Evaluating and Optimizing the Performance of Dual-Hop LoRa Network using Genetic Algorithm

Aspiring to support vast IoT applications, Long Range (LoRa) developed by Semtech has been most adopted LPWAN technology as it provides low power consumption, long transmission range, massive scalability and low deployment cost. The current work focuses on enhancing the LoRa network performance by employing cooperative communication. Cooperative communication has emerged as the promising technology enabling efficient utilization of the communication resources by allowing the end devices to cooperate with each other in data transmission. The performance of dual hop LoRa network is investigated in terms of coverage probability and latency. Further to optimize the performance of the network, Genetic Algorithm (GA) has been utilized for both non-cooperative and cooperative scenarios. A mathematical model has been formulated and based on that an optimization problem has been defined in terms of estimated received power which gives the optimal transmission configuration of the LoRa node at which the received power is maximum. The optimized value of the received power is further utilized to optimize the performance of the network in terms coverage probability and latency. MATLAB simulations are carried out to highlight the goodness of the proposed model. Simulation results shows that by employing cooperative communication the performance of the LoRa network enhances by 22% and 8% improvement in coverage probability and latency respectively. Also, a significant improvement in coverage probability and latency can be observed in both non-cooperative and cooperative scenarios by employing GA.