Behavioral Modeling of a Radio Frequency Wireless Power Transfer System for Batteryless Internet of Things Applications DOI Creative Commons

Polyana Camargo de Lacerda,

André Mariano, Glauber Brante

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 86974 - 86984

Published: Jan. 1, 2024

Language: Английский

Semantic Slicing across the Distributed Intelligent 6G Wireless Networks DOI
Lauri Lovén, Hafiz Faheem Shahid, Le Ngu Nguyen

et al.

Published: Sept. 11, 2023

In the age of Internet Things (IoT) and expanding computing continuum, it's crucial to manage share resources at edges networks. This position paper presents a new concept known as 'semantic slicing'. approach harnesses power artificial intelligence (AI), wireless networks, edge computing, sensing technologies enable novel applications, optimize resource allocation, streamline data processing decision-making across complex systems spanning continuum. Semantic slicing applies deep understanding specific application requirements intelligently allocate distribute tasks in strategy allows for creation that are not only more efficient responsive, but also better equipped adapt variety applications services.

Language: Английский

Citations

5
Optimizing beamforming in IoT-WET: A symbiotic-based approach under imperfect channel state information DOI
Mai T. P. Le, Vien Nguyen‐Duy‐Nhat, Hieu V. Nguyen

et al.

Physical Communication, Journal Year: 2024, Volume and Issue: 65, P. 102370 - 102370

Published: April 27, 2024

Language: Английский

Citations

1
Harnessing Bio-Inspired Optimization and Swarm Intelligence for Energy-Aware TinyML in IoT DOI

P. Kalyanakumar,

S. Srinivasa Pandian,

S. Boopalan

et al.

2022 International Conference on Inventive Computation Technologies (ICICT), Journal Year: 2024, Volume and Issue: unknown

Published: April 24, 2024

This research investigates the integration of bio-inspired optimization and swarm intelligence principles with TinyML for development energy-aware Internet Things (IoT) devices. A novel model algorithm, termed "BioSwarmML," is introduced evaluated against existing algorithms through comprehensive simulation analyses employing suitable metrics. The proposed framework aims to enhance energy efficiency in IoT applications by leveraging collective derived from behaviors. "BioSwarmML" algorithm designed draw inspiration natural processes, incorporating techniques such as genetic algorithms, simulated annealing, evolutionary strategies. Concurrently, are integrated emulate decentralized self-organizing behaviors observed biological systems. amalgamation optimize consumption models on devices, facilitating sustainable adaptive learning processes. Simulation involve a comparative study established evaluating BioSwarmML based metrics consumption, accuracy, latency. results demonstrate efficacy achieving superior while maintaining competitive performance terms accuracy responsiveness. comparison sheds light advantages applications, showcasing its potential widespread adoption ecosystems. contributes advancement energy-efficient systems introducing algorithmic paradigm that aligns international journal standards. showcases promising avenue enhancing sustainability TinyML-driven offering valuable addition body knowledge field.

Language: Английский

Citations

1
A Smart Solar Monitoring system using IOT DOI
S. Durgadevi,

Sura Shalini,

Thirupura Sundari

et al.

2022 International Conference on Communication, Computing and Internet of Things (IC3IoT), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 5

Published: April 17, 2024

A smart solar monitoring system using IOT describes a that uses various sensors and devices to monitor control panels' performance. This provides real-time data on energy generation consumption, enabling users optimize usage make informed decisions regarding management. The consists of components, including sensors, microcontrollers, communication protocols, cloud services. These components work together collect analyse data, provide alerts notifications, generate reports use technology in systems improves efficiency, reduces costs, enables remote control, making it an ideal solution for

Language: Английский

Citations

1
Behavioral Modeling of a Radio Frequency Wireless Power Transfer System for Batteryless Internet of Things Applications DOI Creative Commons

Polyana Camargo de Lacerda,

André Mariano, Glauber Brante

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 86974 - 86984

Published: Jan. 1, 2024

Language: Английский

Citations

1