# Resource Allocation to Massive Internet of Things in LoRaWANs
## 2 Literature Review
### Link- and System-Based Approaches  
The capacity of an LoRaWAN in terms of the maximum number of EDs that can communicate in a single and multi-cell scenario is analyzed in [3] under realistic traffic conditions in the simulation and the testbed. Furthermore, use cases for LoRaWAN are shown based on the traffic type, payload size, and minimum required packet success ratio (\(SR\)) [19]. It is certain that increasing the network capacity in terms of the offered traffic in all the use cases can provide a satisfactory \(SR\). Furthermore, [3] shows that the \(SR\) increases with the number of GWs. In the unconfirmed mode, the traffic load has a low effect on the \(SR\), as the geometric coverage plays a significant part, resulting in an \(SR\) of 55% or 93% depending on the number of GWs. Furthermore, in [3], the intra-SF interference is limited to SF values of 7, 9, and 12. To improve \(SR\), each ED is allocated with an SF based on the GW sensitivity by measuring the received power that a GW would receive from the ED during the initial deployment, as presented in [5]. Based on the measured received power, each ED picks an SF based on the GW sensitivity threshold defined for every SF. However, this work only focuses on enhancing the packet success ratio in the UL transmission and does not take into consideration the DL transmission. The reliability and scalability of the LoRa (RS-LoRa) scheme aims to enhance the reliability and scalability of an LoRaWAN under an ideal network situation [20]. RS-LoRa works in two phases: In the first phase, GW groups the EDs within its coverage by obtaining the received signal strength indicator (RSSI) and SF on each channel. In the second phase, EDs get their SFs, transmit power, and a channel based on the RSSI. This grouping based on the RSSI decreases collision by choosing a suitable SF, and enhancing the network reliability, scalability, and capture effect. Another work presents a system-level simulation with heterogeneous traffic in [21]. The primary purpose of the work in [21] is to enhance the \(SR\) by allocating possibly the lowest SFs based on the SNR of the ED packet. Hence, the scheme in [21] decreases the ToA for each ED and reduces the probabilities of collision by suitable SF allocation. A similar approach called an adaptive spreading factor selection (ASFS) algorithm aims to allocate SFs to EDs in the presence of channel activity detection to improve the throughput of a single and multi-hop LoRaWAN network [22]. The performance of the proposed SF allocation algorithm shows improved throughput in comparison with SX1272 and SX1301 chips. EXtending the performance of LoRa (EXplora-AT) [23] aims to improve the data extraction rate by proposing two SF allocation approaches; EXplora-SF and EXplora air-time (EXplora-AT). EXploRa-SF allocates SFs to the entire network by considering the best RSSI while EXplora-AT aims to allocate a fair SF based on the air-time to EDs in the network by considering an \"ordered water-filling [24]\" approach to guarantee low ToA and channel fairness. The performance of both methods has been compared to the adaptive data rate (ADR) scheme, where the results of EXploRa-SF show improved results compared to ADR. In contrast, EXplora-AT shows even better performance under a high traffic load. The performance of EXplora-AT has been further enhanced by EXplora K-means (EXplora-KM) and EXplora-Time symbol (EXplora-TS), as presented in [25]. EXplora-KM identifies the crowded regions having high collision probability, thus increasing SF. On the other hand, EXplora-TS considers the heterogeneous traffic load, where EDs having a higher payload size are served based on priority. The performance of these two approaches has been compared to EXplora-AT, where results show a 21% increase. The authors in [26] further improved the performance of EXplora-AT and EXploRa-SF by considering the capture effect.