Efficient Multiple Green Energy Base Stations Far-Field Wireless Charging for Mobile IoT Devices

Sha, Qiuyu; Liu, Xilong; Ansari, Nirwan

doi:10.1109/jiot.2022.3232091

Cited by 18 publications

(12 citation statements)

References 41 publications

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“…In order to assess the efficacy of the proposed model for energy-efficient machine learning in IoT edge devices, comprehensive experimentation was conducted across multiple contextual datasets. The model's performance was meticulously compared against three well-established methods, denoted as [2], [8], and [12]. The outcomes of these experiments, as depicted in the following tables, illuminate the transformative impact of the proposed model on energy efficiency, speed, and overall performance in the IoT edge environment sets.…”

Section: Results Analysismentioning

confidence: 99%

“…Table 1 presents a comparative analysis of energy efficiency achieved by the proposed model and the three benchmark methods. Notably, the proposed model demonstrates a substantial improvement in energy efficiency, outperforming [2], [8], and [12] by 12.4%, 20.7%, and 16.3%, respectively. This enhancement in energy efficiency signifies the model's potential to significantly extend the operational lifespan of IoT edge devices, reducing the need for frequent energy replenishment.…”

Section: Methodsmentioning

confidence: 96%

“…[12] Sha et al introduced "Efficient Multiple Green Energy Base Stations Far-Field Wireless Charging for Mobile IoT Devices" [12], discussing efficient wireless charging for IoT devices, which is pertinent to our paper's energy optimization context.…”

Section: Review Of Existing Modelsmentioning

confidence: 99%

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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: Results Analysismentioning

confidence: 99%

Section: Methodsmentioning

confidence: 96%

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

Rosemaro,

Pandit

2023

RJCSE

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“…The full disconnection of WET from the fuelbased grid not only has a positive impact on the [4] Efficient WET PBs' deployment, efficient beamforming, enhanced RF-EH designs X X [10] Massive WET PBs' (devices') architecture & deployment, programmable medium, X X resource scheduling, distributed ledger technology [17] Security Blockchain, contract theory, lightweight consensus protocol X [11] Green IoT Ambient EH, WET, wired energy trading X [12] Green WET Ambient EH, green PBs deployment X X [13] Green WET Green PBs deployment, millimeter wave communications X [16] Efficient WET Concurrent ambient EH and WET to charge IoT devices X [14] Green WET Green PBs deployment, dynamic PBs-to-IoT devices association X X [5] Efficient WET Directional antennas, dynamic PBs-to-IoT devices association X [7] Efficient WET Trajectory planning of mobile PB X X [8] Efficient WET Trajectory planning of mobile PB, directional antennas X X [6] Efficient WET Optimal deployment of PBs, directional antennas X X [9] Efficient WET Trajectory optimization of a flying PB, intelligent reflective surface X X [15] Green WET Distributed green energy storage, nomadic WET X X This work Sustainable WET Green WET, secure energy transactions, gPBs's flexible deployment, X X X minimize the overall expenses, ubiquitous charging with minimum RF pollution environment but also encourages the use of selfsufficient PBs which can provide a ubiquitous charging service, as we discuss next.…”

Section: Disconnecting Wet From the Gridmentioning

confidence: 99%

On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer

Rosabal

López

Alves

et al. 2021

IEEE Internet Things J.

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Wireless energy transfer (WET) is emerging as an enabling green technology for Internet of Things (IoT) networks. WET allows the IoT devices to wirelessly recharge their batteries with energy from external sources such as dedicated radio frequency transmitters called power beacons (PBs). In this paper, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint. Optimal positions for the PBs are determined by maximizing the average incident power for the worst location in the service area since no information about the sensor deployment is provided. Such network planning guarantees the fairest harvesting performance for all the IoT devices. Numerical simulations evidence that our proposed optimization framework improves the energy supply reliability compared to benchmark schemes. Additionally, we show that although both, the number of deployed PBs and the number of antennas per PB, introduce performance improvements, the former has a dominant role. Finally, our proposal allows to extend the coverage area while keeping the total power budget fixed, which additionally reduces the level of electromagnetic radiation in the vicinity of PBs.

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“…One of the primary obstacles in establishing such communication is the presence of physical barriers like walls—structures which can significantly disrupt data flow, diminish detection accuracy, and affect the system’s overall performance. These buildings are characterized by their integration of IoT (Internet of Things) technologies, facilitating advanced automation and real-time control over various subsystems, such as energy management, security, and environmental controls [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Also, it is highly anticipated that 5G and 6G communication technologies are utilized in smart buildings and expansive spaces [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Smart Building Surveillance Systems in Thin Walls: An Efficient Barrier Design

Lee,

Kim

2024

Sensors

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This paper introduces an efficient barrier model for enhancing smart building surveillance in harsh environment with thin walls and structures. After the main research problem of minimizing the total number of wall-recognition surveillance barriers, we propose two distinct algorithms, Centralized Node Deployment and Adaptation Node Deployment, which are designed to address the challenge by strategic placement of surveillance nodes within the smart building. The Centralized Node Deployment aligns nodes along the thin walls, ensuring consistent communication coverage and effectively countering potential disruptions. Conversely, the Adaptation Node Deployment begins with random node placement, which adapts over time to ensure efficient communication across the building. The novelty of this work is in designing a novel barrier system to achieve energy efficiency and reinforced surveillance in a thin-wall environment. Instead of a real environment, we use an ad hoc server for simulations with various scenarios and parameters. Then, two different algorithms are executed through those simulation environments and settings. Also, with detailed discussions, we provide the performance analysis, which shows that both algorithms deliver similar performance metrics over extended periods, indicating their suitability for long-term operation in smart infrastructure.

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Efficient Multiple Green Energy Base Stations Far-Field Wireless Charging for Mobile IoT Devices

Cited by 18 publications

References 41 publications

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

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

On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer

Enhancing Smart Building Surveillance Systems in Thin Walls: An Efficient Barrier Design

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