Sensor Networksppt - Sensor Networks.ppt

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					Sensor Networks

    ICS 203A
   Crista Lopes
• RFID systems
• Sensor networks
  – Testbeds and protocols
  – Architectures and Network Programming
  – Operating Systems and In-network
•   Supply-chain global tracking
•   Localized tracking
•   Routing in conveyer belts
•   Security
         Bar Codes vs. RFID
• tag, backed by data   • tag, backed by
  processing system       massive data
                          processing system

• line-of-sight         • no line-of-sight
• immutable info        • reprogrammable
• zero cost (…)         • non-zero cost
                          (target price: 10 cents)
                        • more dynamic
System architectures
[Wireless] Sensor Networks
• Industrial control and monitoring
    – lighting, a/c, machinery
• Environmental monitoring
    – fire, pollution, wild life, agriculture, etc.
•   Health monitoring
•   Traffic monitoring and control
•   Building structures monitoring
•   Asset tracking and supply chain mngmt
•   Security and military sensing
•   …
Testbeds & Protocols
SensorNet Architectures For
 Indoor Location Detection
         David Starobinski
        Ioannis Paschalidis
         Ari Trachtenberg
         Boston University
         NSF NOSS Meeting
       Colorado School of Mines
    Indoor Location Detection:
     An Enabling Technology
• Smart homes and
• Tracking personnel
  and equipment (e.g.,
  in hospitals)
• Assisting visibly
  impaired population
• Disaster recovery
    Indoor Location Detection:
      Technical Challenges
• Most approaches use signal landscape
• Problem: signal landscape highly varying
  in both time and space:
  – Multi-path fading
  – Long-term dependencies (e.g., doors opening
    and closing)
          Research Thrusts
1. Robustness
   Provide resiliency to sensor failures and
    signal changes
   Theory of information and identification
2. Optimization
   Determine optimal sensors placement and
   Theory of detection, large deviation, and
    non-linear programming
Testbed - Implementation
     Outreach Activities:
  BU SensorNet Consortium

• Liaison between academia and industry
• Foster collaboration and technology
• Train and educate students
          Web Information
• Laboratory of Networking and Information

• Center for Information and Systems
Communication Patterns
for Collaborative Reasoning
in Sensor Networks

                   Leonidas Guibas
                   Sebastian Thrun
                   Computer Science
                   Stanford University
Co-optimization of Networking with
 Application-Specific Processing

      Sensor Net Applications

          Network Stack
     Communication Structure
• Communication patterns in a
  sensor network are shaped
  – the structure of the physical
    phenomena being monitored
  – the task to be accomplished
  – the nature of errors and
    uncertainty in the
  – the state of the sensor nodes
    themselves (energy reserves,
     Communication Patterns For
         Generic Tasks
• A. Going from limited local       Algebraic Topology,
  information to global             Data Analysis and
                                    Mining, Shape Spaces
   – Qualitative understanding of             Henri Poincare
     sensor layouts, signal
     landscapes, etc.
• B. Distributed lightweight
  probabilistic reasoning           Bayesian Estimation,
                                    Decision Theory,
   – Integration of evidence from   Probabilistic Learning
     multiple sources
   – maintenance of multiple
                                                 Thomas Bayes
     A1. Understanding the Sensor
             Field Layout       a landmark

An understanding of the topology of the
field layout can be essential for many
tasks in the network, from routing to
information discovery and the proper
integration of evidence.

This topological information may be
derivable from local connectivity data        The geodesic Voronoi diagram
among the nodes and not require
expensive geometric operations, such as
node localization.

Such lightweight global analysis
techniques can be both very efficient and
very robust.

                                            The combinatorial Delaunay complex
          A2. Understanding Signal
We may want to qualitatively understand
sampled signal landscapes, to gain a
deeper understanding of the phenomena
being monitored, be they forest fires or
moving vehicles.

The qualitative features of the landscape
determine how we might go about
planning escape routes from the fire or
forming sensor collaboration groups to
track the vehicles.

Since noise will be invariably present in
the data, we must develop methods that
can focus on the essential features of the
landscape and not be sidetracked by
irrelevant distractions.
A3. Creating a Society of Nodes
To perform its tasks, a sensor network
needs to organize itself in various ways.

Certain nodes may have to assume
specialized functions and become
clustereheads or gateways.

Nodes need to form `districts’ and `towns’,
providing higher-level named abstractions
for applications to use

information highways have to be built and
brokerage services established to link
information providers and information         A double ruling derived via a Morse
seekers.                                      function ≡ distance to boundary
           General Approach
1. Use lightweight information, such as direct
   node connectivity, to extract the overall
   structure of the sensor field
2. Enable `geometric’ operations and predicates,
   such as geographic routing, even though no
   explicit geometry is known
3. Organize nodes according to their sensed data
   into collaboration groups
4. Provide ways for information providers and
   seekers to meet
5. Build natural information highways
  Advantages of Topological Methods

• Can naturally extract global knowledge
  from local information
• Many topological algorithms can easily be
  implemented in a distributed fashion
• Topological persistence and other
  techniques can provide information filters
  for suppressing noise features
     B1. Integration of Evidence
• Energy conservation argues for in-
  network information aggregation
• The aggregation of probabilistic
  information is tricky. In order to
  avoid overcounting the same
  evidence multiple times,
  information about where
  information originates and how it
  is routed must be maintained
• Current loopy belief propagation
  methods are too expensive for
  direct implementation on sensor
B2. Distributed Identity Management
• The identities of tracked                                ?
  vehicles can become confused
  when they pass near each
  other                                                    ?
• That confusion may persist
  even after they separate and
  the identity ambiguity must be   Action at a distance?
  maintained in the network
• Sensor data later on may
  disambiguate one of the
• The network must also
  disambiguate the other ...
                                        Sensor confirms
          General Approach
1. User poses query, plus a cost-for-
   information ratio
2. System propagates query through
   sensor net (decentralized message
3. Sensor nodes respond with data
4. Internal nodes integrate uncertain
   information with Bayesian techniques
5. Result communicated back to user
  Advantages of Bayesian Reasoning

• Can resolve `inconsistent’ information
• Can provide a measure of uncertainty
• Can reason with partial/incomplete
• Can be entirely decentralized (e.g., loopy
  belief propagation)
  Advantages of Decision Theory
• Enables user to specify importance of
• Provides natural `pruning’ technique
  – for avoiding flooding a network with low-
    priority queries
  – for terminating probabilistic inference
           Project Summary
• Qualitative understanding of signal
  landscapes and sensor layouts
• Lightweight distributed probabilistic
• A test-bed implementation within a
  university building
                   The End
Another kind of sensor network exhibiting
distributed reasoning based on local interactions ...
Funneling Impulses in Sensor Networks

   PI’s: P. Jelenkovic, N. Maxemchuk, V. Misra,
                   D. Rubenstein
  RA: P. Cheung

  Columbia University
      Sensor Network            Conventional
1)   Impulse of arrivals   1) Uncorrelated arrivals
2)   Many sources to few   2) Many sources to
     sinks (funnel)           many sinks
3)   Compression in        3) .Preserves Data
4)   Hostile Environment
• Impulse in a funnel: PCF rather than DCF -
• Funnel: Density of sensors to extend network
• Compression: Generic and Application Specific
• Hostile environment:
Persistence of data waiting to be forwarded
Multipath routing
• Develop Mathematical Model of Sensor
• Test the models by simulation
Use data from real applications in the
     Experimental Applications
1. Predict the spread of a fire
• With FDNY using NIST Model
2. Early warning for shock waves from an
    earthquake (few seconds )
• With Lamont-Doherty Earth Observatory
3. Spread of biological contaminants or poison gas
• With Mechanical Engineering Dept.
          Compression Model
•    Physical Event is a 4-D Bandwidth limited
•    2 types of frames - like MPEG
1.   Full information
2.   Differential information
•    2 phases per frame
1.   Push: Low sequency components - generic
2.   Pull: Additional Components from specific
     locations - Application specific
     Compression Technique
1. Interpolate Non-uniform samples to obtain
   uniform samples
2. Form and Forward low sequency components
   in a frame
3. Request additional components in specific
• Difference between picture compression and
   fire readings
Temperature Thresholds in a Frame
Sensor Networks for Undersea
  Seismic Experimentation
         PI: John Heidemann
      Co-PIs: Wei Ye, Jack Wills
    Information Sciences Institute
   University of Southern California
      Why Undersea Sensor
• Vision: to reveal previously unobservable
  phenomena (Pottie)
• Goal: to expand senor-net technology to
  undersea applications
• Numerous potential applications
  – Oilfields: seismic imaging of reservoir
  – Environmental: pollution monitoring
  – Biology: fish or micro-organism tracking
  – Geology: undersea earthquake study
  – Military: undersea surveillance
       Our Focus Application
 Seismic imaging for undersea oilfields

  – Collaborate with USC’s
    ChevronTexaco Center
    for Interactive Smart
    Oilfield Technologies
• Current technology
  – High cost
  – Perform rarely, about    Photo courtesy Institute of Petroleum

    once every 1-3 years
 Our Approach: Undersea Sensor
• Dense sensor networks are largely
  changing terrestrial sensing today
• Bring the concept to undersea
  – Enable low-cost, frequent operation
  – Buoys      Radio
     Exploit dense sensors, close observation


  Current Undersea Networking
• Sparse networks, small number of nodes, long-
  range acoustic communication
  –   Navy Spawar (Rice): Seaweb network ~20 nodes
  –   Woods hole & MIT (Stojanovic)
  –   Northeastern Univ. (Proakis)
  –   Navy Postgraduate School (Xie)
• Cable networks: high speed, high cost
  – Neptune Network (Several Universities led by Univ. of
       • 3000km fiber-optic/power cables; $250 million in 5 years
• Instead we focus on low-cost, wireless and
  dense networks
        Our Research: Acoustic
• Water significantly absorbs radio waves
• Existing work on acoustic comm.
  – Focus on reliability and bandwidth utilization
    (push to higher bit rates)
  – COTS acoustic modems are long range (1-
    90km), power hungry and costly
• Our focus is to develop short-range (50-
  500m), low power (similar to Mica2 radio),
  low-rate (≤10kbps), and low cost acoustic
  comm. hardware
     Our Research: Networking
• Large and varying propagation delay
  breaks/degrades many existing protocols
  – Sound is over 5 magnitude slower than radio
  – Time sync, localization, MAC protocols
• Will investigate time-sync and localization
  algorithms that takes the propagation
  delay into account
• Will investigate efficient MAC protocols
  suitable for large latency
            Long-Term Energy
    Application only runs once a month
    – Nodes sleep for a month to conserve energy
    – Will investigate new energy management
      schemes for long sleep time
       • Inspired by work at Intel Portland
• Delay tolerant data transport
    – Large sensor data and low-bandwidth acoustic
    – Will investigate suitable DTN techniques
       • Delay tolerant networking research group (
• Project goal: expanding sensor-network
  technology to undersea applications
• Research directions
  – Hardware for low-power, short-range acoustic
  – Networking protocols and algorithms suitable
    for long propagation delay
  – Long term energy management
• Project website
   Exploring the Design Space of
Sensor Networks Using Route-aware
           MAC Protocols
   Injong Rhee and Bob Fornaro
      Department of Computer Science
       North Carolina State University
        Motivation and Goal

         New MAC schemes

                               Existing Sensor MAC

• Expanding design space
  – Under extremely low energy budget
    Testbed: Wildlife tracking
•   Endangered animals in NC (Red wolves, black bears, etc.)
•   Current telemetry techniques are not adequate.
•   Sensor networks can improve monitoring of these animals
•   Our teams have been working with wildlife biologists and NC
    zoology association on this project.
 Design choices :
     Existing approaches

Tradeoff         TDMA:
(coupling of     Good service
Throughput and                   802.11
                 Medium energy
Response time)                   Good service
                                 High energy
Our approach: Route-aware
             • On-demand routing
               paradigm (Directed diffusion,
SINK           SPIN, etc)
             • Route-awareness: the MAC
               layer of a node knows
               whether it is on a “currently
               active routing path” or not.
                – If not on such a path, it
                  switches off its radio.
                – Reduce idle listening

                                     Power consumption of node subsystems
                   Power (mW)   10
Route-aware MAC (RASMAC)
             • If off, how does it know of a
               new active path?
                – Software: Periodic
                – Hardware: passive
                  radio-powered trigger
             • Decoupling of throughput
               and response time.
             • Periodic synchronization
                 – Response time
             • Wake-up time duration (or
               frequency) while on active
                 – Throughput
   Performance results:
           Route-aware MACs
Extremely low   RA-SMAC:
Energy budget   Low energy


Architecture and Programming
  Creating an Architecture for
Wireless Sensor Networks – in a
   David Culler, Scott Shenker, Ion

   Electrical Engineering and Computer
     University of California, Berkeley

        NETS/NOSS Infosession
        Sensor Network Networking
                    Hood                             TinyDB
             FTSP                       Regions
Transport                    SPIN
                 TTDD                    Deluge      Trickle    Drip
 Routing               MMRP                               Arrive
                 CGSR          TORA            Ascent                   MintRoute
                AODVDSR    ARA    GSR             GPSR         GRAD
Scheduling     DSDV   DBF       TBRPF
                                              SPAN        GAF           FPS
Topology                PC          ReORg
              PAMAS                  SMAC        WooMac
                                             TMAC                             BMAC
 Link                  WiseMAC                       Pico

                                              Bluetooth                    802.15.4
 Phy         RadioMetrix                                       eyes
                             RFM                                       nordic
 The “Internet Architecture”
                      • End-to-end flows
                         – Pt-to-pt dominantly
      application        – Many applications sharing
                           the network
                      • Over best effort packet
                        delivery service
                      • Opaque, universal routing
network        IP       service
                      • Agnostic to physical link
             link       and application
                      • Radical simplification of a
                        really hard problem
                         – Efficiency cost
                         – Quality cost
           What role a “sensor net
Env. Monitoring                             Active
                       Detection/Alarm                • Wide range of long-lived
                                         Environments   applications
                         Tracking                     • Diverse, constrained,
          Structures                Distr. Control
                                                        evolving resources
                                                          – Low duty cycle
                                                          – Small tables
                                                          – Loss, noise & change
                                                      • Embedded in & adapting to
                                                        phy. env.
                                                      • In-network processing, not
                                                      • Highly application specific
                                                      • WSN needs a “narrow
                                                      • Few applications over
                                                        many nodes
      Emerging view of sensor
Compose what they need                 Tracking                                      Sensing
                                       Application                                  Application

 Multiple     Pt-Pt      Neighborhood              Aggregation                     Data          Robust
 Network     Routing        Sharing                  N --- 1                     Collection   Dissemination
 Layer         1-1          1-k / k-1                                               N-1            1-N

                         Rich Common Link Interface (SP)



  Link and                  IEEE


  Physical                  802.15.4
Six Aspects of a Sensor Network
• Design Principles
   – Guidelines and constraints, what functionality, what state
   – To what are we agnostic
• Functional Architecture
   – Logical building blocks/protocols, interfaces, interconnections,
• Programming Architecture
   – API/ISA – what logical data types and operations are expressible
• Protocol Architecture
   – Distributed algorithms to provide each component service, defn. of
     the information exchanged between instances
   – Most existing work is of this form
• System Support Architecture
   – Capabilities of the node to support the network arch.
• Physical Architecture
   – Set of nodes, interconnects, communication fabrics upon which
     network is constructed
                       Areas of Work
•   Physical Architecture
    –   Multitier, non-homogeneous (patches, transit, internet)
    –   SNA should not require unconstrained nodes
    –   Should utilize unconstrained nodes to reduce burden on constrained ones
    –   Mobility within physically embedded context
•   Programming Architecture
    – SNA will define consistent interfaces that encompass seven communication
       abstractions underlying range of programming models
    1. Dissemination
    2. Collection
    3. Aggregation
    4. Localized Neighborhood
    5. Point-to-point
    6. Data-centric storage
    7. Attribute-based routing
•   Functional Architecture
•   Protocol Architecture
•   System Support Architecture
•   Design Principles
                  Areas of Work (2)
•   Physical Architecture
•   Programming Architecture
•   Functional Architecture
    – Thin-waist as expressive interface to best-effort 1-hop broadcast - SP
    – implement over a range of links, utilize by a range of network protocols
    – Higher level optimization within control & info exposed by SP
•   Protocol Architecture
    – address-free protocols over SP, focusing on general, yet efficient
      techniques for defining forwarding predicate and reusable mech. For
      duplicate detection, suppression, and transmission scheduling
    – Name based: simple set of primitives at SP layer that allow network
      layer services to dictate and use naming schemes
         •   Discovery, formation, maintenance, forwarding
         •   Application-independent portions support sharing of partner networks
    – In-network storage: provide soft-state abstraction as building-block
      for variety of address-free and name-based network protocols
    – active in-network storage: identify minimalist actions that are flexible
      enough to higher levels to express meaningful predicates and queries
•   System Support Architecture
•   Design Principles
                 Areas of Work (3)
•   Physical Architecture
•   Programming Architecture
•   Functional Architecture
•   Protocol Architecture
    – Key cross-layer issues: discovery, time coordination, power
      management, network management security
    – Focus on cooperative interfaces
• System Support Architecture
    – SNA independent of particular OS, but implemented on one
    – extend TinyOS to better support SNA processing
        •   Encapsulation, Buffer management, Robustness, Scheduling
• Design Principles
    – Initial set guide the SP approach
    – Refined through the process
           Goal: Open, Interactive
            Community Process
• Push-and-pull
    – Actively pull in components developed by the community
    – Actively push out the framework
    – Interactive dialog on both
• Community Workshops – early and often
    – First one ~march 04
        • Initial framework for feedback on direction
        • Establish key collaboration participants in sub-areas
    – Annual follow-ons
• Winnowing process for interfaces, components
        • Experience, feedback, planning, prioritize, next steps
• Network stack(s) openly available to entire program at all times
    – On testbeds as they emerge
• Series of course materials
    – Intend to be shared and circulated
  Lightweight and Flexible
Sensor Network Management

   Kang G. Shin and Daniel L. Kiskis
   Real-time Computing Laboratory
          EECS Department
      The University of Michigan
              Our Key Motivations
• Self-organization in sensor networks
   – Essential for large-scale, unattended deployment
   – Required for evolving over time
   – It is pervasive
       • Across scales
       • Across services
• Better software engineering needed
   – Build or buy/borrow
   – Large number of home-grown components
   – Composing and configuring a system is difficult
       • Incompatibility
       • Hidden architectural assumptions
       • Redundancy
   – Resource constraints
• Take management-centric view of self-
  – Common management functions
     •   Start/stop service
     •   Query and modify parameters
     •   Signal events
     •   Invoke functions
  – Common management information
     • Objects
     • Attributes
     • Parameter and event types
• Derive management models
           Approach, cont’d
• Develop network service management
  – Encapsulate common management
  – Standardize management information
  – Management protocols
• Evolve and evaluate through core services
  – Routing
  – Hierarchical cluster management
  – Network bootstrapping
AgletBus Architecture
   Implementation and Evaluation
• Sensor network
  –   Mica and Mica2 Motes
  –   TinyOS and NesC
  –   iMotes and Stargates
  –   Parallel monitoring for
      debugging and
• Simulation
  – NS-2
              Expected Results
• Management models for sensor networks
• Network management infrastructure
  – Management software components
     • Lightweight, robust, and flexible
     • Shared code base for inter-node communication
  – Common interfaces
• Benefits
  – Consistent, standardized management functions
  – Reduced code footprint
  – Improved software reliability

Real-Time Computing Laboratory
Sensor Coordination using
   Active Dataspaces
        Steven Cheung

NSF NOSS Informational Meeting
      October 18, 2004
Why sensor network programming hard?

 Deploying new or                             Applications
 additional sensors
                                  Intermittent end-to-end
      Limited CPU power           connectivity
      and memory
                                Hibernation        Attacks

          Locality                Data aggregation

   Sensor nodes
Need: High-level programming abstraction

                                       Focus of this project

Optimizing CPU    Naming
& memory use
                  Deploying new or
Aggregation       additional sensors
   Security       Intermittent end-to-end
 Scalability      connectivity
   Locality       Hibernation
        Sensor nodes
Our Approach: Active dataspace (ADS)

• ADS is an active data repository that provides
  associative operations for data access
• Inspired by the tuple space model [Gelernter 85],
  developed for parallel computing
• Every data tuple (or record) contains a list of
• Basic TS operations:
  –   in is used to remove tuples from TS
  –   rd to read tuples
  –   out to create data tuples
  –   eval to create “active” tuples
              Features of ADS
• Data-centric model
• Time-uncoupling: Data consumers and producers do not
  need to be active at the same time
• Identity-uncoupling: Endpoints do not need to know each
  other’s identities
• Stable network paths between endpoints need not exist
• Virtual tuples support data generation on demand
• Tuple set operator and cardinality constraint to facilitate
  in-network aggregation
• Search constraint for specifying the scope and
  preferences for tuple selection to exploit locality
          Expected results
• High-level programming model and
  language to ease sensor network
  programming for a wide range of
  application domains
• Architecture and techniques to implement
  a resource-efficient, adaptive, and
  trustworthy ADS system
• Evaluation studies using a prototype ADS
High-Level, Efficient Sensor
  Network Programming

 NOSS Informational Meeting
     October 18, 2004
         Eddie Kohler
     Cross-Service Concerns
• Two sampling modalities, two sampling periods
   – Light (   ) and temperature (   )


• Result: interference and inefficiency
   – Reduce sleep time, data aggregation
   – Small changes to periods can reduce costs
• Sampling periods a cross-service concern
   – Each component must be aware of the others
   – Does this prevent efficient application-level component
Programming Languages for Sensors

• Use a programming language to solve this
  programming problem
• Goal: Smart sensor network service libraries
• System designers build parameterized libraries
   – Examples: temperature sensing, sensor value smoothing,
     routing tree formation, link quality estimation, query
     processing, …
   – More flexible application components than conventional nesC
• Scientists plug libraries together to build applications
   – The libraries weave themselves into an efficient program
  Sensor Network Application
       Construction Kit
• Language
   – Transitive path connections let independent services create
     a shared message path:
     MsgSrc ..> TreeDispatch ..> Network
   – Partially constrained parameters address cross-service
     issues: TimerM(period >= 20)
• Compiler
   – Expands a SNACK program and weaves together the results
   – Shares components as much as possible
• Component and service library
   – Components work effectively with the SNACK language
   – Parameters, path connections, dynamic memory, packet
     format, …
   – Services can build real applications
         SNACK Expansion (1)
                         Application Specification
         Tree Dispatch   Light Sampler   Tree Dispatch   Temp Sampler

• Sample light and temperature, send both up a routing
     LightSampler -> [collect::Put16] TreeDispatch;
     TempSampler -> [collect::Put16] TreeDispatch;
• Pretty simple!
                 SNACK Expansion (2)
                                   Partial Expansion: Dispatch
Tree Dispatch Service                Light        Tree Dispatch Service              Temperature
                                    Sampler                                            Sampler
                Msg Sink Msg Src    Time Src                      Msg Sink Msg Src     Time Src

Null Forward         Dispatch       Light Sense   Null Forward         Dispatch       Temp Sense

 Tree Service        Network        Time Sink      Tree Service        Network        Time Sink

   • The components came from a user-defined service
        – Easy to understand and change

                Compiler expands the services one step…
                SNACK Expansion (3)
                           Further Expansion: Tree and Link Estimator
Tree Dispatch Service                    Light       Tree Dispatch Service                 Temperature
                                        Sampler                                              Sampler
               Msg Sink Msg Src         Time Src                    Msg Sink Msg Src         Time Src

Null Forward          Dispatch         Light Sense   Null Forward          Dispatch         Temp Sense

                      Network           Time Sink                          Network           Time Sink

                  Tree Service                                         Tree Service

                              Link Estimator                                       Link Estimator
      Msg Sink Msg Src                                     Msg Sink Msg Src
                             Msg Sink Msg Src                                     Msg Sink Msg Src
               Tree                                                 Tree
                                 Link Estimator                                       Link Estimator
         Link Estimator                                       Link Estimator
                                   Network                                              Network
            Network                                              Network

                            … then all the way …
    SNACK Expansion (4)
              Post Compilation: Merged Services

                          Msg Sink Msg Src    T ime Src

         Null Forward 1       Dispatch 1     T emp Sense

         Null Forward 2       Dispatch 2     Light Sense

                                T ree        T ime Sink

                            Link Estimator


… then contracts to a minimal, efficient

• SNACK an important step in
  mote programming practice
   – Readable
   – Reusable libraries
   – Efficient, too
• Next steps
   –   More real applications (ESS)
   –   Non-mote platforms
   –   Heterogenous deployments
   –   Multi-program systems
Operating Systems and
In-network Processing
  Ultra Low-Power, Self-
Configuring, Wireless Sensor

           Steve Wicker
 School of Electrical and Computer
         Cornell University
   Our Network Model:
Numerous, Cheap, and Small
• Large numbers of
  small, low power
  sensors distributed
  (randomly) across
  coverage area          Berkeley Dust Mote
• Exploit redundancy
• Adaptive link and
• Distributed
  reporting tools       Wadsworth/Cornell Biosensor
     Research Team: 2004 NSF
                                        Software Tools
• Goal: Development of platform
  technologies for low-power
  sensor networks.                        MagnetOS
• Cross-Disciplinary team
   – ECE - Wicker, Tong, Manohar
   – CS - Birman, Sirer                Self-Configuration
• Approach:
   – Tie operating system and low-
     power processor technologies to        MAC
     self-configuring network theme
   – Develop extensive testbed for
     testing and demonstrating            Low-Power
     technologies                         Processor
    Platform Technologies: SNAP
  Asynchronous Processor (Manohar,
• Clockless logic
   – Spurious signal
     transitions (wasted
     power) eliminated
   – Hardware only active
     if it is used for the
                             Processor   Bus   Year   E/op      Ops/sec
     computation             Atmel       8     200?   1-4 nJ    4 MIPS

• MIPS: high-
                             StrongARM   32    200?   1.9 nJ    130 MIPS
                             MiniMIPS    32    1998   2.3 nJ*   22 MIPS

  performance                Amulet3i
                             80C51 (P)
                                                      1.6 nJ*
                                                      1 nJ**
                                                                80 MIPS
                                                                4 MIPS

   – 24pJ/ins and 28 MIPS    Lutonium
                                                      43 pJ
                                                      24 pJ
                                                                4 MIPS
                                                                28 MIPS
     @ 0.6V
Cornell Testbed Configuration
Network Self-Configuration
 Through Game Theory

• Motivations: efficiency and scalability
• Efficiency - ability of market-based distributed control
  mechanisms to move complex networks toward
  optimal operating points.
• Scalability - distributed decision-making inherent in
  market settings.
   – Interaction and decisions are local, obviating the need for a
     global perspective (both memory and computationally-
• Critical Tools: Equilibrium concepts, utility-based
  decision making, and bargaining.
      Prisoner’s Dilemma

• Two partners in a legal firm are caught over billing.
  There is not sufficient evidence for a full conviction.
  They are each offered a deal.      Marley
                            Don't Confess           Confess

            Don't Confess   1 year, 1 year      10 years, 0 years
                  Confess   0 years, 10 years    3 years, 3 years

• Original version due to Merrill Flood and Melvin
  Dresher at RAND Corporation (Santa Monica, CA).
• Read The Nature of Rationality by Robert Nozick.
     Rationality by Design

• Artificial agents can be programmed to strictly
  follow decision rules.
• Shifts game theory emphasis from modeling
  and explanation to design.
   – Agents follow local set of rules.
   – Game theory predicts emergent functionality
• Result: Distributed, scalable control
   – Exploit characteristics specific to sensor nets.
   – Emergent routing protocols
   – Distributed power and coverage control
  Theoretical Results for Aloha
• Theorem 1: For an MPR Aloha system with cost
  parameter c, arrival distribution l, and discount
  rate d, there exists an equilibrium strategy s that
  is not necessarily unique.

• By first showing that the underlying Markov
  chain is irreducible and aperiodic, we can
  determine stability regions and asymptotic
   – For some value of c, the throughput achieved by a
     system with selfish users is identical to that of the
     optimal centrally-controlled system.
                           [MacKenzie and Wicker, ICC2003]
       Operating Systems

• MagnetOS (Gun Sirer, CS)
  – Provide a unifying single-system image abstraction
  – Converts applications into distributed components that
    communicate over a network
  – Transparent component migration
  – Power-efficient
    COE Sensor Networking Effort
• Collaboration: 6 departments, 12+ faculty
• Graduate student support
• Sensor networking testbed
   – Convincing technology demonstrations
• Incorporation of other interested faculty at Cornell
   – Earth and Atmospheric Science
   – Agricultural School
   – Veterinary Schools
         COllaborative Multiscale
        Processing Architecture for
            Sensor networkS
     Richard Baraniuk (ECE)    Peter Druschel (CS)
Matthias Heinkenschloss (CAAM) David B. Johnson (CS)
                 T. S. Eugene Ng (CS)
• Measurement, monitoring, tracking of physical
  – pollutants, wildfire, …

• Solution involves a class of distributed algorithms
  – digital signal processing
      (estimation, compression, detection, classification, …)
  – data assimilation

• Research agenda: Integrate needs of network layer
  and application processing
  – In-network application processing
  – Application-aware network layer
  – Application processing adapted to network realities
         Multiscale Data Assimilation
Example: Determination of the source of a contaminant from
  measurements of the concentration

 Perform data assimilation inside sensor network
 – Domain decomposition
 – Multiscale

 – Preferred communication paths due to physics of the problem
          Multiscale Signal Processing

• For piecewise-smooth sensor data, multiscale wavelet-based
  compression can reduce network communication

• But … practical constraints foil standard approaches to wavelets
   – irregular sampling
   – no natural multiscale hierarchy
    Characteristics of Processing
• Communications are highly organized
• Locality in communications
• Multiscale communications

Exploit these characteristics in a sensor network
to achieve low energy consumption and high
solution fidelity simultaneously
            Research Plan
• Self-organized network overlays
  – Proximity-based hierarchical overlay
  – Localized short-cuts
• Network services
  – Localization and neighbor discovery
  – Synchronization
• Practical distributed processing algorithms
  – Examples: wavelet compression, data
• Implement in small testbed
   Hierarchy Self-Organization
Nodes self-elect to become drums; drums send out limited propagation
   beacon floods; other nodes associate with a drum to form cells.
Use level k drums to create level k+1 drums and cells.
        Localized Short-Cuts
Neighbor sensors in different cells need to
  communicate efficiently
• Solving environmental monitoring problems requires
  sophisticated application aware       in-network
• We are developing a new sensor network processing
  architecture to realize these solutions
• Key ideas
   – Application-aware sensor network layer
   – Practical and robust multiscale processing algorithms
• Demonstrate ideas in small-scale testbed
Semantic Networking of Sensor
         Systems for
   In-Network Processing
    Alanyali, Little, Venkatesh, ECE
              Kunz, Biology
         Phillips, Geography
           Boston Unversity
• Environmental applications are
  fundamentally limited by energy
• Require long-term deployment
• Characterized by stasis, punctuated by
  extreme events on short time scales
• Broad frontier of scientific inquiry devoid of
  viable instrumentation
     How Study Ecosystem of
             Primary Goal:
     Brazilian Free-tailed Bats?
• Kunz et al.

• How Fundamental Ecological Questions
        impact ecosystem?
• Millions of bats
• Foraging area in 1000’s
  of sq Km

• How instrument with
• Correlate enviromental
  parameters with
  occurrence of bats
• Measure what we can…in
  a SNET
        High-Level Approach
• Semantic, attribute-based routing
• In-network, distributed information
• Application guided by discipline experts --
  biology, geography (bats, soil moisture
     Attribute-Based Routing -
• Sensors assigned attribute values (e.g., location,
  sensed parameters)
• Define relationships within the attribute scheme
  (e.g., containment, neighbors, etc.)
• Use attributes to define clustering and overlay
• Addressing achieved with attributes
• Explicit use of attribute hierarchy in
  routing/addressing -- not fixed -- permits
  intersection of different addressing schemes,
S. Venkatesh, M. Alanyali : M-Ary Hypothesis Testing in Sensor Networks: CISS04
S. Venkatesh, Y.Shi, W. Karl: Performance Guarantees in Sensor Networks: ICASSP04

Inferencing as In-Network

                            “ What is it”

      Is it a plume of toxin? What
             kind of a plume?
     Are conditions right for insect
                   Fusion Center Model
• Setup:
   – Y - measurements
   – decisions: {0,1}                        phenomena
   – fixed # bits communicated

• Broadcast/multihop                Y1           Y2          Yn
  transmission to fusion center
   – Energy inefficient

                                   S1    …        Sk
                                                             …    SM
• Issues
   – Fusion center evaluates the
     rules (quality of each         d1          dk           dn
     sensor)                                 fusion center
   – Intractable -- with every
     sensor’s rules                             Fusion
   – Single point of failure                    Center
                S. Venkatesh, M. Alanyali : M-Ary Hypothesis Testing in Sensor Networks: CISS04

             Distributed Classification
• Setup:
   – decisions: {p1, p2} (30% plume A, 70% plume B)
        •   don’t make local decisions
        •   sensor j to its neighbor k
        •   Belief propagation -- converges to centralized sol.
        •   A collaborative algorithm                                               4

• Benefits:
   –   Short distance comm.                                                                 3
   –   Lower delays in comm.                                               2
   –   Lower energy in comm.
   –   Arbitrary network                                                                1
   –   Works with severe quantization of values
   –   Does not require fusion center
• Energy conservation via in-network
  processing and attribute-based routing
• Environmental event detection leading to
  more detailed data collection and SNET
• Targeting application for understanding
  bat ecosystem

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