# Resource Allocation to Massive Internet of Things in LoRaWANs
## 6 Conclusions  
In this study, we analyzed the intra- and inter-SF collisions and proposed two SF assignment schemes for the confirmed and unconfirmed modes in LoRaWAN. Through simulation results, we observed that the transmissions arriving at the GW with the same or different SFs could be correctly received, only when their respective SINR values are above a certain threshold. It was also shown that a higher SF is highly vulnerable to interference due to high ToA. Therefore, assigning higher SFs to EDs far away from the GW might not necessarily solve the network congestion as both the intra- and inter-SF interferences exist in the LoRaWAN networks. The first scheme tackled the higher SF issue by decreasing it in the worst case and certainly enhanced the \(SR\), \(\
exists D\), and \(\
exists D\)\(D_{ack}\) delays in comparison with the existing typical SF scheme. Furthermore, the second scheme played a vital role in assigning different SF to EDs in the same region; as a result, it reduced the impact of interference on the network and enhanced the success ratio. We believe that the two proposed SF assignment methods can be used for IoT application services, offering a high transmission success ratio without the sacrifice of energy consumption and computation cost.  
Figure 8: Delay analysis of the ED sensitivity, GW sensitivity, and random-based SF allocation schemes under the LOG and OH path loss models by varying the distance in an urban environment: (**a**) end-to-end delay (**b**) ACK delay.  
Figure 9: The success ratio of the SF allocation schemes in the unconfirmed mode under the condition of GW radius = 3410 m: (**a**) SR under LOG; (**b**) SR under OH.  
**Author Contributions:** Conceptualization, A.F., and J.-Y.P.; methodology, A.F., and D.-H.K.; software, A.F.; validation, A.F., D.-H.K., and J.-Y.P.; formal analysis, A.F.; investigation, A.F., and D.-H.K.; resources, A.F.; data curation, A.F.; writing--original draft preparation, A.F.; writing--review and editing, A.F., and J.-Y.P; visualization, A.F.; supervision, J.-Y.P; project administration, J.-Y.P. Funding acquisition, J.-Y.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2018-0-00691, Development of Autonomous Collaborative Swarm Intelligence Technologies for Disposable IoT Devices).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
LoRaWAN long-range wide area network SF spreading factor ED end device IoT internet of things MAC media access control CSS chirp spread spectrum DR data rate RW receive window DL downlink NS network server UL uplink ToA time-on-air SNR signal-to-noise ratio GW gateway RF radio frequency AWGN additive white Gaussian noise CIR cumulative interference ratio RS-LoRa reliability and scalability of LoRa RSSI received signal strength indicator ASFS adaptive spreading factor selection EXPLoRa EXtending the performance of LoRa EXplora-AT EXplora air-time EXplora-KM EXplora K-means EXplora-TS EXplora-Time symbol E-ADR enhanced ADR PLR packet loss ratio MCS modulation and coding schemes QoS quality of service OH Okumura-Hata LOG log-distance ADR adaptive data rate PER packet error rate