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Distributed Field Estimation with One–bit Sensors

  • Ye Wang
  • Nan Ma
  • Manqi Zhao
  • Prakash Ishwar
  • Venkatesh Saligrama

We study the problem of reconstructing a temporal sequence of unknown spatial data fields in a bounded geographical region of interest at a data fusion center from finite bit-rate messages generated by a dense noncooperative network of noisy low-resolution sensors (at known locations) that are statistically identical (exchangeable) with respect to the sensing operation. The interchangeability assumption reflects the property of an unsorted collection of inexpensive mass-produced sensors that behave in a statistically identical fashion. We view each data field as an unknown deterministic function over the geographical space of interest and make only the minimal assumption that they have a known bounded maximum dynamic range. The sensor observations are corrupted by bounded, zero-mean additive noise which is independent across sensors with arbitrary dependencies across field snapshots and has an arbitrary but unknown distribution but a known maximum dynamic range. The sensors are equipped with binary analog-to-digital converters (ADCs) (comparators) with random thresholds that are independent across sensors with arbitrary dependencies across snapshots and are uniformly distributed over a known dynamic range. These modeling assumptions partially account for certain real-world scenarios that include (i) the unavailability of good initial statistical models for data fields in yet to be well studied natural phenomena, (ii) unknown additive sensing/observation noise sources, (iii) additive model perturbation errors, (iv) substantial variation of preset comparator thresholds accompanying the mass-manufacture of low-precision sensors, (v) significant temperature fluctuations across snapshots affecting hardware characteristics, and (vi) the use of intentional dither signals for randomized scalar quantization.

Keywords

Sensor Network Fusion Center Reconstruction Scheme Noisy Observation Sensor Observation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Ye Wang
    • 1
  • Nan Ma
    • 1
  • Manqi Zhao
    • 1
  • Prakash Ishwar
    • 1
  • Venkatesh Saligrama
    • 1
  1. 1.Boston UniversityBostonUSA

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