Compressed Sensing in Wireless Sensor Networks without Explicit Position Information
Journal article, 2017

Reconstruction in compressed sensing relies on knowledge of a sparsifying transform. In a setting where a sink reconstructs a field based on measurements from a wireless sensor network, this transform is tied to the locations of the individual sensors, which may not be available to the sink during reconstruction. In contrast to previous works, we do not assume that the sink knows the position of each sensor to build up the sparsifying basis. Instead, we propose the use of spatial interpolation based on a predetermined sparsifying transform, followed by random linear projections and ratio consensus using local communication between sensors. For this proposed architecture, we upper bound the reconstruction error induced by spatial interpolation, as well as the reconstruction error induced by distributed compression. These upper bounds are then utilized to analyze the communication cost tradeoff between communication to the sink and sensor-to-sensor communication.

Author

CHRISTOPHER LINDBERG

Chalmers, Signals and Systems, Kommunikationssystem, informationsteori och antenner, Communication Systems

Alexandre Graell i Amat

Chalmers, Signals and Systems, Kommunikationssystem, informationsteori och antenner, Communication Systems

Henk Wymeersch

Chalmers, Signals and Systems, Kommunikationssystem, informationsteori och antenner, Communication Systems

IIEEE Transactions on Signal and Information Processing over Networks

2373-776X (eISSN)

Vol. 3 404-415

Cooperative Situational Awareness for Wireless Networks (COOPNET)

European Commission (FP7), 2011-05-01 -- 2016-04-30.

Areas of Advance

Information and Communication Technology

Subject Categories

Communication Systems

Signal Processing

DOI

10.1109/TSIPN.2016.2623091