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