# Sparsity-Aware Sensor Collaboration for Linear Coherent Estimation

arXiv: https://arxiv.org/abs/1408.6566 · HTML: https://mlsanity.com/paper/1408.6566/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2014.

## Built on by (most cited first, 3 of 3)

- [Empowering Graph Representation Learning with Test-Time Graph Transformation](https://mlsanity.com/paper/2210.03561/) (2022)
- [Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation](https://mlsanity.com/paper/2403.01467/) (2024)
- [Robust Graph Contrastive Learning with Information Restoration](https://mlsanity.com/paper/2307.12555/) (2023)

## Abstract (arXiv)

In the context of distributed estimation, we consider the problem of sensor collaboration, which refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. While incorporating the cost of sensor collaboration, we aim to find optimal sparse collaboration schemes subject to a certain information or energy constraint. Two types of sensor collaboration problems are studied: minimum energy with an information constraint; and maximum information with an energy constraint. To solve the resulting sensor collaboration problems, we present tractable optimization formulations and propose efficient methods which render near-optimal solutions in numerical experiments. We also explore the situation in which there is a cost associated with the involvement of each sensor in the estimation scheme. In such situations, the participating sensors must be chosen judiciously. We introduce a unified framework to jointly design the optimal sensor selection and collaboration schemes. For a given estimation performance, we show empirically that there exists a trade-off between sensor selection and sensor collaboration.