1. TRANSFER: Transactions Routing for Ad-hoc NetworkS with eFficient EneRgy
Ahmed Helmy
Computer and Information Science and Engineering (CISE)
University of Florida (UFL)
web:
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
sensor
Pocket PC
PDA
Mobile
phone
Handheld
Computing
capability
GPS& location
GPS & location
Computing capability
Source (S)
Target
sensor
Pocket PC
PDA
Mobile
phone
Handheld
Computing
capability
GPS & location
Computing capability
Source (S)
Target
Zone of S
Contact (C1)
Contact (C2)
Zone of C1
Zone of C2
(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
(Straightening algorithm)
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. Single-shot Policy NoC=3 D=2 R=3 r=3 Ahmed Helmy - UFL Q contact-2

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

11. Rotation-like effect in the step search policy
Attempt 3
Attempt 1
Attempt 2
Attempt 2
Attempt 3 Q
Attempt 1
Attempt 1
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
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, Eflood and 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