Ultra-Low Energy Wireless Sensor and Monitor Networks

1. Ultra-Low Energy WirelessSensor and Monitor Networks Jan M. Rabaey http://www.eecs.berkeley.edu/~jan In cooperation with Profs B. Brodersen, A. Sangiovanni-Vincentelli, K. Ramchandran, P. Wright, and the PicoRadio group.

2. The New Internet “The Berkeley Endeavour project”

3. The Vision Localizers “Tiny devices, chirping their impulse codes at one another, using time of flight and distributed algorithms to accurately locate each participating device. Several thousands of them form the positioning grid … Together they were a form of low-level network, providing information on the orientation, positioning and the relative positioning of the electronic jets… It is quite self-sufficient. Just pulse them with microwaves, maybe a dozen times a second …” Pham Trinli, Thousands of years from now Vernor Vinge, “A Deepness in the Sky,” 1999

4. PicoRadio’s Meso-scale low-cost (< 0.5 $) sensor-computation-communication nodes for ubiquitous wirelessdata acquisition that minimize power/energy dissipation • Minimize energy (<5 nJ/(correct) bit) for energy-limited source • Minimize power (< 100 mW) for power-limited source (enablingenergy scavenging) By using the following strategies • self-configuring networks • fluid trade-off between communication and computation • Integrated System-on-a-Chip, using aggressive low-energy architectures and circuits Target date: 2004

5. Security Dense network of sensor and monitor nodes Environment monitoring and control Object tagging Identification The Smart Home

6. Wireless in the Home Source: IEEE Spectrum, December 99

7. Industrial Building Environment Management • Task/ambient conditioning systems allow thermal condition in small, localized zones (e.g. work-stations) to be individually controlled by building occupants“micro-climates within a building” • Requires dense network of sensor/monitor nodes • Wireless infrastructure provides flexibility in composition and topology

8. Wireless node Offices Entrance Exhibits Cafe The Interactive Museum

9. The Enhanced User Interface The “virtual” keyboard (Kris Pister, UCB) • Other Applications: • Disaster mitigation, traffic management and control • Integrated patient monitoring, diagnostics, and drug administration • Automated manufacturing and intelligent assembly • Toys, etc

10. System Requirements and Constraints • Large numbers of nodes — between 0.05 and1 nodes/m2 • Cheap (<0.5$) and small ( < 1 cm3) • Limited operation range of network — maximum 50-100 m • Low data rates per node — 1-10 bits/sec average • up to 10 kbit/sec in rare local connections to potentially support non-latency critical voice channel • Low mobility (at least 90% of the nodes stationary) • Crucial Design Parameter: Spatial capacity(or density)— 100-200 bits/sec/m2

11. Today Estimated: 2002-2003 Integrated radio + sensor on–a-chip Fact or Fiction?

12. Functional & Performance Requirements Functional & Performance Requirements Network Architecture Node Architecture Performance analysis Performance analysis How to get there? Network level Constraints Node level Think Energy!

13. Opportunities • Exploit the application properties • Sensor data is correlated in time and space • Sensor networks are “query-based” • Sensing without precise localization seldom makes sense • Duty cycle of sensor nodes is very small • And use node architectures that excel in the “common case” • Stream-based data flow processing for baseband • Concurrent Finite State Machines for protocol stack

14. a: computation energy for transceiving a single bit b: transmission cost factor for a single bit g: path-loss exponent (2..4) e: overhead (in extra bits needed for transmission of a single bit) Pstandby: standby power (eg., due to need to keep receiver on) Power & energy dissipationin ad-hoc wireless networks * These equations assume perfect power control

15. a: computation energy for transceiving a single bit b: transmission cost factor for a single bit g: path-loss exponent (2..4) e: overhead (in extra bits needed for transmission of a single bit) Pstandby: standby power (eg., due to need to keep receiver on) Saving Power at the Application Layer • actual_bits/sec: source coding Opportunity: sensor data is correlated in time and space Trading off communication for computation

16. Answer: no performance loss! (under certain conditions) based on non-constructive information-theoretic argument (Slepian-Wolf). Engineering question: How do we develop a practical and constructive framework to optimize global network usage? Distributed Source Coding (Ramchandran) • Sensor networks present major spatial data correlation, but exploitation requires major intra-node communication Good theoretical question: How much performance loss if such communication unavailable?

17. a: computation energy for transmission of a single bit b: transmission cost factor for a single bit g: path-loss exponent (2..4) e: overhead (in extra bits needed for transmission of a single bit) Pstandby: standby power (eg., due to need to keep receiver on) Saving Power at the Network Layer • dist: network partitioning using multi-hop • e: cost of network discovery and maintenance Opportunities: • exploit application (i.e. sensoring) properties • merge with localization

18. Example: 1 hop over 50 m 1.25 nJ/bit 5 hops of 10 m each 5  2 pJ/bit = 10 pJ/bit Multi-hop reduces transmission energy by 125! (assuming path loss exponent of 4) Source Dest log(b/a) Optimal number of hops needed for free space path loss. Communicating over Long DistancesMulti-hop Networks But … network discovery and maintenance overhead

19. Low-Energy Network Strategies • Reactive Routing good for rarely used routes • Proactive Routing good for frequently used routes • Need solution that is more adequate for problem at hand:class (contents)-based and location (geographic)-based addressing. (discovering n routes) (discovering one route)

20. Example: Directed Diffusion (Estrin) • Application-aware communication primitives • expressed in terms of named data(not in terms of the nodes generating or requesting data) • Example: “Give me temperature information” • Nodes diffuse the interest towards producers via a sequence of local interactions • This process sets up gradients in the network which channel the delivery of data • Reinforcement and negative reinforcement used to converge to efficient distribution • Intermediate nodes opportunistically fuse interests, aggregate, correlate or cache data • Other options: swarm intelligence

21. Distributed Positioning Merging locationing with networking leads to pruningof overhead messaging

22. a: computation energy for transceiving a single bit b: transmission cost factor for a single bit g: path-loss exponent (2..4) e: overhead (in extra bits needed for transmission of a single bit) Pstandby: standby power (eg., due to need to keep receiver on) Saving Power at the Media Access Layer (!) • Pstandby: Power-management of inactive nodes • e: Cost of collisions and retransmissions (interference) Opportunities: distribution of communication in time and frequency • rendez-vous scheduling in local neighborhood • usage of multiple virtual channels reduces interference

23. Where Does Energy Go? When idle: Channel Monitoring Collision and Retransmission Signaling overhead (header, control pkts)

24. Mostly-Sleepy MAC Layer Protocols • Computational energy for receiving a bit is larger than the computational energy to transmit a bit (receiver has to discriminate and synchronize) • Most MAC protocols assume that the receiver is always on and listening! • Activity in sensor networks is low and random • Careful scheduling of activity pays off big time, but … has to be performed in distributed fashion

25. PicoMAC Spread Spectrum Multi-Channel Scheme To Reduce Collision Rate To Reduce Signaling Overhead (Shrink Address Space) • Truly Reactive Messaging Power Down the Whole Data Radio Reduce Monitoring Energy Consumption by 103 Times Wakeup Radio will Power Up Data Radio for Data Reception

26. a: computation energy for transceiving a single bit b: transmission cost factor for a single bit g: path-loss exponent (2..4) e: overhead (in extra bits needed for transmission of a single bit) Pstandby: standby power (eg., due to need to keep receiver on) Saving Power at the Physical Layer • a: Choice of data rate, modulation, physical channel access • e: Cost of framing, channel coding, synchronization, CRC Opportunities: radio’s with fast acquisition (and probably less perfect channel)

27. The Mostly Digital Radio

28. Integration/Power Tradeoff • Example:High carrier frequency allows small integrated passive elements, but increases power consumption • Why? • Increase Id to boost device ft • Increased power consumption in PLLs

29. PicoRadio Implementation Issues • Dynamic nature of PicoRadio networks requires adaptive, flexible solution • Traditional programmable platforms cannot meet the stringent low-power requirements • 3 orders of magnitude in energy efficiency between custom and programmable solutions • Configurable (parameterizable) architectures combine energy efficiency with limited flexibility • System-on-a-chip approach enables integration of heterogeneous and mixed mode modules on same die • Predicted improvements: factor 10 each year!

30. ReconfigurableProcessor/Logic Pleiades 10-80 MOPS/mW ASIPs DSPs 2 V DSP: 3 MOPS/mW Embedded Processors SA110 0.4 MIPS/mW The Energy-Flexibility Gap 1000 Dedicated HW 100 Energy Efficiency MOPS/mW (or MIPS/mW) 10 1 0.1 Flexibility (Coverage)

31. (Re)configurable Computing:Merging Efficiency and Versatility Spatially programmed connection of processing elements. • “Hardware” customized to specifics of problem. • Direct map of problem specific dataflow, control. • Circuits “adapted” as problem requirements change.

32. Architecture Comparison LMS Correlator at 1.67 MSymbols Data Rate Complexity: 300 Mmult/sec and 357 Macc/sec 16 Mmacs/mW! Note: TMS implementation requires 36 parallel processors to meet data rate - validity questionable

33. Intercom TDMA MACImplementation alternatives • ASIC: 1V, 0.25 mm CMOS process • FPGA: 1.5 V 0.25 mm CMOS low-energy FPGA • ARM8: 1 V 25 MHz processor; n = 13,000 • Ratio: 1 - 8 - >> 400 Idea: Exploit model of computation: concurrent finite state machines, communicating through message passing

34. Small footprint direct-downconversion R/F frontend Digital baseband processing implemented on combination of fixed and configurable datapath structures Protocol stack implemented on combination FPGA/reconfigurable state machines Embedded microprocessor running at absolute minimal rates Envisioned PicoNode Platform Embedded uP Reconfigurable State Machines FPGA Dedicated DSP Reconfigurable DataPath

35. PicoNode I • Flexible platform for experimentation on networking and protocol strategies • Size: 3”x4”x2” • Power dissipation < 1 W (peak) • Multiple radio modules: Bluetooth, Proxim, … • Collection of sensor and monitor cards Motorola StarTac Cellular Battery (3.6V) Serial Port Window Casing Cover Connectors for sensor boards Pico Radio Test Bed

36. Memory Sub-system Embedded Processor (Xtensa) Interconnect Network (Sonics Silicon Backplane) Programmable Protocol Stack(FPGA) FixedProtocol Stack Baseband Processing Direct down-conversion front-end (Yee et al) PicoNode II (two-chip) Program-mable logic Software running on processor Custom analog circuitry Mixed analog/ digital Fixed logic Protocol ADC Digital Baseband processing Analog RF DAC Chip 1 Chip 2

37. The Holy Grail: Energy Scavenging SOURCE: P. Wright & S. Randy UC ME Dept.

38. Example: MEMS Variable Capacitor Out of the plane, variable gap capacitor Integrated Manufacturing Lab Up to 10 mW of power demonstrated

39. PicoRadio Design Challenges PicoNode Architecture Design Positioning Network Architecture Performance Analysis Energy Constraints Conceptual Modeling Use Cases