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Energy-adaptive Data Loss Recovery in Energy Harvesting Wireless Sensor Networks
http://doi.org/10.5626/JOK.2025.52.8.700
Wireless sensor networks often face limited lifespans due to constrained battery capacity. Specifically, the multi-hop transmission mechanism causes energy depletion in nodes located near the sink, as data converges at these nodes, making them susceptible to energy exhaustion and transmission failures. To overcome the lifetime limitations of sensor nodes, energy harvesting techniques have been employed as a promising solution. However, harvested energy cannot be stored beyond the battery capacity, leading to surplus energy that goes unused. In this paper, we propose a novel scheme to address data loss by effectively utilizing this excess harvested energy. Our method retransmits lost data during transmission errors by leveraging surplus energy that exceeds the battery threshold. Additionally, if extra energy remains, the scheme performs redundant transmissions to enhance data reliability. Simulation results demonstrate that our proposed scheme successfully collects more data compared to conventional approaches, highlighting its effectiveness in recovering from transmission errors.
Efficient Distributed Training Method Considering the Energy Level of Edge Devices in Solar-powered Edge AIoT Environments
Yeontae Yoo, IKjune Yoon, Dong Kun Noh
http://doi.org/10.5626/JOK.2023.50.8.720
Solar-powered IoT devices periodically harvest energy and therefore can fundamentally solve the energy limitation of battery-based IoT devices. However, a careful energy consumption policy is required due to the variation in the amount of energy harvested. There is a growing interest in the AI-distributed training models that can improve the quality and performance of training by conducting small training at each edge node and sharing the results with neighbors. However, the straggler node problem may occur in such distributed models, significantly decreasing the overall training speed and exponentially reducing the lifespan of the IoT network due to insufficient energy of specific nodes. This study proposes a technique to prevent the occurrence of straggler nodes as much as possible for efficient AI-distributed training in an AIoT environment composed of solar-powered devices. The proposed scheme uses an approximate computing technique that adapts energy consumption by adjusting the accuracy according to each node’s harvested energy while retaining the minimum accuracy required by the application. Among various approximation computing schemes, this study uses a data-level approximation scheme that adjusts the accuracy by adjusting the sampling rate of the sensing data. The experimental results confirm that the proposed scheme reduces the generation of straggler nodes by efficient and balanced use of each node’s harvested energy.
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