Routing and Performance Evaluation of Disruption Tolerant Networks

1. Routing and Performance Evaluation of Disruption Tolerant Networks Mouhamad IBRAHIM Ph.D. defense Advisor: Philippe Nain INRIA Sophia Antipolis 14 November, 2008

2. Thesis outline Part I: Design and performance evaluation of routing protocols for disruption tolerant networks Part II: Design and performance evaluation of medium access control protocol for IEEE 802.11 standard

3. Routing in mobile ad hoc networks • Mobile Ad Hoc Networks (MANETs) • No fixed infrastructure • Nodes communicate in a peer to peer mode with other nodes • Nodes work as routers: Store-Forward • Routing in MANETs: Main assumption • Existence of end-to-end paths between Source-Destination pairs

4. Routing challenges in MANETs • Instability of wireless paths: node mobility, low node density, interferences,… • Does not help to establish and maintain routes • Appearance of Disruption/Delay Tolerant Networks (DTNs): disconnected mobile networks • Often there is no end-to-end path among Source-Destination pairs  take advantage of node mobility to perform routing Store-Carry-Forward

5. V1 S Store-Carry-Forward: how does it work? R V2 D V3

6. Routing approaches for DTNs • Classification based on the degree of knowledge that nodes have about their future contact opportunities • Four classes of routing techniques: • Scheduled-contact based routing • Controlled-contact based routing • Predicted-contact based routing • Opportunistic-contact based routing

7. Opportunistic-contact based routing • Flooding mechanism Epidemic routing protocol • Limit the number of hops Multicopy Two-hop Relay protocol • Limit the number of copies Spray-and-Wait protocol • Question: To what extent we can push the performance if we increase number of contact opportunities: Throwboxes

8. Throwboxes (1) • Throwboxesare fixed relays with better storage and energy capabilities • Battery powered for short term use or solar panel for long term use Photos are taken from http://prisms.cs.umass.edu/dome/

9. Throwbox (2) • Operate in Store-Forward paradigm • Promising approach to route messages in DTNs • Adding one throwbox on UMass DieselNet improves packet delivery by 37% and reduces message delivery delay by 10%[1] • Research still in its early stage!! Part I: Evaluate and design routing techniques for opportunistic DTNs augmented by throwboxes [1] N. Banerjee et al. An energy-efficient architecture for DTN throwboxes. Infocom 2007.

10. Opportunistic DTNs: Inter-meeting times • Characteristic of inter-meeting times among nodes • Random mobility: • Inter-meeting times mobile/mobile have shown to follow an exponential distribution [Groenevelt et al.: The message delay in mobile ad hoc networks. Performance Evaluation, 2005] • Human mobility: • Inter-meeting times mobile/mobile have shown to follow power law distribution [Chaintreau et al.: Impact of human mobility on the design of opportunistic forwarding algorithms. Infocom, 2006]

11. Opportunistic-contact: Random mobility Random Waypoint model (RWP) Random Direction model (RD) X2 V2 T2, V2 X1 α2 V1 T1, V1 R α1 R • Directions (αi) are uniformly distributed(0, 2π) • Speeds (Vi) are uniformly distributed (Vmin,Vmax) • Travel times (Ti) are exponentially /generally distributed • Next positions (Xi)s are uniformly distributed • Speeds (Vi)s are uniformly distributed(Vmin,Vmax)

12. Simulation N = 1 Exponential –μx Simulation N = 5 Exponential –5μx Simulation N = 10 Exponential –10μx Mobile/box inter-meeting times CCDF on a linear-log scale: log(Pr(τ > x))= log(e - μ x )= - μ x

13. Contact time C1 C2 C3 Time τ1 τ2 τ3 Parameter μ (1) • Stationary probability to find the mobile within neighborhood of a box f(.,.)  stationary spatial pdf of the mobility model • Using Renewal theory, we have

14. Parameter μ (2) • Unconditioning on throwbox location within the network area LxL • Case of Random Direction model: mobile nodes are uniformly distributed[1] and hence independent of throwboxes pdfdistribution!! pdf of throwboxes distribution Stationary pdf of location for mobility model [1] P. Nain et al. Properties of random direction models. Infocom 2005.

15. Parameter μ (3) • Case of Random Waypoint model: mobile nodes are distributed around the center[3] • μ depends on throwboxes spatial distribution Throwboxes uniformly distributed Throwboxes generally distributed, e.g. [3] J.-Y. Le Boudec and M. Vojnovic. Perfect simulation and stationarity of a class of mobility models, Infocom 2005.

16. Performance evaluation of relaying protocols in DTNs with throwboxes • Epidemic routing protocol (ER) • Multicopy two-hop relay protocol (MTR)

17. B1 B2 Epidemic routing protocol Epidemic Routing  flooding protocol R V2 D V3 V1 S

18. B1 B2 Multicopy two-hop protocol (MTR) Copies make at MAX two hops between Source/Destination R V2 D V3 V1 S

19. B1 V1 S B2 Network model M throwboxes N-1 mobile relay nodes Mobile/box: Exponential with μ R V2 Mobile/mobile: Exponential with λ[4] D V3 Destination node Source node [4] R. Groenevelt, P. Nain, and G. Koole. The message delay in mobile ad hoc networks. Performance Evaluation, 2005.

20. Metrics of interest • Distribution and mean value of • Delivery delay T user side • Total number of generated copies G when one packet is to be send from source to destination  network operator side

21. Markov analysis • Two-dimensional continuous time absorbing Markov chain I(t) = (R(t),B(t)) as follows: • For t < T: • R(t) {1,2,…,N}  number of mobile nodes holding a copy of the packet (source included) • B(t) {0,1,2,…,M}  number of throwboxes holding a copy of the packet (assumed fully disconnected) • For t > T, I(t)= {a} absorbing state, i.e. when destination receives the packet

22. MTR protocol: Delivery delay (1) Approach to solve: Stochastic analysis • Delivery delay TMTR is the minimum of N + M mutually independent R.V.s TMTR = (DSD, Dr1, Dr2,…, DrN-1, DB1,…, DBM) • Hence distribution of TMTR reads as source  relay  destination: sum of twoexponentials with rate λ source  throwbox  destination: sum of two exponentialswith rate μ source  destination: exponential with rate λ

23. MTR protocol: Delivery delay (2) and mean of TMTR reads as • Using fluid model, we obtained also asymptotic expression for E[TMTR] when N or M go large

24. MTR protocol: # of generated copies • Define Pra(n,m) as probability that last visited state before absorption is state (n,m) • Pra(n,m) is sum of probabilities of different paths joining state (1, 0) to state (n,m) • These probabilities are all equal. Their total number is • The probability distribution of GMTR reads as

25. m*(i,j)is the (i,j)thentry of M-1 Epidemic protocol: Delivery delay Approach to solve: Theory of absorbing Markov chain • Delivery delay TER represents time to absorption Q = infinitesimal generator of Markov chain • M = transition matrix among non-absorbing states

26. Epidemic: # of generated copies • Define Pra(n,m) as probability that last visited state before absorption is state (n,m) • Case of epidemic protocol: transition ratesare state dependent approach reported by [Gaver et al.: Finite Birth-And-Death Models in Randomly Changing Environments, 1984] • The probability distribution of GER follows then

27. Case of connected Throwboxes • Underlying assumption: Pass a copy to one throwbox to let all the others infected • Same expressions hold by substituting M  1 μ  M μ

28. Model validation: Delivery delay Throwboxes disconnected and uniformly distributed Throwboxes disconnected and RWP stationary distributed Throwboxes connected and uniformly distributed Throwboxes connected and RWP stationary distributed Epidemic protocol RWP model

29. Model validation: Delivery delay Throwboxes disconnected and uniformly distributed Throwboxes disconnected and RWP stationary distributed Throwboxes connected and uniformly distributed Throwboxes connected and RWP stationary distributed MTR protocol RWP model

30. Performance evaluation framework for throwboxes-augmented DTNs Objective: Framework to evaluate and analyze performance of various routing strategies for DTNs extended with throwboxes

31. Proposed five routing strategies (1) • Main idea: define possible message forwarding interactions among the Source, Mobile relays, Throwboxes and the Destination • Ultimate goal: exploit throwboxes presence to minimize copies generations at mobile nodes

32. Infected throwbox Mobile relay • Destination Infected mobile relay Mobile relay • Source Throwbox • Destination Strategy I Strategy II Strategy III Strategy IV Strategy V Relay Destination Relay Destination Relay Throwbox Relay Throwbox Relay Throwbox Source Relay Relay  Destination Relay Destination Source Relay Proposed five routing strategies (2) • Common forwarding interactions: Particular interactions for each strategy

33. Metrics of interest Under a given routing strategy s: 1- Mean delivery delay between a Source/Destination E[Ts] • Mean number of valuabletransmissionsE[Gs], i.e. those made only by mobile nodes plus the source 2- Mean number of mobile relays infected by the source, Is 3- Mean number of infected throwboxes, Ks 4- Proba. Source delivers message to destination, PrSs 5- Proba. Mobile relay delivers message to destination, PrRs

34. Modeling framework (1) • Three-dimensional continuous time absorbing Markov chain As(t) = (Is(t), Js(t), Ks(t))as follows: • For t < Ts, As(t) = (Is(t), Js(t), Ks(t)): Is(t) Number of mobile nodes infected by the source Js(t) Number of mobile nodes infected by the throwboxes Ks(t) Number of infected throwboxes • For t > Ts: As(t) = {a} absorbing state

35. a θ(i,j,k) 3- Mean number of throwboxes Ks  S(i,j,k) i,j,k i,j+1,k 2- Mean number of mobile relays Is  β(i,j,k) 1- Mean sojourntimeTs  α(i,j,k) Fs(i,j,k) is mean value of metric Fs till absorption starting from (i,j,k) i+1,j,k 5- Proba. delivery by relay PrRs  Mean value of metric Fs at (i,j,k) γ(i,j,k) 4- Proba. delivery by source PrSs  i,j,k+1 Modeling framework (2)

36. N,0,M θ(N,0,M) a Modeling framework (3) • Values of Fs are known at last states  only one possible transition to state {a}, e.g. • Iterating recursive equation till initial state (1,0,0): Known!

37. Modeling framework (4) • To compute E[Ts] and G[Ts] under a given strategy  Define corresponding state spaceEs and infinitesimal generatorQs(t)

38. Framework validation Strategy II: Analytical versus simulation results

39. Comparing E[T] and E[G] with respect to Epidemic protocol Strategy II Strategy IV Strategy V

40. Diameter of epidemic protocol Context: Opportunistic DTNs running epidemic protocol WITHOUT throwboxes Objective: Examine the mean length of forwarding path

41. Diameter of epidemic protocol • Instance of epidemic tree: • XS,D denote number of intermediate hops between S and D  Aim is to compute E[XS,D]: diameter of epidemic protocol S R2 R1 R3 R5 D R4

42. 1 1 1 1 1 1 2 2 3 2 2 4 3 2 2 3 4 3 3 4 3 4 4 4 Diameter computation (1) Approach to solve: Theory of recursive tree • Recursive tree is like any tree on a graph, however, nodes are labeled with their joining instants to the tree Example: recursive tree of order 4 E[Xi,j] is known for random tree

43. Diameter computation (2) • Conditioning on possible labels of the destination among the N nodes • Look to the impact of limiting number of forwarding hops on relaying performance • Using the framework, we analyze different dissemination algorithm with limited number of hops

44. Epidemic protocol: Limiting # of hops Max. hop = 2 Max. hop = 3 Max. hop = 4 Max. hop = 5

45. Part II

46. Adaptive Backoff Algorithm for IEEE 802.11 • Motivation: IEEE 802.11 performs poorly in congested network • Following a successful transmission, source station chooses backoff duration randomly in {0,…,CW0} • Objectives: • Adaptive algorithm aware of active stations • Maximize system throughput and minimize end-to-end delay Inadequate for large networks

47. How to transmit at optimal transmission probability τ* • Bianchi model[5]: • Transmission probability • Our idea: m= log(CWmax/CW0) [5] G. Bianchi. Performance analysis of the IEEE 802.11 distributed coordination function. JSAC 2000.

48. Estimating # of active stations • Active stations are decoding all transmitted packets on the channel  identify emitting stations • Stations counts signs of life coming from others stations • signs of life: error free data and RTS packets • Measured during virtual transmission times • Samples used as input to a corrected WMA filter Ňk: sample at kth period CWk: window at kth period α, β : correcting factors

49. Adaptive Standard Group departure Group departure Group entrance Group entrance Algorithm performance

50. Conclusions (1) • Accurate approximation for meeting rate between a mobile/throwbox: • For two common mobility model • For general throwboxes spatial distribution • Explicit expressionsfor the distribution and the mean of delivery delay and number of generated copies • Under epidemic and MTR protocols • Asymptotic expressions for these means under MTR