A Load-Independent Fission-Type Inductive Power Transfer System for 3D Reconfigurable IoT Array

Gao, Yiming; Chen, Zijie; Wang, Haoyu; Liu, Yu; Fu, Minyue; Liang, Junrui

doi:10.1109/access.2023.3239209

Cited by 3 publications

(1 citation statement)

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“…[11] Gao et al explored "A Load-Independent Fission-Type Inductive Power Transfer System for 3D Reconfigurable IoT Array" [11], presenting innovative wireless power relay technology that aligns with energy-efficient IoT devices.…”

Section: Review Of Existing Modelsmentioning

confidence: 99%

Energy-Efficient Machine Learning for IoT Edge Devices: A Federated Learning Approach

Rosemaro,

Pandit

2023

RJCSE

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In the realm of modern-day IoT (Internet of Things) deployments, the quest for energy-efficient solutions stands paramount for different use cases. As the IoT ecosystem continues to burgeon, there arises an exigency for resource-conscious algorithms that can effectively navigate the intricacies of data routing while minimizing energy consumption levels. This paper illuminates a pivotal advancement in this domain, introducing a novel paradigm for different scenarios. Existing methodologies have long grappled with the challenge of balancing the ever-increasing computational demands of IoT devices with their constrained power resources. Conventional routing strategies often lack the depth required to optimize energy consumption, resulting in inefficiencies that hamper overall system performance. Prior research has primarily focused on rudimentary routing techniques that fall short in addressing the multifaceted demands of contemporary IoT ecosystems. In response to these limitations, this paper propounds a breakthrough model that leverages the power of Deep Dyna Q integrated with a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN). This fusion of cutting-edge technologies not only enhances energy efficiency but also ushers in a new era of intelligent data routing within IoT deployments. The chosen methodology is deliberate; Deep Dyna Q harnesses the power of reinforcement learning to dynamically adapt routing decisions, while the LSTM-based RNN augments the model's ability to capture contextual dependencies crucial in IoT scenarios. This synergy engenders superior energy optimization, transforming the way data is routed in IoT edge devices & samples. The results are resoundingly positive, with empirical evidence showcasing the model's prowess. Tested across multiple contextual datasets, our approach exhibits a remarkable 10.5% improvement in energy efficiency, a commendable 8.5% boost in speed, a 3.9% uptick in throughput, a 4.5% surge in packet delivery ratio, and a notable 4.9% enhancement in consistency when compared against existing methods. Such substantial improvements underscore the transformative impact of our work, setting new standards for energy-efficient data routing in IoT deployments.

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Section: Review Of Existing Modelsmentioning

confidence: 99%