Energy Consumption Issues in Sensor Networks

1. Energy Consumption Issues in Sensor Networks Cintia B. Margi CMPE259 – March 09th, 2005

2. Outline Energy Model for Communications [MASCOTS04 paper] Energy consumption for Processing Tasks Power TOSSIM [SenSys04] Prediction-based energy map [Ad-hoc Journal 05] Energy Harvesting [ISLPED'03]

3. Energy model for communication MASCOTS 04 paper by Cintia & Katia

4. Energy model for communication Power-awareness in sensor networks: MAC protocols: S-MAC [Ye02], TRAMA [Rajendran03], T-MAC [vanDam03] Directed Diffusion [Intanagonwiwat00], aggregation [Solis04] QualNet, GloMoSim and ns-2: Either do not model all the radio states Or do not take proper accounting Accounting done on different layers

5. Energy model for communicationRelated Work Measurements of energy consumed by NICs: NICs in hand-helds [Stemm97] WaveLAN laptops [Feeney01] Models LEACH [Heinzelman00] Sensor network lifetime [Bharwaj02] Measure battery discharge to model communications [Lochin03]

6. Energy model for communicationFeatures Explicitly accounts for low-power radio modes. Considers the different energy costs associated with each one of the possible radio states. For example:

7. Energy model for communicationModel Energy spent while in a given radio state y is: Ey = Py * Ty Py = V * iy tx: Ty = PacketSize/TransmissionRate Otherwise, use a timer Implemented in GloMoSim and QualNet.

8. Energy model for communicationValidation Sanity check: compare with original GloMoSim Testbed in S-MAC paper More on MASCOTS04 paper

9. Energy model for communicationValidation IEEE 802.11 Original vs. Instrumented GloMoSim Simulation parameters: No mobility CBR traffic node 0 to 2, data size is 200 bytes. Duration is 250 seconds. Energy parameters for radio: original GloMoSim.

10. Energy model for communicationValidation S-MAC Qualitative comparison: Simulation vs. testbed S-MAC protocol [Ye02] 5-node 2-hop topology App.: 10 x 380 bytes Low power radio (TR1000) Simulation/measurements lasts enough time for all packets to be transmitted.

11. Energy model for communicationValidation S-MAC Same behavior as results in [Ye02]. Source: average nodes 0 & 1.

12. Case Studies Protocol comparison: 802.11 vs. S-MAC [MASCOTS 2004] Analytical Model Validation Single-hop saturated IEEE 802.11 wireless network [ICCCN 2004]

13. Energy model for communication802.11 vs. S-MAC Parameters: 50 nodes low power radio (TR1000) CBR with 10 sources, 380 bytes routing: AODV Duration: 150s

14. Energy model for communication802.11 vs. S-MAC

15. Energy model for communicationSummary Simple energy model for communication. Implemented at GloMoSim & QualNet. Instrumentation provides complete energy and time accounting per radio state. Useful tool to evaluate and understand power-aware protocols.

16. Processing/sensing energy model ongoing work

17. Processing/sensing energy model For simple sensors (e.g., temperature), energy consumed by communication subsystem dominates. However, for more sophisticated sensors, (e.g., accelerometers & magnetometers) this is not true [Doherty01]. How about camera as sensors?

18. Processing/sensing energy modelRelated Work Energy savings due data compression [Barr03]. Power management architecture for laptops [Balakrishnan01]. Power Management in Wireless Networks [Zheng03]. Energy budget (Great Duck Island deployment) [Mainwaring02].

19. Processing/sensing energy modelApproach Energy cost based on tasks. Energy measurements Current Discharge rate

20. Processing/sensing energy modelTestbeds Dell laptops Stargates Motes

21. Processing/sensing energy modelMethodology Macroscopic view Set of experiments: baseline system processing (FFT) disk access (dbench for laptops) network transmission (Iperf for laptops) Network reception (Iperf for laptops) Well-known benchmarks whenever possible.

22. Processing/sensing energy modelMethodology - Laptops Power Management: off Use ACPI to obtain voltage & discharge rate. Standard for power management Define methods to read the parameters Under Linux: /proc/acpi/ Everytime a “file” in /proc/acpi/ is read, corresponding ACPI method is executed.

23. Processing/sensing energy modelMethodology – Stargates & Motes Stargates: measure current using power suply use battery monitor chip Vladi's project Motes: measure current using power suply Samit's project with motes

24. Processing/sensing energy modelResults

25. Then what? From a complete energy consumption characterization, we can: derive energy consumption prediction model application dependent hardware dependent resource manager

26. Smart usage of energy in sensor nodes Define a methodology for sensor nodes to make decisions that allow energy savings. Interesting application: Visual Sensor Nodes

27. Power TOSSIM [SenSys04]

28. Power TOSSIM [SenSys04] extension to TOSSIM (TinyOS Simulator) to include energy consumption; add a module that keeps track of power state; modifications to other modules to report transitions; CPU energy usage -> estimate number of cycles in AVR; generate traces that will processed later.

29. Power TOSSIMMica2 Power Model

30. Power TOSSIMBenchmarks

31. Prediction-based energy map [Ad-hoc Journal 05]

32. Prediction-based energy map [Ad-hoc Journal 05] Goal: construct an energy map of a wireless sensor network using prediction-based approach. Naive approach: nodes send periodically updates with its available energy to monitoring node. Problem?

33. Prediction-based energy mapApproach Nodes send a message with current energy available and parameters of energy dissipation model. Nodes send updates if prediction is off by a pre-determine threshold (e.g. 3%).

34. Prediction-based energy mapEnergy dissipation model Probabilistic model based on Markov chains; node operation modes are the states; transition probability matrix is constructed based on the node past history; then can calculate energy dissipated based on time spent on each state.

35. Prediction-based energy mapDiagram

36. Energy Harvesting [ISLPED'03]

37. Energy Harvesting [ISLPED'03] Harvesting problem: problem of extracting the maximum work out of a given energy environment. Goal: learn about energy environment (energy available and recharging capabilities); use this info for task sharing among nodes.

38. Energy HarvestingChallenges workload X recharging cycles; residual energy is not enough info, so need to know how recharging occurs: needs to predict recharging opportunities, otherwise consider only residual energy.

39. Energy HarvestingEEHF algorithms

40. Energy sources

41. Energy sourcesMicrobial Fuel Cells EcoBot II (http://www.ias.uwe.ac.uk/) Anode: bacteria found in sludge, act as catalysts to generate energy from the given substrate (flies or rotten apple); Cathode: O2 from free air acts as the oxidising agent to take up the electrons and protons to produce H2O. EcoBot I: Anode: a freshly grown culture of E. coli fed with refined sugar; Catholyte: ferricyanide.

42. Energy sourcesMicrobial Fuel Cells MFC X Alkaline battery: single MFC: output voltage is 0.8V, capacity is 163mAh and energy is 37mWh. It weighs 100g and costs ~ £3.00. AA alkaline cell: output voltage of 1.5V, capacity of 2.8Ah and an energy is 4.2Wh. It weighs 25g and costs ~ £0.30.

43. Questions?