Wireless Integrated Network Sensors

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Wireless Integrated Network Sensors : 

Wireless Integrated Network Sensors Barbara Theodorides April 15, 2003

Paper : 

Paper G. J. Pottie and W. J. Kaiser, Wireless Integrated Network Sensors, Communications of ACM, 43(5), May 2000.

WINS : 

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 : 

A general picture Internet sensing signal processing / event recognition wireless communication low power networking local area worldwide user

Concerned about… : 

Concerned about… The Physical principles dense sensor network Energy & bandwidth constraints distributed & layered signal processing architecture WINS network architecture WINS nodes architecture

Physical Principles : 

Physical Principles When are distributed sensors better? A. Propagation laws for sensing All signals decay with distance e.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

Physical Principles (cont) : 

Physical Principles (cont) What are the fundamental limits driving the design of a network of distributed sensors? B. Detection & Estimation Detector: given a set of observables {xj} determines which of the hypotheses {hi} are true Target 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, SNR Complexity: dimension of feature space, #hypotheses Either a longer set of independent observations or high SNR Decrease the #features and the #hypotheses

Physical Principles (cont) : 

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 & #hypotheses cons: complexity Deploy a dense sensor network - homogeneous environment within the detection range - reduces #environmental features size of decision space attractive method

Physical Principles (cont) : 

Physical Principles (cont) C. Communication Constraints Spatial separation (e.g. low lying antennas) Surface roughness, reflecting & obstructing objects However  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) : 

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 time as 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 : 

Signal-Processing Architecture We want: low false-alarm & high detection probability Processing Hierarchy Precision Cost

Signal-Processing Architecture (cont) : 

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 : 

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) : 

WINS Network Architecture (cont) Link Sensor Network to the Internet Layering of the protocols (and devices) is needed WINS 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 : 

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) : 

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

WINS Node Architecture (cont) : 

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 ? : 

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 : 

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

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