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TEMPLE. UNIVERSITY. Deadline-sensitive Opportunistic Utility-based Routing in Cyclic Mobile Social Networks. Mingjun Xiao a , Jie Wu b , He Huang c , Liusheng Huang a , and Wei Yang a a University of Science and Technology of China, China b Temple University, USA
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TEMPLE UNIVERSITY 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, China b Temple University, USA c Soochow University, China
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 route 1 message route 2 sender receiver route k • Concept: Utility-based routing • Valuable message: route (more reliable, costs more) • Regular message: route (less reliable, costs less) 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
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
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
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:
Solution: DOUR • Compute Expected Utility Example:
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
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).
Solution: DOUR • Determine Optimal Forwarding Sequence Example:
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 salways 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 • Metrics Average 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.
