Connecting the Physical World with Pervasive Networks

ECET 581 Reading Report

Due: 9/12/06

By Douglas A. Schultz

Summary• This article discusses instrumenting the

physical world with pervasive networks of sensor-rich, embedded computation.

• Authors:– Deborah Estrin– University of California, Los Angeles– David Culler and Kris Pister– University of California, Berkeley– Gaurav Sukhatme– University of Southern California

Mark Weiser• (July 23, 1952 - April 27, 1999) was a chief

scientist of Xerox PARC and widely considered to be the father of Ubiquitous computing (also known as Ubicomp) and calm technology.

• Weiser’s Vision: A world in computing is so pervasive, that everyday devices can sense their relationship to us and each other.

• Technology has improved to the point where Weiser’s vision can become reality, applied to computing and social applications.

Pervasive Networks

• Systems should fulfill two of Weiser’s visions.– Ubiquity, by injecting computation into the physical

world with high spatial density.– Invisibility, by having the nodes and collective nodes

operate autonomously.

• Systems must have reusable building blocks for sensing , computing and manipulating the physical world.

• Systems must not contain specialized instrumentation.

Opportunity Ahead

• We need the ability to easily deploy sensors, computation and actuation into our world.

• The devices must be general purpose that can adapt and organize to support different applications and environments.

• Taxonomy of emerging system types for the next decade.

Challenges

• Most serious are System Challenges.

• The many distributed system elements, limited access to elements, and extreme environmental dynamics all combine to force review of:- Layers of abstraction- Kinds of hardware acceleration used- Algorithmic techniques

Immense Scale

• Many small devices will be in these new systems.

• In 5 to 10 years device size could be as small as a cubic millimeter.

• Measurement fidelity and availability will come from the quantity of redundant measurements and their correlation.

Limited Access

• Devices will be embedded where a wired connection is impossible or too expensive to use.

• Communications will have to be wireless and nodes will have to rely on renewable or harvested energy.

• Scale could be so large no one person could ever touch all the devices.

• Energy sources will limit the amount of activity, as in sensor measurements, per unit time.

Extreme Dynamics

• Since the system is nodal and tied to the constantly changing physical world it will have extreme dynamics.

• Reaction to environmental changes will directly effect the devices performance.

• Most of the time the device senses no change and uses low power.

• When an event occurs, then high and low-level data, flow from sensors and actuators must be managed effectively.

• These systems must continuously adapt to resource and event activity.

A taxonomy of systems.

• New system design must come from hands on experience with new technology.

• The use of many small low power connected devices will be applied to solve problems where individual devices are impractical.

• System reuse and evolution is key to pervasiveness.

Physically embedded systems

• Scale, variability, and autonomy relate to the system elements and data being captured.

Scale

• 1st characteristic of system design• Space and time factors, effect the sampling interval,

overall system coverage, and the total number of sensor nodes.– Sampling: What you are trying to measure determines the

sampling scale. The application also effects the sampling scale. If it’s event detection, (lower resolution) vs. event or signal reconstruction, (higher resolution).

– Extent: Also effects the scale. Environmental systems could be 10 kilometers vs. a building or room system.

– Density: System density is the measure of sensor nodes per footprint of input stimuli. High density systems can extend the life of sensors nodes and reduce noise by redundant measurements.

Variability

• 2nd characteristic of system design.• Static systems use design time optimization. • Dynamic systems use run time optimization.

– Structure: Ad hoc vs. engineered. Structure vs. bio-complexity monitoring. Also combinations of both.

– Task: Variability takes the form of how much can we tweak the system for single mode operation.

– Space: Variability in space equals mobility. Applies to nodes and what you are trying to measure.

Autonomy• 3rd aspect of system design.• Higher system autonomy, indicates less human

involvement, which requires more complex internal processing.– Modalities: Autonomous systems depend on multiple

sensory modalities. This lowers system noise, and helps identify measurement anomalies.

– Complexity : Autonomy makes the system model more complex. A system that just delivers data for a human to process is less complex. A system that executes depending on system state, and inputs over time, and must execute a programming language is much more complex.

Where are we now? (1)

• Weiser suggested a need for different size devices, from the size of a pin to a whole building.– Small packages in the physical world.– PDA’s have had wireless LAN capabilities but

this requires a large battery pack. Bluetooth (short range wireless network) has recently been added to PDA’s.

– PDA’s now have cell phone capabilities, and both support GPS.

Where are we now? (2)

Sensors have been further reduced in size due to advances in MEMS (microelectromechanical systems).Small CMOS low-power radios are also being developed. For example, Crossbow developed by UC and DARPA. (See picture of UC Berkeley Mote.)

Where are we now? (3)

• As device size decreases and complexity increases, several new OS have been developed.– Vxworks (www.windriver.com)– Geoworks (www.geoworks.com)– Chorus (www.sun.com/chorusos)– Small footprints and TCP/IP capabilities have been

added.– Windows CE adds a subset of windows to PDA’s.– Unix variants i.e. Linux provide real-time multitasking

support.

Where are we now? (4)

• An effective networked node must have a runtime environment that supports;– Scheduling,– Device Interface,– Networking,– Resource management,– Concurrent data flows from sensor to network

to controllers.

Tiny OS• The TinyOS (tiny operating system environment)

is component based.• Traditional scheduling loops are replaced by

fine-grain multi-threading.

• TinyOS provides fine-grain power management, extensive concurrency, with limited processing resources.

• Open-source OS.

Sensing and actuation

• Interacting with the real world involves energy exchange in two forms:– sensing and actuation.– Sensing, a sensor, converts (temperature, light intensity, etc.) to

information.– Actuation lets a node convert information into action, while it’s

main role is improve sensing.– An Actuator, moves part of itself, relocates itself or moves other

items in the environment.– To deal with uncertainty in sensing and actuation filtering is used

at each node and overlapping measurement areas.– A new approach is to hybridize control, a planner and reactive

system communicate via a 3rd software layer.

Localization

• Nodes must know their location. • Where am I? • In relation to a map, other nodes or global coordinate

system.• Scale and autonomy play a large role in location

computation. Recent trends use algorithms to localize large networks autonomously.

• Localization can be seen as a sensor-fusion problem.• One recent example of coarse localization let the nodes

build a map of their environment.• Another algorithm builds a mesh where nodes are point

masses and springs represent relationships between nodes.

Distributed system architecture.

• Constraints imposed by battery power will make or break these systems.

• Systems will not be able to constantly stream data out to a computer for analysis.

• Computation must be along side the sensors so data will be processed locally.

• Self configuring systems will turn off redundant nodes to conserve energy.

• Data-centric vs. address-centric network architecture using directed diffusion.

Distributed system architecture (2)

• Directed diffusion separates data and node identity. Used by Linux systems and a subset runs on TinyOS.

• Higher-tier resources will be necessary to compliment the lower level data nodes. An example could be a roving robot that replaces batteries or recharges the batteries of the nodes.

Where are we headed?

• Thousands of devices embedded in buildings, bridges, water ways, highways, and protected areas to monitor health and detect critical events.

• Advances in miniaturization mean we can now put instruments in the experiment, instead of conduction the experiment inside an instrument.

• System architecture will have to support interrogating, programming , and manipulating the real world.

• Embedded systems will need to self-organize, spatial reconfiguration is needed.

Conclusion

• Interdisciplinary approaches will be needed to solve system problems with; – Networking– Operating systems – Databases– Artificial intelligence– Virtual reality– Signal processing

• Training will have to be applied at the graduate and undergraduate level.

About the authors (1)Deborah Estrin is a professorof computer science atthe University of California,Los Angeles. Her researchinterests include design ofnetwork and routing protocolsfor large global networks,sensor networks, andapplications for environmental monitoring. Shehas a PhD in computer science from the MassachusettsInstitute of Technology. She has servedon several program committees and editorialboards. Contact her at the Computer ScienceDept., UCLA, 3713 Boelter Hall, 420 WestwoodPlaza, Los Angeles, CA 90095; destrin@cs.ucla.edu.

About the authors (2)David Culler is a professorof computer science at theUniversity of California,Berkeley, and director ofIntel Research at UCB. Hisresearch interests includeembedded networks ofsmall wireless devices, parallelcomputer architecture, parallel programminglanguages, and high-performance communication.He has a PhD from MIT. Contacthim at Computer Science Division #1776, 627Soda Hall, UCB, Berkeley, CA 94720-1776;culler@cs.berkeley.edu.

About the authors (3)Kris Pister is an associateprofessor in the ElectricalEngineering and ComputerSciences Department atUCB. His research interestsinclude the developmentand use of standard MEMSfabrication technologies,micro robotics, and CAD for MEMS. He has aBA in applied physics from the University ofCalifornia, San Diego, and an MS and PhD inelectrical engineering from UCB. Contact himat the Electrical Eng. and Computer ScienceDept., UCB, 497 Cory Hall, Berkeley, CA 94720-1770; pister@eecs.berkeley.edu.

About the authors (4)Gaurav Sukhatme is anassistant professor in theComputer Science Departmentat the University ofSouthern California andthe associate director of itsRobotics Research Laboratory.His research interestsinclude embedded systems, mobile robot coordination,sensor fusion for robot fault tolerance,and human–robot interfaces. He has an MS andPhD in computer science from USC. He is a memberof the IEEE, AAAI, and ACM and has servedon several conference program committees.Contact him at the Computer Science Dept.,MC0781, USC, 941 West 37th Place, LosAngeles, CA 90089-0781; gaurav@usc.edu.