Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications (1703.05401v2)

Abstract: In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, is investigated. In particular, to enable reliable uplink communications for IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the three-dimensional (3D) placement and mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs' locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total transmit power of the IoT devices is reduced by 45% compared to a case in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28% enhanced system reliability compared to the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs.

• The paper introduces a novel two-step framework that jointly optimizes UAV 3D placement and uplink power control to reduce IoT device transmit power.
• It employs sequential quadratic programming for UAV positioning and iterative algorithms for device association, achieving a 45% reduction in transmit power.
• The study demonstrates enhanced system reliability by up to 28% and highlights trade-offs between UAV mobility and energy consumption for practical IoT applications.

Energy-Efficient Internet of Things Communications with UAV Deployment

The paper "Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications" authored by Mohammad Mozaffari et al., investigates a framework for the optimized deployment and mobility of multiple UAVs functioning as aerial base stations to facilitate energy-efficient uplink communications for IoT devices. This paper addresses the challenge of minimizing the total transmit power required for IoT devices while ensuring reliable communications through the strategic placement and movement of UAVs, device-UAV association, and uplink power control.

Contributions and Methodology

The primary contribution of this paper lies in its novel two-step iterative framework which comprehensively optimizes the following:

1. 3D Placement and Mobility of UAVs: The proposed methodology dynamically determines the optimal 3D positions of UAVs to serve IoT devices based on their spatial distribution and activation patterns.
2. Device-UAV Association and Uplink Power Control: It iteratively optimizes device association and the transmit power of each IoT device to minimize overall transmit power while satisfying device-specific SINR requirements.

The approach is divided into two main optimization problems:

• First Subproblem: With fixed UAV positions, the paper offers an iterative algorithm for jointly finding the optimal device association and transmit power.
• Second Subproblem: Given the device association, the optimal UAVs' 3D locations are determined using a sub-optimal, yet efficient, approach based on sequential quadratic programming (SQP).

Numerical Results and Analysis

Simulation results illuminate the effectiveness of the proposed framework:

• Energy Efficiency: The proposed method reduced the total transmit power of IoT devices by 45% compared to scenarios with stationary UAVs.
• System Reliability: Enhanced reliability, with improvements up to 28%, demonstrates the advantage of dynamically optimizing UAV positions.
• Interference Management: Increasing the number of orthogonal channels significantly reduced the overall transmit power required by IoT devices due to decreased interference.

Theoretical Insights and Trade-offs

The paper provides valuable theoretical insights into the trade-offs in UAV deployment:

• Mobility vs. Transmit Power: Increased UAV mobility results in lower device transmit power due to optimized UAV placement, which reduces communication distances and interference.
• Number of Update Times: More frequent updates lower the transmit power of devices but require greater energy expenditure for UAV mobility.

Implications and Future Work

The implications of this work are profound for the deployment of UAVs in IoT networks:

• Practical Applications: The framework can be applied to various IoT scenarios such as environmental monitoring, smart grids, and traffic control, where energy efficiency and reliable data collection are paramount.
• Theoretical Contributions: The introduction of a joint optimization model bridging UAV placement and mobility and its interaction with IoT communication parameters is significant.

For future developments, improvements can be focused on:

• Refinement of Mobility Patterns: Exploring adaptive UAV mobility schemes to further minimize energy consumption.
• Integrating Advanced Machine Learning Techniques: Using predictive analysis to forecast IoT device activation patterns can refine UAV deployment strategies.
• Tests in Real-world Scenarios: Implementing and testing the proposed framework in real-life urban IoT settings can validate the theoretical findings and uncover new considerations.

Conclusion

The research presented in this paper addresses a critical aspect of modern IoT networks by proposing a significant enhancement in the deployment and operational efficiency of UAVs. Its substantial reduction in IoT device transmit power alongside improved system reliability positions it as a noteworthy reference for future studies targeting the energy-efficient operation of IoT ecosystems with UAV-based support.