UAV Uplink Scheduling and Trajectory Optimization for Energy Efficiency
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Solution Overview
Problem
The limited on-board energy of UAVs restricts their endurance and communication time in data acquisition systems, necessitating a method to maximize energy efficiency in UAV uplink communication with ground sensors.
Innovation Solution
The method jointly optimizes the UAV flight trajectory, sensor wake-up scheduling, and time slot allocation using a combination of time division multiple access (TDMA) and successive convex approximation techniques to maximize energy efficiency, defined as the transmission bit information per unit energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the UAV communicates with all sensors continuously, then the information transmission amount increases, but the energy consumption increases proportionally
Solution Approach 1:
The patent implements periodic sensor wake-up scheduling where sensors alternate between active and sleep states. The UAV communicates with sensors in periodic time slots rather than continuously, allowing sensors to wake up only when needed for data transmission. This periodic operation pattern reduces overall energy consumption while maintaining necessary information transmission throughput.
Solution Approach 2:
The patent performs preliminary optimization of the UAV flight trajectory and sensor wake-up scheduling before actual data acquisition. By pre-planning the optimal flight path and determining which sensors should be active in each time slot, the system minimizes energy consumption while ensuring all necessary data is collected. This preliminary action prevents wasted energy during actual operation.
2Duration of action of moving object
If the UAV flies faster to complete data acquisition quicker, then the communication time increases, but the energy consumption increases
Solution Approach 1:
The patent optimizes the UAV flight speed as a variable parameter rather than operating at maximum speed. By adjusting the flight speed to an optimal value that balances communication time and energy consumption, the system achieves extended communication duration without proportional energy increase. The optimization considers the relationship between flight speed, channel quality, and energy consumption to determine the best operating speed.
Solution Approach 2:
The patent implements dynamic trajectory optimization where the UAV flight path and speed are adjusted adaptively based on sensor locations, data priorities, and energy constraints. Rather than following a fixed high-speed route, the UAV dynamically modifies its flight characteristics to maximize communication time while managing energy consumption efficiently throughout the data acquisition mission.
3Productivity
If the UAV serves multiple sensors simultaneously, then the productivity increases, but the network fairness deteriorates
Solution Approach 1:
The patent segments the data acquisition process into discrete time slots, with each time slot dedicated to serving a specific sensor or a small group of sensors. This time-division segmentation ensures that each sensor receives dedicated attention and fair treatment, preventing any single sensor from being starved of resources. The segmentation approach maintains productivity by systematically cycling through all sensors while ensuring network fairness.
Solution Approach 2:
The patent implements periodic time-division multiple access (TDMA) scheduling where each sensor is allocated specific time slots for communication with the UAV. This periodic scheduling ensures that all sensors receive equal opportunities for data transmission over time, maintaining network fairness. Meanwhile, the systematic rotation through all sensors ensures that data acquisition productivity is maintained by keeping the UAV continuously engaged in productive communication tasks.
Data Source
AI summary
A design method of a high energy efficiency unmanned aerial vehicle (UAV) green data acquisition system belongs to the technical field of data acquisition and optimization for UAV uplink communication. Firstly, a system optimization objective is constructed; and in a uplink communication network of a single UAV and ground sensors, the UAV receives data periodically. Secondly, according to a constructed optimization problem, the optimization objective is maximization of EE({W},{t},{S}). Finally, an original problem is decomposed into two approximate concave-convex fractional sub-problem based on a block coordinate descent method and a successive convex approximation technique to obtain a suboptimal solution; an overall iterative algorithm is proposed: in each iteration, by solving the sub-problems, wake-up scheduling S, time slot t and UAV trajectory W are alternately optimized. The solution obtained in each iteration is used as the input of next iteration. The present invention can jointly optimize the UAV flight trajectory, the sensor wake-up scheduling and the flight time slot to ensure that the transmission information amount and energy consumption of the sensors satisfy system requirements, while maximizing the energy efficiency of the system.