The lifetime of a UAV-assisted wireless network is determined by the amount of energy consumed by the UAVs during flight, data collection, and transmission to the ground station. Routing protocols are commonly used for data transmission in a communication network. However, because of the mobility of UAVs, using a routing protocol with a single communication technology results in higher delay and more energy consumption in a UAV-assisted wireless network. To overcome this, we propose two reinforcement learning (RL) algorithms, Q-learning and deep Q-network (DQN), for energy-efficient data transmission over a hybrid BLE/LTE/Wi-Fi/LoRa UAV-assisted wireless network. We consider BLE, LTE, Wi-Fi, and LoRa for communication over a UAV-GS link. The RL algorithms take any random network as input and learn the best policy to output the network with less energy consumption. The reward/penalty is chosen in such a way that the network with the highest energy consumption is penalized and the one with the lowest is rewarded, thereby minimizing total network energy consumption. Based on learning, it creates a hybrid BLE/LTE/Wi-Fi/LoRa UAV-assisted wireless network by assigning the best communication technology to a UAV-GS link. Further, we compare the performance of proposed RL algorithms with a rule-based algorithm and random hybrid scheme. In addition, we propose a theoretical framework for constructing hybrid network for both free space and free space multipath path loss models. We demonstrate the performance comparison of the proposed work with the conventional shortest path routing algorithm in terms of network energy consumption and average network delay using extensive results. Finally, the effect of the velocity of the UAV and the number of packets on the performance of the proposed framework is analyzed.

Wilson Ayyanthole Nelson

Sreenivasa Reddy Yeduri

Ajit Jha

Abhinav Kumar

Linga Reddy Cenkeramaddi

IEEE/ACM Transactions on Networking

IEEE/ACM TRANSACTIONS ON NETWORKING 的高层次目标是发表来自通信/计算机网络领域的理论或实验探索的高质量原创研究成果，涵盖各种物理层技术上的各种信息传输网络，有线（各种引导媒体：例如，铜、光学）和无线（例如，射频、声学（例如，水下）、红外线），或这些的混合。该杂志欢迎报告新经验和实际系统实验的应用贡献。

The IEEE/ACM Transactions on Networking aims to publish high-quality original research results from the field of communication and computer networking. These studies can be theoretical or experimental, covering various physical layer technologies for information transmission networks, including wired networks (such as copper and optical media) and wireless networks (such as radio frequency, acoustic (e.g., underwater), and infrared), as well as hybrid applications of these technologies. The journal welcomes reports of new experiences and practical system experimental contributions.

IEEE/ACM Transactions on Networking 的目标是发表通信和计算机网络领域的高质量原创研究成果。这些研究可以是理论性的，也可以是实验性的，涵盖各种物理层技术的信息传输网络，包括有线网络（如铜缆和光纤）和无线网络（如射频、声学（例如水下声学）和红外线），以及这些技术的混合应用。该期刊欢迎报告新的经验和实际系统实验的应用贡献。

RL-Based Energy-Efficient Data Transmission Over Hybrid BLE/LTE/Wi-Fi/LoRa UAV-Assisted Wireless Network

Two reinforcement learning algorithms, Q-learning and deep Q-network, are proposed for energy-efficient data transmission over a hybrid BLE/LTE/Wi-Fi/LoRa UAV-assisted wireless network and a theoretical framework for constructing hybrid network for both free space and free space multipath path loss models is proposed.

Category	Quartile
计算机科学	3
计算机科学, 计算机理论方法	2
计算机科学, 计算机硬件	3
计算机科学, 工程电子与电气	3
计算机科学, 电信学	3

RL-Based Energy-Efficient Data Transmission Over Hybrid BLE/LTE/Wi-Fi/LoRa UAV-Assisted Wireless Network

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