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Self Adaptive Contention Aware RoutingProtocol for Intermittently Connected Mobile

NetworksAhmed Elwhishi, Pin-Han Ho, K. Naik, and Basem Shihada

Abstract—This paper introduces a novel multi-copy routing protocol, called Self Adaptive Utility-based Routing Protocol (SAURP),for Delay Tolerant Networks (DTNs) that are possibly composed of a vast number of devices in miniature such as smart phonesof heterogeneous capacities in terms of energy resources and buffer spaces. SAURP is characterized by the ability of identifyingpotential opportunities for forwarding messages to their destinations via a novel utility function based mechanism, in which a suite ofenvironment parameters, such as wireless channel condition, nodal buffer occupancy, and encounter statistics, are jointly considered.Thus, SAURP can reroute messages around nodes experiencing high buffer occupancy, wireless interference, and/or congestion, whiletaking a considerably small number of transmissions. The developed utility function in SAURP is proved to be able to achieve optimalperformance, which is further analyzed via a stochastic modeling approach. Extensive simulations are conducted to verify the developedanalytical model and compare the proposed SAURP with a number of recently reported encounter-based routing approaches in termsof delivery ratio, delivery delay, and the number of transmissions required for each message delivery. The simulation results show thatSAURP outperforms all the counterpart multi-copy encounter-based routing protocols considered in the study.

Index Terms—Encounter-based Routing, DTN.

F

1 INTRODUCTION

Delay Tolerant Network (DTN) [1] is characterized bythe lack of end-to-end paths for a given node pair forextended periods, which poses a completely differentdesign scenario from that for conventional mobile ad-hoc networks (MANETs) [13]. Due to the intermittentconnections in DTNs, a node is allowed to buffer amessage and wait until the next hop node is foundto continue storing and carrying the message. Such aprocess is repeated until the message reaches its destina-tion. This model of routing is significantly different fromthat employed in the MANETs. DTN routing is usuallyreferred to as encounter-based, store-carry-forward, ormobility-assisted routing, due to the fact that nodalmobility serves as a significant factor for the forwardingdecision of each message.

Depending on the number of copies of a messagethat may coexist in the network, two major categoriesof encounter-based routing schemes are defined: single-copy and multi-copy. With the single-copy schemes [5],no more than a single copy of a message can be carriedby any node at any instance. Although simple andresource efficient, the main challenge in the implemen-

• Ahmed Elwhishi, Pin Han Ho, and K. Naik are with the Department ofElectrical and Computer Engineering, University of Waterloo, Ontario,Canada.E-mail: aelwhish@engmail.uwaterloo.ca, p4ho@uwaterloo.ca,snaik@uwaterloo.ca.

• Basem Shihada. is with Department of Computer Science, KAUST,Thuwal, Saudi Arabia.E-mail: basem.shihada@kaust.edu.sa

tation of single-copy schemes lies in how to effectivelydeal with the interruptions of network connectivity andnode failures. Thus, single-copy schemes have beenreported to seriously suffer from long delivery delaysand/or large message loss ratio. On the other hand,multiple-copy (or multi-copy) routing schemes allow thenetworks to have multiple copies of the same messagethat can be routed independently and in parallel so as toincrease robustness and performance. It is worth notingthat most multi-copy routing protocols are flooding-based [3], [4] that distribute unlimited numbers of copiesthroughout the network, or controlled flooding-based[20], [7] that distribute just a subset of message copies,or utility-based approaches [2] that determine whethera message should be copied to a contacted node simplybased on a developed utility function.

Although improved in terms of performance, thepreviously reported multi-copy schemes are subject tothe following problems and implementation difficulties.First, these schemes inevitably take a large number oftransmissions, energy consumption, and a vast amountof transmission bandwidth and nodal memory space,which could easily exhaust the network resource. Sec-ond, they suffer from contention in case of high trafficloads, when packet drops could result in a significantdegradation of performance and scalability. Note thatthe future DTNs are expected to operate in an envi-ronment with a large number of miniature hand-helddevices such as smart phones, tablet computers, personaldigital assistants (PDAs), and mobile sensors. In such ascenario, it may no longer be the case that nodal contactfrequency serves as the only dominant factor for the

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message delivery performance as that assumed by mostexisting DTN literature. Therefore, limitations on powerconsumption, buffer spaces, and user preferences shouldbe jointly considered in the message forwarding process.

To cope with the above-mentioned deficiency, a familyof multi-copy schemes called utility-based controlledflooding [15], [21], [14], [12] has been proposed. Theclass of schemes generate only a small number of copiesto ensure that the network is not overloaded with thelaunched messages. Although being able to effectivelyreduce the message delivery delay and the numberof transmissions, most of the utility-based controlledflooding routing schemes in literature assume that eachnode has sufficient resources for message buffering andforwarding. None of them, to our best knowledge, hassufficiently investigated how the protocol should takeadvantage of dynamic network status to improve theperformance, such as packet collision statistics, wirelesslink conditions, nodal buffer occupancy, and batterystatus. Note that the nodal buffer status could serve as anindicator how much the opportunity cost is by acceptinga forwarded message, while the channel condition isan indicator how likely the contact could be an eligibleone; or in other words, how likely a message can besuccessfully forwarded during the contact. They areobviously essential parameters to be considered in theutility function.

With this in mind, we introduce a novel DTN routingprotocol, called Self Adaptive Utility-based Routing Pro-tocol (SAURP), that aims to overcome the shortcomingsof the previously reported multi-copy schemes. Ourgoal is to achieve a superb applicability to the DTNscenario with densely distributed hand-held devices.The main feature of SAURP is the strong capability inadaptation to the fluctuation of network status, trafficpatterns/characteristics, user encounter behaviors, anduser resource availability, so as to improve networkperformance in terms of message delivery ratio, messagedelivery delay, and number of transmissions.

The contributions of the paper are as following• We develop a novel DTN routing scheme which

incorporates with some parameters that have notbeen jointly considered in the literature. The param-eters include link quality/availability and buffer oc-cupancy statistics, which are obtained by samplingthe channels and buffer space during each contactwith another node.

• We introduce a novel transitivity update rule, whichcan perfectly match with the proposed routingmodel and the required design premises.

• We introduce a novel adaptive time-window updatestrategy for maintaining the quality metric functionat each node, aiming at an efficient and optimal de-cision making process for each active data message.

• An analytical model is developed for the proposedSAURP, and its correctness is further verified. Weshow via extensive simulations that the proposedSAURP can achieve significant performance gain

over the previously reported counterparts under theconsidered scenarios.

The rest of the paper is organized as follows. SectionII provides a review of the related work. Section IIIdescribes the proposed SAURP in detail. Then, SectionIV analyzes the proposed SAURP by coming up with ananalytical model for the delivery ratio and end-to-endmessage delivery delay. Section V provides the simula-tion results and comparisons with the counterparts. InSection VI, we conclude the paper.

2 RELATED WORK

Most (if not all) previously reported encounter-basedrouting schemes have focused on nodal mobility, whichhas been extensively exploited as the dominant factorin the message forwarding decision. Those schemes con-tributed in the context of introducing new interpretationsof the observed node mobility in the per-node utilityfunction. Spyropoulos et al. in [6], [12] developed rout-ing strategies that use different utility routing metricsbased on nodal mobility statistics, namely Most MobileFirst (MMF), Most Social First (MSF), and Last SeenFirst (LSF). Nelson et al. [29] proposed an enhancedversion of MSF by taking the number of message replicastransferred during each contact in proportion to the per-node utility function, which is in turn determined bythe evolution of the number of nodal encounters duringeach time-window. Lindgren et al. in Jones et al. in[19] introduced a novel utility function for DTN routingby manipulating the minimum expected inter-encounterduration between nodes. Ling et al. in [23] designed afeedback adaptive routing scheme based on the influencefactors solely determined by the node mobility, wherea node with higher mobility is given a higher factor,and messages are transmitted through nodes with higherinfluence factors.

Lindgren et al. in [2] introduced a DTN routingscheme which predicts encounter probability betweennodes. Burgess et al. in [38] introduced a routing pro-tocol which bases its decisions on whether to transmitor delete a message on the path likelihood. The pathlikelihood metric is based on historic information of thenumber of encounters between nodes. Y. Liao et al. in[37] introduced a routing scheme that combines erasure-coding with an estimation routing scheme and selec-tively distributes messages blocks to relay nodes. Thedecision of forwarding a message depends on the contactfrequency and other factors such as buffer occupancy,and available battery power level.

Balasubramanian et al. in [22] introduced a routingscheme as resource allocation. The statistics of availablebandwidth and the number of message replicas currentlyin the network are considered in the derivation of therouting metric to decide which message to replicatefirst among all the buffered messages in the custodiannode. The derivation of the routing metric, nonetheless,is not related to buffer status. Along the similar line

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of research, Khrifa et al. in [43] proposed a forwardingand dropping policy for a limited buffer capacity. Thedecision under this policy is made based on the valueof per-message marginal utility. This policy nonethe-less was designed to suit homogeneous nodal mobility.Lee et al. in [39] introduced a comprehensive routingscheme as resource allocation that jointly optimizes linkscheduling, routing, and replication. This frameworkallows the developed solutions to be adaptive to variousnetwork conditions regarding nodal interferences andconnections/disconnections. Tan et al. in [36] introduceda routing strategy based on calculating the expected end-to-end path length as a metric in forwarding messagesmainly based on the reciprocal of the encounteringprobability. It is defined as the expectation of messagetransmission latency through multi-hop relays.

Another scheme called delegation forwarding wasintroduced in [14], where a custodian node forwards amessage copy to an encountered node if the encounterednode has a better chance to “see” the destination. Thekey idea is that a custodian node (source or relay)forwards a message copy only if the utility function(represented by the rate of encounters between nodepairs) of the encountered node is higher than all thenodes so far “seen” by a message, and then currentcustodian will update its utility value of that message tobe equal to that of the encountered node. Spyropouloset al. in [6] propsed routing scheme called Spray andFocus, which is characterized by addressing an upperbound on the number of message copies (denoted as L).In specific, a message source starts with L copy tokens.When it encounters another node B currently withoutany copy of the message, it shares the message deliveryresponsibility with B by transferring L/2 of its currenttokens to B while keeping the other half for itself. Whenit has only one copy left, it switches to a utility forward-ing mechanism based on the time elapsed since the lastcontact. This scheme has proven to significantly reducethe required number of transmissions, while achievinga competitive delay with respect to network contentionssuch as buffers space and bandwidth.

Some studies have investigated the impact of hu-man mobility and their social relations on the routingalgorithms [32], [33], [34], [35], [31], [17], [41], lead-ing to a class of social network based message for-warding schemes. With these schemes, the variation innode popularity and the detectability of communitiesare employed as the main factors in the forwardingdecisions. Bubble-rap [34] is a representative protocolby considering the importance of individuals in a socialnetwork for making the message forwarding decision.Mosli et al. in[17] introduced a DTN routing schemeusing utility functions calculated from an evaluation ofcontext information. The derived cost function is usedas an assigned weight for each node that quantifies itssuitability to deliver messages to an encountered noderegarding a given destination. Other schemes are basedon content-based network service [40] as a novel style

of communication that associates source and destinationpairs based on actual content and interests, rather thanby letting the source to specify the destination.

Although previously reported studies such as [6],[7], [2], [14], [31], [22] have made great efforts in im-proving the DTN routing techniques, they are subjectto various limitations in the utility function updateprocesses. The schemes such as [2], [38] that take thenumber of encounters as the main factor in the messageforwarding decision, may suffer from multiple falselydetected contacts. This happens when a node exhibitsan intermittent connection with another node, e.g., dueto a communication barrier. Further, a permanent orquasi-permanent neighbor will cause the utility functioncalculation invalid in message forwarding. For example,if node A and B remain in the transmission range ofeach other for a long period without disconnection, therewill be only one contact counted between the two nodesirrespective of the long duration of the contact. A routingdecision based on the number of contacts makes node Ba less suitable candidate for carrying a message fromnode A than other nodes that have a larger numberof contacts, even though node B could actually be thepreferred candidate for carrying the messages.

Although the abovementioned schemes can capturethe mobility properties in order to come up with ef-ficient forwarding strategy, they may not be able toacquire accurate knowledge about network dynamicsand unpredicted contacts. More importantly, the channelcapacity and buffer occupancy status have never beenjointly considered in the derivation of utility functionsfor hop-by-hop message forwarding. It is clear that thesetwo factors could only be overlooked/ignored when theencounter frequency is low since the routing protocolperformance is dominated by node mobility, while thenetwork resource availability does not play an importantrole. However, in the scenario that the nodal encounterfrequency is large and each node has many choicesfor message forwarding in a short time, the networkresource availability is envisioned to serve as a criticalfactor for performance improvement and should be uti-lized in the derivation of utility functions.

Motivated by the above observations, this paper in-vestigates encounter-based routing that jointly considersnodal contact statistics and network status includingwireless channel condition and buffer occupancy. Ourgoal is to reduce the delivery delay and the numberof transmissions under stringent buffer space and linkcapacity constraints. This is a desired feature of a DTNespecially in the scenario where each mobile node ishand-held device with limited resources.

3 SELF ADAPTIVE UTILITY-BASED ROUTINGPROTOCOL (SAURP)

The proposed SAURP is characterized by the abilityof adapting itself to the observed network behaviors,

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which is made possible by employing an efficient time-window based update mechanism for some network sta-tus parameters at each node. We use time-window basedupdate strategy because it is simple in implementationand robust against parameter fluctuation. Note that thenetwork conditions could change very fast and makea completely event-driven model unstable. Fig. 1 illus-trates the functional modules of the SAURP architecturealong with their relations.

The Contact Statistics (denoted as CS(i)) refers tothe statistics of total nodal contact durations, channelcondition, and buffer occupancy state. These values arecollected at the end of each time window and used asone of the two inputs to the Utility-function Calculationand Update Module (UCUM). Another input to theUCUM, as shown in Fig. 1, is the updated utility denotedby 4T (i)

new, which is obtained by feeding 4T (i) ( theinter-contact time between any node pair, A and B)through the Transitivity Update Module (TUM). UCUMis applied such that an adaptive and smooth trans-fer between two consecutive time windows (from cur-rent time-window to next time-window) is maintained.4T (i+1) is the output of UCUM, and is calculated at theend of current time window W (i). 4T (i+1) is thus usedin time window W (i+1) for the same tasks as in windowW (i).

Forwarding Strategy Module (FSM) is applied at thecustodian node as a forwarding decision making processwhen encountering any other node within the currenttime window based on the utility value (i.e., 4T (i)).

It is important to note that CS, TUM, FSM, and mes-sage vector exchange are event-driven and performedduring each contact, while UCUM is performed at theend of each time-window. The following subsectionsintroduce each functional module in detail.

3.1 Contact Statistics (CS)

To compromise between the network state adaptabilityand computation complexity, each node continuouslyupdates the network status over a fixed time window.The maintained network states are referred to as ContactStatistics (CS), which include nodal contact durations,channel conditions, and buffer occupancy state, and arefed into UCUM at the end of each time window. The CScollection process is described as follows.

Let two nodes A and B be in the transmission rangeof each other, and each broadcasts a pilot signal per ktime units in order to look for its neighbors within itstransmission range. Let T(A,B), Tfree, and Tbusy representthe total contact time, the amount of time the channel isfree and the buffer is not full, and the amount of timethe channel is busy or the buffer is full, respectively, atnode A or B during time window W (i). Thus, the totalduration of time in which node A and B can exchangeinformation is calculated as:

Tfree = T(A,B) − Tbusy (1)

Note that the total contact time could be accumulatedover multiple contacts between A and B during W (i).

3.2 Utility-function Calculation and Update Module(UCUM)

UCUM is applied at the end of each time window and isused to calculate the currently observed utility that willbe further used in the next time window. The two inputsto UCUM in time window W (i) are: (i) the predictedinter-contact time (4T (i)), which is calculated accordingto the previous time-window utility (i.e., 4T (i)), as wellas an update process via the transitivity property update(introduced in subsection 3.3), and (ii) the observed inter-encounter time obtained from the current CS(i) (denotedas 4T (i)

cs ).

3.2.1 Calculation of Inter-encounter Time (4T (i))

An eligible contact of two nodes occurs if the durationof the contact can support a complete transfer of at leasta single message between the two nodes. Thus, in theevent that node A encounters B for a total time durationTfree during time window W (i), the number of eligiblecontacts in the time window is determined by:

n(i)c =

⌊TfreeTp

⌋(2)

where Tp is the least time duration required to transmita single message. Let 4T (i)

cs(A,B) denotes the averageinter-encounter time duration of node A and B in timeW (i). Obviously, 4T (i)

(A,B) = 4T (i)(B,A). We have the fol-

lowing expression for 4T (i)cs(A,B):

4T (i)cs(A,B) =

W (i)

n(i)c

(3)

4T (i)cs(A,B) describes how often the two nodes en-

counter each other per unit of time (or, the encounterfrequency) during time window W (i) considering theevent the channel is busy or the buffer is full.

Thus, inter-encounter time of a node pair intrinsicallyrelies rather on the duration and frequency of previouscontacts of the two nodes than simply on the number ofprevious contacts or contact duration. Including the totalduration of all the contacts (excluding the case when thechannel is busy or the buffer is full) as the parameter isexpected to better reflect the likelihood that nodes willmeet with each other for effective message exchange.

With this, the proposed routing protocol does notpresume any knowledge of future events, such as nodevelocity, node movement direction, instants of time withpower on or off; instead, each node keeps network statis-tic histories with respect to the inter-encounter frequencyof each node pair (or, how often the two nodes encountereach other and are able to perform an effective messageexchange).

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Transitivity Update Module (TUM)

∆T(i)∆T(i+1)

∆T(i)new

Utility -function

Calculation and Update

Module (UCUM)

Forwarding Strategy

Module (FSM)

CS(i)

���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

Transitivity Update Module (TUM)

Forwarding Strategy

Module (FSM)Utility-

function Calculation and Update

Module (UCUM)

∆T(i+1)new ∆T(i+2)

next time-window W (i+1)

current time -window W(i)

CS(i+1)

Fig. 1. The SAURP Architecture

3.2.2 Time-window Transfer Update

Another important function provided in UCUM is forthe smooth transfer of the parameters between consecu-tive time windows. As discussed earlier, the connectivitybetween any two nodes is measured according to theamount of inter-encounter time during W (i), which ismainly based on the number of contacts (i.e., nc) and thecontact time (i.e., Tfree). These contacts and contact du-rations may change dramatically from one time windowto the other and address significant impacts on the pro-tocol message forwarding decision. Hence, our schemedetermines the next time window parameter using twoparts: one is the current time window observed statistics(i.e., 4T (i)

cs ), and the other is from the previous timewindow parameters (i.e., 4T (i)), in order to achieve asmooth transfer of parameter evolution. The followingequation shows the derivation of4T (i+1) in our scheme.

4T (i+1) = γ.4T (i)cs + (1− γ)4T (i) (4)

The parameter γ is given by

γ =| 4T (i) −4T (i)

cs |

max(4T (i),4T (i)cs )

, where4T (i), 4T (i)

cs > 0 (5)

If 4T (i)cs > W , which happens if if n(i)c = 0, then

4T (i+1) = 2W

n(i−1)c

. This case represents a worst casescenario, i.e. unstable node behavior, or low quality ofnode mobility. Hence, the 4T (i+1) value should be low.

4T (i+1) represents the routing metric (utility) valuethat is used as input to the next time window. This valueis maintained as a vector of inter-encounter time that isspecific to every other node, which is employed in thedecision making process for message forwarding.

3.3 The Transitivity Update Module (TUM)

When two nodes are within transmission range of eachother, they exchange utility vectors with respect to themessage destination, based on which the custodian nodedecides whether or not each message should be for-warded to the encountered node. With a newly receivedutility vector, transitivity update [2] is initiated. Wepropose a novel adaptive transitivity update rule, whichis different from the previously reported transitivity up-date rules [2]. [6]. The proposed transitivity update rule

is characterized as follows: (1) it is adaptively modifiedaccording to a weighting factor α, which is in turn basedon the ratio of 4T (i) of the two encountered nodesregarding the destination rather than using a scaling con-stant. Note that the weighting factor α determines howlarge impact the transitivity should have on the utilityfunction. (2) It can quantify the uncertainty regardingthe position of the destination by only considering thenodes that can effectively enhance the accuracy of theutility function.

The transitivity property is based on the observationthat if node A frequently encounters node B and Bfrequently encounters node D, then A has a good chanceto forward messages to D through B. Such a relation isimplemented in the proposed SAURP using the follow-ing update strategy:

4T (i)

(A,D)new= α4T (i)

(A,D)+ (1− α)(4T (i)

(A,B)+4T (i)

(B,D)) (6)

where α is a weighting factor that must be less than 1to be valid:

α =4T (i)

(A,B) +4T(i)

(B,D)

4T (i)

(A,D)

, 4T (i)

(A,D)> 4T (i)

(A,B)+4T (i)

(B,D)(7)

α has a significant impact on the routing decision rule.From a theoretical perspective, when a node is encoun-tered that has more information for a destination, thistransitivity effect should successfully capture the amountof uncertainty to be resolved regarding the position ofthe destination.

To ensure that the transitivity effect can be successfullycaptured in the transitivity update process, an updateshould be initiated at node A regarding D only when4T (i)

(A,D) > 4T(i)(B,D). Otherwise, the transitivity property

for node A is not useful since node A itself is a bettercandidate for carrying the messages destined to nodeD rather than forwarding them through B. This rule isapplied after nodes finish exchange messages.

3.4 The Forwarding Strategy Module (FSM)

The decision of message forwarding in SAURP is mainlybased on the utility function value of the encounterednode regarding the destination, and the number ofmessage copy tokens. If more than one message copy

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are currently carried, the weighted copy rule is applied;otherwise the forwarding rule is applied.

3.4.1 Weighted Copy RuleThe source of a message initially starts with L copies. Inthe event that any node A that has n > 1 message copytokens and encounters another node B with no copieswith 4T (i)

(B,D) < 4T(i)(A,D), node A hands over some of

the message copy tokens to node B and keeps the restfor itself according to the following formula:

NB =

⌊NA

(4T (i)

(A,D)

4T (i)

(B,D)+4T (i)

(A,D))

)⌋(8)

where NA is the number of message tokens that node Ahas, 4T (i)

(B,D) is the inter-encounter time between node

B and node D, and 4T (i)(A,D) is the inter-encounter time

between nodes A and D. This formula guarantees thatthe largest number of message copies is spread to relaynodes that have better information about the destinationnode. After L message copies have been disseminatedto and carried by the encountered custodian nodes,each custodian node carrying the message performsmessage forwarding according to the forwarding rule asdescribed in the next subsection. It may be noted herethat the idea of weighted copy rule was firstly examinedin [37] and our previous study [15], and has been provedto achieve improved delivery delay.

3.4.2 The Forwarding Rule• If the destination node is one hop away from an

encountered node, the custodian node hands overthe message to the encountered node and completesthe message delivery.

• If the inter-encounter time value of the encounterednode relative to that of the destination node is lessthan that of the custodian node by a threshold value,4Tth, a custodian node hands over the message tothe encountered node.

The complete mechanism of the forwarding strategy inSAURP is summarized as shown in Algorithm 1.

4 ANALYTICAL MODEL OF SAURPIn this section a statistical analysis is conducted to eval-uate the performance of SAURP. Without loss of general-ity, Community-Based Mobility Model [6] is employed inthe analysis. The problem setup consists of an ad hoc net-work with a number of nodes moving independently ona 2-dimensional torus in a geographical region, and eachnode belongs to a predetermined community. Each nodecan transmit up to a distance K ≥ 0 meters away, andeach message forwarding (in one-hop) takes one timeunit. Euclidean distance is used to measure the proximitybetween two nodes (or their positions) A and B. Aslotted collision avoidance MAC protocol with Clear-to-Send (CTS) and Request-to-Send (RTS), is implementedfor contention resolution. A message is acknowledged

Algorithm 1 The forwarding strategy of SAURPOn contact between node A and BExchange summary vectorsfor every message M at buffer of custodian node A do

if destination node D in transmission range of Bthen

A forwards message copy to Bend if

else if 4T (i)(A,D)> 4T

(i)(B,D) do

if message tokens >1 thenapply weighted copy rule

end ifelse if 4T (i)

(A,D) > 4T(i)(B,D) +4Tth then

A forwards message to Bend else if

end else ifend for

S Dp3

p2

p1

p4

Fig. 2. Paths of message copies to destination

if it is received successfully at the encountered nodeby sending back a small acknowledgment packet to thesender.

The performance measures in the analysis include theaverage delivery probability and the message deliverydelay. The analysis is based on the following assump-tions.• Nodes mobility is independent and heterogeneous,

where nodes have frequent appearance in somelocations.

• Each node in the network maintains at least one for-warding path to every other node. Fig. 2 illustratesthe paths that a message copy may take to reach thedestination.

• Each node belongs to a single community at a time(representing some hot spots such as classrooms,office buildings, coffee shops), and the residing timeon a community is proportional to its physical size.

• The inter-contact time 4T(A,B) between nodesA and B follows an exponential distributionwith probability distribution function (PDF),P4T(A,B)

(t) = β(A,B).e−β(A,B)t, where t is the time

instance.It has been shown that a number of popular user

7

mobility models have such exponential tails (e.g., Ran-dom Walk, Random Waypoint, Random Direction, andCommunity-based Mobility [9], [26]). In practice, recentstudies based on traces collected from real-life mobilityexamples argued that the inter-contact time and thecontact durations of these traces demonstrate exponen-tial tails after a specific cutoff point [18]. Based on themobility model of the nodes, the distribution of the inter-contact time can be predicted and calculated using timewidow updates shown in (4). Thus, parameter βAB iscalculated as βAB = 1

4T(A,B).

4.1 Delivery Probability

In order to calculate the expected message delivery ratio,any path of message m between S and D is a k − hopsimple path, denoted as l, which is represented by aset of nodes and links denoted as {S, h1 h2 ....hk−1, D},and {e1, e2, ..., ek}, respectively. The cost on each edge,denoted as {β1, β2, .., βk}, is the inter-contact rate (orfrequency) of each adjacent node pair along the path.According to the forwarding policy of SAURP, the valuesof inter-contact rate should satisfy {β1 < β2 < .. < βk}.The path cost, PRl(t), is the probability that a messagem is successfully forwarded from S to D along path lwithin time t, which represents a cumulative distributionfunction (CDF). The probability density function of apath l with k − hop for one message copy can becalculated as convolution of k probability distributions[27] which is calculated as:

Prl(t) = p1(t)⊗ p2(t)⊗ ...pk(t) (9)

Theorem 1. Let the probability distribution function(PDF) for the message delivery along a one-hop path i bedenoted as pi(t) = βie

−βit. Thus, the PDF for a k − hopsimple path l with an edge cost {β1, β2, ...,βk} can beexpressed as

Prl(t) =

kl∑i=1

C(kl)i pi(t) (10)

where the coefficients are given as follows:

C(kl)i =

kl∏j=1,i6=j

βjβj − βi

(11)

The proof is provided in Appendix.The probability of message delivery on forwarding

path l between any source S, and destination D, withinexpiration time T is expressed as:

Fl(T ) = PRl(Tdl < T ) =´ T0Prl(t)dt

=∑kli=1 C

kli

´ T0Pi(t)dt

PRl(Tdl < T ) =

kl∑i=1

C(kl)i .(1− e−βiT ) (12)

If there are L− 1 copies (excluding the message at thesource) of message m traversing through L− 1 indepen-dent paths in the network, the maximum probability ofmessage delivery can be written as

PRmax(Td < T ) = max{PRSD, PR1, PR2, .., PRL−1} (13)

where PRSD and PRl are random variables represent-ing the delivery probability in case of direct messagedelivery between S and D, and through one of L − 1paths, respectively. The expected delivery probability ofmessage m with L− 1 copies traversing on L− 1 pathsis calculated as:

PR(Td < T ) = 1− PRSD (TSD > T )

L−1∏l=1

(1− PRl(Tdl < T ))

(14)

PR(Td < T ) = 1− e−βSDTL−1∏l=1

(kl∑i=1

C(kl)i

(e−βiT

))(15)

By assuming X totally generated messages in thenetwork, the average of the delivery probability in thenetwork is calculated as

PR =1

X

X∑m=1

PRm (16)

4.2 Delivery DelayTheorem 2. The expected total time required to delivera message from S to D along an individual path l canbe calculated as

E[Dl] =´∞0PRl(Tdl > t) =

∑kli=1 C

(kl)i .´∞0e−βitdt

E[Dl] =

kl∑i=1

C(kl)i .

1

βi(17)

Let message m have L − 1 copies (excluding themessage at the source) traversing on L− 1 independentpaths. The minimum delivery delay can be written as:

DSD = min{TSD, Td1, Td2, ..TdL−1} (18)

where TSD and Tdl are a random variables represent-ing the delivery delay through direct path between Sand D and through one of L− 1 paths, respectively. Theexpected delay of message m, E[DSD], can be calculatedas

E[DSD] =´∞0P (Td > t) =´∞

0e−βSDt

∏L−1l=1

(∑kli=1 C

(kl)i .e−βit

)dt

= 1βSD

´∞0βSDe

−βSDt∏L−1l=1

(∑kli=1 C

(kl)i .e−βit

)dt =

8

1

βSDE

{L−1∏l=1

(kl∑i=1

C(kl)i .e−βiTSD

)}, TSD <∞ (19)

The above relation gives an upper bound on thedelivery delay since it is conditioned to TSD, TSD < ∞and can be taken as point of reference.

The average delivery delay of message m can becalculated intuitively as:

E[ED(S,D)

]=

[1

L

(TSD +

L−1∑l=1

Tdl

)].

1

PR(Td < T )(20)

TSD is included in (19) only if TSD <∞.By assuming X totally generated messages in the net-

work, the average delivery delay can thus be calculatedas

DR =1

X

X∑m=1

Dm (21)

4.3 Validation of Analytical ModelIn order to evaluate the accuracy of the mathematicalexpressions in this analysis, SAURP is examined undertwo network status scenarios. In the first scenario, thenetwork is operating under no congestion, i.e., all thenodes have infinite buffer space, and the bandwidth ismuch larger than the amount of data to be exchangedbetween any two encountered nodes. In the secondscenario, the network is operating under limited re-sources, i.e., the forwarding opportunities can be lostdue to high traffic, limited bandwidth, limited bufferspace, or contention (i.e., more than one node withinthe transmission range are trying to access the wirelesschannel at the same time). For both scenarios, 50 nodesmove according to community-based mobility model[6] in a 300x300 network area. The transmission rangeis set to 30 to enable moderate network connectivitywith respect to the considered network size. The trafficload is varied from a low traffic load (i.e., 20 messagesgenerated per node in 40,000 time units) to high trafficload (i.e., 80 messages generated per node in 40,000 timeunits). A source node randomly chosen a destination andgenerates messages to it during the simulation time. Inthis analysis the message copies are set to 5 (i.e., forminga maximum of 5 paths).

Examining SAURP under the two scenarios is veryimportant; in case of no congestion, the best path thatis taken by a message is mainly based on the inter-encounter time, while under congestion, the messagewill be buffered for longer period of time and enforcedto take longer path to go around the congested arearesulting in more dropping rate and longer deliverydelay.

To enable accurate analysis, the simulation programis run for a period of time ( warm up period of 10,000

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time units) such that each node can build and maintainthe best forwarding paths with every other node in thenetwork. These forwarding paths are mainly based onthe congestion degree (traffic loads values) considered inthe analysis. The forwarding path is cached by followingthe trajectories of the generated messages during thewarm up stage between every source destination pair inthe network. These messages are forwarded from nodeto node according to SAURP routing mechanism.

In this analysis, we simplified the calculation by lim-iting our study to only the best two of forwarding pathsamong all other paths and compare the simulation andtheoretical results of delivery ratio and delivery delay.In most cases, a message takes the best forwarding paththat based on the inter-encounters history if the networkis not congested and the buffers operate under theircapacity limit.

Fig. 3 and Fig. 4 compare the theoretical and simula-tion results in terms of delivery ratio and delivery delayof the considered scenarios.

As seen from the figures, when the network resourcesare enough to handle all the traffic loads (Scenario 1),there is no dramatic change in the obtained delivery ratioand delivery delay for all traffic loads. That is becausemessages follow the best forwarding paths that lead tobest performance. The simulation and analytical plots forSAURP present close match and validates the generalityof the analytical expressions. Additionally, it is evidentthat (16) and (20) are tight for all degrees of traffic loads.When the network resources are limited (i.e, scenario 2),the contention and the overhead of MAC layer increase,resulting in longer forwarding paths, higher drop rate,and longer delivery delay. The simulation and analyticalplots are still providing close match with small divergein case of high traffic loads.

Although the contention does affect the accuracy ofour theoretical expressions, the error introduced forSAURP is not large (20%), even for large traffic loads.Therefore, we believe the analytical expression is usefulin assessing the performance in more realistic scenarioswith contention. As an evident by these plots, the actualdelay obtained by SAURP becomes increasingly worse

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than what the theory predicts. This demonstrates theneed to add an appropriate contention model when itcomes to modeling flooding-based schemes. A first effortin that direction can be found in [28].

5 PERFORMANCE EVALUATION

5.1 Experimental Setup

To evaluate the SAURP, a DTN simulator similar to thatin [30] is implemented. The simulations are based on twomobility scenarios; a synthetic one based on communitybased mobility model (CBMM) [6], and a real-worldencounter traces collected as part of the Infocome 2006experiment, described in [42].

The problem setup consists of an ad hoc network witha number of nodes moving independently in a geograph-ical region, and each node belongs to a predeterminedcommunity. Each node can transmit up to a distanceK ≥ 0 meters away, and each message transmissiontakes one time unit. A slotted collision avoidance MACprotocol with Clear-to-Send (CTS) and Request-to-Send(RTS), is implemented for contention resolution. A mes-sage is acknowledged if it is received successfully at theencountered node by sending back a small acknowledg-ment packet to the sender. The performance of SAURPis examined under different network scenarios and iscompared with some previously reported schemes listedbelow.

• PROPHET [3]• Spray and Focus (S&F) [6]• Most mobile first (MMF)[25]• Delegation forwarding (DF) [14]• Self-Adaptive utility-based routing protocol

(SAURP)• Self-Adaptive routing protocol (SARP) [15]

The performance comparison under the considered mo-bility scenarios is in terms of average delivery delay,delivery ratio, and the total number of transmissionsperformed for all delivered messages.

5.2 CBMM Scenario5.2.1 Evaluation ScenariosIn the simulation, 110 nodes move according to thecommunity-based mobility model [6] in a 600 x 600meter network in a given geographical region. The simu-lation duration is 40,000 time. The message inter-arrivaltime is uniformly distributed in such a way that thetraffic can be varied from low (10 messages per nodein 40,000 time units) to high (70 messages per nodein 40,000 time units). The message time to live (TTL)is set to 9,000 time units. Each source node selects arandom destination node, begins generating messagesto it during simulation time.

We analyze the performance implication of the follow-ing. First, the performance of the protocols is evaluatedwith respect to the impact of the number of messagecopies. Second, with respect to the low transmissionrange and varying buffer capacity under high trafficload. Third, with respect to the moderate-level of connec-tivity and varying traffic load. Fourth, the performanceof the protocols is examined in terms of the bandwidth.Finally, the performance of the protocols is examined interms of the level of connectivity changes.

Impact due to Number of Message CopiesWe firstly look into impact of the number of messagecopies toward the performance of each protocol. Thetransmission range K of each node is set to 30 meters,leading to a relatively sparse network. In order to re-duce the effect of contention on any shared channel,the traffic load and buffer capacity is set to medium(i.e., 40 generated messages per node ) and high (i.e.1,000 messages per node), respectively. The number ofmessage copies is then increased from 1 to 20 in orderto examine their impact on the effectiveness of each pro-tocol. The proposed SAURP is compared with the S&Fand MMF schemes, since each scheme has a predefinedL to achieve the best data delivery. Note that the value ofL depends on the application requirements, the mobilitymodel considered, and the design of the protocol.

Fig. 5 shows the results on message delivery delay,delivery ratio, and number of transmissions under dif-ferent numbers of copies of each generated message. Ascan be seen, the L value has a significant impact on theperformance of each scheme. It is observed that bestperformance can be achieved under each scheme witha specific value L.

In the next scenarios, the number of message copiesis fixed at 15 for the S&F scheme, 10 for the SARP andSAURP, and 18 for the MMF. These L values can serve asa useful rule of thumb for producing good performance.

The Effect of Buffer SizeIn this scenario the performance of SAURP regardingdifferent buffer sizes is examined under a low transmis-sion range (i.e., K = 30) and a high traffic load (i.e., 50

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messages generated per node). Due to the high trafficvolumes, we expect to see a significant impact upon themessage forwarding decisions due to the degradation ofutility function values caused by buffer overflow. Notethat when the buffer of the encountered node is full,some messages cannot be delivered even though the en-countered node metric is better than the custodian node.This situation results in extra queuing delay, especially inthe case that flooding-based schemes are in place. Fig. 6shows the experiment results where the buffer space wasvaried from 5 (very limited capacity) to 200 (relativelyhigh capacity) messages to reflect the performance of theprotocols under the considered traffic load. As shown inFig. 6, when the buffer size is small (50 messages or less)

the performance of the protocols is very sensitive to thechange of buffer capacity.

It is observed that the SAURP scheme produced thebest performance in all scenarios, since it takes thesituation that a node may have a full buffer into consid-eration by degrading the corresponding utility metric,it produced the best performance. In specific, SAURPyielded a shorter delivery delay than that of PROPHETby 230%, S&F by 50%, and SARP by 22%. SAURPcan achieve a higher delivery ratio than DF by 73%,PROPHET by 79%, S&F by 66%, and SARP by 17%.Although SUARP produced more transmissions thanMMF and DF, it yielded a smaller delivery delay thanthat of MMF by 82%, and DF by 66%. As the buffer

11

size increased, the performance of all protocols improvedespecially for MMF and SARP. When the buffer size islarger than the traffic demand, the SARP scheme hasyielded a competitive performance due to the relaxationof buffer capacity limitation. SAURP still yielded the bestperformance with a smaller number of transmissionsthan S&F by 33%.

The Effect of Traffic LoadThe main goal of this scenario is to observe the perfor-mance impact and how SAURP reacts under differentdegrees of wireless channel contention. The networkconnectivity is kept high (i.e., the transmission rangeis set to as high as 70 meters) under different trafficloads, while channel bandwidth is set relatively quitesmall (i.e., one message transfer per unit of time) inorder to create congested environment. We have twoscenarios for nodal buffer capacity: 1) unlimited capacity;and 2) low capacity (15 messages). Fig. 7 shows theperformance of all the routing algorithms in terms of theaverage delivery delay, delivery ratio, and total numberof transmissions.

It is observed that PROPHET produced the largestdelivery delay and requires a higher number of trans-missions compared to all the other schemes, thus it is notincluded in figure 7(c). PROPHET produced an order ofmagnitude more transmissions than that by SAURP.

As shown in Fig. 7(a), 7(b), and 7(c), when the trafficload is increased, the available bandwidth is decreasedaccordingly, which causes performance reduction. Whenthe traffic load is moderate (i.e., less that 50 messages), itis clear that the delivery delay is short in all the schemes,while SAURP outperforms all other protocols and MMFis the second best. This is because in MMF, the effectof buffer size is relaxed, which makes nodes buffer anunlimited number of messages while roaming amongcommunities. SAURP can produce delay shorter thanthat of PROPHET, MMF, DF, S&F, and SARP by 350%,52%, 400%, 250%, and 57%, respectively. Regarding thedelivery ratio, SAURP, MMF, S&F, and SARP can achieveexcellent performance of 98%, while the PROPHET rout-ing degrades below 60% for high traffic loads. DF canachieve delivery ratio above 92%.

As expected, the performance of all the schemes de-grades as wireless channel contention is getting higher,especially when the traffic load exceeds 50 messagesper node during the simulation period. We observedthat SAURP can achieve significantly better performancecompared to all the other schemes, due to the considera-tion of busy links in its message forwarding mechanism,where the corresponding routing-metric is reduced ac-cordingly. This results in the ability of rerouting the con-tended messages through the areas of low congestion.However, such a rerouting mechanism makes messagestake possibly long routes and results in more transmis-sions than that of MMF and DF. In summary, the deliverydelay obtained by the SAURP in this scenario is shorterthan that of PROPHET by 330%, MMF by 66%, S&F

by 88%, DF by 233%, and SARP by 30% respectively.Regarding delivery ratio, SAURP can achieve as high as93%, compared with 90 % by SARP, 87% by MMF, 77%by DF, and 88% by S&F. Even though DF produced thelowest number of transmissions, it is at the expense ofthe worst delivery delay.

As the buffer capacity is low (e.g., 15 messages) andthe traffic load is high, the available bandwidth de-creases and the buffer occupancy increases accordingly,which makes the performance of all protocols degraded,especially for the PROPHET and MMF. It is observedthat PROPHET produced the largest delivery delay. It isnotable that SAURP outperforms all the multiple-copyrouting protocols in terms of delivery delay and deliveryratio under all possible traffic loads. When the trafficload is high, SAURP yielded shorter delivery delay thanthat of SARP by 28%, MMF by 53%, SF by 41%, DFby 47%, and PROPHET by 233%. Although SAURPrequires more transmissions compared to the MMF andDF, the number is still smaller than that produced byS&F. SAURP can achieve delivery ratio above 76% forhigh traffic loads, while the SARP, PROPHET , DF,S&F, and MMF degrades by 66%, 47%, 51%, 62%, and55%, respectively. Fig. 8(a), 8(b), and 8(c) shows theperformance of all techniques under this scenario.

The Effect of Channel Bandwidth and Traffic LoadTo examine the effect of channel bandwidth, the networkconnectivity is set to moderate (under moderate trans-mission range by setting K = 50), and the link capacityis set five times higher than that used in the previousscenarios in order to avoid bottlenecks in the trafficloads. Fig. 9 shows the performance of all the routingprotocols in terms of the average delivery delay, deliveryratio, and total number of transmissions.

As the link bandwidth increased, it can be seen fromFig. 9 that the performance of all routing schemes hasimproved with respect to delivery delay and deliveryratio, because the buffer capacity is unlimited and thecontention on the bandwidth is relaxed. SARP achievedthe best performance, while SAURP achieves the secondbest compared to the other schemes. It outperformsMMF scheme, since MMF is coupled by the numberof message copies. Compared to PROPHET, DF, andS&F, SAURP has a shorter delay by 450%, 390%, and83%, respectively. Meanwhile, SAURP needed much lesstransmissions compared to that by S&F. Even thoughDF produced the lowest number of transmissions, ithas the worst performance in terms of delivery delayand delivery ratio. All protocols achieved a deliveryratio above 90%. Compared to other protocols, SAURPmaintains the second highest delivery ratio after SARP:above 96%.

The above results show that channel bandwidth hassignificant impact on the performance of the protocols.If the available bandwidth is much higher than thetotal traffic load, flooding based schemes [11] can yielddelivery delay as SAURP at the expense of taking far

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more transmissions. On the other hand, if the channelbandwidth is limited, SAURP and the spraying schemesoutperform the flooding-based schemes because of thecontention caused by limited bandwidth.

The Effect of ConnectivityThis scenario studies the performance impact due tonetwork topology connectivity. In the scenario, the levelof connectivity is increased from very sparse to highlyconnected by varying the value of K while observingthe resultant impact on the performance . We are partic-ularly interested to investigate the SAURP mechanismin response to heavy traffic loads which result in highcontention on the wireless channel. The buffer capacityis kept low (15 messages), and the traffic load is con-siderably high (60 messages per node). Fig. 10 showsthe average delay, delivery ratio, and the number oftransmissions as a function of transmission range.

SAURP outperforms all the schemes in terms of deliv-ery delay while taking noticeably fewer transmissionsthan that by S & F and and SARP schemes underall connectivity considered in the simulation. Whenthe network is sparsely connected, SAURP can achieveshorter delivery delay than all other schemes, that isbecause the performance of other schemes is affectedby the uncertainty of buffer occupancy status. On theother hand, when the network is moderate-connected,SARP can achieve commutative-level of delivery delaycompared to SAURP with more transmissions. As thenetwork becomes almost connected and the traffic loadis high, the uncertainty of both buffer occupancy statusand the availability of bandwidth affect the performanceof the other techniques. As a result, SAURP outperformsall other schemes in terms of delivery delay and deliveryratio.

5.3 Real Trace ScenarioIn order to evaluate SAURP in realistic environment,the performance of the scheme is examined using realencounter traces. These data sets comprise of contacttraces between short-range Bluetooth enabled devicescarried by individuals in Infocome 2006 conference en-vironment. More details about the devices and the datasets, including synchronization issues can be found in[42]. In order to observe the performance impact andhow SAURP reacts under congested environment, we

set the bandwidth, buffer capacity, and the distributionof the contact time such that congested environment isformed. The channel bandwidth is set relatively quitesmall (i.e., one message transfer per unit of time), andthe buffer size is set to 10, under different levels of trafficdemand.

Fig. 11 shows the performance of all the routing algo-rithms in terms of the average delivery delay, deliveryratio, and total number of transmissions.

As the buffer capacity is low and the traffic load ishigh, the available bandwidth decreases and the bufferoccupancy increases accordingly, which makes the per-formance of all protocols degraded, especially for thePROPHET and MMF. It is observed that PROPHET pro-duced the largest delivery delay. It is subject to at least2.1 times of longer delivery delay than that by SARP.It is notable that SAURP outperforms all the multiple-copy routing protocols in terms of delivery delay anddelivery ratio under all possible traffic loads. When thetraffic load is high, SAURP yielded shorter deliverydelay than that of MMF by 52%, SF by 30%, and DFby 40%. Although SAURP requires more transmissionscompared to the MMF and DF, the number is still smallerthan that produced by S&F. SAURP can achieve deliveryratio above 76% for high traffic loads, while the SARP,POPHET, DF, S&F, and MMF degrades by 67%, 38%,53%, 60%, and 50%, respectively.

6 CONCLUSION

The paper introduced a novel multi-copy routingscheme, called SAURP, for intermittently connected mo-bile networks that are possibly formed by densely dis-tributed and hand-held devices such as smart phonesand personal digital assistants. SAURP aims to explorethe possibility of taking mobile nodes as message carriersin order for end-to-end delivery of the messages. Thebest carrier for a message is determined by the predictionresult using a novel contact model, where the networkstatus, including wireless link condition and nodal bufferavailability, are jointly considered. We provided an ana-lytical model for SAURP, whose correctness was furtherverified via simulation. We further compared SAURPwith a number of counterparts via extensive simulations.It was shown that SAURP can achieve shorter deliverydelays than all the existing spraying and flooding basedschemes when the network experiences considerable

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Fig. 11. The effect of traffic load under trace based scenario

contention on wireless links and/or buffer space. Thestudy provides a significance that when nodal contactdoes not solely serve as the major performance factor, theDTN routing performance can be significantly improvedby further considering other resource limitations in theutility function and message weighting/forwarding pro-cess.

APPENDIX

Proof of (10):First we compute the convolution needed in the proof.

e−ax ⊗ e−bx =´ x0e−a(x−u)e−budu = e−ax e

(a−b)x−1a−b

= e−bx−e−axa−b

for two hop (k = 2)

P1+2 = P1 ⊗ P2 = β1β2´ x0e−(β1−β2)te−β2xdt

= β1β2β2−β1

(e−(β1x−β2x)

)= β1β2

[e−β1x

β2−β1+ e−β2x

β1−β2

]= −C(2)

1 P1(x)− C(2)2 P2(x)

For k ≥ 3, by inductive we can get

Pk−1 =∑kl−1i=1 Ckl−1i .Pi(x)

Pk = P1+2+...k−1 ⊗ Pk =[∏k−1i=1 βi

]∑kl−1j=1

eβjx∏k−1

i=1(βk−βj)

⊗ Pk

=∑kl−1i=1 Ckl−1i

(βk

βk−βiPi(x) +βi

βi−βkPk(x))

If we consider Ckli = Ckl−1i . βiβi−βk , we get

Pk =∑kl−1i=1 Ckli .Pi(x) +

∑kl−1i=1 Ckl−1i . βi

βi−βkPk(x)

For the second term, we have∑kl−1i=1 Ckl−1i

βiβi−βk =

∑kl−1i=1

βiβi−βk

.(∏k−1

j=1, j 6=iβj

βj−βi

)=∏k−1j=1 βj .

∑kl−1i=1

∏k−1j=1, j 6=i

1βj−βi

=∏k−1j=1

βjβj−βk = Cklk

Therefore, we have

fk(x) = Pk =∑kl−1i=1 Ckli Pi(x) + Cklk Pk(x)

=∑kli=1 C

kli Pi(x)

CDF:Fk(x) = P (Td < T ) =

´ T0fk(x)dx =∑kl

i=1 Ckli

´ T0Pi(x)dx =

∑kli=1 C

kli .(1− e−βiT )

CCDF:

= P (Td > T ) =´∞Tfk(x)dx =

∑kli=1 C

kli .e−βiT

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