Decentralized Machine Learning Framework for the Internet of Things: Enhancing Security, Privacy, and Efficiency in Cloud-Integrated Environments DOI Open Access

José Guilherme Rezende Ramos Salles Gonçalves,

Muhammad Shoaib Ayub, Аinur Zhumadillayeva

и другие.

Electronics, Год журнала: 2024, Номер 13(21), С. 4185 - 4185

Опубликована: Окт. 25, 2024

The Internet of things (IoT) presents unique challenges for the deployment machine learning (ML) models, particularly due to constraints on computational resources, necessity decentralized processing, and concerns regarding security privacy in interconnected environments such as cloud. In this paper, a novel ML framework is proposed IoT characterized by wireless communication, dynamic data streams, integration with cloud services. integrates incremental algorithms robust model exchange protocol, ensuring that preserved, while enabling devices participate collaborative from distributed across networks. By incorporating gossip-based communication ensures energy-efficient, scalable, secure exchange, fostering effective knowledge sharing among devices, addressing potential threats inherent cloud-based ecosystems. framework’s performance was evaluated through simulations, demonstrating its ability handle complexities real-time processing resource-constrained environments, also mitigating risks within

Язык: Английский

TinyML: Tools, applications, challenges, and future research directions DOI
Rakhee Kallimani, Krishna Pai, Prasoon Raghuwanshi

и другие.

Multimedia Tools and Applications, Год журнала: 2023, Номер 83(10), С. 29015 - 29045

Опубликована: Сен. 9, 2023

Язык: Английский

Процитировано

34
Exploring the Potential of Distributed Computing Continuum Systems DOI Creative Commons
Praveen Kumar Donta, Ilir Murturi, Víctor Casamayor Pujol

и другие.

Computers, Год журнала: 2023, Номер 12(10), С. 198 - 198

Опубликована: Окт. 2, 2023

Computing paradigms have evolved significantly in recent decades, moving from large room-sized resources (processors and memory) to incredibly small computing nodes. Recently, the power of has attracted almost all current application fields. Currently, distributed continuum systems (DCCSs) are unleashing era a paradigm that unifies various resources, including cloud, fog/edge computing, Internet Things (IoT), mobile devices into seamless integrated continuum. Its infrastructure efficiently manages diverse processing loads ensures consistent user experience. Furthermore, it provides holistic solution meet modern needs. In this context, paper presents deeper understanding DCCSs’ potential today’s environment. First, we discuss evolution up DCCS. The general architectures, components, discussed, benefits limitations each analyzed. After that, our discussion continues constitute part DCCS achieve computational goals futuristic applications. addition, delve key features perspective provide comprehensive overview emerging applications (with case study analysis) desperately need architectures perform their tasks. Finally, describe open challenges possible developments be made unleash its widespread for majority

Язык: Английский

Процитировано

23
REVIEW ON THE EVOLUTION AND IMPACT OF IOT-DRIVEN PREDICTIVE MAINTENANCE: ASSESSING ADVANCEMENTS, THEIR ROLE IN ENHANCING SYSTEM LONGEVITY, AND SUSTAINABLE OPERATIONS IN BOTH MECHANICAL AND ELECTRICAL REALMS DOI Creative Commons

Joachim Osheyor Gidiagba,

Nwabueze Kelvin Nwaobia,

Preye Winston Biu

и другие.

Computer Science & IT Research Journal, Год журнала: 2024, Номер 5(1), С. 166 - 189

Опубликована: Янв. 15, 2024

This study provides a comprehensive review of the evolution and impact Internet Things (IoT)-driven predictive maintenance, focusing on advancements in technology, their role enhancing system longevity, promoting sustainable operations mechanical electrical systems. The primary objective was to assess how IoT integration has transformed traditional maintenance approaches, leading improved durability reliability. Utilizing systematic literature methodology, involved sourcing data from peer-reviewed journals, conference proceedings, industry reports. A content analysis approach employed analyze data, themes such as technological advancements, sustainability considerations, industry-specific applications maintenance. Key findings reveal significant applications, particularly advanced analytics, artificial intelligence, machine learning strategies. These have led more accurate timely interventions, contributing enhanced longevity operational efficiency. also highlights emergence green practices challenges opportunities future landscape concludes that IoT-driven is pivotal for industrial operations, with lying addressing through innovative solutions robust regulatory frameworks. Recommendations policy include fostering prioritizing energy Future research directions involve exploring emerging technologies investigating long-term environmental impacts deployments. Keywords: Predictive Maintenance, System Longevity, Sustainable Operations, Things.

Язык: Английский

Процитировано

11
Shaping Sustainable Futures: Public Policies and Renewable Energy Insights Based on Global Bibliometric Analysis DOI Open Access
Armenia Androniceanu, Cristina Veith, Ștefan Alexandru Ionescu

и другие.

Sustainability, Год журнала: 2024, Номер 16(12), С. 4957 - 4957

Опубликована: Июнь 10, 2024

The paradigm of sustainable energy is gaining ground at the historical juncture present worldwide push for development. This being driven by latest technological advancements and a maturing process public policy evolution toward support transition. paper analyzes, with bibliometric analysis, specialized literature in order to capture main themes interest, as well their evolution, thus offering panoramic view research trends significance implementing correct environmental measures policies. Covering period from 1991 2024, our exploration filters 2990 articles Web Science database using query that intersects “sustainable energy”, “renewable development”, nuanced consideration political landscape shapes these domains. Using advanced capabilities R program, methodology employed facilitates workflow extraction allowing detailed examination proliferation over decades. provides significant results, demonstrating increasing impact through international collaborations, importance high-impact journals on sustainability policies, growing focus energy” “CO2 emissions”. analysis relevance term groups development correlation between economic growth CO2 emissions confirms emerging trends. Furthermore, critical directions future necessity formulating coherent policies are highlighted.

Язык: Английский

Процитировано

8
The Role of Lightweight AI Models in Supporting a Sustainable Transition to Renewable Energy: A Systematic Review DOI Creative Commons
Tymoteusz Miller, Irmina Durlik, Ewelina Kostecka

и другие.

Energies, Год журнала: 2025, Номер 18(5), С. 1192 - 1192

Опубликована: Фев. 28, 2025

The transition from fossil fuels to renewable energy (RE) sources is an essential step in mitigating climate change and ensuring environmental sustainability. However, large-scale deployment of renewables accompanied by new challenges, including the growing demand for rare-earth elements, need recycling end-of-life equipment, rising footprint digital tools—particularly artificial intelligence (AI) models. This systematic review, following Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines, explores how lightweight, distilled AI models can alleviate computational burdens while supporting critical applications systems. We examined empirical conceptual studies published between 2010 2024 that address energy, circular economy paradigm, model distillation low-energy techniques. Our findings indicate adopting significantly reduce consumption data processing, enhance grid optimization, support sustainable resource management across lifecycle infrastructures. review concludes highlighting opportunities challenges policymakers, researchers, industry stakeholders aiming integrate principles into RE strategies, emphasizing urgent collaborative solutions incentivized policies encourage low-footprint innovation.

Язык: Английский

Процитировано

1
Revisiting Edge AI: Opportunities and Challenges DOI
Tobias Meuser, Lauri Lovén, Monowar Bhuyan

и другие.

IEEE Internet Computing, Год журнала: 2024, Номер 28(4), С. 49 - 59

Опубликована: Июль 1, 2024

Язык: Английский

Процитировано

5
Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT Communication DOI Open Access

Clément Rup,

Eddy Bajic

Sustainability, Год журнала: 2024, Номер 16(3), С. 1160 - 1160

Опубликована: Янв. 30, 2024

The industrial Internet of things (IIoT) is a major lever in Industry 4.0 development, where reducing the carbon footprint and energy consumption has become crucial for modern companies. Today’s IIoT device infrastructure wastes large amounts on wireless communication, limiting lifetime increasing power battery requirements. Communication capabilities seriously affect responsiveness availability autonomous IoT devices when collecting data retrieving commands to/from higher-level applications. Thus, objective optimizing communication remains paramount; addition to typical optimization methods, such as algorithms protocols, new concept emerging, known wake-up radio (WuR). WuR provides novel on-demand schemes that can increase efficiency. By expanding lifespan while maintaining high reactivity performance, approach paves way “place-and-forget” deployment methodology combines small with an extended highly responsive functionality. technology, applied devices, facilitates green IIoT, thereby enabling emergence (OD-IoT) concept. This article presents analysis state-of-the-art technology within paradigm details OD-IoT Furthermore, this review overview applications their impact including relevant industry use cases. Finally, we describe our experimental performance evaluation WuR-enabled commercially available off shelf. Specifically, focused range consumption, successfully demonstrating applicability strong potential it benefits offers sustainable systems.

Язык: Английский

Процитировано

4
Featuring Wave and Tidal Energy Transformation With Artificial Intelligence and Machine Learning in Urban Growth and Living DOI
Bhupinder Singh

Advances in environmental engineering and green technologies book series, Год журнала: 2025, Номер unknown, С. 395 - 420

Опубликована: Фев. 21, 2025

Harnessing raw energy from the sea for sustainable urban living, driven by Artificial Intelligence (AI) and Machine Learning (ML), through wave tidal conversion could be a paradigm-shifting breakthrough. These renewable sources are able to utilize non-stop movement of oceans tides, which will work in decreasing carbon footprints cities. Through AI ML algorithms, capture, storage, distribution process is made way efficient predicting patterns or enhancing grid integration. Together, these technologies provide fast online decisions, dependability scalability units. AI-based solutions waves conversion, therefore, can become key signaling point addressing an ever-increasing demand most modern-day infrastructural platforms as well means forces global climate change mitigation consider their toward providing smarter greener futures our communities.

Язык: Английский

Процитировано

0
Solar-powered light-modulated microwave programmable metasurface for sustainable wireless communications DOI Creative Commons
Han Wei Tian,

Ya Lun Sun,

Xin Ge Zhang

и другие.

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Март 14, 2025

Programmable metasurface holds big promise in wireless communications by virtue of its powerful capability controlling electromagnetic waves. However, challenges exist for the programmable achieving self-sufficient renewable energy supply and flexible reliable multi-domain information transmissions. Here, we report a solar-powered light-modulated microwave (SLMPM) integrating photovoltaic module to acquire from modulated light sunlight simultaneously. Such an SLMPM enables direct, real-time, transmissions domains under direct exposure, with flexibility implement various modulation schemes. Its low power consumption on-board harvesting allows 24 hours light-to-microwave transmission 8 sole input. A hybrid communication system real-time image is demonstrated show outstanding features SLMPM. We believe that can contribute sustainable advancement future communications, rendering them more cost-effective, energy-efficient, environment-friendly, ubiquitous. Achieving sustainability crucial yet poses significant challenges. To address this, authors propose demonstrate enabling communications.

Язык: Английский

Процитировано

0
Building Sustainable Internet of Things Ecosystem Connectivity DOI

Nallam Vani Annapurna Bhavani,

Bhupendra Kumar, Chandan Kumar Sahoo

и другие.

Advances in computer and electrical engineering book series, Год журнала: 2025, Номер unknown, С. 53 - 76

Опубликована: Фев. 7, 2025

This chapter explores the integration of internet things with sustainable connectivity, leveraging synergies between deep learning, cloud, and edge computing. The increasing scale complexity IoT systems necessitates efficient data processing, real-time analytics, low-latency response. throws light on how techniques learning in analysis led to smart decision-making adaptive behavior based dynamic environments. text discusses use cloud computing for big storage computation, while emphasizes local computation network traffic ease delays, addressing challenges related energy use, resource utilization, resilient networks. these technologies provides a framework designing sustainable, connected ecosystems that enhance operational efficiency minimize environmental impact.

Язык: Английский

Процитировано

0