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CS 851 Wireless Sensor Networks Introductory Lecture

CS 851 Wireless Sensor Networks Introductory Lecture. Professor Jack Stankovic Department of Computer Science University of Virginia September 2003. Purpose of this Lecture. Get you to think differently Regardless of whether you are new to WSN or have been working with them

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CS 851 Wireless Sensor Networks Introductory Lecture

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  1. CS 851Wireless Sensor NetworksIntroductory Lecture Professor Jack Stankovic Department of Computer Science University of Virginia September 2003

  2. Purpose of this Lecture • Get you to think differently • Regardless of whether you are new to WSN or have been working with them • Introduce the basic key issues and their implications • Reduce work to its essence

  3. The field is exploding

  4. Smart Spaces Smart School Smart Factory Smart City • Other Applications • Battlefields/Surveillance • Earthquake areas • Environmental Monitoring • Airport security • Emergency Response • Location Services

  5. More Applications • Interface with the Internet • Handheld PDAs/laptops • Element in pervasive computing From your reading did you find interesting applications or ideas about applications that were Surprising?

  6. Ad Hoc Wireless Sensor Networks • Sensors • Actuators • CPUs/Memory • Radio

  7. Research Questions • What are the correct HW elements to make solutions at the OS/middleware/application levels easier? • Current motes are only 1 possible platform • How about DSPs? Special security HW? • What capacities (cpu speed, memory, bandwidth, power, etc.) and their fundamental limitations, have if any, on solutions

  8. Sensor/Actuator Clouds Resource management, team formation, networking, … Heterogeneous Homogeneous • Severe constraints • power, memory, bandwidth, cpu, cost, ...

  9. Background: Challenge fundamental assumptions underlying distributed systems technology • How the problems change • Key Areas to be Addressed • Routing • Power Management • Localization • Security • Paradigms • Theory • Other Issues • Examples: key research problems/solutions • Spatial-Temporal Routing • Application Independent Data Aggregation • Localization Realities

  10. How the Problems Change • Environment • connect to physical environment (large numbers, dense, real-time) • massively parallel interfaces (sometimes) • faulty, highly dynamic, non-deterministic • wireless (indirect impact on remote entity) • power management critical • Network • structure is dynamically changing • sporadic connectivity • new resources entering/leaving • large amounts of redundancy • self-configure/re-configure • individual nodes are unimportant - route/query to AREA

  11. How the Problems Change • OS/Middleware • manage aggregate performance • control the system to achieve required emerging behavior • How do we know it works? • self-organizing (self-*) • fuzzy membership and team formation • manage power/mobility/real-time/security tradeoffs • geographical/location based (spatial) • real-time/real world (temporal) • data centric

  12. Examples • Can you give me examples of simple decentralized algorithms that exhibit aggregate behavior?

  13. Implications • Fundamental Assumptions underlying distributed systems technology has changed • wired => wireless (limited range, high error rates) • unlimited power => minimize power • Non-real-time => real-time • fixed set of resources => resources being added/deleted • each node important => aggregate performance • New solutions necessary

  14. Example: Resource Management • Measure communication errors • if too many • increase communication power or if a mobile node it might move closer to the destination

  15. Example: Consensus • Classical consensus: all correct processes agree on one value • No power constraints • No real-time constraints • Does not scale well to dense networks • Approximate agreement (some work here) - on sets of values (physical quantities) • New Solutions ?

  16. Termination: every correct processor eventually decides some value Uniform Agreement: no two processors decide differently Group Membership: join/leave - everyone knows who is in the group Termination: “at least n” correct processors decide some value by time t Group Agreement: at least n processors decide the same value within epsilon Area/Function Membership: join/leave an area or by function New Concept of Consensus Classical New Definitions

  17. Example: Group Management (Tracking) Base Station

  18. Group Management - API • Create_Group(name,function,criterion,atleast,accuracy) - implicit and explicit • Destroy_Group(name) • Join() • Leave() • Move_COG() • Expand() -- to gain sensing confidence • Shrink() -- to save power • Commit(grp_ID) - to synchronize group re-configurations

  19. What’s Hard • Multiple targets • Crossing targets • False Alarms • Depends on (changing) environment, sensors, confidence tradeoffs, noise, lost messages, …) • Speed of targets • Uniqueness of targets • Classify targets • Proper abstractions • Save power/min. commun.

  20. The Essence • Power • Other limited resources (BW, CPU, …) • Extreme Scale • Changing “everything” / uncertainty • Aggregation • unimportant individual nodes • decentralized, very simple algorithms • What I do impacts you (collisions) – mutual exclusion

  21. Six Themes • Routing • Power • Localization • Security • Paradigms • Theory • Are there others? Yes…..

  22. Routing • Solutions must be • Power aware • Robust to lost messages, dead motes, voids • Real-time • Communication range variations • Moving end points • Amount of state information • Extreme Scale • Secure

  23. Power • Example Algorithms • AFECA – power up and power down with time proportional to the number of neighbors • GAF – create grid and keep at least one mote alive in each grid (rotate among them in the grid) • SBPM – no grids; non-deterministic; minimize connectivity; decentralized; complete sensing coverage (60% savings over no power management) • Differentiated Surveillance • 50% less energy than “best” other solution

  24. Power • Other power savings: • Vary transmission power • Turn off devices not needed • On – all devices on • Off – microprocessor in low power state so that registers/memory are not lost and clock interrupt can occur • Checking – microprocessor and radio are on • Choose routes that minimize power • Aggregate messages to save power

  25. Localization • Space (localization) and Time (clock sync) Basis • Environmental monitoring – where and when events occurred • Localization is a function of • Hardware available, cost requirement, signal propagation model, timing and energy requirements, network makeup, nature of environment, node and beacon density, time sync, communication costs, error requirements, device mobility, …

  26. Security • What is the single most important issue that could prevent WSNs from wide scale deployment? • Security • 2nd issue -> Privacy • At application level • Authenticity and integrity • Security of each service (examples) • Routing: • non-secure if a single node is captured! • Eavesdrop or change message • Flood • Insidious unintended consequences of collecting data • Monitor oceans for fish migration (data mine location of submarine fleet)

  27. Security • Localization • Attacker can report he is close to everyone • Chirp then wait then transmit to give false location (normally chirp and transmit simultaneously – measure signals difference) • Network Discovery • Provide false messages to create false topology • Prevent convergence

  28. Paradigms • Virtual Machines • SQL and data services models • EnviroTrack • Tie to physical systems/physics • Swarm computing • Biological metaphors

  29. Theory • Theory of computation for WSN • Emerging behavior of local/decentralized algorithms • New graph theory • New spatial-temporal analysis • Aggregate control theory • Utilization Equivalent Bounds • Modeling and Analysis • What are the fundamental scientific questions

  30. Other Key Issues (1) • Sensing/communication range ratio • Sensing/communication/power tradeoffs Communication Range Sensing Range What if the opposite?

  31. Other Key Issues (2) • Reality programming • Robust to faults • Sensor realities • Don’t believe one reading • Hysteresis • Sensor fusion • Activation delays • Avoid false alarms • Self-Calibration

  32. Other Key Issues (3) • Limited capacities • Rapid dynamics • Scaling factors and implications on behaviors • Extreme scaling • Insidious interactions • High density with many motes off to enable long system lifetime; turn on when activity happens then too many with many collisions and poor response

  33. Other Key Issues (4) • Architecture – hierarchy of control/capability/functionality • Size of targets/events (point/area) X Explosion Fire

  34. Middleware Services • Non-traditional • Configuration service • Automatic calibration • Network programming • Reset services • Management services

  35. Middleware Services • Real-Time Routing • SPEED – spatial-temporal concept • Application Independent Data Aggregation • AIDA – feedback control • Localization • APIT – realities of wireless world

  36. Sensor Net Routing • End-to-end • Real-time • Collisions • Congestion Destination Source Assumption: Nodes know location

  37. SPEED USE VELOCITY

  38. Application Independent Data Aggregation • Expensive to acquire the “channel” • Small data packets • Group data packets into 1 MAC packet • Works in additionto other data aggregation techniques which are based on semantics

  39. Major Architectural Difference

  40. FIXED SCHEME • Accumulate N packets • N: degree of aggregation • FIXED • On Demand • Adaptive/FC • T: Time out for old packets when accumulation rate is slow

  41. DYNAMIC/Adaptive FC • Adaptive choice of N • Take into account the output Queue delay • Delay is used to adjust the output queue push rate and degree of aggregation

  42. Localization • Determine the geographic location of each node with a high degree of accuracy (necessary for application) • Applications • search and rescue • disaster relief • target tracking • Protocols • location aware routing • guaranteeing sensing coverage • location directory services • Fundamental and Enabling Service

  43. Radio Model in Evaluation DOI = 0.2 DOI = 0.05 Radio Model DOI = Degree of Irregularity

  44. Known: Signal strength is not good indicator of distance over the entire region Hypothesis: Signal strength IS accurate enough for nodes very close to each other! X

  45. Testing Hypothesis

  46. Summary • (Much) Current Distributed Systems Technology • wired networks, powerful nodes, highly reliable nodes, interaction with users, fixed numbers of resources/team members, unlimited power, ... • Embedded (Large Scale) Distributed Systems • wireless, simple nodes, unreliable nodes, interaction with the environment, resources being added and deleted continuously, power management needed, …

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