# LoRaWAN Behaviour Analysis through Dataset Traffic Investigation
## 6 Conclusions  
In this work, we dealt with Low-Power Wide-Area-Network and specifically with LoRaWAN. These networks use lightweight protocols to manage efficiently power-consumption, signal range coverage and data transmission in IoT frameworks. The goal is twofold: (i) to present into detail some methodologies (and to provide relevant software tools) able to capture the behaviour of these kind of network by analysing traffic traces stored in datasets; (ii) to test and perform the analysis of some data collected in a quite rich datasets identified in the LoED one.  
That dataset is quite a large, contains 11,263,001 real world LoRaWAN packets and is collected from a wide network deployed in London counting nine different Gateways and more than 1481 distinct physical end-devices. This dataset is interesting not only for the dimension of the network from which the data are collected but also because the network is used for multiple applications and for a long period of time.  
The result of the work is a data analysis allowing us to understand how a real LoRaWAN network works and in particular how all its components cooperate to reach the desired performance. More, we also checked if, in this case study, all the specifications are respected.  
We have performed six different analyses. Each analysis focuses on a particular feature of LoRaWAN and for each of them we have explained the strategy used to carry it out and we have presented the obtained results.  
As expected all the obtained results verify the LoRaWAN specification. By looking at the results we can observe how the gateway modifies the radio indicators such as Spreading Factor and \(RSSI\) in order to guarantee good performances at any time and at any conditions.  
Conceptualization, P.S. and F.G.C.; methodology, P.S.; software, F.G.C.; validation, P.S., F.G.C. and F.C.; formal analysis, F.G.C.; investigation, P.S.; resources, P.S.; data curation, F.G.C.; writing--original draft preparation, P.S., F.G.C. and F.C.; writing--review and editing, P.S., F.G.C. and F.C.; visualization, P.S and F.G.C.; supervision, F.C.; project administration, F.C. All authors have read and agreed to the published version of the manuscript.  
Part of the activities are related to the H2020 ELEGANT project (Grant agreement ID: 957286). The Authors thank the ELEGANT consortium.  
Not applicable.  
The dataset LoED is publicly available here [[https://zenodo.org/record/4121430#.YjMRoX_MJhE](https://zenodo.org/record/4121430#.YjMRoX_MJhE)]([https://zenodo.org/record/4121430#.YjMRoX_MJhE](https://zenodo.org/record/4121430#.YjMRoX_MJhE)) (accessed on 30 January 2022).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ABP & Activation By Personalization \\ ADR & Adaptive Data Rate \\ BW & Bandwidth \\ CR & Coding Rate \\ CSS & Chirp Spread Spectrum \\ DevAddr & Device Address \\ DevEU & Device Extended Unique Identifier \\ DevNonce & Device Nonce \\ ED & End Device \\ FCnt & Frame Counter \\ FCntDown & Frame Counter Downlink \\ FCntUp & Frame Counter Uplink \\ \end{tabular}