Multi-Objective Stochastic Gradient Based ADR Mechanism for Throughput and Latency Optimization in LoRaWAN

In Long Range Wide Area Networks (LoRaWAN), the goal of Adaptive Data Rate (ADR) is to allocate resources to End Devices (ED) like Transmission Power (TP) and Spreading Factor (SF). The EDs are designed in a way that they can choose optimal configuration resource parameters from a set of LoRa physical layer parameters. The SF parameter has to be chosen correctly, as an incorrect one may cause collisions and interference if multiple nodes have the same SF. This paper focuses on throughput and latency optimization using an effective ADR mechanism for LoRaWAN-based IoT networks. The objective of this study is to maximize the total throughput. SF should be used by multiple nodes as it will have less Time on Air (ToA), but it may cause collision, contention, and co-spreading factor interference problems. The idea is to find an optimal SF allocation to end devices and the optimal number of total devices using the same SF to avoid collision and interference. This paper proposes a multi-objective stochastic gradient descent method to solve the constrained optimization problem for optimizing throughput and latency. This work compares throughput and latency results for the static, quasi-static, and dynamic environments. Trade-offs between latency and throughput for the simulated scenarios are also presented. The simulation results show that the throughput obtained using this technique is higher than the naive ADR approach and the existing gradient descent methods.