Improved Decision Fusion Model for Wireless Sensor Networks over Rayleigh Fading Channels

dc.contributor.authorJamoos, Ali
dc.date.accessioned2020-10-26T10:52:25Z
dc.date.available2020-10-26T10:52:25Z
dc.date.issued2017
dc.description.abstractThis paper deals with decision fusion in wireless sensor networks (WSNs) over Rayleigh fading channels. The likelihood ratio test (LRT) is considered as the optimal fusion rule when applied at the fusion center (FC). However, applying the LRT at the FC requires both the channel state information (CSI) and the local sensors’ performance indices. Acquiring such information is considered as an overhead in energy and bandwidth constrained systems such as WSNs. To avoid these drawbacks, we propose a modification to the traditional three-layer system model of a WSN where the LRT is applied as a local decision making method at the sensors level. Applying the LRT at the sensors level does not require the CSI or the local sensors’ performance indices. It only requires the signal-to-noise ratio (SNR). Moreover, a new fusion rule based on selection combining (SC) is suggested. This fusion method has the lowest complexity when compared to other diversity combining based fusion rules such as the equal gain combiner (EGC) and the maximum ratio combiner (MRC). Simulation results show that the performance of the proposed model outperforms the traditional model. In addition, applying the EGC at the FC in the proposed model provides comparable performance to the traditional model that applies the LRT at the FC.en_US
dc.identifier.urihttps://dspace.alquds.edu/handle/20.500.12213/6235
dc.language.isoen_USen_US
dc.publisherMDPI Technologiesen_US
dc.subjectwireless sensor networksen_US
dc.subjectdecisions fusionen_US
dc.subjectfading channelsen_US
dc.subjectlikelihood ratio testen_US
dc.subjectEGCen_US
dc.subjectMRCen_US
dc.subjectSCen_US
dc.titleImproved Decision Fusion Model for Wireless Sensor Networks over Rayleigh Fading Channelsen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
[8].pdf
Size:
487.79 KB
Format:
Adobe Portable Document Format
Description:
article
License bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.61 KB
Format:
Item-specific license agreed upon to submission
Description: