1. 1 This Century Challenges: Embedding the Internet Deborah Estrin UCLA Computer Science Department destrin@cs.ucla.edu http://lecs.cs.ucla.edu/estrin

2. 2 Enabling Technologies EmbeddedNetworked Sensing Control system w/ Small form factor Untethered nodes Exploit collaborative Sensing, action Tightly coupled to physical world Embed numerous distributed devices to monitor and interact with physical world Network devices to coordinate and perform higher-level tasks Exploit spatially and temporally dense, in situ, sensing and actuation

3. 3 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

4. 4 “The network is the sensor” (Oakridge Natl Labs) Requires robust distributed systems of thousands of physically-embedded, often untethered, devices.

5. 5 From Embedded Sensing to Embedded Control Embedded in unattended “control systems” –Different from traditional Internet, PDA, Mobility applications that interface primarily and directly with human users –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 Critical concerns extend beyond traditional networked systems –Usability, Reliability, Safety –Robust interacting systems under dynamic operating conditions –Often mobile, uncontrolled environment, –Not amenable to real-time human monitoring 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 Physical Distributed Micro (Embedded Networked Sensing) Centralized (Traditional Sensor Systems) Macro (Shared Scientific Instruments (telescopes)) Virtual (Internet)

7. 7 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 dynamics of physical world cause systems to operate in ad hoc fashion –Measure and adapt to unpredictable environment –Exploit spatial diversity and density of sensor/actuator nodes Achieve desired global behavior with adaptive localized algorithms –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

8. 8 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 Energy-constrained (un-tethered, small-form-factor), unattended systems cant tolerate communication overhead of indirection Embedded systems can’t rely on human intelligence, elasticity, to compensate for system ambiguities

9. 9 ENS Research Focus Critical research needed in “systems” –Component technology (sensors, low power devices, RF) is far ahead of our ability to exploit Must develop, distributed, in-network, autonomous event detection capabilities –Adaptive Self-Organization to achieve reliable, long-lived, operation in dynamic, resource-limited, harsh environment. –Collaborative, multi-modal, processing and active database techniques –Primitives for programming aggregates to create an autonomous, adaptive, monitoring capability across 1000s of nodes –Sensor coordinated actuation will enable truly self-configuring and reconfiguring systems by allowing for adaptation in physical space –Safety, Predictability, Usability, particularly as we embed sophisticated behaviors in previously-”simple” objects. Strive toward an Architecture and associated principles by building working systems, studying them, iterating –Analogous to TCP/IP stack, soft state, fate sharing, and eventually, self-similarity, congestion control… –What is our stack, metrics, system taxonomy…

10. 10 Sample Layered Architecture Application processing, Distributed query processing, QOT tradeoffs Routing Self-configuring network topology MAC, Time, Location Phy: comm, sensing, actuation, SP User Queries, External Database Data dissemination, aggregation, storage, caching

11. 11 Metrics Efficiency –System lifetime/System resources Resolution/Fidelity –Detection/Identification Latency –Response time Robustness –To variable system and input state] –Security to malicious or buggy nodes Scalability –Over space and time

12. 12 Systems Taxonomy: Dimensions Spatial and Temporal Scale –Sampling interval –Extent –Density (of sensors relative to stimulus) Variability –Ad hoc vs. engineered system structure –System task variability –Mobility (variability in space) Autonomy –Multiple sensor modalities –Computational model complexity Resource constrained –Energy, BW –Storage, Computation

13. 13 Traffic/Load/Event Models: Dimensions Frequency (spatial, temporal) –Commonality of events in time and space Locality (spatial, temporal) –Dispersed vs. clustered/patterned Mobility –Rate and pattern

14. 14 Constructs for in network processing Nodes pull, push, and store named data (using tuple space) to create efficient processing points in the network –e.g. duplicate suppression, aggregation, correlation Nested queries reduce overhead relative to “edge processing” Complex queries support collaborative signal processing –propagate function describing desired locations/nodes/data (e.g. ellipse for tracking) Interesting analogs to emerging peer-to-peer architectures

15. 15 Directed Diffusion Basic idea –name data (not nodes) with externally relevant attributes Data type, time, location of node, SNR, etc –diffuse requests and responses across network using application driven routing (e.g., geo sensitive or not) –optimize path with gradient-based feedback –support in-network aggregation and processing Data sources publish data, Data clients subscribe to data –However, all nodes may play both roles A node that aggregates/combines/processes incoming sensor node data becomes a source of new data A sensor node that only publishes when a combination of conditions arise, is a client for the triggering event data –True peer to peer system Implemented defines namespace and simple matching rules in the form of filters –Linux (32 bit proc) and TinyOS (8 bit proc) implementations

16. 16 Of more interest than simple Aggregation are Nested Queries (Source: Heidemann et. al.) Use application-level information to scope and process data. user audio light sensors flat nested

17. 17 Nested Query Evaluation (A real experiment w/sub-optimal hardware) Nested queries greatly improve event delivery rate Specific results depend on experiment –placement –limited quality MAC General result: app-level info needed in sensor nets; diffusion is good platform events successfully received (%) number of light sensors flat nested 60 1234 80 40 20

18. 18 Sub-optimal aggregation tree constructions (From Krishnamachari et.al.) On a general graph if k nodes are sources and one is a sink, the aggregation tree that minimizes the number of transmissions is the minimum Steiner tree. NP-complete Center at Nearest Source (CNSDC): All sources send through source nearest to the sink. Shortest Path Tree (SPTDC): Merge paths. Greedy Incremental Tree (GITDC): Start with path from sink to nearest source. Successively add next nearest source to the existing tree. AC: Distinct paths from each source to sink.

19. 19 Source placement: event-radius model (From Krishnamachari et.al.)

20. 20 Comparison of energy costs (From Krishnamachari et.al.)

21. Opportunism always pays; Greed pays only when things get very crowded (From Intanagowiwat et.al. ns-2 more detailed simulations)

22. 22 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

23. 23 Adaptive Topology Schemes SPAN Benjie Chen, Kyle Jamieson, Robert Morris, Hari Balakrishnan, MIT, http://www.pdos.lcs.mit.edu/papers/span:wireless01 http://www.pdos.lcs.mit.edu/papers/span:wireless01 –Goal: preserve fairness and capacity while providing energy savings (minimize number of coordinators while still preserving network capacity). –Mechanism: elects coordinators to create backbone topology. –Limitation: Depends on ad-hoc routing protocol to get list of neighbors and connectivity matrix between them. ASCENT Alberto Cerpa and Deborah Estrin, UCLA, http://lecs.cs.ucla.edu/~cerpa/ASCENT-final-infocom- pdf1.3.pdf http://lecs.cs.ucla.edu/~cerpa/ASCENT-final-infocom- pdf1.3.pdf –Goal: exploit the redundancy in the system (high density) to save energy while providing a topology that adapts to the application needs –Mechanism: empirical adaptation. Each node assesses its connectivity and adapts participation in multi-hop topology based on the measured operating region. –Limitation

24. 24 Performance Results (From Chen et. al. simulations)

25. 25 Performance Results (From Cerpa, 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.

26. 26 Programming Paradigm How do we task a 1000+ node dynamic sensor network to conduct complex, long-lived queries and tasks ?? Map isotherms and other “contours”, gradients, regions –Record images wherever acoustic signatures indicate significantly above-average species activity, and return with data on soil and air temperature and chemistry in vicinity of activity. –Mobilize robotic sample collector to region where soil chemistry and air chemistry have followed a particular temporal pattern and where the region presents different data than neighboring regions. Pattern identification: how much can and should we do in a distributed manner?

27. 27 Towards a Unified Framework for ENS General theory of massively distributed systems that interface with the physical world –low power/untethered systems, scaling, heterogeneity, unattended operation, adaptation to varying environments Programming the Collective –What local behaviors will result in global tasks –Programming model for instantiating local behavior and adaptation –Abstractions and interfaces that do not preclude efficiency Large-scale experiments to challenge assumptions behind heuristics –Measurement tools –Data sets

28. 28 Pulling it all together Collaborative Signal Processing and Active Databases Adaptive Self-Configuration Sensor Coordinated Actuation Environmental Microsensors CENS Core ResearchAcademic Disciplines Networking Communications Signal Processing Databases Embedded Systems Controls Optimization … Biology Geology Biochemistry Structural Engineering Education Environmental Engineering Networking Communications Signal Processing Databases Embedded Systems Controls Optimization … Biology Geology Biochemistry Structural Engineering Education Environmental Engineering

29. 29 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/ DARPA Programs http://dtsn.darpa.mil/ixo/sensit.asp http://www.darpa.mil/ito/research/nest/ Related projects at UCLA and USC-ISI http://cens.ucla.edu http://lecs.cs.ucla.edu http://www.isi.edu/scadds Many other emerging, active research programs UCB: Culler, Hellersein, BWRC, Sensorwebs, CITRIS MIT: Chandrakasan, Balakrishnan Cornell: Gherke, Wicker Univ Washington: Boriello UCSD: Cal-IT2