An Energy-Efficient Flooding Algorithm in ad hoc Network (EFA)

1. An Energy-Efficient Flooding Algorithm in ad hoc Network (EFA) Concrete Mathematic Final presentation of term project Professor:Kwangjo Kim Group 16: Tran Minh Trung, Nguyen Duc Long

2. An Energy-Efficient Flooding Algorithm in Ad-Hoc Network (EFA) • Related Works • Problem statement • Proposed Solution • Simulation & Evaluation

3. Related Works • Related work (Previous paper: PAODV, APRA, MMBCR) • Congested node, Week node: Reject or relay the coming connection -> Reduce the network connectivity • Single path from source to destination: Slow transmission speed, Increase control packet over head -> Waste energy Proposed solution • Disjoint a single path in to multiple paths (dependent on energy capacity of each sub path) • Balance the power consumption between Strong node and week node -> reduce the partition problem (1) • Increase Network Connectivity -> Reduce routing discovery phase (2) • 1,2 -> Increase Network life time

4. Problem statement (1) Run “Routing discovery phase” again many times -> Waste time + Energy consuming Run “Routing discovery phase” fewer time -> Save time, Energy

5. Problem statement (2) • Ad Hoc model: • Directed Graph G(V, E) where V is the set of all nodes and E is the set of all directed links (i, j) where i, j  V. • Ni Set of all neighbors nodes of node (i) (i) (j) • Directed graph: G(V,E) • Weighted link: E(i,j) • Set of neighbor node: N(i)

6. Problem statement (2) • Node (i) • Energy available: • In case of serving j node at the same time • Otherwise f(i,k) f(j,i) i e(i) = er(i) - erq(i) ∑f(j,i) =∑f(i,k) e(i) = er(i)

7. Problem statement (3) • Directed link (i,j) • exist if and only if J  Ni • Energy capacity of link (i,j) • Life time of a routing path = Life time of each link or each node e(2)=40 e(1)=10 Source Dest. e(5)=10 e(i,j) = Min (e(i),e(j)) e(3)=60 e(4)=40 Link 1: e(sd)= Min (e(3),e(4))=40 Link 2: e(sd)= Min (e(1),e(2))=10 …

8. Path 1 is created with energy capacity = 4, Hop count = 4 RREQ(1) RREQ(1) 4 4 RREQ(2) RREQ(2) RREQ(1) RREQ(2) 8 8 8 8 RREQ(1) 4 RREQ 10 Path 1 is created with energy capacity = 8, Hop count = 3 RREQ Flooding method

9. Lexicographic order • A routing path will be chosen dependent on 3 information • Fresh sequence number: F(i) • Min Energy capacity:E(i) • Hop count to destination: H(i) • The path will be selected dependent on lexicographic order • Path i: (F(i), E(i), H(i)) • The number of path is dependent on the total energy requirement, and the energy available of all possible paths

10. X X X X Node with energy level 1 X X X Node with energy level 2 RREQ for link level 1 RREQ for link level 2 Node with energy level 3 RREQ for link level 3 X X X • Full capacity: 10 • Capacity levels: • Level 1: 1 -> 4 • Level 2: 4 -> 7 • Level 3: 7 -> 10 X X X The mesh network example (1) Eliminated because of containing weaker node X Eliminated because of backward flooding (Increase hop count) X X

11. Node with energy level 1 Node with energy level 2 RREP for link level 1 RREP for link level 2 Node with energy level 3 RREP for link level 3 • Full capacity: 10 • Capacity levels: • Level 1: 1 -> 4 • Level 2: 4 -> 7 • Level 3: 7 -> 10 The mesh network example (2)

12. Simulation & Evaluation(1) • Simulation model: • 10 - 50 mobile nodes • are generated randomly in an area of 500M*500M. • The moving speed of each node is 5m/s. • 2-20 connections is established during 900 seconds simulation times. • The energy model: • initial energy of each node is 20mW. • The energy usage for receiving and sending each packet are txPower = 0.6mW and rxPower = 0.3mW respectively.

13. Simulation & Evaluation(2) Routing overhead (control messages) Expiration sequences of nodes

14. Simulation & Evaluation(3) Route reliability End to End Delay

15. Conclusion (1) • Final achievement: • Use concrete mathematic knowledge for writing higher quality paper • Graph theory • Directed graph • Weighted link • Lexicographic Order • Set theory • Experiences in dealing with NS2, Perl, gnuplot

16. Conclusion (2) • Contribution: • Proposed new Energy-Efficient Flooding Algorithm for Ad-Hoc routing protocol • Simulation results shows betters performance • Future plan: • Complete full paper (With more different & complicated scenarios – mobility measurement) • After getting review & advice from Profesor -> submit to international conference • Apply some Stochastic and Mathematical model -> Journal paper

17. Progress after midterm report • Writing simulation program by NS2 ( Tran Minh Trung) • Generate scenarios by TCL script • Apply energy model to standard scenarios • Write simulation results to log files • Analisys simulation log files ( Nguyen Duc Long) • Write perl modules (Collect & Split data) • Write drawing script by gnuplot (linux)

18. Reference • Paper: • T.M Trung, S.-L. Kim, “An Adaptive Power Aware Routing Algorithm for Ad Hoc Networks”, Submitted to ICWC – Toronto Canada 2003 • H.X.Tung, T.M Trung, V.D Liem, P.V Su, “Power – Aware Ad-Hoc Ondemand Distance Vector Routing Protocol”, KISA 2003 • Charles E. Perkins, Elizabeth M. Belding-Royer, and Samir Das. "Ad Hoc On Demand Distance Vector (AODV) Routing." IETF Internet draft, draft-ietf-manet-aodv-10.txt, March 2002 (Work in Progress). • C.-K. Toh, H. Cobb, and D.A. Scott, “Performance evaluation of battery-life aware routing schemes for wireless Ad Hoc Networks” in Proc. IEEE, ICC • Books & Link: • Discrete Mathematics and Its Applications, 4th edition , Kenneth H. Rosen, McGRAW-HILL, 1999 • http://mathworld.wolfram.com/LexicographicOrder.html