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

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

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

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

Architectural Overview NoC: Number of Contacts Ahmed Helmy - UFL

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

Overlap Problem and Solution B avoids going through L’s neighbors x, y, z (Straightening algorithm) Ahmed Helmy - UFL

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Backup Slides Ahmed Helmy - UFL

Ahmed Helmy - UFL

Ahmed Helmy - UFL

Ahmed Helmy - UFL

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

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

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