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Embedded Systems and Sensor Networks Pete Broadwell <[email protected]> Joe Polastre <[email protected]> Introduction Network-enabled embedded systems currently are approaching widespread use. We make a case for the “access network” approach to converging such networks with larger networks, and present wireless sensor networks as a case study. Talk Outline • Introduction to embedded systems – Design considerations – Networking options • Strategies for network convergence – Access networks – Service discovery • Sensor networks: a case study – Operating environment – Networking implementation – Supported applications Embedded Systems Pete Broadwell <[email protected]> What is an embedded system? • Hardware and software components • Part of a larger system • Operates without human intervention Tiny Webserver • Example: – Single-board microcomputer – Software stored in ROM – Runs special-purpose app until turned off Types of embedded systems • Sensors* – Collect data – Passive interaction with environment • Actuators* – Control machines – May introduce changes into environment • Beacons* – No sensing or actuation – Can alert other sensors to changes in environment * All can benefit from being networked! Why the interest in embedded systems? • Embedded systems are becoming ubiquitous – Moore’s Law: more computing power in smaller devices • Example: laboratory temperature alarm Embedded devices: Traditional electronics: temperature sensor +5V controlling ROM comparator thermistor comm. bus interface +5V speaker comm. bus environment monitor Why network them? • Some embedded systems have no use for network connectivity – Example: my car’s ABS (or do they?) • Others benefit from network access – Example: refrigerator orders milk when it’s low • It’s easy: ubiquitous large network access – Infrared – Wireless – Cable, telephone, power lines… Motivations for networked embedded systems “Smart spaces” Stanford iRoom Remote actuation Remote surgery Access to sensor network data (more later) Embedded systems design issues • Power consumption • OS/programming API – Real-time? Event-driven? • Communication – Medium? Protocol? • Localization • Monitoring • Security Communications decisions • Medium choices: – Infrared – Wireless – Fiber • Protocol choices: – IrDA – Bluetooth – Ultra Wideband (eventually) – PicoNet • Messaging format choices: – Active messages (asynchronous – RPC (synchronous) – Proprietary • Network setup choices: – Ad-hoc or static – TCP/IP compatibility – Internet connectivity OS/Programming model • Example: Windows XP Embedded – Componentized version of consumer OS – Device-specific “enabling features” XP Embedded configuration screen • Embedded Linux is similar Computation in the network • Embedded systems push functionality into the network – Leaving data processing/decision making to supervisor is slow and wasteful • One solution: Active Messages – Facilitate asynchronous intra-network computation – May support distributed queries of sensors (treating sensor networks as a DB) Relation to network convergence • Embedded systems employ an extremely diverse range of programming models and communication methods. • Common thread: connectivity exists among hosts, as well as between hosts and a central supervisor entity with greater computing resources. A case for the Access Network approach to convergence Treat networks of embedded systems as “access networks” Internet Unresolved issue: service discovery • How do hosts on a large network discover services offered by networked embedded systems? • Service discovery protocols – – – – – – Sun’s Jini Microsoft’s UPnP Salutation Bluetooth* PicoNet* IrDA* * Per-connection only Service Discovery Protocols: Electronic eavesdropping example Nosy Dan’s eavesdropping device Base Station 1. “Register service: Mote 1 Mote 1 listening in Room 1” Nosy Dan’s competitor Base Station 6.3. “Establish “Requestconnection service: With Mote 1” Listening in Room 1” Nosy Dan Room 1 Room 2 4. “Lookup service: 5. “Reply: Mote 1 Listening in Room 1” in Room 1” Listening 2. “Register service: Mote 1 listening in Room 1” LAN Lookup server • An Internet-scale solution to this problem has yet to be developed. Sensor Networks Joe Polastre <[email protected]> Emerging Extremes and Convergence • Planetary Services • Open Internet Services • Internet Services • Servers • Workstations • Personal Computers • PDAs / HPCs/ smartphones • Microscopic sensor/embedded networks From David Culler’s Invited Lecture at USC, February 28, 2001 Network Convergence Sensor Networks • Concurrency intensive – • • • • Self-organized and reactive control provides real time services via different network mechanisms • Different elements of the converged network have varied loads • • May or may not have UI Network is adaptive population usage & physical stimuli robustness Hands-off (no UI) Dynamic configuration, discovery – – data streams and real-time events, not command-response Huge variation in load – – Converged Network • Concurrency intensive – service discovery major part of huge, all-encompassing network Complimentary roles – tiny semi-autonomous devices empowered by infrastructure – infrastructure services connected to the real world Sensor Networks • Existing Research Platforms TinyOS/Mica SmartDust WINS NG 2.0 – Berkeley Platform – Sensoria – (Pister) Berkeley 2001 (Culler) 330µm TX Drivers Power input ambient light sensor Photodiode ADC Power Oscillator 13 state Sensor input 0-100kbps CCR or diode FSM controller 1mm Optical Receiver Sensor Integration The TinyOS Platform Application Model Environment monitoring IP DB SerialForwarder Traditional network RF Remote control console for motes IP SerialForwarder SerialForwarder IP Inventory tracking Services… What about Sensors? • Variety of sensors & actuators available – All-in-one sensor board includes light, temperature, microphone, sounder, accelerometer, and magnetometer – Environmental monitoring sensor board includes light, calibrated temperature, thermopile, humidity, barometric pressure – Remote control sensor board includes external pin connections to control physical devices including RC vehicles Multi-Network Data Acquisition --- Demo --- • Two motes are sensing light and reporting the results back to a base station • Base station allows IP clients to connect and read sensor data or control motes from anywhere on Internet Robust Communication Geographic Routing: QoS multi-hop data acquisition • GeoCast (Navas and Imielinski 1996) – Architecture for addressing and routing in wide are networks • GeoMote (Pete, Joe, Rachel 2001) – Sensor network implementation of GeoCast: lower power, adhoc – Primary Services: • Geographic Multicast • Nearest Neighbor Service Discovery • Geographic Network Reprogramming and Reconfiguration • Low Power Pursuer/Evader Games Geographic Routing Architecture Client Process Event Router Gateway Host Event Client Process Direct Message Low-Power Pursuer Evader Geographic vs. Internet Architecture • Geographic (sensor) – Routers may never talk to Hosts and vice versa – Gateways are entry/exit points but have no routing info – Broadcast medium dependant on distance from source • Internet – Functions of the gateway and router are typically merged – Gateways perform routing functions and are entry/exit points – Broadcast medium dependant on physical network Directed Diffusion • Data-Centric • Register “interests” in the network – < Attribute, Value > pairs • Nodes diffuse the interest towards producers via a sequence of local interactions • Gradients determine path of data • Achieve efficient distribution of data through reinforcement and negative reinforcement Illustrating Directed Diffusion Setting up gradients Sending data Source Source Sink Sink Reinforcing stable path Source Source Sink Sink Recovering from node failure Illustration courtesy of Deborah Estrin, UCLA Distributed Algorithms • Completely new area to investigate robust distributed algorithms on sensor networks – Example: New Distributed Algorithm for Connected Dominating Sets in Wireless Ad-Hoc Networks ---Alzoubi et. al. – Connected Dominating Set Typically Used as a backbone for wireless networks— useful to compose the backbone dynamically Connected Dominating Set 1) Set the rank of each node 0 2) Lowest Rank Among Neighbors DOMINATOR Start Dominator INVITE JOIN 3) If all lower ranking 1 neighbors dominee DOMINEE INVITE JOIN then you are dominator 2 2 4) Invite black nodes DOMINATOR to participate in 3 dominating tree 1 DOMINEE JOIN INVITE 2 2 DOMINATOR 3 3 DOMINEE 4 4 This is all occurring over RF broadcasts! 3 Sensor Network as a Database Two Projects: • Intel Research w/ UC Berkeley: TAG – Tiny Aggregate Queries in Ad-hoc Sensor Networks – Sam Madden, Wei Hong, Joe Hellerstein, Mike Franklin, David Culler • Cornell University: Cougar – Towards Sensor Database Systems – Querying the Physical World – Philippe Bonnet, Johannes Gehrke, Praveen Seshdri Databases vs. Sensor Networks • Database – – – – – – Single Physical Device Static data Centralized Failure is not an option Plentiful resources Administrated • Sensor Network – – – – – – – – – Numerous Devices Streaming data Large number of nodes Multi-hop network No global knowledge about the network Frequent node failure Energy is the scarce Resource, limited memory Autonomous Want “to combine and aggregate data streaming from sensors.” Sounds like a database… Fjords Use Fjords to handle lack of reliabilty and streaming push data • Allows arbitrary combinations of push/pull amongst devices – Operators assume non-blocking queue interface between each other. – Queues implement push vs. pull • Pull from A to B : Suspend A, schedule B until it produces data. A cannot go forward until B produces data. • Push from B to A : A polls, scheduler thread invokes B until it produces data. A can process other inputs while waiting for B. – Supports parallelism between operators Fjording the Stream Querying Streaming Sensor Data P u s h Pull Social Networks / Active Badges • Sensor networks can record social interactions by detecting proximity • Not just a convergence of sensors and Internet, but other “networks” too! • First attempt to monitor social network at UCB NEST Retreat, January 2002 • UCLA: iBadge Prototype – Investigate behavior of children in a Kindergarten Social Network Visualization Wagner Culler Social Network Results Conclusions and Future Directions Future Directions • Everyone disagrees over whether sensors should directly communicate via IP – – – – Sensors: Routing is data-centric and energy-aware Internet: Routing is bandwidth and latency-centric If so, we need IPv6 NOW! Do sensors need TCP/IP overhead since the transport medium is unreliable? • Networked Sensors may choose to elect some nodes to participate in networking and others to acquire data – Partitions the network into two sets, end-hosts and infrastructure, like current Internet Conclusions • Research opportunities in sensor networks is infinite (or nearly infinite) – – – – Algorithms Network Architecture / Routing Data Acquisition / Aggregation Network Convergence of Devices • Computing will continue to move within the network • Sensors and Embedded Systems will enabled ubiquitous computing efforts • Connecting Embedded Devices to Traditional Networks can be very powerful: – Environmental Monitoring – Autonomous Actuation (eg: “Smart” home) References • See www.cs.berkeley.edu/~polastre/cs294-2002sp • Links to relevant papers and more information on Embedded and Sensor Networks Embedded Systems and Sensor Networks