1. 1 Sensor Networks for Environmental Monitoring: Lessons for DERNs? Deborah Estrin Director, NSF Science and Technology Center for Embedded Networked Sensing (CENS) Professor, UCLA Computer Science Department destrin@cs.ucla.edu http://lecs.cs.ucla.edu/estrin

2. 2 Embedded Networked Sensing Potential Micro-sensors, on- board processing, and wireless interfaces all feasible at very small scale –can monitor phenomena “up close” Will enable spatially and temporally dense environmental monitoring Embedded Networked Sensing will reveal previously unobservable phenomena Seismic Structure response Contaminant Transport Marine Microorganisms Ecosystems, Biocomplexity

3. 3 “The network is the sensor” (Oakridge National Labs) Requires robust distributed systems of thousands of physically-embedded, unattended, and often untethered, devices.

4. 4 New Design Themes Long-lived systems that can be untethered and unattended –Low-duty cycle operation with bounded latency –Exploit redundancy and heterogeneous tiered systems Leverage data processing inside the network –Thousands or millions of operations per second can be done using energy of sending a bit over 10 or 100 meters (Pottie00) –Exploit computation near data to reduce communication Self configuring systems that can be deployed ad hoc –Un-modeled physical world dynamics makes systems appear ad hoc –Measure and adapt to unpredictable environment –Exploit spatial diversity and density of sensor/actuator nodes Achieve desired global behavior with adaptive localized algorithms –Cant afford to extract dynamic state information needed for centralized control

5. 5 From Embedded Sensing to Embedded Control Embedded in unattended “control systems” –Different from traditional Internet, PDA, Mobility applications –More than control of the sensor network itself Critical applications extend beyond sensing to control and actuation –Transportation, Precision Agriculture, Medical monitoring and drug delivery, Battlefied applications –Concerns extend beyond traditional networked systems Usability, Reliability, Safety Need systems architecture to manage interactions –Current system development: one-off, incrementally tuned, stove- piped –Serious repercussions for piecemeal uncoordinated design: insufficient longevity, interoperability, safety, robustness, scalability...

6. 6 Sample Layered Architecture Resource constraints call for more tightly integrated layers Open Question: Can we define an Internet-like architecture for such application- specific systems?? In-network: Application processing, Data aggregation, Query processing Adaptive topology, Geo-Routing MAC, Time, Location Phy: comm, sensing, actuation, SP User Queries, External Database Data dissemination, storage, caching

7. 7 ENS Research Some building blocks for experimental systems –Fine grained time and location –Adaptive MAC –Adaptive topology –Data centric routing New designs motivated by new combination of constraints and requirements

8. 8 Fine Grained Time and Location (Elson, Girod, et al.) Unlike Internet, the location of nodes in time and space is essential for local and collaborative detection Fine-grained localization and time synchronization needed to detect events in three space and compare detections across nodes GPS provides solution where available (with differential GPS providing finer granularity) Acoustic or Ultrasound ranging and multi-lateration algorithms promising for non-GPS contexts (indoors, under foliage…) Fine grained time synchronization needed to support ranging

9. 9 Tiered System Design: IPAQs and UCB Motes Localization –Mote periodically emits coded acoustic “chirp s” (511 bits) –IPAQs listen for chirps (buffer time series - mote can’t do this) –run matched filter and record time diff btwn emit- and receive-time of coded sequence –Share ranges with each other via 802.11; trilaterate –IPAQs currently configured with their position; future: range to each other; self- configure Time sync –Allows computation of acoustic time-of-flight –One IPAQ has a “MoteNIC” to sync mote and IPAQ domains

10. 10 Major sources of energy waste Idle listening when no sensing events, Collisions, Control overhead, Overhearing Major components in S-MAC Massage passing Periodic listen and sleep Combine benefits of TDMA + contention protocols Tradeoff latency and fairness for efficiency Energy Efficient MAC design (Wei et al.) 0 0.02 0.04 0.06 0.08 0.1 0. 12 0.14 050100150200250300 Average Dissipated Energy (Joules/Node/Received Event) Network Size Diffusion Omniscient Multicast Flooding 0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0 50100 150 200 250 300 Average Dissipated Energy (Joules/Node/Received Event) Network Size Diffusion Omniscient Multicast Flooding Over 802.11-like MAC Over energy-aware MAC

11. 11 Adaptive Topology: An example of Self-Organization with Localized Algorithms Self-configuration and reconfiguration essential to lifetime of unattended systems in dynamic, constrained energy, environment –Too many devices for manual configuration –Environmental conditions are unpredictable Example applications: –Efficient, multi-hop topology formation: node measures neighborhood to determine participation, duty cycle, and/or power level –Beacon placement: candidate beacon measures potential reduction in localization error Requires large solution space; not seeking unique optimal Investigating applicability, convergence, role of selective global information

12. 12 Context for creating a topology: connectivity measurement study (Ganesan et al) Packet reception over distance has a heavy tail. There is a non- zero probability of receiving packets at distances much greater than the average cell range 169 motes, 13x13 grid, 2 ft spacing, open area, RFM radio, simple CSMA Can’t just determine Connectivity clusters thru geographic Coordinates… For the same reason you cant determine coordinates w/connectivity

13. 13 Example Performance Results (ASENT) (Cerpa et al., Simulations and Implementation) Energy Savings (normalized to the Active case, all nodes turn on) as a function of density. ASCENT provides significant amount of energy savings, up to a factor of 5.5 for high density scenarios.

14. 14 Data Centric vs. Address Centric approach Address Centric Distinct paths from each source to sink. Traditional IP model Works well when energy (and thus communication) is not at a premium Data Centric Name data (not nodes) with externally relevant attributes Data type, time, location of node, SNR, etc Publish/Subscribe Support in-network aggregation and processing where paths/trees overlap Essential difference from traditional IP networking

15. 15 Comparison of energy costs (Krishnamachari et.al.) Address Centric Shortest path data centric Greedy tree data centric Nearest source data centric Lower Bound Data centric has many fewer transmissions than does Address Centric; independent of the tree building algorithm.

16. 16 ENS Research in progress Work in progress--in network processing mechanisms and models –Fine grained data collection, methods, tools, analysis, models (D. Muntz (UCLA), G. Pottie (UCLA), J. Reich (PARC)) –Collaborative, multi-modal, processing among clusters of nodes (e.g., F. Zhao (PARC), K. Yao (UCLA) –Enable lossy to lossless multi-resolution data extraction (Ganesan (UCLA), (Ratnasamy (ICSI)) –Primitives for programming the “sensor network” (Estrin (UCLA), Database perspective: S. Madden (UCB)) –Modeling capacity and capability (M. Francischetti (Caltech), PR Kumar (Ill), M. Potkonjak (UCLA), S. Servetto (Cornell)) Future areas--constructing models –Architecture design principles –Global properties: responsiveness, predictability, safety

17. 17 Follow up Embedded Everywhere: A Research Agenda for Networked Systems of Embedded Computers, Computer Science and Telecommunications Board, National Research Council - Washington, D.C., http://www.cstb.org/ http://www.cstb.org/ Related projects at UCLA and USC-ISI http://cens.ucla.edu http://lecs.cs.ucla.edu http://rfab.cs.ucla.edu http://www.isi.edu/scadds Many other emerging, active research programs, e.g., UCB: Culler, Hellerstein, BWRC, Sensorwebs, CITRIS MIT: Balakrishnan, Chandrakasan, Morris Cornell: Gehrke, Wicker Univ Washington: Boriello Wisconsin: Ramanathan, Sayeed UCSD: Cal-IT2 DARPA Programs http://dtsn.darpa.mil/ixo/sensit.asp http://www.darpa.mil/ito/research/nest/