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Research supported by NSERC

Non-bifurcated Routing in Wireless Multi-hop Mesh Networks by Abdullah-Al Mahmood and Ehab S. Elmallah Department of Computing Science University of Alberta Ahmed Kamal Department of Electrical and Computer Engineering Iowa State University. Research supported by NSERC. Outline Introduction

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Research supported by NSERC

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  1. Non-bifurcated Routing in Wireless Multi-hop Mesh NetworksbyAbdullah-Al Mahmood and Ehab S. ElmallahDepartment of Computing ScienceUniversity of AlbertaAhmed KamalDepartment of Electrical and Computer EngineeringIowa State University Research supported by NSERC

  2. Outline • Introduction • Problem Formulation • Solution Approach • Some Related Work • Simulation Results • Concluding Remarks

  3. Introduction • General Objectives • Fixed wireless broadband access • Support applications with different service requirements (e.g., high data rate, low delay jitter etc.)

  4. Gateway Internet Internet Mesh BS Gateway Mesh BS Mesh BS (y) Mesh BS Mesh BS Mesh BS (x) Subscribers d1(x) d2(x) Demand D(x) … d|D(x)|(x) Introduction

  5. Gateway Internet Internet Mesh BS Gateway Mesh BS Mesh BS (y) Mesh BS Mesh BS Mesh BS (x) Subscribers s1(x) Accepted Demand S(x) s2(x) … s|D(x)|(x) Introduction flow f(x, y)

  6. 3,4 3,4 3,4 Packets 1,2, … 7 1,2,5,6,7 1,2,5,6,7 Problem with Splitting Flows • Example: Routing streaming data End user experiences poor stream quality or unusual delay

  7. System Model • Non-bifurcated Flows • A sequence of uniquely identifiable packets • Indivisibly follows the same path without rerouting • Routers do not need synchronization • Interference follows protocol model (conforming to RTS-CTS-DATA-ACK sequence in IEEE 802.11 family of standards)

  8. Problem Formulation with Single Channel • Notations • f(X,Y): aggregate flow between sets of routers X and Y • f(D(x)), f(S(x)): sum of flow values in the vectors D(x) and S(x) respectively • EintT(x): set of edges having one end within interference range of x • f(EintT(x)): sum of flow values along edges in EintT(x) • C(x): Available channel capacity at router x

  9. EintT(x) ≤ C(x) f(V, x) x f(x, V) Problem Formulation with Single Channel Objective: Subject to: • Channel capacity constraint: l(x) = f(x, V) + f(V, x) + EintT(x)

  10. f(V, x) + f(S(x)) Incoming Flows and Accepted Flows f(x, V) Outgoing Flows = y di(x) di(x) di(x) g x Problem Formulation with Single Channel • Flow conservation constraint: • Flow indivisibility constraint: di(x)ÎD(x) is assigned a single route from x to a gateway in G

  11. Remarks • We seek assignment of flows to edges • Implementation: we use source routing in order to realize a set of computed flows • Challenges • Achieved flow values = planned flows? • Any improvement over a sophisticated (ad-hoc) routing protocol (e.g. DSR)?

  12. 1/9 3/8 4/4 s b a t 5/9 7/8 0/4 s b a t Solution Approach • Flow augmenting paths (FAP) are used in classical network flow problems • Example: • Our problem under certain constraints is NP-complete • Traditional FAPs do not work for our problem

  13. Solution Approach • We propose the use of Interference Constrained FAPs (IC-FAP) • IC-FAPs take into account interference in system model • Challenges • Finding IC-FAPs efficiently (i.e., in polynomial time?) • Suitability of using IC-FAPs for finding near optimal solutions (i.e. Are IC-FAPs sufficient?)

  14. IC-FAP: An Example

  15. An IC-FAP Search Heuristic • Idea • Forward to nodes closer to gateways • Keep track of interference and consider alternative paths

  16. j 1 (4,4):g,f h i k (3,5):g 1 g f e d 1 c b a (4,0):g,f (0,0):g,f,c,b 1 (3,0):g,f,c An Example

  17. Some Related Work • [Draves et al.: MobiCom 2004]: “Routing in multi-radio, multi-hop wireless mesh networks” • Proposed a metric based on transmission delays • Chooses channel that is likely to decrease delay

  18. Some Related Work • [Raniwala and Chiueh.: INFOCOM 2005]: “Architecture and algorithms for an IEEE 802.11-based multi-channel wireless mesh network” • A heuristic solution • Three stages : tree construction, node to interface binding and interface to channel binding • Key idea is balancing traffic load

  19. Some Related Work • [Kodialam and Nandagopal: MobiCom 2005]: “Characterizing the capacity region in multi-radio multi-channel wireless mesh networks” • Models the problem as a linear program • Provides a framework for estimation of capacity region • Flows are allowed to split among multiple paths • Assumes synchronous operation of routers

  20. Some Related Work • [Alicherry et al.: Journal on Selected Areas of communication 2006]: “Joint channel assignment and routing for throughput optimization in multiradio wireless mesh networks” • Assumes a synchronous model • Formulates solution as a linear programming relaxation • The algorithm works in stages of solution refinement • The final solution allows bifurcation of flows

  21. Simulation Results Topology

  22. Simulation Results • Findings: The average throughput is higher at different traffic loads. • Comparison with DSR : Average throughput

  23. Simulation Results • Findings: The minimum throughput is also higher at different traffic loads. • Comparison with DSR : Minimum throughput

  24. Simulation Results • Comparison with DSR : Delay Jitter • Findings: The delay jitter is comparatively less

  25. Simulation Results: Insights • One source of improvement from DSR results from route stability • DSR is not designed for (max-min) fairness

  26. Concluding Remarks • The

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