Metaverse for Intelligent Transportation Systems (ITS): A Comprehensive Review of Technologies, Applications, Implications, Challenges and Future Directions

IEEE Transactions on Intelligent Transportation Systems, Journal Year: 2024, Volume and Issue: 25(7), P. 6290 - 6308

Published: Jan. 16, 2024

Intelligent transportation systems (ITS) have made significant advancements in enhancing safety, reliability, and efficiency. However, challenges persist security, privacy, data management, integration. Metaverse, an emerging technology enabling immersive simulated experiences, presents promising solutions to overcome these challenges. By establishing secure communication channels, facilitating virtual simulations for safe testing training, centralized management with real-time analytics, metaverse offers a transformative approach address While has found extensive applications across industries, its potential remains largely untapped. This comprehensive review delves into the integration of ITS, exploring key technologies like reality, digital twin, blockchain, artificial intelligence, their specific context ITS. Real-world case studies, research projects, initiatives are compiled showcase metaverse's It also examines societal, economic, technological implications ITS highlights associated Lastly, future directions identified unlock full systems.

Language: Английский

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Blockchain-Enhanced Sensor-as-a-Service (SEaaS) in IoT: Leveraging Blockchain for Efficient and Secure Sensing Data Transactions

Information, Journal Year: 2024, Volume and Issue: 15(4), P. 212 - 212

Published: April 10, 2024

As the Internet of Things (IoT) continues to revolutionize value-added services, its conventional architecture exhibits persistent scalability and security vulnerabilities, jeopardizing trustworthiness IoT-based services. These architectural limitations hinder IoT’s Sensor-as-a-Service (SEaaS) model, which enables commercial transmission sensed data through cloud platforms. This study proposes an innovative computational framework that integrates decentralized blockchain technology into IoT design, specifically enhancing SEaaS efficiency. research contributes optimized with operations simplified public key encryption. Furthermore, this introduces advanced model featuring trading for among diverse stakeholders. At core, presents a unique blockchain-based data-sharing mechanism manages multiple aspects, from enrollment validation. Evaluations conducted in standard Python environment indicate proposed outperforms existing models, demonstrating approximately 40% less energy consumption, 18% increased throughput, 16% reduced latency, 25% reduction algorithm processing time. Ultimately, integrating lightweight authentication using cryptography within establishes model’s potential efficient secure IoT.

Language: Английский

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Navigating the Sea of Data: A Comprehensive Review on Data Analysis in Maritime IoT Applications

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(17), P. 9742 - 9742

Published: Aug. 28, 2023

The Internet of Things (IoT) is significantly transforming the maritime industry, enabling generation vast amounts data that can drive operational efficiency, safety, and sustainability. This review explores role potential analysis in IoT applications. Through a series case studies, it demonstrates real-world impact analysis, from predictive maintenance to efficient port operations, improved navigation environmental compliance. also discusses benefits limitations highlights emerging trends future directions field, including growing application AI Machine Learning techniques. Despite promising opportunities, several challenges, quality, complexity, security, cost, interoperability, need be addressed fully harness IoT. As industry continues embrace becomes critical focus on overcoming these challenges capitalizing opportunities improve operations.

Language: Английский

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Real-Time Anomaly Detection for Water Quality Sensor Monitoring Based on Multivariate Deep Learning Technique

Sensors, Journal Year: 2023, Volume and Issue: 23(20), P. 8613 - 8613

Published: Oct. 20, 2023

With the increased use of automated systems, Internet Things (IoT), and sensors for real-time water quality monitoring, there is a greater requirement timely detection unexpected values. Technical faults can introduce anomalies, large incoming data rate might make manual erroneous difficult. This research introduces applies pioneering technology, Multivariate Multiple Convolutional Networks with Long Short-Term Memory (MCN-LSTM), to monitoring. MCN-LSTM cutting-edge deep learning technology designed address difficulty detecting anomalies in complicated time series data, particularly monitoring real-world setting. The growing reliance on sensor networks continuous driving development deployment approach. As these technologies become more widely used, rapid precise identification or aberrant points becomes critical. difficulties, inherent noise, high influx pose significant hurdles anomaly processes. technique takes advantage by integrating networks. combination approaches offers efficient effective multivariate allowing identifying flagging patterns values that may signal issues. Water have far-reaching repercussions, influencing future analyses leading incorrect judgments. Anomaly must be avoid inaccurate findings ensure integrity tests. Extensive tests were carried out validate utilizing information obtained from installed scenarios. results studies proved MCN-LSTM’s outstanding efficacy, an impressive accuracy 92.3%. level precision demonstrates technique’s capacity discriminate between normal abnormal instances real time. big step forward It improve decision-making processes reduce adverse outcomes caused undetected abnormalities. unique has promise defending human health maintaining environment era systems IoT contributing safety sustainability supplies.

Language: Английский

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LoRaWAN-based hybrid internet of wearable things system implementation for smart healthcare

Internet of Things, Journal Year: 2024, Volume and Issue: 25, P. 101124 - 101124

Published: Feb. 15, 2024

This study introduces the design and development of an Internet Wearable Things-based Hybrid Healthcare Monitoring System (IoWT-HHMS) for smart medical applications. The system incorporates wearable sensing units real-time, remote monitoring vital health parameters such as Blood Pressure (BP), Heart Rate (HR), Body Temperature (BT). A key innovation is a hybrid wireless network communication mechanism within IoWT-HHMS, utilizing FiPy microcontroller. supports both short- long-range connectivity integrates algorithm efficient data acquisition updating to IoT platform. IoWT-HHMS has undergone extensive testing validation across various scenarios, including sensor functionality, performance Wi-Fi LoRaWAN networks, connectivity, accuracy assessment using Datacake dashboard. tests evaluated crucial aspects reliability, power consumption, latency. results demonstrate system's high stability in reading parameters. Comparisons with reference devices reveal impressive levels Systolic BP (SBP), Diastolic (DBP), HR, BT, recording 96.37%, 95.17%, 97%, 98.57% accuracy, respectively. Both networks proved reliable indoor outdoor settings, maintaining transmission over distances up 1.5 km without loss. In conclusion, developed shows great promise effective real-time patients' conditions innovative mechanism.

Language: Английский

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Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

Batteries, Journal Year: 2024, Volume and Issue: 10(6), P. 204 - 204

Published: June 13, 2024

In recent years, the rapid evolution of transportation electrification has been propelled by widespread adoption lithium-ion batteries (LIBs) as primary energy storage solution. The critical need to ensure safe and efficient operation these LIBs positioned battery management systems (BMS) pivotal components in this landscape. Among various BMS functions, state temperature monitoring emerge paramount for intelligent LIB management. This review focuses on two key aspects health management: accurate prediction (SOH) estimation remaining useful life (RUL). Achieving precise SOH predictions not only extends lifespan but also offers invaluable insights optimizing usage. Additionally, RUL is essential estimation, especially demand electric vehicles continues surge. highlights significance machine learning (ML) techniques enhancing while simultaneously reducing computational complexity. By delving into current research field, aims elucidate promising future avenues leveraging ML context LIBs. Notably, it underscores increasing necessity advanced their role addressing challenges associated with burgeoning vehicles. comprehensive identifies existing proposes a structured framework overcome obstacles, emphasizing development machine-learning applications tailored specifically rechargeable integration artificial intelligence (AI) technologies endeavor pivotal, researchers aspire expedite advancements performance present limitations adopting symmetrical approach, harmonizes management, contributing significantly sustainable progress electrification. study provides concise overview literature, offering state, prospects, utilizing monitoring.

Language: Английский

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Radiometric Infrared Thermography of Solar Photovoltaic Systems: An Explainable Predictive Maintenance Approach for Remote Aerial Diagnostic Monitoring

Smart Cities, Journal Year: 2024, Volume and Issue: 7(3), P. 1261 - 1288

Published: May 28, 2024

Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels susceptible to thermal degradation defects that can impact their performance, thereby necessitating timely accurate fault detection maintain optimal generation. The considered case study focuses on an intelligent diagnosis (IFDD) system for the analysis radiometric infrared thermography (IRT) in a predictive maintenance setting, enabling remote inspection diagnostic monitoring power plant sites. proposed IFDD employs custom-developed deep learning approach which relies convolutional neural networks effective multiclass classification defect types. is challenging task issues such as IRT data scarcity, defect-patterns’ complexity, low image acquisition quality due noise calibration issues. Hence, this research carefully prepares customized high-quality but severely imbalanced six-class thermographic dataset panels. With respect previous approaches, numerical temperature values floating-point used train validate models. trained models display high accuracy efficient anomaly diagnosis. Finally, create trust system, process underlying model investigated with perceptive explainability, portraying most discriminant features, mathematical-structure-based interpretability, achieve feature clustering.

Language: Английский

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Latest Innovations in Internet of Things (IoT): Digital Transformation Across Industries

INNOVATIVE Journal Of Social Science Research, Journal Year: 2024, Volume and Issue: 4(3), P. 1027 - 1037

Published: May 6, 2024

This research article explores recent advances in IoT technology and its huge impact on various sectors. method uses a qualitative approach involving in-depth interviews thematic analysis. key innovations such as wearable devices, smart medical predictive maintenance systems, IoT-based transportation solutions. The findings highlight how these are driving digital transformation across industries, leading to improved healthcare delivery, increased manufacturing efficiency, optimized operations, sustainable agricultural practices, personalized retail experiences. study also aligns empirical with theoretical frameworks regarding IoT, transformation, industry-specific applications, emphasizing the strategic importance of leveraging catalyst for innovation, competitiveness, value creation. provides valuable insights businesses, policymakers researchers looking leverage drive achieve growth industries.

Language: Английский

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A Comprehensive Review on Cybersecurity Issues and Their Mitigation Measures in FinTech

Iraqi Journal for Computer Science and Mathematics, Journal Year: 2024, Volume and Issue: 5(3)

Published: Jan. 6, 2024

The fourth industrial revolution has seen the evolution and wide adoption of game-changing disruptive innovation, "financial technologies (FinTech), around globe. However, security FinTech systems networks remains critical. This research paper comprehensively reviews cybersecurity issues their mitigation measures in FinTech. Four independent researchers reviewed relevant literature from IEEE Xplore, ScienceDirect, Taylor & Francis, Emerald Insight, Springer, SAGE, WILEY, Hindawi, MDPI, ACM, Google Scholar. key findings analysis identified privacy issues, data breaches, malware attacks, hacking, insider threats, identity theft, social engineering distributed denial-of-service cryptojacking, supply chain advanced persistent zero-day salami man-in-the-middle SQL injection, brute-force attacks as some significant experienced by industry. review also suggested authentication access control mechanisms, cryptography, regulatory compliance, intrusion detection prevention systems, regular backup, basic training, big analytics, use artificial intelligence machine learning, sandboxes, cloud computing technologies, blockchain fraud for issues. tackling will be paramount if is to realize its full potential. Ultimately, this help develop robust mechanisms achieve sustainable financial inclusion.

Language: Английский

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Channel Characterization and Modeling for VLC-IoE Applications in 6G: A Survey

IEEE Internet of Things Journal, Journal Year: 2024, Volume and Issue: 11(21), P. 34872 - 34895

Published: July 26, 2024

Language: Английский

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