Wireless Integrated Network Sensors

1. Wireless Integrated Network Sensors Barbara Theodorides April 15, 2003

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

3. 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.

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

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

6. In free space favor large array • However, almost every scenario of interest distributed array • regardless the array size  objects behind walls 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

7. 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

8. 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

9. 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.

10. 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 can clock at much lower speeds  consume less energy ASICs maintain a cost advantage

11. If application & infrastructure permit: process data locally / multihop routing Play the probability game only to the extent we have to Signal-Processing Architecture • We want: low false-alarm & high detection probability Processing Hierarchy Precision Cost

12. 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

13. 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

14. 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

15. 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

16. sensor signal processing for event detection Processing event classification & identification wireless internet interface interface actuator control continuously vigilant operation low-duty cycle operation 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

17. 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

18. 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

19. 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