An Energy-Efficient Architecture for DTN Throwboxes

1. An Energy-Efficient Architecture for DTN Throwboxes Presenter: Zhe Chen Author: Nilanjan Banerjee, Mark Corner, Brian N. Levine

2. What are Disruption Tolerant Networks ? • DTNs are sparse networks with low node density • Nodes are largely disconnected • Transfer data through intermittent contacts • Come naturally from the applications they support • Wildlife tracking • Underwater exploration and monitoring • Or from fragility and failures in the network itself • Major natural disasters • Jamming and Noise • Power Failure

3. Examples of DTN UMass DieselNet [Burgess et al. Infocom 06]

4. Limitations of Mobile DTNs • Do you have enough capacity in your DTN? • Most influential factor in DTN performance? • the frequency and number of contact opportunities • How can we increase contacts? • more mobile nodes=$$$$ • or change the mobility pattern of nodes • (mobility patterns inherent to a particular network)

5. Observation Place a relay and create a virtual contact Route A Route B

6. Solution : Throwboxes • Throwboxes: stationary battery powered relays • has radios and storage • cheap, small, easy to deploy • solar power=perpetual operation • Challenges • where do we place these boxes ? [Wenrui et al. : Mass 06] • make them ultra low power for perpetual operation

7. Solution : Throwboxes • Throwboxes: stationary battery powered relays • has radios and storage • cheap, small, easy to deploy • Challenges • Previous paper: where to place these boxes thus maximize network performance: ? [Wenrui et al. : Mass 06] • Power management: trade of between nodes’ lifetime and connection opportunities • Aim: maximize performance and simultaneously meet individual energy constraints

8. Outline • Design Goals • Throwbox Architecture • Mobility Prediction Engine • Lifetime Scheduler • Throwbox Prototype and Deployment • Experimental Results • Power Savings • Routing Performance • Conclusions

9. Throwbox Design Goals • Small form factor, portable and cheap • Can be placed practically anywhere in the network • Design should be general • Applicable to wide variety of DTNs • Should not use prior information about mobility patterns • Run perpetually on solar panels of the size of the box • Translates to a small average power constraint • Optimization goal: maximize the number of packets forwarded

10. Primary source of overhead • Energy cost of neighbor discovery • Idle, on and off, searching contacts • DTN networks • Sparse, is it worth the cost of waking the node

11. Overview

12. Mobility Measurement and Prediction • Buses transmit: pos, dir, and speed. • Throwbox predicts: • if bus will reach data-range before tier-1 can be woken? • length of time in range(is it worth?) • Track the probability the node enters data-range given series of cells it must traverse • Statistics kept on each cell • Markovian assumption allows simple calculation

13. Scheduling • Each contact incurs fixed cost to wake tier-1 platform. • Most efficient strategy: wake for largest contacts • saves energy, but mostly designed to limit power • 0-1 Knapsack problem reduces to this scheduling problem • choose items to carry s.t. (∑weight ≤ capacity) and maximizes ∑value. • C1 ... Cnevents, each has • total energy cost ei(weight), bytes transferred di (value) • Energy constraint P ∙t (capacity) • Solution is subset of events s.t. (∑ei ≤ P∙t) and maximizes ∑di

14. Token Bucket Approach new tokens ? Events Battery capacity Takenevents Ignored or skipped events • Take this event, next event, or both? • Token rate = average power constraint Estimate the size & energy cost ignore if insufficient tokens Compute tokens generated till next event based on tracking inter-arrival times If sufficient tokens for both events • take current event If current event larger than next connection take it otherwise wait for next one

15. Experimental Setup • How effective is our energy management design? • compare with single platform periodic wake up (PSM*) • Two-platform with mobility prediction (WoW*) • Can we really run it on solar-power? • At reduced consumption does it still help? • use the successful delivery metric • Use trace-based simulation and deployment • equipped 40 busses with XTend radios • placed three Throwboxes for several weeks • record contact opportunities with buses (both radios)

16. Throwbox Placement Throwbox deployed on bikes in UMassDieselNet

17. Power Savings (equivalent transfers) • 20x less power than periodic wakeup • 5x less power than just mobility prediction

18. Routing performance • Throwbox at 80mW equivalent to best case.

19. Conclusions • Placing relays in DTNs can produce huge performance boost • Motivates studies on adding Meshes or Infostations to DTN • Tiered Architecture can produce substantial energy savings • Can lead to 31 times less energy consumption • Need for systems to adapt to variable solar power • Multi-radio systems are energy efficient in sparse networks • Need for more efficient use of the XTend channel • Low bitrate radio can be used to gather packet info • Need to integrate power management into routing

20. Energy performance • Need larger cell, but perpetual operation possible • Unanswered questions about solar variation