TRANSFER : Transactions Routing for Ad-hoc NetworkS with eFficient EneRgy

1. TRANSFER: Transactions Routing for Ad-hoc NetworkS with eFficient EneRgy Ahmed Helmy Computer and Information Science and Engineering (CISE) University of Florida (UFL) email: helmy@ufl.edu web: www.cise.ufl.edu/~helmy Wireless Networking Lab: nile.cise.ufl.edu Ahmed Helmy - UFL

2. Motivation • Most current ad hoc routing approaches • Setup/maintain optimal (e.g., shortest) routes (DSR, AODV, ZRP,..) • Incur high route discovery cost, warranted for long-lived flows where cost is amortized over flow duration • In Small Transactions • Cost is dominated by route discovery (vs. data transfer) • Design Goal: reduce cost for small transactions • Example small transactions: resource discovery query, text messaging, sensor network query, etc. Ahmed Helmy - UFL

3. Approach • Avoid flooding-based approaches and instead of flat architecture use hierarchical architecture • Instead of complex hierarchy formation use loose hierarchy (zone-based) • Instead of bordercasting (as in ZRP) query only a few selected contact nodes • Contacts act as short cuts to bridge zones and reduce degrees of separation between querier & resource • Borrows from the concept of small worlds Ahmed Helmy - UFL

4. Flooding vs. Contact-based Query Target Target Contact (C2) GPS& location capability GPS & location capability Computing capability Computing capability PDA Zone of C1 PDA Contact (C1) Zone of C2 GPS & location capability GPS & location capability Handheld Handheld Mobile phone Pocket PC Pocket PC Mobile phone Zone of S Source (S) Computing capability Source (S) Computing capability sensor sensor sensor sensor sensor sensor sensor sensor sensor sensor sensor sensor sensor sensor sensor sensor (a) Flooding from source (S) to discover Target (b) Query from source (S) using contacts C1 and C2 to discover Target Ahmed Helmy - UFL

5. Architectural Overview NoC: Number of Contacts Ahmed Helmy - UFL

6. Contact Selection Scheme • Reactive (on-demand) contact selection • Choose contacts with reduced proximity overlap • Proximity overlap reduction mechanisms • use the proximity information at the border (if available as link state) to reduce the overlaps • use the neighbor-neighbor avoidance mechanism • use disjoint paths (as possible) to reach contacts Ahmed Helmy - UFL

7. Overlap Problem and Solution B avoids going through L’s neighbors x, y, z Ahmed Helmy - UFL

8. Search Policies • Levels of contacts defined by maximum depth D • Several search policies investigated: • Single-shot uses 1 attempt (minimum latency) • Level-by-level uses several attempts with depth level increased by 1 for every attempt • Step uses several attempts with depth increased exponentially 1,2,4,8,… (minimum overhead) • In multi-attempts use the rotation effect • choose different level-1 contacts for different attempts to increase network coverage • Use loop detection and re-visit prevention Ahmed Helmy - UFL

9. contact-2 contact-2 contact-2 contact-2 contact-2 contact-1 contact-1 contact-2 Q contact-2 contact-1 contact-2 contact-2 Single-shot Policy NoC=3 D=2 R=3 r=3 Ahmed Helmy - UFL

10. contact-2 contact-2 contact-2 contact-1 contact-1 contact-1 Q contact-1 contact-1 contact-2 contact-1 contact-2 contact-2 contact-2 contact-2 Level-by-level or Step Policy contact-2 NoC=3 D=2 R=3 r=3 Ahmed Helmy - UFL

11. Attempt 1 Attempt 1 Attempt 1 Attempt 3 Attempt 2 Attempt 2 Attempt 3 Q Attempt 2 Attempt 3 Rotation-like effect in the step search policy Ahmed Helmy - UFL

12. Evaluation and Analysis • Trade-off between success rate vs. energy • Simulation uses fallback to flooding upon failure • Parameter analysis (optimum r, NoC, D) • Main evaluation metric is total energy consumption • Energy consumption due to various components • Proximity maintenance: function of mobility m/s • Query overhead: function of query rate query/s • Total Consumption: function of q (query/s)/(m/s) QMR Ahmed Helmy - UFL

13. The Communication Energy Model • Based on IEEE 802.11 • Accounts for energy consumption due to transmission and reception • Accounts for differences between broadcast and unicast messages • Energy consumed by a broadcast message (Eb): • Eb=Etx+g.Erx=Etx(1+f.g), where g is ave. node degree. • Energy consumed by a unicast message (Eu): • Eu=Etx+Erx+Eh=Etx(1+f+h), where f=Erx/Etx and h=Eh/Etx, Eh energy consumption due to handshake. • For this study we use f=0.64, and h=0.1 Ahmed Helmy - UFL

14. Simulation Setup • Random node layout • Random way point mobility model [0,20] m/s • Random src-dst pair selection • R=3 to limit storage and proximity overhead Ahmed Helmy - UFL

15. Optimum Number of Contacts (NoC) Reduced coverage frequent fallback to flooding N=1000 nodes Increased query threads , r=3, D=33 (5 attempts max) - Optimum NoC=3, resulting in (near) perfect coverage Ahmed Helmy - UFL

16. Optimum contact distance (r) N=1000 nodes , NoC=3, D=33 (5 attempts max) - Optimum r=3, resulting in min overlap and max coverage Ahmed Helmy - UFL

17. Optimum depth of search (D) 2 attempts 3 attempts N=1000 nodes 4 attempts 5 attempts , NoC=3, r=3 - D=33 (5 attempts max) results in (near) perfect coverage - High order attempts (4th & 5th) only search unvisited parts of the network (due to re-visit prevention) and achieve increased coverage without excessive overhead Ahmed Helmy - UFL

18. Scalability Analysis and Comparisons (1) Per-Query Energy Consumption (NoC=3, r=3, D=33) - Total query energy consumption = f(query rate) query/s - Define per-query energy as Estep, Efloodand Eborder Ahmed Helmy - UFL

19. Comparisons (contd.) (2) Proximity (Zone) Maintenance Energy Consumption - For TRANSFER Z(R)=Z(3), for ZRP Z(2R-1)=Z(5) (extended zone) - Proximity cost=f(mobility) m/s Ahmed Helmy - UFL

20. Comparisons (contd.) Total Energy Consumption: Proximity + Query Energy • To combine the proximity energy, f(mobility), and the query energy, f(query rate) • The query-mobility-ratio (QMR) metric, q, in query/s/(m/s) is used for normalization • Total Step Energy: ETstep=Z(R)+q.Estep • Total Flood Energy: ETflood=q.Eflood • Total ZRP Energy: ETborder=Z(2R-1)+q.Eborder • Define total energy ratios (TER): Ahmed Helmy - UFL

21. Comparisons (contd.) (3.a) Total Energy Consumption (vs. Flooding) - For high query rates achieves energy savings of 90-95% over flooding Ahmed Helmy - UFL

22. Comparisons (contd.) (3.b) Total Energy Consumption (vs. ZRP bordercasting) - For high query rates achieves energy savings of 75-86% over ZRP Ahmed Helmy - UFL

23. Summary/ Conclusions • Developed a contact-based architecture for energy-efficient routing of small transactions • Introduced effective contact selection scheme • Investigated several search policies (e.g., Step) • Analyzed performance of TRANSFER and showed favorable parameter settings for a wide array of networks • Achieved gains for high query rates 75-95% as compared to flooding and ZRP Ahmed Helmy - UFL

24. Backup Slides Ahmed Helmy - UFL

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28. Query Resolution Latency - For single-shot: minimum number of attempts (~1) - For step: number of attempts scales well with network size Ahmed Helmy - UFL

29. Comparisons ODC: on-demand routing with caching (DSR-like) MDS: minimum dominating set algorithm Smart-fld: smart flooding (location-based heuristic) Ahmed Helmy - UFL

30. Comparisons ODC: on-demand routing with caching (DSR-like) MDS: minimum dominating set algorithm Smart-fld: smart flooding (location-based heuristic) Ahmed Helmy - UFL