Algo-rithm 1 line 6). A pseudo-algorithm of MCFA is given byAlgorithm 2; while two nodes become within proximity,forwarding decisions take into consideration the utility Uto decide whether to forward a message or not. We notethat if a node meets the destination, the message is alwaysforwarded even though SI� THRESHOLD. Based on an off-line study using the described datasets, we set the thresh-old value in this paper to 0.36. Note that this value can bededuced dynamically over time relying on local networkproperties.

Algorithm 2. MCFA (Ni).

tbp1: while Ni in contact with Nj do2: exchangeðbi;RANKðNiÞ3: while m 2 SendBufferðNiÞ do4: if ½Uðm;Ni;NjÞP THRESHOLD AND

RANKðNjÞP RANKðNiÞ� OR Nj ¼ destinationðmÞ then5: Forwardðm;NjÞ6: end if7: end while8: if receivingðm;NiÞ then9: bi ¼ ðbi þ 1Þ=DT10: end if11: end while


5.3. Evaluation

While PFA remains simple and does not use additionalstorage, it may achieve poor performance in particular sce-narios. Fig. 7 compares the number of forwarded messagesover time using PFA and MCFA by the most popular nodeusing the CoNext07 experiment (see Table 1). We showthat PFA tends to send all message towards the most pop-ular nodes causing buffer saturation early. Such saturationwill block the most popular nodes from sending andreceiving messages even when they are critically neededto deliver a particular message later in time. On the otherhand, MCFA uses the most popular nodes only when theyare critically needed or not heavily used. MCFA, therefore,maintains a constant message delivery rate in order toreach critical nodes when needed.

So far, we have been assuming that node rank remainsconstant during the experiment, and our tests use the rankof the node at the end of the experiment. We, therefore,study the evolution of ranking techniques over time. Weplot in Fig. 8 PeopleRank values of four representativenodes (unpopular, popular and semi-popular nodes) overtime using the CoNext07 experiment. We show that ranksmay change only at the beginning of the experiment, andstabilizes after 6–7 h. We note that we choose to plot thenodes that considerably change their ranks over time;other nodes compute their final rank after only 20 min to1 h. To summarize, after the initial bootstrapping phase,which last for a short period of time in comparison withthe network lifetime (i.e., many days or months.), noderank remains relatively constant, and socially popularnodes remain popular throughout the duration of theexperiment.

Next, we evaluate the performance of three state-of-the-art social forwarding algorithms (PeopleRank, LC, andFR) relying on the two previously described datasets (Info-com06, and CoNext07). We also use a data-driven artificialtrace to evaluate the scalability of PFA and MCFA in largescale social networks.

Fig. 7. Number of forwarded messages over time using PFA and MCFA bya popular node in the CoNext07 dataset.

5.3.1. PFA and MCFA in small social environmentsWe adopt a real-trace driven approach to analyze and

evaluate the trade-off between the efficiency and fairnessof PFA and MCFA in order to provide a more realistic eval-uation platform.

Next, we compare our distributed algorithms, PFA andMCFA, performance to the offline approach based on thereal mobility traces shown in Table 1. In Fig. 9, we comparethe satisfaction index SI and the message distributionamong nodes using PFA and the offline approach. In addi-tion, we compare the success rate of these forwardingalgorithms shown in the legends of each figure. We showthat PFA outperforms all the offline rules we have testedand achieves one of the best SI/success rate trade-offs.PFA also ensures a more fair distribution of the burdenamong the nodes; the satisfaction index (SI) of popularand semi-popular nodes remain close to zero and verifyEq. (1) with a relatively small constant C ¼ 3. PFA is explic-itly prohibiting forwarding messages when the burden isnot fairly distributed among neighbors; popular nodesare more likely to meet all other nodes as well as eachother. The burden is therefore fairly distributed amongthem. We note that, PFA is also efficient given that it keepsthe success rate close to the optimal (2% less compared tothe original PeopleRank success rate performance).

We compare in Fig. 10 the performance of our algo-rithms PFA and MCFA. We show that while PFA ensuresthe best load balance among nodes, MCFA uses the mostpopular nodes more often in order to reduce the end-to-end delay and achieve a better success rate; MCFA reducesthe load of most popular nodes by 10–20% while keepingthe success delivery ratio near to optimal (success ratedegradation of only 1%).

5.3.2. ScalabilityOwing to the lack of large-scale experimental data sets,

we artificially create San Francisco trace, a data set that usesthe San Francisco taxi cab trace [27] coupled with three

Fig. 8. Node ranking over time; PeopleRank values of four representativenodes (unpopular, popular and semi-popular nodes) over time using theCoNext07 experiment.

Fig. 9. Comparison of FOG and the offline technique (Infocom06 data set).

Table 2Properties of the San Fransisco modified data set.

San Francisco

CoNext07 Infocom06 Dartmouth

Duration 3d 3d 3dMobility pattern Bluetooth Bluetooth WiFi# Nodes 27 47 100Median inter-contact 10 mn 15 mn 6 mnMedian contact time 240 s 150 s 160 s


human mobility traces in order to represent three sub-communities of the San Francisco area such as the airport,downtown, and the sunset areas. To the best of our knowl-edge, this is the largest data set that captures humanmobility contacts as well as human social properties inlarge scale networks.

The San Francisco taxi trace contains mobility traces oftaxi cabs in San Francisco. It contains GPS coordinates ofapproximately 500 taxis collected over 30 days. Each tracecontains the reported time and location for each taxi. Weincorporate traces for the duration of 3 days, interpolatethe movement of the cabs, and then generate the contactsbetween these taxis. We assume a contact has occurredwhen a taxi comes within proximity of 100 m from anothertaxi. The contacts trace thus contains the starting timestamp of when the contact has occurred and the endingtime stamp of when the contact has finished. It also con-tains the IDs of the two cabs that happened to be in contactwith each other during that time.

We artificially incorporate three real human mobilitytraces in the three different areas of San Francisco city;Infocom06, CoNext07, and Dartmouth campus (see Table 2

Fig. 10. Comparison of PFA and MCFA algorithms.

for more details). Taxis are moving between these areas.Contacts between taxis and nodes within an area are addedbased on the same contact time and inter-contact time dis-tribution [4] of the corresponding area.

We evaluate the performance of PFA integrated witheach of the three rank-based forwarding algorithms inlarge scale networks relying on the San Francisco modifieddataset. Figs. 11–13 plot for each rank-based algorithm (a)the normalized number of forwarded messages and (b) thenormalized success rate performance using the originalalgorithm and the PFA-based extension. We show thatPFA and MCFA ensure better distribution of resourcesamong nodes while keeping the success rate performanceclose to optimal. However, while PFA and MCFA givealmost near load distribution using PeopleRank, LC andFR rankings, MCFA-based extensions achieve bettersuccess delivery ratios within a given delay d.

For example, we show that PFA-FR (i.e., PFA-basedextension of FR algorithm) ensures fairly distributedallocation of the forwarding burden among nodes with aminimal decrease in success rate by only 4% within a10 min timescale compared to the original FR success rate.MCFA-FR uses slightly more often the most popular nodes(2–3% more than PFA-FR), however it achieves consider-ably better success delivery ratio with almost 5% improve-ment compare to PFA-FR within a 10 min delay.

Finally, we note that MCFA-PeopleRank and PFA-Peo-pleRank yield a significantly high improvement comparedto the FAIR algorithm (described in Section 3) in large scalenetworks: +20% to 25% in the success rate within a 10 mintimescale. Using PeopleRank, highly ranked nodes carrythe highest burden since they are more likely to forward

Fig. 11. Comparison of PFA, MCFA implemented on top of LC algorithm (using the modified SF dataset).

Fig. 12. Comparison of PFA, MCFA implemented on top of FR algorithm (using the modified SF dataset).

Fig. 13. Comparison of PFA, MCFA implemented on top of PeopleRank algorithm (using the modified SF dataset).


a message to its destination. PFA-PeopleRank is thereforeensuring an acceptable trade-off between efficiency andfairness in large scale networks.

5.3.3. CostCost, in addition to end-to-end delay and success rate

metrics, is a very important evaluation metric in opportu-nistic networks. We define the cost of a forwarding algo-

rithm as the fraction of contacts involved in theforwarding process. PFA and MCFA are designed to fairlydistribute the burden among semi-popular and popularnode. However it was shown in previous work [25] thatefficiency/cost trade-off may be achieved when most pop-ular node carry the largest burden in delivering messages.In this section, we study the impact of the fair distributionof the burden given by PFA and MCFA on the overall cost.


Fig. 14 compares the cost of PFA-PeopleRank, MCFA-PeopleRank to FAIR (absolute fairness) and the originalversion of PeopleRank using three different data sets (Info-com06, CoNext07, and modified SF data sets). The originalversion of PeopleRank performs well and achieves the bestnormalized cost in all scenarios we have tested; People-Rank, as a social-based ranking algorithm, was designedto provide a decent cost/efficiency trade-off. We note thatrank-based forwarding algorithms can be fair and keep thecost and the performance close to the optimal. PFA-People-Rank outperforms the FAIR algorithm while using only 2–6% more replicas of the message m compared to the costof the original PeopleRank algorithm. We also note thatMCFA achieves one of the best trade-offs between effi-ciency, fairness, and cost; it ensures fairness among mostof the nodes in the network while keeping the cost andsuccess delivery ratio close to optimal. MCFA uses 1–4%less message replication compared to PFA and achievesbetter success delivery ratio. However, it has been shownin the previous section that MCFA may use the most pop-ular nodes only ‘‘if needed’’ to achieve considerably bettersuccess ratio.

Finally, it is crucial to be aware that, while the totalnumber of replicas in the network increases, the cost in-crease per node is not as significant as shown in the previ-ous results. Moreover, as the number of nodes in thenetwork grows, reflecting more realistic deployment envi-ronments, the increase in cost of MCFA algorithm becomessignificantly smaller. In fact, it has been shown that exist-ing state-of-the-art rank based forwarding algorithmspresent many drawbacks in large scale networks wheremultiple sub-communities may exist [24]. MCFA uses itsestimation of the number of message replicas to fairly dis-tribute the burden among more semi-popular nodes whichincreases the probability to reach distant destinations.

6. Related work

Most interactions between people rely on the establish-ment of the sense of fair treatment. Computer networkingcommunication, and more particularly peer-to-peer file

Fig. 14. Normalized cost of extended PeopleRank versions.

sharing applications and services take into considerationthe fair treatment of users. Fairness is therefore particu-larly important and challenging since it is considered a ma-jor incentive for peer-to-peer service usage in today’sInternet. Theses challenges are more critical in infrastruc-ture-less wireless networks given the lack of centralizedand trusted mechanisms that control the fair treatmentof users. With the recent shift in research interest fromcentralized services to distributed services in mobile com-munication, fairness is becoming an interesting field ofinvestigation in many research topics such as resourceallocation, congestion control, and network routing.

In peer-to-peer mobile networks such as mobile ad hocnetworks (MANETs) [6,17,11,16], wireless sensor networks[30,15,12], or delay tolerant networks (DTNs) [14,19], mul-ti-hop wireless communication between users may failwith the absence of the sense of fairness between partici-pating nodes. In DTNs, a device has to decide whether ornot to forward data to an intermediate node that itencounters. Such forwarding decisions are typically guidedby the desire to reduce the number of replicas of dataitems in the network to conserve bandwidth as well asby the desire to reduce end-to-end delay.

Current forwarding techniques in DTN are generally de-signed to efficiently, and excessively over-use popularnodes to guide and improve forwarding decisions[7,8,28,1,19,21,9,29]. Rank-based forwarding techniquescurrently represent one of the most promising methodsfor addressing the message forwarding challenge [5,25,3].Nodes in these techniques are ranked based on their socialprofiles or contact history to identify those that have ahigher probability of successfully forwarding the messageto the destination. While these techniques have demon-strated great efficiency in performance [5,26,25,3], theydo not address the rising concern of fairness amongst var-ious nodes in the network. Higher ranked nodes typicallycarry the largest burden in delivering messages, which cre-ates a high potential of dissatisfaction amongst them. Pro-viding fairness is then an important networking goal sincethe unfair treatment of users is considered as a disincen-tive to participation in the communication process. It hasbeen shown that ranking-based forwarding algorithmsprovide good performance relying on identifying and over-using popular nodes in the network [5,26,25]. Such wellranked nodes are more likely than others to deliver a mes-sage to its destination in a shorter delay. As a direct conse-quence, an absolute fair treatment of users causes asignificant end-to-end delay and message delivery perfor-mance degradation. It is then primordial to considerwhether there exists a tradeoff relationship between fair-ness and efficiency.

Fair sharing of resources has been largely studied in thecontext of classical networks. Previous work has been in-spired by the well known max–min fairness [33] or Jain’sfairness index [13] in order to improve a fair allocation/scheduling of resources in the Internet. In the context ofwireless networks, researchers use the link qualityvariation of access points to maximize aggregate through-put [18,20]. In the context of DTNs many assumptions aremade regarding resource constraints (storage andbandwidth), and strictly adhere to max–min fairness for


end-to-end delay minimization [10,1]. In this paper, we ad-dress the above issue more generally. We assume unlim-ited resources in the network and propose a real timedistributed framework to improve forwarding decisionsin order to avoid dissatisfaction among popular nodes,and therefore ensure an efficiency–fairness tradeoff usinglocal information.

7. Conclusion

Our work addresses the rising concern of fairnessamongst various nodes in mobile-based opportunistic net-works. Rank-based forwarding algorithms are typically de-signed to reduce the number of data replicas in the networkto conserve bandwidth and reduce end-to-end delay. Inthese algorithms, higher ranked nodes carry a much heavierburden in delivering messages, which can create high levelsof dissatisfaction amongst them. In this paper, we have pro-posed FOG a real-time distributed framework that maxi-mizes the overall user satisfaction using existing state-of-the-art rank-based forwarding algorithms; it helps rank-based forwarding algorithms utilize potential nodes to for-ward messages while satisfying fairness properties. FOGimplements different opportunistic fairness algorithmsand selects the suitable one regarding given network prop-erties. In this paper, we have introduced two real-time dis-tributed algorithms; Proximity Fairness Algorithm (PFA)and Message Context Fairness Algorithm (MCFA).

We have adopted a real-trace driven approach to studyand analyze the trade-offs between the efficiency, cost, andfairness of rank-based forwarding algorithms in mobileopportunistic networks. Our preliminary results show thatmobile opportunistic communication between users mayfail with the absence of fairness. Furthermore, an absolutefair treatment of users also yields inefficient communica-tion performance. Our analysis shows that FOG uses bothfairness algorithms PFA and MCFA to ensure relative equal-ity in the distribution of resource usage among neighbornodes while maintaining a high delivery success rate, andcost performance close to optimal. While the simplicity isconsidered as one of PFA algorithm strengths, we haveshown that MCFA uses fewer message replicas comparedto PFA and achieves a higher success delivery ratio.

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Abderrahmen Mtibaa, Ph.D., is a postdoctoralresearch associate in the School of ComputerScience at Carnegie Mellon University inQatar. Graduated on June 2010 from the Uni-versity of Paris VI and Technicolor ParisResearch Lab. His current areas of interestinclude mobile opportunistic networks/DTN,wireless and ad hoc networks, mobility mod-els, protocol design, routing/forwarding, net-work communities and social networking.

Khaled A. Harras is currently an AssistantTeaching Professor in the Department ofComputer Science at Carnegie Mellon Uni-versity. He is the founder and director of theNetworking Systems Lab and currentlyresides at CMU’s campus in Qatar. Khaled’smain research interests include delay anddisruption tolerant networks, specificallyprotocol and architectural challenges inextreme networking environments, multi-media wireless sensor networks, social per-vasive systems, and multi-interface

networking and communication. He is a member of IEEE and the ACM.

fairness-related challenges in mobile opportunistic networking

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