Power Harvesting & Storage & Management Strategies in Wireless Sensor Networks

Motivating Application: Battlefield Surveillance

Other Applications Wildlife Monitoring Alarm System Flock Protection Border Surveillance

Power is a Critical Issue Ad Hoc NetworksSensor Networks Scale10~100s nodes1000s~100,000 nodes10-100X Density2~4 Neighbors10~30 Neighbors5-10X Bandwidth11Mbps ( e.g )50Kbps20~40X EnergyLarge (111Wh)Small (3.3Wh)>30X Requirement1hour ~ 1 day3 ~ 6 month100X CostHigh per unitMuch cheaper per unit<1/10X RechargeableYesNo or use energy scavenger Failure rateVery lowHigh ApplicationMost non-real-timeTime-sensitive Quality Spec.LosslessAllow packet loss ContextLocation-independentLocation-dependent MobilityIntermittent movementContinuous movements

Technology Trends Relative improvements in laptop computing technology from 1990–2003.

What will you learn in this lecture? A Survey of Energy Harvesting & Storage Strategies A Survey of Power Management Strategies Power management at a single node (Localized) Power management at scale (Distributed) How to balance Energy Harvesting with Energy Consumption Eon: A language and runtime for perpetual systems

Where Energy Comes From?

Power Management High Low Timer = 1 hr … Timer = 5 hr … Timer = 10 hr Low High EON

Feasible Sources of Energy Photovoltaic solar cells Amorphous Crystalline Vibrations Piezoelectric Capacitive Inductive Radio-Frequency (RF) Thermoelectric conversion Human power Wind/air flow Pressure variations Harvesting technologyPower density Solar cells (outdoors at noon) 15 mW/cm 2 Piezoelectric (shoe inserts) 330 μW/cm 3 Vibration (small microwave oven) 116 μW/cm 3 Thermoelectric (10 o C gradient) 40 μW/cm 3 Acoustic noise (100dB)960 nW/cm 3 Power densities of energy harvesting technologies

Solar and Ambient Light Sources Noon on a sunny day mW/cm 2 Office Lights: 7.2 mW/cm 2 Collectors Silicon –15% - 30% efficient –.6 V open potential - needs series stacks Poly-Silicon –10% - 15% efficient Photoelectric Dyes 5% to 10% efficient BWRC - BMI - Solar Powered PicoRadio Node

Solar Cell Characteristics % efficiency outdoors <1% efficiency indoors Needs power management scheme Maximum power point might need tracking V-I characteristics of a Solar World solar panel

Temperature Gradients Exploit gradients due to waste heat / ambient temp Maximum power = Carnot efficiency 10˚C differential - (308K - 298K) /308 = 3.2% Through silicon this can be up to 110 mW/cm 2 Methods Thermoelectric (Seebeck effect) ~ 40µW/cm 10˚C Piezo thermo engine (WSU) ~ 1 mW/mm 2 (theoretical) Bahr et al. WSU -Piezo thermo engine

Human Power Burning 10.5 MJ a day Average power dissipation of 121 W Areas of Exploitation Foot Using energy absorbed by shoe when stepping 330 µW/cm 2 obtained through MIT study Skin Temperature gradients, up to 15˚C Blood Panasonic, Japan demonstrated electrochemically converting glucose

Air Flow Power output/ efficiencies vary with velocity and motors Applications exist where average air flow may be on the order of 5 m/s At 100% efficiency ~1 mW/cm MEMS turbines may be viable

Piezoelectric energy via Vibrations Roundy, UC Berkeley - Piezo Bender Materials (notably crystals and certain ceramics) to generate an electric potential in response to applied mechanical stress. Sources HVAC Engines/Motors Existing Designs Roundy ~ 800 µW/cm 3 (similar to clothes dryer)

Wireless Energy Transfer :Microwave & RF

Where Energy is Stored

Feasible Devices for Energy Storage Batteries Li-ion NiCaD NiMH Ultracapacitors Maxwell Samsung NEC Micro-fuel cells Micro-heat engines Radioactive power sources Maxwell 5V 2F 2.7 mAhr ultracapacitor VoltaFlex thin film rechargeable lithium batteries

Macro Batteries Macro Batteries - too big Zinc air (3500 J/cm 3 ) High power density Doesn’t “stop” Alkaline (1800 J/cm 3 ) Standard for modern portable electronics Lithium ( J/cm 3 ) Standard for high power portable electronics Micro Batteries - on the way Lithium Ni/NaOH/Zn

Battery Capacity Curve Battery model shows the Rate Capacity Effect

MEMS Fuel Cell Fraunhofer S.J. Lee et. al., Stanford University Current Generation Toshiba 1 cm 3 hydrogen reactor Produces 1watt Transients may be too slow for low duty cycles Next Generation Planar Arrays Fraunhofer mW/cm 2 Stanford - > 40 mW/cm 2 (more room for improvement)

Capacitors/ Ultra capacitors Ultra capacitors Good potential for secondary storage Energy density on order of 75 J/cm 3 Work being done to shrink them Capacitors Useful for on chip power conversion Energy density too low to be a real secondary storage component

Micro Heat Engines MEMS scale parts for meso scale engine 1 cm 3 volume 13.9 W Poor transient properties Micro size heat engine ICE’s, thermoelectrics, thermoionics, thermo photo voltaics via controlled combustion Meant for microscale applications with high power needs

Radioactive Approaches High theoretical energy density Power density inversely proportional to half life Demonstrated power on the order of nanowatts Environmental concerns

Where Energy Goes in a Sensor Node SensorsRadio CPU TinyOS Power Management Subsystem Dynamic Voltage Scaling Scalable Signal Processing Dynamic Modulation Scaling Coordinated Power Management

Energy microcontroller (CPU) How to save energy in microcontroller? Turn it off !! Design low-complexity algorithms Dynamic Voltage Scaling Multiple Sleep Modes (Atmega128L as an example) Idle mode: stop CPU only Power-save mode: turn off cpu, on-chip flash, ADC and other I/O Standby mode: turn off cpu, bus and main clock Power-down mode: stop everything except one interrupt line A separate processor for computation intensive tasks

Energy Sensors How to save energy at sensor-level? Turn sensors off!! Sample based on changing rate of phenomena Use low power sensor components Magnetic sensor: 19.4mw PIR sensor: 0.88mw Acoustic: 1.73mw Selective & incremental wakeup Use the interrupt-based scheme Vs. the polling-based scheme

Energy Flash How to save energy at Flash? Turn radio off!! Radio: mw Tuning the transmission power according to the node density P=0.01mW (-20 dBm) 15.8/25.8mw P=0.3 mW (-5 dBm) 16.7/41.4mw P=1 mW (0 dBm) 31.2/49.5mw LPL: B-MAC, LPL-Short Preamble: X-MAC, Synchronized Polling.

Energy Radio How to save energy at radio-level? Turn radio off!! Radio: mw Tuning the transmission power according to the node density P=0.01mW (-20 dBm) 15.8/25.8mw P=0.3 mW (-5 dBm) 16.7/41.4mw P=1 mW (0 dBm) 31.2/49.5mw LPL: B-MAC, LPL-Short Preamble: X-MAC, Synchronized Polling.

Energy Radio Energy aware routing/MAC Routing based on Energy Metrics Turn off radio while a node is not intended receiver Collision avoidance MAC Reduce the number of retransmission Multi-path routing to balance energy Deplete energy evenly across the network, preventing premature network partition. Limited Forwarding according the node density Forward to nearest neighbor

Energy Network Explore through redundancy and collaboration Converge Tracking & Classification Communication

Energy Coverage How to save energy in providing coverage? Selectively turn on a subset of nodes in high density networks Full coverage in space Partial coverage in space Duty cycle scheduling Turn on a node only a portion of time Controlled Coverage Placement Provide coverage to critical path

Energy Tracking How to save energy during the tracking process? Initial Detection There is no need to turn on all sensors for initial detection Localized wakeup Network wide wakeup process is very energy consuming Group based tracking with local aggregation Reduce false alarms

Energy Communication How to wakeup? Duty cycle the radio trade-off between energy and latency Wake-up circuit & protocols exploiting them instantly wake up remote receiver radio when needed minimize spurious wake ups & interference, and their impact match destination address in addition to preamble cheap directional antennas Sensor-triggered node wakeup event sensor network user Zz z Path nodes need to be woken up Zz z Radio modePower (mW) Transmit14.88 Receive12.50 Idle12.36 Sleep0.016

Eon: A language and runtime for perpetual systems Jacob Sorber, Alexander Kostadinov, Matthew Garber, Matthew Brennan †, Mark Corner, Emery Berger University of Massachusetts Amherst † University of Southern California

Example: Tracking Turtles State of the art: Radio Telemetry On-shell GPS tracking Small, lightweight, waterproof Need to last forever—a perpetual system Wood turtle (Clemmys insculpta)

Daily Solar Production Varies Weather and mobility = uncertain energy budget

Energy Consumption Varies GPS energy/reading is also uncertain

Challenges for Perpetual Systems Variable energy budget Size is limited Can’t overprovision Always on GPS = 3 hour life Need an adaptive solution Writing energy-aware code is difficult

Eon Language and Runtime First energy-aware programming language Tight link between program and runtime Explicit data flow and energy preferences Measure energy harvesting and consumption Automatically conserve energy as needed execute an alternate implementation adjust fine grained timers

Eon Programming Language Coordination language Structure: Directed Acyclic Graph Nodes = code written in C/NesC Edges = map node outputs to inputs Execution starts at events Flow = path from event source to handler Annotate Flows Describe how to conserve energy GPSTimer GetGPS StoreData

// Predicate Types typedef valid TestValid; //Node declarations GPSTimer() => (); GPSFlow() => (); GetGPS() => (GpsData_t data, bool valid); HandleGPS(GpsData_t data, bool valid) => (); LogData(GpsData_t data, bool valid) => (); LogTimeout(GpsData_t data, bool valid) => (); ListenRequest() => (msg_t msg); ReadData(msg_t msg) => (msg_t msg); SendData(msg_t msg) => (); HandleRequest(msg_t msg) => (); // Eon States // there is always an implicit BASE state stateorder {(HiGPS, Respond)}; // Sources source ListenRequest => HandleRequest; source timer GPSTiumer => GPSFlow; // Adjustable Timer Limits GPSTimer:[HiGPS] = (1 hr, 10 hr); GPSTimer:[*] = 10 hr; // Flows GPSFlow = GetGPS -> HandleGPS; HandleRequest:[*,*][Respond] = ReadData -> SendData; HandleGPS:[*,valid][*] = LogData; HandleGPS:[*,*][*] = LogTimeout; GPSFlow Example Eon Program GPSTimer ListenRequest GetGPS LogTimeout LogData ReadData SendData valid? Respond?(1 hr – 10 hr) // Adjustable Timer Limits GPSTimer:[HiGPS] = (1 hr, 10 hr); HandleRequest HandleGPS HandleRequest:[*,*][Respond] = ReadData -> SendData;

Runtime System Basic flow execution Choose sustainable energy state What do we need to know? Solar energy Energy consumption Not provided by most hardware. High Low Timer = 1 hr … Timer = 5 hr … Timer = 10 hr Low High

Hardware Support Measures Energy harvested Per-flow energy Battery fullness Energy independence Easily change hardware No offline profiling Charge control: Heliomote Only required for energy adaptation

Goal: Avoid empty and full battery Predict outcomes per state Detailed predictions are complex Too complex for motes Near-sighted approximation Choosing an energy state 50% 100% 0% Time (hours, future) Battery

Choosing an energy state (cont) Choose between High and Low Timer ranges: find two settings Avoid empty battery Avoid full battery Any setting in between is sustainable Done! Now do it again. High(Max) High(Min) Low Avoid Waste Avoid Empty

Conclusion Energy management is crucial part of sensor network research First-class consideration Energy Harvesting Solar is widely used for it high energy density Energy Storage Ultra-capacitor is a promising direction for energy storage. Energy Conservation Save as much energy as possible Energy Balancing keep energy demand and supply in balance.