Embedding the Internet: This Century Challenges

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

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 Ecosystems, Biocomplexity Marine Microorganisms

Enabling Technologies Embednumerous distributed devices to monitor and interact with physical world Networkdevices tocoordinate and perform higher-level tasks Embedded Networked Exploitcollaborative Sensing, action Control system w/ Small form factor Untethered nodes Sensing Tightly coupled to physical world Exploit spatially and temporally dense, in situ, sensing and actuation

“The network is the sensor” (Oakridge National Labs) Requires robust distributed systems of thousands of physically-embedded, often untethered, devices. • Technical Challenges • Energy constraints imposed by unattended, untethered, micro-scale systems. • Level of dynamics ( Environmental: obstacles, weather, terrain; System: large number of nodes, failures.) • Scaling challenges due to very large numbers of distributed nodes.

New Design Themes Massively distributed, untethered, and unattended systemsto cover spatially distributed phenomena in natural, obstructed, environments • In-network procesing • 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 sources 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 (redundancy) of sensor/actuator nodes • Adaptive localized algorithms to achieve desired global behavior • 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

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...

ENS Research Focus Network Self-Organization Human interface Database policies and architecture Theoretical framework Programming models Connection to infrastructure Node Localization Communication Links Cooperative Detection Mobility and navigation Target Identification Algorithms Modeling of Environment System Energy Management Actuation Sensors Calibration • Algorithms, architecture, reference implementations, to achieve distributed, in-network, autonomous event detection capabilities • Strive toward an Architecture and associated principles • Develop working systems and extract reusable building blocks • Analogous to TCP/IP stack, soft state, fate sharing, and eventually, self-similarity, congestion control…

Enabling Technologies • Microsensors and actuators • Low power wireless and media access • Integrated, small form factor, devices • Software • Interfaces • Smart dust • Tiered architectures • Time and location synchronization • See presentations by Culler, Goldsmith, Mitra, Pister

Adaptive Self-Organization • Goal: achieve reliable, long-lived, operation in dynamic, resource-limited, harsh environment. • Adapt • Topology to achieve efficient communciation, sensing, processing, or dissemination coverage (may be application and data driven) • Aggregation/processing points to achieve efficient compression • How well can we do with localized algorithms that do not rely on centralized control or global knowledge ? • Metrics: system lifetime, quality of “detection” • Models and metaphors from biology and physics • See presentations by Albert, Doyle, Francescheti, Goldsmith, Krishnamachari, Kumar

Collaborative, multi-modal, processing • In network processing must extend beyond signal processing, on a single node • Collaborative signal processing • Localization • Compression • Supression of redundant detections • Sensor fusion • … • See presentations by Effros, Potkonjak, Pottie, Ramachandran, Zhao

Sensor coordinated actuation • Actuation needed for fully self-configuring and reconfiguring systems • Allow for adaptation in physical space • Services provided • Energy delivery • Calibration • Localization • Sample collection • Node placement • Static sensors can assist mobile elements with navigation, search, coordination • See presentations by Hogg, Sukhatme

Primitives for Programming the Collective • 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 • Nested behaviors to identify multi-parameter “events” • Record images or mobilize robotic sample collection in response to event detection. • See presentations by Culler, Sukhatme

Safety, Predictability, Usability • As we embed sophisticated behaviors in previously-”simple” objects. • Support effective mental models that allow for correct interactions, adaptations, diagnosis • Design themes • Achieve isolation • Constrain interactions • See presentations at some future workshop…

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 • Understanding and designing for the collective • Local-global (global properties that result…local behaviors that support) • Programming model for instantiating local behavior and adaptation • Abstractions and interfaces that do not preclude efficiency • Large-scale experiments to challenge assumptions behind heuristics

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

Future Directions • Tremendous opportunities for expanding research on horizon • Driven from bottom up by sensor development (e.g., BioMEMS) • Pulled from the top by emerging applications (e.g., medical, space exploration) • Critical Concerns: Security, Privacy, and Safety • ENS systems in human environments will greatly alter human experience and intensify design requirements For further information see http://lecs.cs.ucla.edu/estrin Or email to destrin@cs.ucla.edu Recommended reading: NRC Report Embedded Everywherehttp://www4.nationalacademies.org/cpsma/cstb.nsf/web/pub_embedded

Embedding the Internet: This Century Challenges