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🔥 Content Introduction
In the drone system constructed by quadcopters, energy-efficient data collection in wireless sensor networks (WSN) is crucial. This network consists of numerous small sensor nodes distributed throughout the drone’s body and surrounding environment, each equipped with sensing, data processing, and wireless communication capabilities, responsible for collecting various critical data such as aircraft attitude, motor temperature, ambient pressure, wind speed, etc. This data is significant for ensuring the stable operation of the aircraft and optimizing flight performance. However, limited by the energy supply of the drone, typically powered by a limited-capacity lithium battery, the energy reserves of the nodes are extremely precious, making energy-efficient data collection a pressing challenge.
To achieve energy-saving goals, the selection of low-power sensors is the primary step. For example, when measuring the aircraft’s attitude, low-power models using advanced MEMS (Micro-Electro-Mechanical Systems) technology should be prioritized. Some low-power gyroscopes can meet high-precision attitude measurement requirements while operating with a current as low as several microamperes, significantly reducing energy consumption compared to traditional models. For measuring motor temperature, thermistor sensors with low self-heating effects can be selected to minimize additional energy consumption caused by self-heating, ensuring that energy consumption remains low while accurately sensing changes in motor temperature.
Optimizing data collection strategies is also critical. By employing adaptive sampling frequency technology, the sampling frequency of sensors can be dynamically adjusted based on the flight status of the aircraft. During stable hovering or routine flight tasks, the sampling frequency for attitude sensors can be appropriately reduced, for example, from 100 times per second to 50 times per second, thereby reducing the amount of data collected and lowering energy consumption. Conversely, when the aircraft performs complex maneuvers, such as rapid turns or dives, the sampling frequency can be promptly increased to 200 times per second to ensure precise capture of attitude changes. For sensors monitoring motor temperature, data can be collected every 5 minutes during stable operation; if there is a sudden change in motor load or increased temperature fluctuations, the collection interval can be shortened to 1 minute, achieving a balance between energy utilization and data accuracy.
At the data transmission level, multi-hop routing and data fusion technologies are effective means of energy saving. In a multi-hop routing mechanism, sensor nodes do not all communicate directly with the drone’s data aggregation center but relay data through neighboring nodes. For instance, a sensor node located at the edge of the drone first sends data to a nearby intermediate node with lower communication energy consumption, which then gradually forwards the data to the aggregation center, avoiding the high energy consumption associated with long-distance communication. Data fusion technology involves merging and processing similar or related data before nodes transmit data. For example, multiple sensor nodes distributed in different locations measuring ambient pressure can first average their measured pressure values before sending out a single processed data point, reducing the amount of data transmitted and lowering communication energy consumption.
The sleep scheduling mechanism is also an indispensable energy-saving strategy. Sensor nodes enter a low-power sleep mode during non-essential working periods. When the aircraft is in long-term stable flight with minimal data variation, other sensors, such as those monitoring ambient light intensity and air quality, can enter sleep mode, shutting down most circuit functions while retaining a minimal amount of circuitry for periodic wake-up and receiving specific wake-up signals. Nodes can be awakened every 30 seconds to quickly check for significant data changes; if no changes are detected, they continue to sleep, significantly reducing energy consumption.
Through the aforementioned methods, including low-power sensor selection, optimized data collection strategies, multi-hop routing and data fusion technologies, and sleep scheduling mechanisms, the wireless sensor network carried by quadcopters can significantly reduce energy consumption while ensuring data collection quality, extending the drone’s endurance, and enhancing its overall operational efficiency, better meeting the complex task requirements in various application scenarios such as aerial photography, logistics transportation, agricultural protection, and emergency rescue.
⛳️ Operational Results
🔗 References
[1] Xue Rui, Han Lu. Research on the Application of Network Coding in Drone Communication Networks [J]. Applied Technology, 2019, 46(6):5. DOI:10.11991/yykj.201812026.
[2] Zhai Bin. Research on Real-Time Simulation Technology of Small Drone Flight Control Systems [D]. Zhengzhou University, 2007. DOI:10.7666/d.y1059869.
[3] Yun Chao, Li Xiaomin, Zheng Zonggui. Design of Hardware-in-the-Loop Drone Simulation System Based on Matlab/Simulink [J]. Computer Measurement and Control, 2012, 20(12):4. DOI:CNKI:SUN:JZCK.0.2012-12-054.
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