Abstract
We study the sensor selection problem for field estimation, where a best subset of sensors is activated to monitor a spatially correlated random field. Different from most commonly used centralized selection algorithms, we propose a decentralized architecture where sensor selection can be carried out in a distributed way and by the sensors themselves. A decentralized approach is essential since each sensor has access only to the information (e.g., correlation) in its neighborhood. To make distributed optimization possible, we decompose the global cost function into local cost functions that require only the information in local neighborhoods of sensors. We then employ the alternating direction method of multipliers (ADMM) to solve the proposed sensor selection problem. In our algorithm, each sensor solves small-scale optimization problems, and communicates directly only with its immediate neighbors. Numerical results are provided to show the effectiveness of our approach.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings |
| Place of Publication | Piscataway, NJ |
| Publisher | IEEE |
| Pages | 4257-4261 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-5090-4117-6 |
| DOIs | |
| Publication status | Published - 2017 |
| Event | ICASSP 2017: 42nd IEEE International Conference on Acoustics, Speech and Signal Processing - The Internet of Signals - Hilton New Orleans Riverside, New Orleans, LA, United States Duration: 5 Mar 2017 → 9 Mar 2017 Conference number: 42 http://www.ieee-icassp2017.org/ |
Conference
| Conference | ICASSP 2017 |
|---|---|
| Abbreviated title | ICASSP |
| Country/Territory | United States |
| City | New Orleans, LA |
| Period | 5/03/17 → 9/03/17 |
| Internet address |
Keywords
- alternating direction method of multipliers
- distributed optimization
- field estimation
- Sensor selection
- sparsity