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@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 programs
Sponsors: DARPA, NSF, Intel, Sun, HS-SEAS
1
Roadmap
• Motivation
– Driving applications
– Need for new systems and algorithms research
– Segue…Some structure: stack, taxonomy, load, metrics
• Recently-developed building blocks
– Time synchronization
– MAC
– Adaptive Topology
– Data centric routing and In-network processing
• Some emerging principles
2
Embedded Networked Sensing Potential
Seismic Structure
response
Marine
Microorganisms
• Micro-sensors, onboard 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
Contaminant
Transport
Ecosystems,
Biocomplexity
3
Enabling Technologies
Embed numerous distributed
devices to monitor and interact
with physical world
Embedded
Network devices
to coordinate and perform
higher-level tasks
Networked
Exploit
collaborative
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
4
“The network is the sensor”
(Oakridge National Labs)
Requires robust distributed systems of thousands of
physically-embedded, unattended, and often untethered,
devices.
5
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, stovepiped
– Serious repercussions for piecemeal uncoordinated design:
insufficient longevity, interoperability, safety, robustness,
scalability...
6
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
7
Sample Layered Architecture
User Queries, External Database
Resource
constraints call
for more tightly
integrated layers
In-network: Application processing,
Data aggregation, Query processing
Data dissemination, storage, caching
Can we define an
Internet-like
architecture for
such applicationspecific
systems??
Adaptive topology, Geo-Routing
MAC, Time, Location
Phy: comm, sensing, actuation, SP
8
Load/Event
Models
Systems Taxonomy
•
•
•
•
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
Metrics
•
•
•
•
•
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
9
ENS Research
• Building blocks for experimental systems
– Fine grained time and location
– Adaptive MAC
– Adaptive topology
These examples illustrate
new combination of
constraints and requirements
– Data centric routing
• Emerging principles…
10
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
11
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 can’t 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
12
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
010101001
CPU 1
Mote
Mote Sender
010101001
CPU 2
Mote
50kb/S (20uS per bit)
– Reception of broadcasts are closely correlated in real time
– First bit arrives at receivers with small variations (and easy to filter)
14
Fine Grained Multi-hop Time Synch Results
Snapshot from Running System that achieves
msec 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
15
Energy Efficient MAC design
(Ye et al.)
0.14
0.12
0.1
Diffusion
Flooding
Omniscient Multicast
0.08
0.06
0.04
0.02
00
50
100
150
200
Network Size
•
•
250
300
Average Dissipated Energy
(Joules/Node/Received Event)
Major sources of energy waste
• Idle listening when no sensing events, Collisions, Control overhead,
Overhearing
(Joules/Node/Received Event)
Average Dissipated Energy
•
0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
00
Flooding
Omniscient Multicast
Diffusion
50
100
150
200
250
300
Network Size
Over energy-aware MAC
Over 802.11-like MAC
Major components in S-MAC
• Message passing
• Periodic listen and sleep
Combine benefits of TDMA + contention protocols
• Tradeoff latency and fairness for efficiency
16
Message Passing
• Problem: In-network processing requires entire message
• Solution: Don’t 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
17
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
Node 1
Node 2
listen
sleep
listen
listen
sleep
sleep
listen
sleep
• Prefer neighboring nodes have same schedule for easy broadcast &
low control overhead
Schedule 1
Schedule 2
Border nodes: two
schedules requires
two broadcasts
18
S-MAC Experimental results
(implemented on UCB Mote over RFM radio)
• Topology and measured energy consumption on source nodes
Energy consumed
Average energy consumption in the source nodes
Source 2
Sink 1
Sink 2
• Each source node sends
10 messages
— Each message has 10
fragments x 40B
• Measure total energy
— Data + control + idle
802.11-like protocol
Overhearing avoidance
S-MAC
1600
Energy consumption (mJ)
Source 1
1800
1400
1200
1000
800
600
400
200
0
2
4
6
8
10
Message inter-arrival period (second)
Message
Inter-arrival period
19
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
20
Context for creating a topology:
connectivity measurement study (Ganesan et al)
Packet reception over distance has a heavy tail. There is a nonzero probability of receiving packets at distances much greater
than the average cell range
Can’t just
determine
Connectivity
clusters thru
geographic
Coordinates…
For the same
reason you cant
determine
coordinates
w/connectivity
169 motes, 13x13 grid, 2 ft spacing, open area, RFM radio, simple
CSMA
21
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
22
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.
23
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
24
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 emerging
peer-to-peer architectures
• Build on a data-centric architecture
for queries and storage
25
Nested Query Evaluation
(A real experiment w/sub-optimal hardware)
• 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
events successfully received (%)
(Heidemann et al.)
nested
80
60
40
flat
20
1
2
3
4
number of light sensors
26
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….Approximations:
• 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.
27
Source placement: event-radius model
28
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
29
Opportunism always pays;
Greed pays only when things get very crowded
(Intanagowiwat et al. ns-2 more detailed simulations)
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
31
(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
32
In search of Principles
…All we have thus far are heuristics/design themes
• Exploit density
• Use “localized” algorithms
• Procrastination Pays
33
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
34
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 doesn’t mean flat fully-decentralized--Exploit 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)
35
Lazy/Procrastinating/Just-in-time
systems
•
Post facto coordination
– Time synchronization
– Sensor calibration
•
Don’t 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?
36
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))
37
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 result…local 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?
38
Pulling it all together
CENS Core Research
Collaborative
Signal
Processing and
Active
Databases
Sensor
Coordinated
Actuation
Adaptive
Self-Configuration
Environmental
Microsensors
Academic Disciplines
Networking
Communications
Signal Processing
Databases
Embedded Systems
Controls
Optimization
…
Biology
Geology
Biochemistry
Structural Engineering
Education
Environmental Engineering
39
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/
40