We consider a binary hypothesis testing problem using Wireless Sensor Networks (WSNs). The decision is made by a fusion center and is based on received data from the sensors. We focus on an energy and spectrum efficient transmission scheme used to reduce the energy consumption and spectrum usage during the detection task. We propose a Density-Based Multiple Access (DBMA) transmission protocol that performs a censoring-type transmission based on the density of observations using multiple access channels (MAC). Specifically, in DBMA, only sensors with highly informative observations transmit their data in each data collection. The sensors transmit a common shaping waveform and the fusion center receives a superposition of the analog transmitted signals. DBMA has important advantages for detection tasks in WSNs. First, it is highly energy and bandwidth efficient due to transmissions saving and narrowband transmission over MAC. Second, it can be implemented by simple and dumb sensors (oblivious of observation statistics, and local data processing is not required) which simplifies the implementation as compared to existing MAC transmission schemes for detection in WSNs. We establish both finite sample analysis and asymptotic analysis of the error probability with respect to the network size and provide conditions for obtaining exponential decay of the error. Numerical examples are provided to demonstrate the DBMA performance.
|Original language||American English|
|Title of host publication||2018 IEEE International Symposium on Information Theory, ISIT 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - 15 Aug 2018|
|Event||2018 IEEE International Symposium on Information Theory, ISIT 2018 - Vail, United States|
Duration: 17 Jun 2018 → 22 Jun 2018
|Name||IEEE International Symposium on Information Theory - Proceedings|
|Conference||2018 IEEE International Symposium on Information Theory, ISIT 2018|
|Period||17/06/18 → 22/06/18|
Bibliographical noteFunding Information:
This research was supported by ISF grant 903/2013.
© 2018 IEEE.