A Novel Six-Dimensional Chimp Optimization Algorithm—Deep Reinforcement Learning-Based Optimization Scheme for Reconfigurable Intelligent Surface-Assisted Energy Harvesting in Batteryless IoT Networks DOI Creative Commons
Mehrdad Shoeibi,

Anita Ershadi Oskouei,

Masoud Kaveh

и другие.

Future Internet, Год журнала: 2024, Номер 16(12), С. 460 - 460

Опубликована: Дек. 6, 2024

The rapid advancement of Internet Things (IoT) networks has revolutionized modern connectivity by integrating many low-power devices into various applications. As IoT expand, the demand for energy-efficient, batteryless becomes increasingly critical sustainable future networks. These play a pivotal role in next-generation applications reducing dependence on conventional batteries and enabling continuous operation through energy harvesting capabilities. However, several challenges hinder widespread adoption devices, including limited transmission range, constrained resources, low spectral efficiency receivers. To address these limitations, reconfigurable intelligent surfaces (RISs) offer promising solution dynamically manipulating wireless propagation environment to enhance signal strength improve In this paper, we propose novel deep reinforcement learning (DRL) algorithm that optimizes phase shifts RISs maximize network’s achievable rate while satisfying devices’ constraints. Our DRL framework leverages six-dimensional chimp optimization (6DChOA) fine-tune hyper-parameters, ensuring efficient adaptive learning. proposed 6DChOA-DRL RIS received power mitigating interference from direct RIS-cascaded links. simulation results demonstrate our optimized design significantly improves data rates under system configurations. Compared benchmark algorithms, approach achieves higher gains harvested power, an improvement at transmit 20 dBm, lower root mean square error (RMSE) 0.13 compared 3.34 standard RL 6.91 DNN, indicating more precise shifts.

Язык: Английский

A review of chalcogenide-based perovskites as the next novel materials: Solar cell and optoelectronic applications, catalysis and future perspectives DOI
George G. Njema, Joshua K. Kibet

Next Nanotechnology, Год журнала: 2024, Номер 7, С. 100102 - 100102

Опубликована: Сен. 11, 2024

Язык: Английский

Процитировано

23
Promises and challenges of indoor photovoltaics DOI Creative Commons
G. Krishnamurthy Grandhi, George Koutsourakis, James C. Blakesley

и другие.

Опубликована: Янв. 29, 2025

Язык: Английский

Процитировано

5
Silicon-perovskite combinational solar cells degradation phenomena for futuristic IOTs powering DOI Creative Commons

Pratibha Giri,

J. P. Tiwari

Deleted Journal, Год журнала: 2025, Номер 7(2)

Опубликована: Янв. 17, 2025

Internet of Things (IoTs) nodes are presently powered through primary and secondary batteries, which face the issue recharging replacing within a limited life span battery. Hence, scientists researchers searching for alternative powering sources or supporting by default battery power. Organic perovskite solar cells (OSCs/PSCs) presented as an to IoT nodes. Another option may be based on silicon-perovskite combinational heterojunction cells, better concerning OSCs/PSCs stability OSCs/PSCs, is hampering commercialization source Herein, case degradation structure Al/Si/MAPbI3/Ag having device parameters such Voc ~ 0.41 ± 0.05, Jsc 3.93 FF 66.56 0.05 efficiency 1.08 will in ambient environment sunshine November 2021 97 h capital city India New Delhi. The T80 tested background under sunlight exposure 4 only, five days indoor testing conditions. observation shows all-characteristic parameter decay with respect lab conditions, only fill factor (FF) driven degradation. analyzed XRD, SEM, PL, EDAX studies degraded fresh devices.

Язык: Английский

Процитировано

1
Advances in Energy Harvesting for Sustainable Wireless Sensor Networks: Challenges and Opportunities DOI Open Access
Muhammad Umer Mushtaq, Hein S. Venter, Avinash Singh

и другие.

Hardware, Год журнала: 2025, Номер 3(1), С. 1 - 1

Опубликована: Фев. 20, 2025

Energy harvesting wireless sensor networks (EH-WSNs) appear as the fundamental backbone of research that attempts to expand lifespan and efficiency positioned in resource-constrained environments. This review paper provides an in-depth examination latest developments this area, highlighting important components comprising routing protocols, energy management plans, cognitive radio applications, physical layer security (PLS), EH approaches. Across a well-ordered investigation these features, article clarifies notable technology, highlights recent barriers, inquires avenues for future revolution. starts by furnishing detailed analysis different methodologies, incorporating solar, thermal, kinetic, frequency (RF) energy, their respective efficacy non-identical operational circumstances. It also inspects state-of-the-art techniques aimed at optimizing consumption storage guarantee network operability. Moreover, integration into EH-WSNs is acutely assessed, its capacity improve spectrum tackle associated technological problems. The present work investigates ground-breaking methodologies PLS uses energy-harvesting measures data security. In article, are explored with respect classical encryption discussed from points view well.The assessment furthers criticizes traditional protocols significance well balance has long been sought between space. closes importance continuous existing challenges leverage newly available means highlighted document. order adequately serve increasingly changing requirements EH-WSNs, will should be geared towards AI some advanced solutions. discusses novel interdisciplinary advancements better performance, security, sustainability WSNs.

Язык: Английский

Процитировано

0
Interfacial positioning of LiI-doped PEDOT:PSS in homojunction bilayer towards hole extraction and perovskite growth for solar cells DOI Creative Commons
Chien‐Tsung Wang,

Kuo-Sheng Chung

Results in Surfaces and Interfaces, Год журнала: 2025, Номер unknown, С. 100506 - 100506

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Benzo[b]thiophene-Series Solid Additives for Improving the Morphology and Photovoltaic Performance of Organic Solar Cells DOI
Zhenjun Shan, Xiao Li, Zhimin Zhang

и другие.

ACS Applied Polymer Materials, Год журнала: 2025, Номер unknown

Опубликована: Май 20, 2025

Язык: Английский

Процитировано

0
Zel+: Wearable Net-Zero-Energy Lifelogging Using Heterogeneous Energy Harvesters for Sustainable Context Sensing DOI

Mitsuru Arita,

Yugo Nakamura, Shigemi Ishida

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Future of PSC DOI
P. Arjun Suresh,

K. V. Arun Kumar,

Ann Rose Abraham

и другие.

Опубликована: Май 23, 2025

Процитировано

0
Real-Time Energy-Efficient framework for Multi-Source Harvesting and Adaptive Communication IIoT Networks DOI
Koushik Sinha,

Hosam Alden Riyadh,

Y. Mohana Roopa

и другие.

Sustainable Computing Informatics and Systems, Год журнала: 2025, Номер unknown, С. 101150 - 101150

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
A Novel Six-Dimensional Chimp Optimization Algorithm—Deep Reinforcement Learning-Based Optimization Scheme for Reconfigurable Intelligent Surface-Assisted Energy Harvesting in Batteryless IoT Networks DOI Creative Commons
Mehrdad Shoeibi,

Anita Ershadi Oskouei,

Masoud Kaveh

и другие.

Future Internet, Год журнала: 2024, Номер 16(12), С. 460 - 460

Опубликована: Дек. 6, 2024

The rapid advancement of Internet Things (IoT) networks has revolutionized modern connectivity by integrating many low-power devices into various applications. As IoT expand, the demand for energy-efficient, batteryless becomes increasingly critical sustainable future networks. These play a pivotal role in next-generation applications reducing dependence on conventional batteries and enabling continuous operation through energy harvesting capabilities. However, several challenges hinder widespread adoption devices, including limited transmission range, constrained resources, low spectral efficiency receivers. To address these limitations, reconfigurable intelligent surfaces (RISs) offer promising solution dynamically manipulating wireless propagation environment to enhance signal strength improve In this paper, we propose novel deep reinforcement learning (DRL) algorithm that optimizes phase shifts RISs maximize network’s achievable rate while satisfying devices’ constraints. Our DRL framework leverages six-dimensional chimp optimization (6DChOA) fine-tune hyper-parameters, ensuring efficient adaptive learning. proposed 6DChOA-DRL RIS received power mitigating interference from direct RIS-cascaded links. simulation results demonstrate our optimized design significantly improves data rates under system configurations. Compared benchmark algorithms, approach achieves higher gains harvested power, an improvement at transmit 20 dBm, lower root mean square error (RMSE) 0.13 compared 3.34 standard RL 6.91 DNN, indicating more precise shifts.

Язык: Английский

Процитировано

1