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A Robust Transmission Scheduling Approach for Internet of Things Sensing Service with Energy Harvesting
Jie Hao1,2, Jing Chen1, Ran Wang1,2
1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
This study addresses energy constraints in Internet of Things (IoT) sensing by developing a robust transmission scheduling approach. It optimizes sensor utility despite unpredictable harvested energy, enhancing IoT service reliability.
Area of Science:
- Computer Science
- Electrical Engineering
- Wireless Communications
Background:
- Internet of Things (IoT) sensing services rely on energy harvesting sensors.
- Maximizing utility under energy constraints is crucial for IoT sensor performance.
- Existing models often overlook the inherent randomness of harvested energy.
Purpose of the Study:
- To develop a robust transmission scheduling optimization approach for IoT sensors.
- To address the challenge of random harvested energy in IoT sensing.
- To maximize sensor utility while respecting energy constraints.
Main Methods:
- Formulated a transmission scheduling optimization problem considering random harvested energy.
- Introduced a flexible harvested energy profiling model.
- Transformed random energy constraints into linear constraints for traditional solving.
Main Results:
- The proposed approach provides robustness against the randomness of harvested energy.
- Successfully optimized transmission scheduling under energy constraints.
- Demonstrated a favorable trade-off between service flexibility and robustness.
Conclusions:
- The developed method effectively manages energy constraints in IoT sensing.
- Robust transmission scheduling is vital for reliable IoT services with energy harvesting.
- The approach offers a practical solution for optimizing IoT sensor operations.
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