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 1 Embedded Networked Sensing Potential Seismic Structure response Marine Microorganisms • Micro-sensors, onboard 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 Contaminant Transport Ecosystems, Biocomplexity 2 “The network is the sensor” (Oakridge National Labs) Requires robust distributed systems of thousands of physically-embedded, unattended, and often untethered, devices. 3 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 4 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, stovepiped – Serious repercussions for piecemeal uncoordinated design: insufficient longevity, interoperability, safety, robustness, scalability... 5 Sample Layered Architecture User Queries, External Database Resource constraints call for more tightly integrated layers In-network: Application processing, Data aggregation, Query processing Data dissemination, storage, caching Open Question: Can we define an Internet-like architecture for such applicationspecific systems?? Adaptive topology, Geo-Routing MAC, Time, Location Phy: comm, sensing, actuation, SP 6 ENS Research • Some building blocks for experimental systems – Fine grained time and location – Adaptive MAC – Adaptive topology New designs motivated by new combination of constraints and requirements – Data centric routing 7 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 8 Tiered System Design: IPAQs and UCB Motes • Localization – Mote periodically emits coded acoustic “chirps” (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; selfconfigure • Time sync – Allows computation of acoustic time-of-flight – One IPAQ has a “MoteNIC” to sync mote and IPAQ domains 9 Energy Efficient MAC design (Wei et al.) 0.14 0.12 0.1 Diffusion Flooding Omniscient Multicast 0.08 0.06 0.04 0.02 00 50 100 150 200 Network Size • • 250 300 Average Dissipated Energy (Joules/Node/Received Event) Major sources of energy waste • Idle listening when no sensing events, Collisions, Control overhead, Overhearing (Joules/Node/Received Event) Average Dissipated Energy • 0.018 0.016 0.014 0.012 0.01 0.008 0.006 0.004 0.002 00 Flooding Omniscient Multicast Diffusion 50 100 150 200 250 300 Network Size Over energy-aware MAC Over 802.11-like MAC Major components in S-MAC • Massage passing • Periodic listen and sleep Combine benefits of TDMA + contention protocols • Tradeoff latency and fairness for efficiency 10 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 11 Context for creating a topology: connectivity measurement study (Ganesan et al) Packet reception over distance has a heavy tail. There is a nonzero probability of receiving packets at distances much greater than the average cell range Can’t just determine Connectivity clusters thru geographic Coordinates… For the same reason you cant determine coordinates w/connectivity 169 motes, 13x13 grid, 2 ft spacing, open area, RFM radio, simple CSMA 12 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. 13 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 14 Comparison of energy costs (Krishnamachari et.al.) Data centric has many fewer transmissions than does Address Centric; independent of the tree building algorithm. Address Centric Shortest path data centric Greedy tree data centric Nearest source data centric Lower Bound 15 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 16 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/ 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/ 17