Characteristics of aggregated traffic in LoRaWAN

Research output: ThesisMaster's thesis

Abstract

Over the past few years, internet-of-Things (IoT) request a large number of smart devices to communicate and exchange information without direct human assistance. As the number of IoT nodes are increasing rapidly, the massive machine-type communication (mMTC) in 5G enables us to integrate the IoT massive traffic and applications without affecting the traditional services. Low-Power Wide-Area Networks (LPWAN) are emerging commercially and considered as fundamental enablers of IoT, Industrial Internet-of-Things (IIoT), and industrial revolution 4.0 because of their license free frequency bands, long range, low power consumption, and low cost. In recent years, LoRaWAN is appearing as one of the most leading LPWAN technologies. The main contribution of this work is examining the characteristics and modeling the aggregated traffic of a large and dense LoRa Network that is deployed as a monitoring system inside Tellus Innovation Arena, University of Oulu, with the concept of IoT-based digital campus as a Wireless Access IoT service of 5GTN. To understand the traffic behaviour, we analyzed the inter-arrival times of the transmissions for different weeks, days, and hours. The statistical presentation of data reveals that the trend of transmissions is exponential, that shows that most of the transmissions were within the inter-arrival time of less than 10 seconds while few of them have inter-arrival time over 20 seconds. After that, we fitted inter-arrival times into the exponential distribution, which helped us to find the mean inter-arrival time of the 5GTN traffic which is further used for the modeling of aggregated traffic. Finally, we performed the transmission compression from a gateway to the Network Server that will be beneficial to efficiently utilize the resources and bandwidth. The results demonstrate that the proposed aggregation mechanism increases the system goodput.
Original languageEnglish
QualificationMaster Degree
Awarding Institution
  • University of Oulu
Supervisors/Advisors
  • Alves, Hirley, Supervisor, External person
Award date18 May 2020
Publisher
Publication statusPublished - May 2020
MoE publication typeG2 Master's thesis, polytechnic Master's thesis

Keywords

  • 5GTN
  • LoRaWAN
  • traffic modeling

Fingerprint

Dive into the research topics of 'Characteristics of aggregated traffic in LoRaWAN'. Together they form a unique fingerprint.

Cite this