TY - JOUR
T1 - Joint Collaboration and Compression Design for Distributed Sequential Estimation in a Wireless Sensor Network
AU - Cheng, Xiancheng
AU - Khanduri, Prashant
AU - Chen, Baixiao
AU - Varshney, Pramod
N1 - Publisher Copyright:
© 1991-2012 IEEE.
PY - 2021
Y1 - 2021
N2 - In this work, we propose a joint collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We design near-optimal collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. The objective function for collaboration design is generally non-convex. We establish correspondence between the sparse collaboration matrix and the non-sparse vector consisting of the nonzero elements of the collaboration matrix. Then, we reformulate and solve the collaboration design problem using quadratically constrained quadratic program (QCQP). The compression design problem is solved using the same methodology. We propose two versions of compression design, one centralized scheme where the compression strategies are derived at the FC and decentralized, where the local sensors compute their individual compression strategies independently. Importantly, we show that the proposed methods can also be used for estimating time-varying random vector parameters. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.
AB - In this work, we propose a joint collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We design near-optimal collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. The objective function for collaboration design is generally non-convex. We establish correspondence between the sparse collaboration matrix and the non-sparse vector consisting of the nonzero elements of the collaboration matrix. Then, we reformulate and solve the collaboration design problem using quadratically constrained quadratic program (QCQP). The compression design problem is solved using the same methodology. We propose two versions of compression design, one centralized scheme where the compression strategies are derived at the FC and decentralized, where the local sensors compute their individual compression strategies independently. Importantly, we show that the proposed methods can also be used for estimating time-varying random vector parameters. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.
KW - Wireless sensor networks
KW - collaboration-compression framework
KW - distributed sequential estimation
KW - energy allocation
KW - non-convex QCQP
KW - semidefinite programming
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U2 - 10.1109/TSP.2021.3114982
DO - 10.1109/TSP.2021.3114982
M3 - Article
AN - SCOPUS:85115812718
SN - 1053-587X
VL - 69
SP - 5448
EP - 5462
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
ER -