Deadline-sensitive Opportunistic Utility-basedRouting in Cyclic Mobile Social Networks

Mingjun Xiaoa, Jie Wub, He Huangc,

Liusheng Huanga , and Wei Yanga

a University of Science and Technology of China, Chinab Temple University, USA

c Soochow University, China

TEMPLEUNIVERSITY

Outline

• Motivation

• Problem

• Solution

• Simulation

• Conclusion

Motivation

• Concept : Utility-based routing [Jiewu 12, 13]– Utility is a composite metric

Utility (u) = Benefit (b) – Cost (c)– Benefit is a reward for a routing– Cost is the total transmission cost for the routing– Benefit and cost are uniformed as the same unit

– Objective is to maximize the (expected) utility of a routing

Motivation

• Concept : Utility-based routing– Valuable message: route (more reliable, costs more)– Regular message: route (less reliable, costs less)

sender receiver

messageroute 1

route 2

route k

Benefit is the successful delivery reward

Motivation

Utility-based

routing

Cyclic Mobile Social Networks

delivery deadline is an important factor for the routing design

Deadline-sensitive

utility-based routing

Problem

• Cyclic Mobile Social Networks– Example

Problem• Cyclic Mobile Social Networks

– Each cyclic MSN can be seen as a weighted graph– Each edge contains a set of probabilistic contacts– Each probabilistic contact:

< contact time, contact probability >

Problem

• Deadline-sensitive utility-based routing– Benefit:

– Utility:

– Expected utility ui (t): the expected utility for node i to send a message to its destination within the deadline t

delivery failed,0

ithin timedelivery w successful,)(

tbtb

ctbtu )()(

Problem

• Example:

Utility for the successful delivery:

u(60)=b-c =20-5=15

Utility for the failed delivery: u(60)=0-c=0-5=-5

Expected utility: u1(60)=0.5*15+0.5*(-0.5)=5

1

Benefit b 20Deadline t =60

Contact time τ =50Probability p =0.5

Cost c =5

Problem

• Problem– Cyclic mobile social network: G=V, E

V: mobile nodes

E: set of probabilistic contacts between nodes

T: cycle

d: destination– Objective: design a deadline-sensitive utility-based

routing algorithm to maximize ui (t) for each node i in V

Solution: DOUR

• Basic idea of DOUR – For single-copy routing– Adopt the opportunistic routing strategy– Nodes iteratively calculate their optimal expected

utility values when they encounter– During the iterative computation, each node determines

an optimal forwarding sequence– Forward messages according to the optimal forwarding

sequence

Solution: DOUR

• Concepts– Forwarding opportunity ⟨, v, p :⟩

the node can send messages to node v at time with the contact probability p

– Forwarding sequence Si (t)

An ordered set of forwarding opportunities in the ascending of contact times

Si (t) = {⟨1, v1, p1 , ⟩ ⟨2, v2, p2 , · · · , ⟩ ⟨m, vm, pm }⟩

0 ≤ 1 ≤ 2 ≤· · ·≤ m ≤ t

Solution: DOUR

• Concepts– Opportunistic forwarding rule

each node i forwards messages via the forwarding opportunities in its forwarding sequence in turn, according to the ascending of contact times, until the messages are successfully forwarded to some node, or all forwarding opportunities are exhausted.

Solution: DOUR

• Concepts– Opportunistic forwarding rule

Example:

Solution: DOUR

• Concepts– Optimal forwarding sequence

the forwarding sequence, through which node i can achieve its optimal expected utility when it forwards messages

)()()(

* |)(maxarg)( tSitOtS

i iii

tutS

Solution: DOUR

• Compute Expected Utility– Theorem 1: Assume that node i has a forwarding

sequence Si(t)={⟨1, v1, p1 , ⟩ ⟨2, v2, p2 , · · · , ⟩ ⟨m, vm, pm }, where the optimal expected utilities of ⟩ v1,…,vm are u1*(t),…, um*(t). The expected utility, which is related to this forwarding sequence, satisfies:

m

jijj

j

hjhtSi ctupptu

i1

*1

1)( )()1(|)(

Solution: DOUR

• Compute Expected UtilityExample:

5565.2

5)0()5.01()3.01()7.01(7.0

)10()3.01()7.01(5.0

)30()7.01(3.0)60(7.0|)60(

*2

*3

*2

*2)60(1 1

u

u

uuu S

Solution: DOUR

• Determine Optimal Forwarding Sequence– Determine all forwarding opportunities of node i for

the deadline t: Oi (t)

– For each subset Si (t) of Oi (t), we compute the related expected utility according to Theorem 1, until we find the forwarding sequence to maximize this expected utility value

)()()(

* |)(maxarg)( tSitOtS

i iii

tutS

Solution: DOUR

• Determine Optimal Forwarding Sequence– Theorem 2: Let Si (t > τ) denote a subsequence of Si (t),

where the contact time of each forwarding opportunity in Si (t > τ) is larger than the time τ. Then,

where ⟨j, vj, pj ⟩ Oi (t).

)(

***|)()()(,,

ji tSiijjijjj tuctutSpv

Solution: DOUR

• Determine Optimal Forwarding SequenceExample:

5|)60( and ,50,3,0.5)60( ,

)60(0,2,0.7 |)60()060(

)60(30,2,0.3 |)60()3060(

)60(50,3,0.5 |)60()5060(

)60(60,2,0.7 |)60()6060(

60,2,0.7 ,50,3,0.5 ,3.0,2,30 ,7.0,2,0)60(

)60(1*1

*1)0(11

*2

*1)30(11

*2

*1)50(11

*3

*1)60(11

*2

1

*1

*1

*1

*1

*1

S

tS

tS

tS

tS

uSThus

Sucu

Sucu

Sucu

Sucu

O

Solution: DOUR

• Determine Optimal Forwarding Sequence

Solution: DOUR

• The Detailed DOUR Algorithm

Solution: DOUR

• Performance of DOUR– Theorem 3: The iterative computation in DOUR will

converge within at most |V| rounds of computation.

– Corollary 4: DOUR can achieve the optimal expected utility for each message delivery.

Solution: m-DOUR

• Deadline Sensitive Utility Model for Multi-copy Routing– Each message has multiple copies to be forwarded– If any one copy arrives at the destination before the

deadline, the message delivery will achieve a positive benefit as the reward.

– If all copies fail to reach the destination, the message delivery will result in zero benefit.

– The utility is the benefit minus the forwarding cost of all copies.

Solution: m-DOUR

• Basic Idea of m-DOUR– We only consider the two-hop k-copy routing

from the source s to the destination d for a given deadline t

– We first derive all forwarding opportunities Os(t)

– We let the source s always dynamically select k best forwarding opportunities from Os(t) to transfer messages until all forwarding opportunities are exhausted.

Simulation

• Real Trace Used– UMassDieselNet Trace

• Algorithms in Comparison– Single-copy routing:

DOUR, MaxRatio, MinDelay, MinCost– Multi-copy routing:

m-DOUR, Delegation, OOF

• MetricsAverage utility, Delivery ratio,

Average delay, Average Cost

Simulation

• Evaluation Settings

Simulation

• Results of Single-copy Routing Algorithms– Average utility vs. Deadline, successful delivery

benefit, forwarding cost

Simulation

• Results of Multi-copy Routing Algorithms– Average utility vs. Deadline, successful delivery

benefit, forwarding cost

Simulation

• Results– Delivery ratio, Average delay, Average cost

Conclusion

• Our proposed algorithm outperforms the other compared algorithms in utility.

• Both of the proposed algorithms provide a good balance among the benefit, delay, and cost.

Thanks!

Q&A