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Wireless Integrated Network Sensors
Barbara TheodoridesApril 15, 2003
Paper
• G. J. Pottie and W. J. Kaiser, Wireless Integrated Network Sensors, Communications of ACM, 43(5), May 2000.
WINS
• Initiated in 1993 at the UCLA, 1G fielded in 1996• Sponsored by DARPA LWIM program began in 1995• In 1998, WINS NG
• Distributed network• Internet access to sensors, controls and processors• Low-power signal processing, computation, and low-cost wireless
networking• RF communication over short distances ( < 30m )• Applications: Industries, transportation, manufacture, health care,
environmental oversight, and safety & security.
A general picture
Internet
sensing
signal processing / event recognition
wireless communication
low powernetworking
event
information
local area worldwide user
Concerned about…
• The Physical principles dense sensor network• Energy & bandwidth constraints distributed & layered
signal processing architecture
• WINS network architecture• WINS nodes architecture
Physical Principles
• When are distributed sensors better?
A. Propagation laws for sensingAll signals decay with distancee.g. electromagnetic waves in free space (~ 1/d2)
in other media (absorption, scattering, dispersion)
distant sensor requires costly operations
If the system is to detect objects reliably, it has to be distributed, whatever the networking cost
• In free space favor large array• However, almost every scenario of interest distributed array
regardless the array size objects behind walls
Physical Principles (cont)
• What are the fundamental limits driving the design of a network of distributed sensors?
B. Detection & EstimationDetector: given a set of observables {xj}
determines which of the hypotheses {hi} are trueTarget presence/absence: based on estimates parameters {fk} of {xj}
Selected Fourier, wavelet transform coefficients Marginal improvement
Formally: Decide on hi if p(hi | {fk}) > p(hj | {fk}) ∀ j ≠ i
Reliability: #independent observations, SNRComplexity: dimension of feature space, #hypotheses
Either a longer set of independent observations or high SNR
Decrease the #features and the #hypotheses
Physical Principles (cont)
Use of practical Algorithms: Apply deconvolution and target-separation machinery to exploit a
distributed array (deal with only 1 target and no propagation dispersal effects)- reduces feature space & #hypothesescons: complexity
Deploy a dense sensor network - homogeneous environment within the detection range - reduces #environmental features size of decision space
attractive method
Physical Principles (cont)
C. Communication Constraints• Spatial separation (e.g. low lying antennas)
Surface roughness, reflecting & obstructing objectsHowever spatial isolation, reuse of frequencies
• Multipath propagation (reflections off multiple objects)Recover ~ space, frequency, and time “diversity”
But for static nodes, time diversity is not an option spatial diversity is difficult to obtain
Diversity in frequency domain• “Shadowing”: dealt with by employing a multihop network
The greater the density, the closer the nodes, and the greater the likelihood of having a link with sufficiently small distance and shadowing losses.
Physical Principles (cont)
D. Energy Consumption
• Limits to the energy efficiency of CMOS communications and signal-processing circuits
• Limits on the power required to transmit reliably over a given distance
Networks should be designed so that radio is off as much of the timeas possible and otherwise transmits only at the minimum required level
ASICs maintain a cost advantage
• ASICs can clock at much lower speeds consume less energy
Signal-Processing Architecture
• We want: low false-alarm & high detection probability
Processing Hierarchy
Human
Sophisticated Methods
Collaboration of WINS nodes
Higher-energy processing & sensing
Energy thresholding
PrecisionCost
If application & infrastructure permit: process data locally / multihop routing
Play the probability game only to the extent we have to
Signal-Processing Architecture (cont)
• Application Specific
e.g. Remote security application• WINS node: 2 sensors (seismic & imaging capability)
Seismic senor requires little power constantly vigilant Simple energy detection triggers the camera’s operation Collaborative WINS nodes (e.g. target location) Send image & seismic record to a remote observer
WINS node: simple processing at low power Radio: does not need to support continuous transmission of images
WINS Network Architecture
Characteristics• Support large numbers of sensor• Low average bit rate communication ( < 1-100 Kbps )• Dense sensor distributions• Exploit the short-distance separation multihop communication• Protocols: designed so radios are off MAC address should include some
variant of time-division access
Time-division protocol Exchange small messages: performance information, synchronization,
bandwidth reservation requests Abundant bandwidth few conflicts, simple mechanisms
At least one low-power protocol suite has been developed feasible to achieve distributed low-power operation in a flat multihop network
WINS Network Architecture (cont)
Link Sensor Network to the Internet• Layering of the protocols (and devices) is neededWINS Gateways: Support for the WINS network and access between
conventional network physical layers and their protocols and between the WINS physical layer and its low-power protocols
System Architect – Responsibilities Application’s requirements (reduced operation power, improved bit rate,
improved bit error rate, reduced cost) How can Internet protocols (TCP, IPv6) be employed?
- need to conserve energy, unreliability of physical channels Where should the processing and the storage take place?
- at the source / reducing the amount of data to transmit
WINS Node Architecture
1993: Initiated at the UCLA 1G of field-ready WINS devices and software was fielded (1996)
1995 : DARPA sponsored - the LWIM project multihop, self-assembled, wireless network
algorithms for operating at micropower levels - the joint, UCLA and Rockwell Science Center of Thousand Oaks,
program platform for more sophisticated networking and signal processing algorithms (many types of sensors,
less emphasis on power conservation)
Lesson: Separate real-time from higher-level functions
WINS Node Architecture (cont)
1998: WINS NG developed by the authors contiguous sensing, signal processing for event detection, local control of actuators, event classification, communication at low power Event detection is contiguous micropower levels Event detected => alert process to identify the event Further processing? Alert remote user / neighboring node? Communication between WINS nodes
sensor
actuator inte
rfac
e signal processing for event detection
control
Processing
event classification & identification
wireless internet interface
continuously vigilant operation low-duty cycle operation
WINS Node Architecture (cont)
Further Generations (Future work): Support plug-in Linux devices Small, limited sensing devices interact with WINS NG nodes in
heterogeneous networks Scavenge energy from the environment photocells
Why WINS ?
• Low power consumption ( 100 μW average ) Separation of real-time from higher level functions Hierarchical signal-processing architecture
• Application specific• Communication facility ( WINS gateways )
Remote user
• Scalable Reduce amount of data to be send scalability to thousands of
nodes per gateway
Conclusion
• Densely distributed sensor networks (physical constraints) • Layered and heterogeneous processing• Application specific networking architectures • Close intertwining of network processing • Development platforms are now available