1. 1 Wireless Sensor Networks: Application Driver for Low Power Systems Deborah Estrin Laboratory for Embedded Collaborative Systems ( LECS ) UCLA Computer Science Department http://lecs.cs.ucla.edu destrin@cs.ucla.edu

2. 2 Applications Scientific: eco-physiology, biocomplexity mapping Infrastructure: contaminant flow monitoring (and modeling) Engineering: monitoring (and modeling) structures www.jamesreserve.edu

3. 3 Common Vision Embed numerous distributed devices to monitor and interact with physical world Exploit spatially and temporally dense, in situ, sensing and actuation Network these devices so that they can coordinate to perform higher-level tasks Requires robust distributed systems of hundreds or thousands of devices

4. 4 Challenges Tight coupling to the physical world and embedded in unattended “control systems ” –Different from traditional Internet, PDA, Mobility applications that interface primarily and directly with human users Untethered, small form-factor, nodes present stringent energy constraints –Living with small, finite, energy source is different from traditional fixed but reusable resources such as BW, CPU, Storage Communications is primary consumer of energy in this environment –R 4 drop off dictates exploiting localized communication and in- network processing whenever possible

5. 5 New Design Themes Long-lived systems that can be untethered and unattended –Low-duty cycle operation with bounded latency –Exploit redundancy –Tiered architectures (mix of form/energy factors) Self configuring systems that can be deployed ad hoc –Measure and adapt to unpredictable environment –Exploit spatial diversity and density of sensor/actuator nodes

6. 6 Approach Leverage data processing inside the network –Exploit computation near data to reduce communication Achieve desired global behavior with adaptive localized algorithms (i.e., do not rely on global interaction or information) –Dynamic, messy (hard to model), environments preclude pre-configured behavior –Cant afford to extract dynamic state information needed for centralized control or even Internet- style distributed control

7. 7 Why cant we simply adapt Internet protocols and “end to end” architecture? Internet routes data using IP Addresses in Packets and Lookup tables in routers –Humans get data by “naming data” to a search engine –Many levels of indirection between name and IP address –Works well for the Internet, and for support of Person-to-Person communication Embedded, energy-constrained (un-tethered, small-form-factor), unattended systems cant tolerate communication overhead of indirection

8. 8 Techniques for building long-lived Exploiting redundancy –Adaptive Self-Configuration –Supporting low-duty cycle operation Exploiting heterogeneity

9. 9 Exploiting Redundancy: Goal To extend system lifetime We may be able to deploy 100 times as many nodes in environments where we can’t increase the battery capacity by factor of 100 To overcome environmental limitations (obstructions) Non line of site conditions, Variable sensor coupling To achieve good coverage with ad-hoc deployment When deployment or operational conditions cant be controlled precisely

10. 10 Exploiting Redundancy example Efficient, multi-hop topology formation goal: exploit redundancy provided by high density to extend system lifetime while providing communication and sensing coverage. –If too many sensors active at the same time, increase energy consumption and competition for communication resources. –If too few nodes active, then lack of communication and/or sensing coverage. –Central control/configuration requires too much communication –Nodes should self-configure to find the right trade-off –Ultimately should adapt based on desired “fidelity”

11. 11 Adaptive Fidelity Examples ASCENT –Node measures number of neighbors and packet loss to determine participation, duty cycle, and/or power level. –Ratio of energy used by Active case (all nodes turn on) to energy used by ASCENT GAF –Uses Geographic information to infer which nodes might be redundant with one another for the purposes of routing Open question: Can we apply Adaptive Fidelity etmore generally?

12. 12 Ratio of energy used by the Active case (all nodes turn on) to the energy used by ASCENT ASCENT provides significant energy savings over the Active case

13. 13 Robustness and Scalability through Adaptation Adaptive mechanisms increase complexity but enable self-configuration for robustness and scalability Self calibration to adapt to variations in sensor response and placement Adjust duty cycle and transmit range as a function of node density and measured range (adaptive fidelity) –Balance increased system life-time with increased resolution Challenge: develop and evaluate localized adaptive algorithms We hope adaptive functions will go beyond “connectivity”…e.g., tracking

14. 14 Supporting low duty cycle operation S-MAC –A MAC designed for wireless sensor networks by increasing and facilitating sleep time and reducing overhearing and contention energy expenditure Triggering and tracking –Use lower-power modalities, devices, to trigger higher power ones –Use active devices to trigger sleeping devices to increase fidelity –Paging channels

15. 15 Supporting low duty cycle operation S-MAC –Message passing –Periodic listen/sleep –Avoid overhearing –Energy Measurement On motes and TinyOS Two-hop network with 2 sources and 2 sinks Under different traffic load

16. 16 Adaptive Tracking Example Sentry nodes active; wake up dormant nodes when necessary. Wakeup wavefront precedes phenomenon being tracked. Information driven diffusion (Zhao, Reich, et.al.): node propagates expression for evaluating best next node(s) in wavefront based on information utility and cost Requires: –low power operating mode with wake up/paging channel –definition of a wakeup wavefront using localized algorithms –time synchronization Network nodes close to tracked event (or with good data on the event) enter fully active state; other nodes dormant/low duty cycle

17. 17 Low Duty Cycle Time Synchronization Pulse synchronization creates locality of synchronized nodes, quickly and efficiently –“External” node generates pulse. Synchronizing nodes compare reception times. –NTP good at correcting frequency –Local pulse good at correcting phase –Use combination

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19. 19 Exploiting Heterogeneity: Tiered Architecture Technological advances will never prevent the need to make tradeoffs Nodes will need to be faster or more energy-efficient, smaller or more capable or more durable. Tiered platform consisting of a heterogeneous collection of hardware. –Larger, faster, and more expensive hardware (sensors) –Smaller, cheaper, and more limited nodes (tags and motes)

20. 20 Tiered Architecture Discover and exploit asymmetry wherever possible –Base stations for aggregating resources; motes for access to physical phenomena –Variable power, distance radios E.g., nodes in ASCENT can adapt by reducing their radio range, using less energy and reducing channel contention. –Multiple modalities E.g., localization with RF, Acoustics, and Imaging

21. 21 Can we eliminate the finite nature of the energy source? Batteries will provide 1J/mm 3 (Pister) When available, solar has a lot (the most) to offer in recharging (Pister) Other possibilities: Charging the batteries on fields of sensors by driving through them ?

22. 22 Current Research Areas Constructs for “in network” distributed processing –system organized around naming data, not nodes Programming large collections of distributed elements Localized algorithms that achieve system- wide properties Time and location synchronization –energy-efficient techniques for associating time and spatial coordinates with data to support collaborative processing Experimental infrastructure

23. 23 Current COTS Infrastructure PC-104+ (off-the-shelf) UCB Mote (Culler/Hill/Pister) Software Directed Diffusion TinyOS (UCB/Culler) Measurement, Simulation

24. 24 Embedded, Everywhere A Research Agenda for Networked Systems of Embedded Computers Fall 2001: Computer Science and Telecommunications Board report (late September) Recommends major areas of research needed to achieve robust, scalable EmNets –predictability, adaptive self- configuration, monitoring & system health, computational models, network geometry, interoperability, social and policy issues Substantive recommendations to DARPA, NIST, & NSF For more information, see www.cstb.org or contact lmillett@nas.edu

25. 25 Future Directions Proposed Center for Embedded Networked Sensing (CENS) –Develop technology architecture, software, components in the context of driving application prototypes Habitat monitoring/Biocomplexity mapping Seismic activity and structure response Contaminant flow monitoring Grades 7-12 science curricula innovations

26. 26 Acknowledgments Funders –DARPA SenseIT and NEST Programs http://www.darpa.mil/ito/research/sensit –NSF Special Projects –Cisco, Intel Collaborators –UCLA LECS students: Bien, Bulusu, Busek, Braginsky, Bychkovskiy, Cerpa, Elson, Ganesan, Girod, Greenstein, Perelyubskiy, Scoellhammer, Yu http:/lecs.cs.ucla.edu/ –USC-ISI Collaborators Govindan, Heidemann, Intanago, Silva, Wei, Zhao http://www.isi.edu/scadds –UCB Intel Lab: Culler, et.al.