Wireless Sensor Networks: In Search of Principles

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Wireless Sensor Networks In Search of Principles

• Deborah Estrin
• Director, NSF Science and Technology Center for
  Embedded Networked Sensing (CENS)
• Professor, UCLA Computer Science Department
• destrin_at_cs.ucla.edu
• http//lecs.cs.ucla.edu/estrin
• Contributors Vlad Bychkovskiy, Alberto Cerpa,
  Jeremy Elson, Deepak Ganesan, Lew Girod, Ramesh
  Govindan, John Heidemann, Bhaskar Krishnamachari,
  Fabio Silva, Wei Ye and members of CENS, LECS,
  and IPAM programsSponsors DARPA, NSF, Intel,
  Sun, HS-SEAS

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Roadmap

• Motivation
• Driving applications
• Need for new systems and algorithms research
• SegueSome structure stack, taxonomy, load,
  metrics
• Recently-developed building blocks
• Time synchronization
• MAC
• Adaptive Topology
• Data centric routing and In-network processing
• Some emerging principles

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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
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Enabling Technologies
Embed numerous distributed devices to monitor and
interact with physical world
Network devices to coordinate 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
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• The network is the sensor
• (Oakridge National Labs)
• Requires robust distributed systems of thousands
  of physically-embedded, unattended, and often
  untethered, devices.

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From Embedded Sensing to Embedded Control

• Embedded in unattended control systems
• Different from traditional Internet, PDA,
  Mobility applications
• 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
• Concerns extend beyond traditional networked
  systems
• Usability, Reliability, Safety
• Need systems architecture and an understanding
  for underlying algorithms to manage interactions
• Current system development one-off,
  incrementally tuned, stove-piped
• Serious repercussions for piecemeal uncoordinated
  design insufficient longevity, interoperability,
  safety, robustness, scalability...

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New Design Themes

• Long-lived systems that can be untethered and
  unattended
• Low-duty cycle operation with bounded latency
• Exploit redundancy and heterogeneous tiered
  systems
• Leverage data processing inside the network
• 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 to reduce
  communication
• Self configuring systems that can be deployed ad
  hoc
• Un-modeled physical world dynamics makes systems
  appear ad hoc
• Measure and adapt to unpredictable environment
• Exploit spatial diversity and density of
  sensor/actuator nodes
• Achieve desired global behavior with adaptive
  localized algorithms
• Cant afford to extract dynamic state information
  needed for centralized control

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Sample Layered Architecture
User Queries, External Database
Resource constraints call for more tightly
integrated layers Can we define
anInternet-like architecture for such
application-specific systems??
In-network Application processing, Data
aggregation, Query processing
Data dissemination, storage, caching
Adaptive topology, Geo-Routing
MAC, Time, Location
Phy comm, sensing, actuation, SP
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Systems Taxonomy
Metrics
Load/Event Models

• Spatial and Temporal Scale
• Extent
• Spatial Density (of sensors relative to stimulus)
• Data rate of stimulii
• Variability
• Ad hoc vs. engineered system structure
• System task variability
• Mobility (variability in space)
• Autonomy
• Multiple sensor modalities
• Computational model complexity
• Resource constraints
• Energy, BW
• Storage, Computation

• Frequency
• spatial and temporal density of events
• Locality
• spatial, temporal correlation
• Mobility
• Rate and pattern

• Efficiency
• System lifetime/System resources
• Resolution/Fidelity
• Detection, Identification
• Latency
• Response time
• Robustness
• Vulnerability to node failure and environmental
  dynamics
• Scalability
• Over space and time

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ENS Research

• Building blocks for experimental systems
• Fine grained time and location
• Adaptive MAC
• Adaptive topology
• Data centric routing
• Emerging principles

These examples illustratenew combination
ofconstraints and requirements
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Fine Grained Time and Location(Elson, Girod, et
al.)

• Unlike Internet, the location of nodes in time
  and space is essential for local and
  collaborative detection
• Fine-grained localization and time
  synchronization needed to detect events in three
  space and compare detections across nodes
• GPS provides solution where available (with
  differential GPS providing finer granularity)
• Acoustic or Ultrasound ranging and
  multi-lateration algorithms promising for non-GPS
  contexts (indoors, under foliage)
• Fine grained time synchronization needed to
  support ranging and many other sensor network
  functions

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Tiered System Design IPAQs and UCB Motes

• Localization
• IPAQs range to each other, create a coordinate
  system
• Mote periodically emits coded acoustic chirps
  (511 bits)
• IPAQs listen for chirps (buffer time series -
  mote cant do this)
• run matched filter and record time diff btwn
  emit- and receive-time of coded sequence
• Share ranges with each other via 802.11
  trilaterate
• Time sync
• Allows computation of acoustic time-of-flight
• One IPAQ has a MoteNIC to sync mote and IPAQ
  domains

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Acoustic Ranging System

• Basic idea
• Sender emits a characteristic acoustic signal
• Receiver correlates received time series with
  time-offsets of reference signal to find peak
  offset

Degree of correlation as a function of time
offset
Amount of Correlation
T
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Reference Broadcast Synchronization

• Distributed system of sensor nodes
• Distinct nodes need inter-node synchronization
• Uses radio channel to relate local clocks of two
  nodes
• Multihop synchronization composition of time
  conversions.
• Can be done post facto
• Eliminates effect of transmission variation
• Receiver latency is low-variance
• Reception of broadcasts are closely correlated in
  real time
• First bit arrives at receivers with small
  variations (and easy to filter)

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Fine Grained Multi-hop Time Synch
ResultsSnapshot from Running System that
achievesmsec time synchronization relative to
NTP ms over lossy wireless
Motes
Lines annotated with offset achieved
between connected clocks
9 ms
2 ms
IPAQ CPUs And Codecs
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Energy Efficient MAC design(Ye et al.)

• Major sources of energy waste
• Idle listening when no sensing events,
  Collisions, Control overhead, Overhearing
  
• Major components in S-MAC
• Message passing
• Periodic listen and sleep
• Combine benefits of TDMA contention protocols
• Tradeoff latency and fairness for efficiency

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Message Passing

• Problem In-network processing requires entire
  message
• Solution Dont interleave different messages
• Long message is fragmented sent in burst
• RTS/CTS reserve medium for entire message
• Fragment-level error recovery
• extend Tx time and re-transmit immediately
• Other nodes sleep for whole message time
• Tradeoff fairness for energy and single-message
  level latency

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Periodic Listen and Sleep

• Problem Idle listening consumes significant
  energy
• Solution Periodic listen and sleep policy and
  mechanism to coordinate
• Turn off radio when sleeping tradeoff latency
  for energy
• Reduce duty cycle to 10 (200ms on/2s off)
• Schedules created using SYNCH
• Prefer neighboring nodes have same schedule for
  easy broadcast low control overhead

Border nodes two schedules requires two
broadcasts
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S-MAC Experimental results(implemented on UCB
Mote over RFM radio)

• Topology and measured energy consumption on
  source nodes

Energy consumed

• Each source node sends 10 messages
• Each message has 10 fragments x 40B
• Measure total energy
• Data control idle

Message Inter-arrival period
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Adaptive Topology An example of
Self-Organization with Localized Algorithms

• Self-configuration and reconfiguration essential
  to lifetime of unattended systems in dynamic,
  constrained energy, environment
• Too many devices for manual configuration
• Environmental conditions are unpredictable
• Example applications
• Efficient, multi-hop topology formation node
  measures neighborhood to determine participation,
  duty cycle, and/or power level
• Beacon placement candidate beacon measures
  potential reduction in localization error
• Requires large solution space not seeking unique
  optimal
• Investigating applicability, convergence, role of
  selective global information

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Context for creating a topology connectivity
measurement study (Ganesan et al)
Packet reception over distance has a heavy tail.
There is a non-zero probability of receiving
packets at distances much greater than the
average cell range
Cant justdetermine Connectivity clusters
thrugeographic CoordinatesFor the same
reason you cant determine coordinates
w/connectivity
169 motes, 13x13 grid, 2 ft spacing, open area,
RFM radio, simple CSMA
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Adaptive Topology Schemes

• Goal exploit the redundancy in the system (high
  density) to save energy while providing a
  topology that adapts to the application needs
• Mechanism empirical adaptation. Each node
  assesses its connectivity and adapts
  participation in multi-hop topology based on the
  measured operating region.
• Does not detect partitions, less efficient cases
  due to lack of global knowledge

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Example Performance Results (ASENT)(Cerpa et
al., Simulations and Implementation)
Energy Savings (normalized to the Active case,
all nodes turn on) as a function of density.
ASCENT provides significant amount of energy
savings, up to a factor of 5.5 for high density
scenarios.
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Directed Diffusion Data Centric Routing

• Basic idea
• name data (not nodes) with externally relevant
  attributes
• Data type, time, location of node, SNR, etc
• diffuse requests and responses across network
  using application driven routing (e.g., geo
  sensitive or not)
• optimize path with gradient-based feedback
• support in-network aggregation and processing
• Data sources publish data, Data clients subscribe
  to data
• However, all nodes may play both roles
• A node that aggregates/combines/processes
  incoming sensor node data becomes a source of new
  data
• A sensor node that only publishes when a
  combination of conditions arise, is a client for
  the triggering event data
• True peer to peer system
• Implementation defines namespace and simple
  matching rules with filters
• Linux (32 bit proc) and TinyOS (8 bit proc)
  implementations

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Diffusion as a construct for in-network
processing

• Nodes pull, push, and store named data (using
  tuple space) to create efficient processing
  points in the network
• e.g. duplicate suppression, aggregation,
  correlation
• Nested queries reduce overhead relative to edge
  processing
• Complex queries support collaborative signal
  processing
• propagate function describing desired
  locations/nodes/data (e.g. ellipse for tracking
  (Zhao et al))
• Interesting analogs to emergingpeer-to-peer
  architectures
• Build on a data-centric architecturefor queries
  and storage

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Nested Query Evaluation(A real experiment
w/sub-optimal hardware)(Heidemann et al.)

• Eevn simple nested queries greatly improve event
  delivery rate
• Specific results depend on experiment
• placement
• limited quality MAC
• General result app-level info needed in sensor
  nets diffusion is a good platform
• Concept of Data Centric vs. Address Centric more
  important than specific implementation

nested
80
60
events successfully received ()
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flat
20
1
2
3
4
number of light sensors
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A more general look at Data Centric vs. Address
Centric approach(Krishnamachari et al.)

• Address Centric
• Distinct paths from each source to sink.
• Data Centric
• Support aggregation in the network where
  paths/trees overlap
• Essential difference from traditional IP
  networking
• Building efficient trees for Data centric model
• Aggregation tree On a general graph if k nodes
  are sources and one is a sink, the aggregation
  tree that minimizes the number of transmissions
  is the minimum Steiner tree. NP-complete.Approxim
  ations
• Center at Nearest Source (CNSDC) All sources
  send through source nearest to the sink.
• Shortest Path Tree (SPTDC) Merge paths.
• Greedy Incremental Tree (GITDC) Start with path
  from sink to nearest source. Successively add
  next nearest source to the existing tree.

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Source placement event-radius model
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Comparison of energy costs
Data centric has many fewer transmissions than
does Address Centric independent on the tree
building algorithm.
Address Centric Shortest path data centric Greedy
tree data centric Nearest source data
centric Lower Bound
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Opportunism always paysGreed pays only when
things get very crowded(Intanagowiwat et al.
ns-2 more detailed simulations)
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Programming Paradigm

• Move beyond simple query with in-network
  aggregation model
• How do we task a 1000 node dynamic sensor
  network to conduct complex, long-lived queries
  and tasks ??
• What constructs does the query language need to
  support?
• What sorts of mechanisms need to be running in
  the background in order to make tasking
  efficient?
• Small databases scattered throughout the network,
  actively collecting data of nearby nodes, as well
  as accepting messages from further away nodes?
• Active messages traveling the network to both
  train the network and identify anomalous
  conditions?
• Storage architecture

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(Still hypothetical) Examples

• Map isotherms and other contours, gradients,
  regions
• Record images wherever acoustic signatures
  indicate significantly above-average species
  activity, and return with data on soil and air
  temperature and chemistry in vicinity of
  activity.
• Mobilize robotic sample collector to region where
  soil chemistry and air chemistry have followed a
  particular temporal pattern and where the region
  presents different data than neighboring
  regions.
• Raises requirements for some global context, e.g.
  average levels
• Emerging role for distributed storage
  architecture
• Pattern identification how much can and should
  we do in a distributed manner?
• Similar to some vision/image analysis problems
  but distributed noisy inputs

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In search of PrinciplesAll we have thus far
are heuristics/design themes

• Exploit density
• Use localized algorithms
• Procrastination Pays

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Exploiting redundancy, density

• Design objectives
• Maximize system lifetime, coverage, accuracy,
  reliability
• NOT to minimize nodes deployed
• Spatial and modal diversity can contribute to all
  objectives, e.g.
• Adaptive topology/load sharing to increase system
  lifetime
• Spatial diversity to achieve coverage around
  obstacles
• Modal diversity to detect outliers in acoustic
  ranging
• Correlated measurements to calibrate

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Localized algorithms

• Localized algorithms and in-network processing
  are mandated by energy constraints and scale
• Challenge is to characterize and constrain global
  behavior that result, e.g., designing for
  predictability in highly uncertain environments
• Localized doesnt mean flat fully-decentralized--E
  xploit self-configuring structure
• Tiered and clustered systems
• Exploit some centralized resources and
  information
• Exploit built-in structures in Globally Ad hoc,
  Locally regular systems (GALORE)

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Lazy/Procrastinating/Just-in-time systems

• Post facto coordination
• Time synchronization
• Sensor calibration
• Dont move a bit until needed Leave data where
  it is detected until needed
• Triggered systems
• Multi-resolution distributed storage
  architecture, Data centric storage (DCS,
  (Ratnasamy (ICSI))
• Not really that simple When and where is data
  needed to detect patterns?

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Some work in progress

• In network processing mechanisms and data, a few
  examples.
• Fine grained data collection, methods, tools,
  analysis, models (D. Muntz (UCLA), G. Pottie
  (UCLA), J. Reich (PARC))
• Collaborative, multi-modal, processing among
  clusters of nodes (e.g., F. Zhao (PARC), K. Yao
  (UCLA)
• Enable lossy to lossless multi-resolution data
  extraction (Ganesan (UCLA), (Ratnasamy (ICSI))
• Primitives for programming the sensor network
  (Estrin (UCLA), Database perspective S. Madden
  (UCB))
• Modeling capacity and capability (M.
  Francischetti (Caltech), PR Kumar (Ill), M.
  Potkonjak (UCLA), S. Servetto (Cornell))

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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 resultlocal
  behaviors that support)
• Programming model for instantiating local
  behavior and adaptation
• Abstractions and interfaces that do not preclude
  efficiency
• Cautionary questions
• Will we be able to generalize away from
  application-specific stove-pipe solutions?
• How to address social concerns about passive
  monitoring?

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Pulling it all together
CENS Core Research
Academic Disciplines
Networking Communications Signal
Processing Databases Embedded Systems Controls Opt
imization Biology Geology Biochemistry Structura
l Engineering Education Environmental Engineering
Adaptive Self-Configuration
Collaborative Signal Processing and Active
Databases
Experimental Systems
Sensor Coordinated Actuation
Environmental Microsensors
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Follow up

• Embedded Everywhere A Research Agenda for
  Networked Systems of Embedded Computers, Computer
  Science and Telecommunications Board, National
  Research Council - Washington, D.C.,
  http//www.cstb.org/
• Related projects at UCLA and USC-ISI
• http//cens.ucla.edu
• http//lecs.cs.ucla.edu
• http//rfab.cs.ucla.edu
• http//www.isi.edu/scadds
• Many other emerging, active research programs,
  e.g.,
• UCB Culler, Hellerstein, BWRC, Sensorwebs,
  CITRIS
• MIT Balakrishnan, Chandrakasan, Morris
• Cornell Gehrke, Wicker
• Univ Washington Boriello
• Wisconsin Ramanathan, Sayeed
• UCSD Cal-IT2
• DARPA Programs
• http//dtsn.darpa.mil/ixo/sensit.asp
• http//www.darpa.mil/ito/research/nest/