# Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN
## 3 Related Literature  
Adaptive data rate schemes have been proposed for LoRaWAN networks to address the issue of interference and packet collisions, which can severely impact the network's performance. In this section, we survey some of the related research on ADR schemes for LoRaWAN. Optimal resource allocation in ADR implementation still requires further research as transmission delays, interference, and energy efficiency remain topical issues. Some research has been undertaken addressing interference and packet collision issues in LoRa networks [18; 20; 31] but is not exhaustive. ED mobility is a phenomenon that is gaining ground [32; 33; 34] and poses its own challenges of frequently varying network conditions. One of the earliest ADR schemes proposed in the literature is a fair adaptive rate algorithm developed by [35], wherein the authors propose a solution to adjust ADR due to network congestion using supervised learning. This technique improves the standard ADR by reducing the probability of ineffective SF adjustments. However, due to the  
Figure 1: Standard ADR model ED side. learning process and the feedback required for every transmission, there is an increase in the transmission latency. EXPLoRa is an algorithm proposed in [36] that considers dense networks to decrease the collision rate and improve throughput. The algorithm distributes equally the number of end nodes utilising different spreading factors (EXPLoRa-AT and another that allocates the SFs by equalising the ToA between different nodes). This method ensures channel usage fairness, although gateway distances are ignored. This approach involves periodically monitoring and managing network behaviour from a centralised entity. The work reported in [37] introduces CA-ADR, a collision-aware algorithm which determines optimal data rates using the average SNR of the most recent packets as a benchmark and the orthogonality of distinct spreading factors to decrease the possibility of collision. This algorithm performs better in comparison to state-of-the-art ADR protocols, albeit in a limited network radius where there are no connectivity constraints, but does not take into consideration the energy efficiency of the network. Collision Avoidance Resource Allocation (CARA) is proffered in [38] wherein the ADR algorithm increases network capacity while decreasing the number of collisions. CARA partitions the available bandwidth of the wireless channel into resource sections corresponding to each channel and SF. This algorithm increases throughput but increases transmission time and energy consumption. A link margin parameter was used in the ADR algorithm in [39] to account for inaccurate link quality estimates. The algorithm resulted in improved SF allocation and a decrease in energy utilisation on individual nodes. A game theory-based strategy was used in [40] to manage the assignment of SFs to different EDs using the same spreading factor. The authors used varying periods for these EDs to share the spreading factor, preventing interference amongst EDs transmitting concurrently. An ADR algorithm that exploits the quasi-orthogonality of spreading factors to create new channels was developed in [41]. The authors delivered higher throughput compared to legacy ADR algorithms but only used SF7-SF9 to evaluate their proposed scheme. The proposed scheme resulted in a lower packet success probability. In our previous work [42], we used fuzzy logic to develop an energy-efficient ADR algorithm FL-ADR. Although this proposed scheme was more energy-efficient compared to legacy schemes, there was a decrease in the packet delivery ratio. In [43], the authors proposed an ADR protocol that assigns SFs by balancing the time-on-air of packets transmitted by every ED. In contrast, in [44], an optimum section of the spreading factors is determined to allocate spreading factors and transmission power to minimise the chance of packet collision within each SF cluster. The algorithms mentioned above focus on the protocol strategy and depend on basic contention frameworks.