Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Channel aware target localization with quantized data in wireless sensor networks
IEEE Transactions on Signal Processing
Tracking in wireless sensor networks using particle filtering: physical layer considerations
IEEE Transactions on Signal Processing
Optimal rate allocation for multi-sensor distributed estimation
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
A collaborative sensor-fault detection scheme for robust distributed estimation in sensor networks
IEEE Transactions on Communications
Timing-Based Mobile Sensor Localization in Wireless Sensor and Actor Networks
Mobile Networks and Applications
Energy aware iterative source localization for wireless sensor networks
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A hole detour scheme using virtual position based on residual energy for wireless sensor networks
ICCSA'11 Proceedings of the 2011 international conference on Computational science and Its applications - Volume Part V
| Hi-index | 35.70 |
A signal intensity based maximum-likelihood (ML) target location estimator that uses quantized data is proposed for wireless sensor networks (WSNs). The signal intensity received at local sensors is assumed to be inversely proportional to the square of the distance from the target. The ML estimator and its corresponding Crameacuter-Rao lower bound (CRLB) are derived. Simulation results show that this estimator is much more accurate than the heuristic weighted average methods, and it can reach the CRLB even with a relatively small amount of data. In addition, the optimal design method for quantization thresholds, as well as two heuristic design methods, are presented. The heuristic design methods, which require minimum prior information about the system, prove to be very robust under various situations