Energy-Efficiency in MANETs

1. Energy-Efficiency in MANETs Task 4: Energy-Efficient Networks Katia Obraczka University of California, Santa Cruz katia@soe.ucsc.edu http://inrg.cse.ucsc.edu/

2. Summary of Activities

3. Efficient Protocols for Power-Constrained Heterogeneous MANETs • Novel energy-efficient data collection algorithms using spatial and temporal data locality. • Novel flexible interconnection protocol to accommodate device heterogeneity and application requirements. • I. Solis, Efficient Protocols for Power-Constrained Heterogeneous Wireless Ad-Hoc Networks, PhD Dissertation, UCSC, 2005. • I. Solis and K. Obraczka, In-Network Aggregation Trade-offs for Data Collection in Wireless Sensor Networks, International Journal on Sensor Networks (IJSNet), Vol 1, No 2, 2006.

4. Robust Routing for Network Fault-Tolerance and Security (Task 3) • Novel game-theoretic stochastic routing framework as proactive alternative to today's reactive approaches to route repair. • Collaboration with Prof. J. Hespanha, affiliated with UCSB’s ICB (Army UARC) and Prof. S. Bohacek at UDel, funded by the Communications&Networking Army CTA. • Integrated and flexible approach to secure routing in MANETs. • C. Lim, Scalable Multi-path Routing for Robust Communication, PhD Dissertation, USC, 2006. • G. Huang, Robust and Secure Routing in MANETs, MSc Theis, UCSC, 2006. • C. Lim, S. Bohacek, J. Hespanha and K. Obraczka, Hierarchical Max-Flow Routing, IEEE Globecom 2005. • S. Bohacek, J. Hespanha, J. Lee, C. Lim and K. Obraczka, A New TCP for Persistent Packet Reordering, IEEE/ACM Transactions on Networking, Vol. 14, No.2, April 2006. • R. Guru, G. Huang and K. Obraczka, An Integrated and Flexible Approach to Robust and Secure Routing in MANETs, IEEE IC3N, August 2005.

5. Modeling Data Networks with Hybrid Systems (Task 5) • New approach to modeling, analyzing, and simulating networks using hybrid systems which combine both continuous-time dynamics as well as discrete-time logic. • Collaboration with Prof. J. Hespanha, affiliated with UCSB’s ICB (Army UARC) and Prof. S. Bohacek at UDel, funded by the Communications&Networking Army CTA. • J. Lee, A Hybrid Systems Modeling Framework for Transport Protocols, PhD Dissertation, USC, 2005. • S. Bohacek, J. Lee, J. Hespanha and K. Obraczka, Modeling Data Communication Networks Using Hybrid Systems, IEEE/ACM Transaction on Networks, 2006, to appear.

6. Mobility Models for Wireless Networks • New approach to modeling mobility in wireless networks using statistical equivalent models (SEMs). • Collaboration with Profs. B. Sanso and A. Kottas, UCSC Applied Math. • K. Viswanath and K. Obraczka, Modeling the Performance of Flooding in MANETs (Extended Version), Computer Communications Journal (CCJ) 2005. • K. Viswanath, K. Obraczka, A. Kottas, B. Sanso, A Statistical Equivalent Model for Random Waypoint Mobility: A Case Study, IEEE SMC SPECTS 2006.

7. Energy Consumption Modeling, Characterization, and Prediction • Models for energy consumption and network lifetime prediction. • Collaboration with Prof. R. Manduchi, UCSC CE. • C. Margi, Energy Consumption Trade-Offs in Power-Constrained Networks, PhD Dissertation, UCSC, 2006. • C. Margi, K. Obraczka, R. Manduchi, Characterizing System Level Energy Consumption in Mobile Computing Platforms, IEEE WirelessCom 2005, June 13-16, 2005 • C. Margi, V. Petkov, K. Obraczka, R. Manduchi Characterizing Energy Consumption in a Visual Sensor Network Testbed, IEEE/Create-Net TridentCom 2006, March 1-3, 2006. • Energy Consumption Trade-offs in Visual Sensor Networks”. C. B. Margi, R. Manduchi , K. Obraczka. SBRC 2006, May 29 - June 02, 2006.

8. Energy-Efficient Medium Access • Novel efficient and flexible medium access framework form MANETs. • Collaboration with Prof. J.J. Garcia-Luna. • V. Rajendran, Medium Access Control Protocols for MANETs, PhD Dissertation, UCSC, 2006. • V. Rajendran, K. Obraczka and J.J. Garcia-Luna, Application-Aware Medium Access for Sensor Networks, 2nd IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), November 2005.

9. Energy Consumption Modeling, Characterization, and Prediction In collaboration with Roberto Manduchi

10. Motivation • Understand energy trade-offs between computation and communication. Why? • Make application-level decisions, e.g., data processing in the node vs. transmission of all data. • Make resource management decisions, e.g., wake up more often. • Main assumption for sensor nets: communication dominates energy consumption. True? • Heterogeneity in MANETs: • Platforms. • Sensors. • Application requirements. Mote Stargate

11. Contributions • Duty cycle energy consumption prediction based on elementary task composition. • Simple lifetime prediction model based on elementary task composition considering different duty cycles. • Case study: visual sensor network.

12. Duty cycle energy consumption prediction • Duty cycle as the node’s “execution unit”. • E.g., image acquisition duty cycle. • Duty cycle composed of “elementary tasks”. • E.g., capture image, transmit image.

13. Approach: task composition • Compose elementary task consumption to obtain duty cycle consumption. • Compose elementary tasks differently for different duty cycles. • Average current does not provide enough information by itself. • Need better granularity: • Charge & duration of a task.

14. Hypothesis • Ti: task i. • q(Ti): average charge for task i. • d(Ti): average duration for task i. • Qdc−j: average charge of duty cycle j. • Ddc−j: average duration of duty cycle j. • n: number of tasks in duty cycle j.

15. First step: task characterization • Thorough energy consumption characterization. • Steady state AND transitory behavior. • Case study: • Visual sensor network.

16. Visual sensor network node

17. Elementary tasks • Sensing/Processing: • Acquire image; • Acquire/save (raw) image; • Acquire/compress/save image; • Acquire/process image: • No object; • Object: then must compress & save sub-image. • Communication: • Transmit image: • Raw image (200KB); • Full compressed image (48KB); • Compressed sub-image (3 different sizes).

18. Elementary tasks • Transitions: • Tasks: • Activate/deactivate webcam; • Activate/deactivate wireless card; • Sleep/wakeup.

19. Elementary tasks: duration • Results are the average of 20 different measurements.

20. Elementary tasks: charge

21. Duty cycles • 6 different duty cycles. • 2 types: • Deterministic: image acquisition/compression. • Conditional: event detection. • If no event is detected, the system is put in sleep or idle mode for T1 = 5 seconds. • Otherwise, the system remains idle for T2 = 3 seconds.

22. Deterministic duty cycles Duty Cycle (c) Duty Cycle (b) Duty Cycle (a) Image Image Image Tx Tx Tx Wait Wait Wait

23. Conditional duty cycles Duty Cycle (d) Duty Cycle (f) Duty Cycle (e) Image Image Image Yes Yes Yes Tx Tx Wait Tx Wait Wait

24. Duty cycles: duration & charge • Relative error: • Ed: -6.6% to 3.4%. • Eq: -9.4% to 9.1%. • Results are the average of 20 measurements.

25. Errors • Webcam & wireless card activation: • Must add 1 second delay after in the duty cycle scripts after issuing commands! • But it is not 1 second in idle! Hardware still working!! • Linux as the OS: • Does not allow complete control. • Wireless interference: • TX task has variable duration; • Transmission errors.

26. Lifetime prediction

27. Simple lifetime prediction model • Follows same hypothesis. • Lifetime: • Lx: node lifetime. • Qb: charge available from the battery. • Qd: average charge for each duty cycle. • Dd: average duration of each duty cycle.

28. Simple lifetime prediction model • Expand formula to include duty cycle prediction: • For conditional duty cycles:

29. Experiments • Same duty cycles presented. • Duty cycle runs continuously, until 1000 mAh is used. • No control on event generation (node was placed in our lab).

30. Simple lifetime prediction modelPrediction vs. Experiments Duty cycle duration: (a): 6.4 s (b): 11.6 s (c): 13.9 s (d) - no object: 11.0 s (e) - no object: 8.3 s (d) & (e) – object: 9.5 s (f) - no object: 6.2 s (f) - object: 4.4 s • Relative error for prediction based on duty cycle model is less than 13%. • Results are the average of 20 measurements.

31. Lifetime prediction model:summary • Simple. • Relative error < 13%. • Allows lifetime estimation for new deployments. • Allows duty cycle trade-off analysis.

32. Future Directions (1) • Formalize lifetime prediction model to include non-deterministic sequence of tasks: • Set of known tasks. • Sequence of tasks not known a priori. • Inputs: info from neighboors, battery, etc. • Could we use it to implement a “resource manager” on the node?

33. Future Directions (2) • Validate this approach using different hardware platforms and sensor network applications. • Would a simpler hardware platform allow better accuracy?

34. Energy-Efficient Channel Access In collaboration with J.J. Garcia-Luna

35. Contributions • First traffic-adaptive MAC protocol. • TRAMA. • Application-aware MAC. • FLAMA (Motes testbed). • MFLAMA. • Framework for energy-efficient, application-aware, multi-channel medium access. • DYNAMMA (UWB radio testbed).

36. MAC state-of-the-art • Far from addressing challenges posed by: • Taking advantage of higher PHY data rates. • Accommodating different applications. • And still achieve energy efficiency.

37. Approach: DYNAMMA (Dynamic Medium Access Framework) • Flexible, energy-efficient, application-aware framework. • Flexible and efficient traffic announcement mechanisms. • Slot structure with reduced idle duration and inter-frame spacing. • Scheduled access (including signalling). • Avoids collisions. • Multi-channel. • Spatial re-use for improved channel utilization.

38. Signaling Slots Burst Data Slots Base Data Slots Superframe N+1 Time slot organization • Collision-free signaling • Gather neighbor Information • Collision-free data exchange • Burst data frame exchange • Collision-free data exchange • Single frame exchange

39. Collision-free signalling. Traffic characterization. Different class of flows based on flow arrival / service rate. Each flow class contends for a “subset” of the channel access slots – prevents idle slot allocation. Multi-channel, collision-free scheduling. One transmitter per channel in the 2-hop. Channel selection based on flow priorities. Ensures that a node does not transmit to a node sleeping or listening on other channels. DYNAMMA: components

40. DYNAMMA performance • Performance analysis by extensive simulations using QualNet and testbed experiments using UWB radio/MAC platform. • Different application scenarios • Synthetic – random exponential traffic (worst case scenario for DYNAMMA, large number of flows). • Data gathering application.

41. Synthetic Traffic

42. Simulation Setup • 16 nodes, square grid topology (18m). • 4000 bytes packet. • DYNAMMA Parameters: • SignalingSlot = 16. • BaseSlots = 16. • BurstSlots = 240, framesPerSlot = 2.

43. Delivery Ratio • Packet losses forDYNAMMA and TRAMAdue to queue drops. • Packet losses for 802.11due to collisions. • Multiple channels helpreducing the queuingdelay.

44. Queuing Delay

45. Energy savings • DYNAMMA implementsidle receive timeouts -i.e. shut off receiver if thetransmission does not startwithin a timeout. • Idle receive timeoutsimproves energy savings.

46. UWB Testbed Radio Mode Control Schedulers Timers Transmit / receive DMA

47. Basic slot duration: 644 us Signal slot duration: 161 us Superframe: 16 signal slots, 256 base slots = 167.440 ms SIFS = 10 us, MIFS = 1.875 us Time base using a 66.66 Mhz crystal with < 10ppm drift. Total drift per superframe 1 us. Three nodes – saturated throughput analysis Two data rates – 53.3 Mbps, & 200 Mbps 4000 bytes payload for 53.3 Mbps 3400 bytes payload and “4” burst per slot for 200 Mbps Testbed experiments

48. Experimental results B A C

49. Conclusion • Flexible framework for energy-efficient, application-aware medium access. • Significant improvements in delay and reliability. • Significant improvements in channel utilization by the use of multiple channels.

50. Future Work • Improve traffic characterization using prediction. • Scheduling optimizations to increase the number of schedules. • Trade-off correctness? • Deal with inconsistencies. • Alternate scheduling approaches to provide guarantied QoS.