Distributed detection with censoring sensors under physical layer secrecy
IEEE Transactions on Signal Processing
PAC vs. MAC for decentralized detection using noncoherent modulation
IEEE Transactions on Signal Processing
Decentralized detection in censoring sensor networks under correlated observations
EURASIP Journal on Advances in Signal Processing
Low-complexity algorithms for event detection in wireless sensor networks
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
A repeated significance test with applications to sequential detection in sensor networks
IEEE Transactions on Signal Processing
Analytical performance of collaborative spectrum sensing using censored energy detection
IEEE Transactions on Wireless Communications
Testing selective transmission with low power listening
REALWSN'10 Proceedings of the 4th international conference on Real-world wireless sensor networks
| Hi-index | 35.69 |
In the censoring approach to decentralized detection, sensors transmit real-valued functions of their observations when "informative" and save energy by not transmitting otherwise. We address several practical issues in the design of censoring sensor networks including the joint dependence of sensor decision rules, randomization of decision strategies, and partially known distributions. In canonical decentralized detection problems involving quantization of sensor observations, joint optimization of the sensor quantizers is necessary. We show that under a send/no-send constraint on each sensor and when the fusion center has its own observations, the sensor decision rules can be determined independently. In terms of design, and particularly for adaptive systems, the independence of sensor decision rules implies that minimal communication is required. We address the uncertainty in the distribution of the observations typically encountered in practice by determining the optimal sensor decision rules and fusion rule for three formulations: a robust formulation, generalized likelihood ratio tests, and a locally optimum formulation. Examples are provided to illustrate the independence of sensor decision rules, and to evaluate the partially known formulations.