Reference : Deep Learning Based Joint Collision Detection and Spreading Factor Allocation in LoRaWAN

	Document type :	Scientific congresses, symposiums and conference proceedings : Poster
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/53945

Title :	Deep Learning Based Joint Collision Detection and Spreading Factor Allocation in LoRaWAN
Language :	English
Translated title :	[en] Deep Learning Based Joint Collision Detection and Spreading Factor Allocation in LoRaWAN
Author, co-author :	Abd Elkarim, Seham Ibrahem []
	Elsherbini, M.M. []
	Mohammed, Ola []
	Khan, Wali Ullah [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
	Waqar, Omer []
	ElHalawany, Basem M. []
Publication date :	Jul-2022
Number of pages :	6
Peer reviewed :	Yes
Audience :	International
Event name :	2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW)
Event date :	10-07 to 13-07-2022
Event organizer :	IEEE
Event place (city) :	Bologna
Event country :	Italy
Keywords :	[en] LoRaWAN ; Deep learnin ; Collision Detection
Abstract :	[en] Long-range wide area network (LoRaWAN) is a promising low-power network standard that allows for long-distance wireless communication with great power saving. L oRa 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.
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/10993/53945
DOI :	10.1109/ICDCSW56584.2022.00043
Other URL :	https://ieeexplore-ieee-org.proxy.bnl.lu/document/9951336

	File(s) associated to this reference
	Fulltext file(s): File Commentary Version Size Access Limited access Deep_Learning_Based_Joint_Collision_Detection_and_Spreading_Factor_Allocation_in_LoRaWAN.pdf Publisher postprint 675.75 kB Request a copy

All documents in ORBi^lu are protected by a user license.

 

University of Luxembourg Library |  Feedback |  Legal notices Site Map