Ali Zulfiqar, Qureshi Kashif Naseer, Mustafa Kainat, Bukhsh Rasool, Aslam Sheraz, Mujlid Hana, Ghafoor Kayhan Zrar
Department of Software Engineering, Bahria University, Islamabad 46000, Pakistan.
Department of Computer Science, Bahria University, Islamabad 46000, Pakistan.
Entropy (Basel). 2022 Nov 4;24(11):1607. doi: 10.3390/e24111607.
The exponential growth of the edge-based Internet-of-Things (IoT) services and its ecosystems has recently led to a new type of communication network, the Low Power Wide Area Network (LPWAN). This standard enables low-power, long-range, and low-data-rate communications. Long Range Wide Area Network (LoRaWAN) is a recent standard of LPWAN that incorporates LoRa wireless into a networked infrastructure. Consequently, the consumption of smart End Devices (EDs) is a major challenge due to the highly dense network environment characterised by limited battery life, spectrum coverage, and data collisions. Intelligent and efficient service provisioning is an urgent need of a network to streamline the networks and solve these problems. This paper proposes a Dynamic Reinforcement Learning Resource Allocation (DRLRA) approach to allocate efficient resources such as channel, Spreading Factor (SF), and Transmit Power (Tp) to EDs that ultimately improve the performance in terms of consumption and reliability. The proposed model is extensively simulated and evaluated with the currently implemented algorithms such as Adaptive Data Rate (ADR) and Adaptive Priority-aware Resource Allocation (APRA) using standard and advanced evaluation metrics. The proposed work is properly cross validated to show completely unbiased results.
基于边缘的物联网（IoT）服务及其生态系统的指数级增长，最近催生了一种新型通信网络——低功耗广域网（LPWAN）。该标准支持低功耗、远距离和低数据速率通信。长距离广域网（LoRaWAN）是LPWAN的一种最新标准，它将LoRa无线技术融入网络基础设施。因此，由于网络环境高度密集，存在电池寿命有限、频谱覆盖范围有限和数据冲突等问题，智能终端设备（ED）的功耗成为一个重大挑战。智能高效的服务供应是网络简化网络并解决这些问题的迫切需求。本文提出了一种动态强化学习资源分配（DRLRA）方法，为终端设备分配诸如信道、扩频因子（SF）和发射功率（Tp）等有效资源，最终在功耗和可靠性方面提高性能。使用标准和先进的评估指标，对所提出的模型与当前实施的算法（如自适应数据速率（ADR）和自适应优先级感知资源分配（APRA））进行了广泛的仿真和评估。对所提出的工作进行了适当的交叉验证，以显示完全无偏的结果。
