A Multi-UAV-Assisted Energy-Saving Data Collection for LoRa Networks
With the rapid development of Internet of Things(IoT)technologies,Low-power Wide-area Networks(LPWANs)have emerged as a key enabler for large-scale,long-range wireless communication.Among LPWAN technologies,LoRa has gained significant attention due to its advantages of wide coverage,low power consumption and cost-effectiveness.However,LoRa networks face challenges in energy efficiency and network lifetime,particularly in complex data collection scenarios with dynamic environments,diverse communication requirements,and fixed gateway deployments.These factors lead to uneven energy consumption among end devices and limit the overall performance of LoRa networks.To address these challenges,this paper proposes an energy-efficient data collection method for large-scale LoRa networks assisted by multiple Unmanned Aerial Vehicles(UAVs).The proposed approach leverages UAVs equipped with LoRa gateways to enable mobile"air-to-ground"data collection from end devices.By formulating the problem as a mixed-integer non-convex optimization problem,the coupling between UAV-LoRa communication parameters and UAV onboard energy constraints is considered.To tackle the complexity of the optimization problem,a series of techniques are introduced to decompose the original problem into several sub-problems.The proposed method jointly optimizes the communication scheduling strategy of end devices,the three-dimensional flight trajectories of UAVs,and the transmission parameters of end devices.Through an iterative process using block coordinate descent,successive convex approximation,and sequential linear programming,a high-quality suboptimal solution is obtained.The objective is to minimize the data transmission energy consumption of LoRa end devices while ensuring collision avoidance among multiple UAVs and satisfying the onboard energy constraints of UAVs.Extensive numerical simulations are conducted to evaluate the performance of the proposed method under three different scales of LoRa networks.The results demonstrate that,compared to existing fixed gateway schemes,the proposed multi-UAV-assisted approach achieves an average improvement of 26.65 times in overall end-device energy efficiency across various network scales.Furthermore,in comparison with fixed UAV flight path schemes,the proposed method attains an average improvement of 6.2 times in overall end-device energy efficiency.The research highlights the advantages of exploiting UAV mobility and multi-gateway deployment to enhance the energy efficiency and prolong the lifetime of large-scale LoRa networks.By dynamically adjusting the positions of UAV-mounted gateways and optimizing the communication parameters,the proposed method effectively balances the energy consumption among end devices and improves the overall network performance.The adaptability and flexibility of the UAV-assisted approach make it particularly suitable for complex IoT scenarios with diverse terrain and limited infrastructure.In conclusion,this paper presents a groundbreaking solution for energy-efficient data collection in large-scale LoRa networks by leveraging the capabilities of multiple UAVs.The proposed method significantly outperforms traditional fixed gateway and UAV-assisted approaches in terms of end-device energy efficiency and network lifetime.The research contributes to the advancement of energy-efficient IoT data collection techniques and paves the way for the deployment of sustainable,scalable,and resilient LoRa networks in a wide range of application domains.Furthermore,the insights gained from this study can be extended to other LPWAN technologies,fostering the development of energy-efficient and robust IoT ecosystems.
国家重点研发计划项目国家自然科学基金面上项目国家自然科学基金面上项目Jiangsu Provincial Key Laboratory of Network and Information SecurityKey Laboratory of Computer Network and Information Integration of the Ministry of Education of China
万方数据
维普
