You are in:Home/Publications/Deep Learning Based Joint Collision Detection and Spreading Factor Allocation in LoRaWAN

Dr. Moataz mostafa El-nahas Ali El-sherbini :: Publications:

Title:
Deep Learning Based Joint Collision Detection and Spreading Factor Allocation in LoRaWAN
Authors: Wali Ullah Khan, Seham Ibrahem Abd Elkarim, MM Elsherbini, Ola Mohammed, Omer Waqar, Basem M ElHalawany
Year: 2022
Keywords: Not Available
Journal: Not Available
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link: Not Available
Full paper Not Available
Supplementary materials Not Available
Abstract:

Long-range wide area network (LoRaWAN) is a promising low-power network standard that allows for longdistance wireless communication with great power saving. LoRa is based on pure ALOHA protocol for channel access, which causes collisions for the transmitted packets. The collisions may occur in two scenarios, namely the intra-spreading factor (intra-SF) and the inter-spreading factor (inter-SF) interference. Consequently, the SFs assignment is a very critical task for the network performance. This paper investigates a smart SFs assignment technique to reduce collisions probability and improve the network performance. In this work, we exploit different architectures of artificial neural networks for detecting collisions and selecting the optimal SF. The results show that the investigated technique achieves a higher prediction accuracy than traditional machine learning algorithms and enhances the energy consumption of the network.

Google ScholarAcdemia.eduResearch GateLinkedinFacebookTwitterGoogle PlusYoutubeWordpressInstagramMendeleyZoteroEvernoteORCIDScopus