Internet of Things最新文献

{"title":"A review of LoRaWAN performance optimization through cross-layer-based approach for Internet of Things","authors":"Melchizedek Alipio ,&nbsp;Carl Christian Chaguile ,&nbsp;Miroslav Bures","doi":"10.1016/j.iot.2024.101378","DOIUrl":"10.1016/j.iot.2024.101378","url":null,"abstract":"<div><div>The Internet of Things (IoT) is proliferating in technology and automation. Some popular IoT applications include environmental monitoring, home automation, agriculture, aquaculture, healthcare, transportation, and logistics. The construction of the IoT system is determined by where it will be used. The design can be tailor-made for short-range or long-range communication, rural or urban areas, indoor or outdoor applications, real-time or delay-tolerant communication, and much more. Low-Power Wide Area Networks (LPWAN) are frequently used for long-distance, energy-efficient, cost-effective communication. In LPWAN technology, Long-Range Wide Area Network (LoRaWAN) is one of the most popular choices because of its remarkable features and openness, making it highly suitable for IoT applications. However, despite its exceptional features, there are still ways to optimize the system to become more efficient and address several challenges during runtime. One possible way to address issues and challenges in LoRaWAN is through cross-layer optimization. This optimization technique violates the restrictions set by the Open System Interconnection (OSI) model and gives freedom to its protocol layers to communicate depending on the intended purpose. This paper surveys the state-of-the-art cross-layer approaches that optimize LoRaWAN for IoT applications. The cross-layer approaches were categorized according to the layer combinations and architecture. In addition, this paper provided observations on the effects of cross-layer optimization in LoRaWAN. Lastly, possible issues and solutions, challenges, and future directives from cross-layer optimization approaches were included.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101378"},"PeriodicalIF":6.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing real-time intrusion detection and secure key distribution using multi-model machine learning approach for mitigating confidentiality threats","authors":"Ju Lu ,&nbsp;Arindam Bhar ,&nbsp;Arindam Sarkar ,&nbsp;Abdulfattah Noorwali ,&nbsp;Kamal M. Othman","doi":"10.1016/j.iot.2024.101377","DOIUrl":"10.1016/j.iot.2024.101377","url":null,"abstract":"<div><div>Ensuring strong security measures against intrusions is of utmost importance in the ever-changing field of information management systems. Conventional Intrusion Detection Systems (IDS) frequently have difficulties in dealing with the ever-changing and intricate characteristics of contemporary cyber threats, particularly in the realm of the Internet of Things (IoT). The current body of research emphasizes the difficulties in attaining both high precision and real-time speed while still preserving the anonymity of data. This work tackles these concerns by presenting a scalable multi-model Machine Learning (ML) technique developed to improve real-time intrusion detection and ensure safe cryptographic key distribution. The suggested solution takes use of the widespread use of IoT devices, which increases the likelihood of advanced cyberattacks. Our approach involves implementing a ML-based automated IDS specifically designed for various IoT environments. These IDS enhance adaptability and accuracy. We also utilize Maximum–Minimum (Max–Min) normalization on the UNSW-NB15 and CICIoT2023 datasets to improve the accuracy of detecting intrusions. Furthermore, we classify a wide range of contemporary threats and typical internet traffic into nine distinct attack categories. To streamline data processing and improve system efficiency, we employ Principal Component Analysis (PCA) for dimensionality reduction. Additionally, we deploy six advanced ML models to optimize detection capabilities and accurately identify threats. Finally, we develop a secure key distribution mechanism using synchronized Artificial Neural Networks (ANNs). The process of mutual learning guarantees the secure distribution of keys among IoT networks, thus reducing the risks to secrecy. This novel methodology not only reinforces the ability to identify intrusions in real-time, but also improves the overall security stance of information management systems. This work significantly contributes to the field of digital security in information management by addressing the limits of current IDS solutions and presenting a complete, multi-faceted security strategy.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101377"},"PeriodicalIF":6.0,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Single-layer Aggregation Framework for Energy-efficient and Privacy-preserving Load Forecasting in Heterogeneous Federated Smart Grids","authors":"Habib Ullah Manzoor,&nbsp;Atif Jafri,&nbsp;Ahmed Zoha","doi":"10.1016/j.iot.2024.101376","DOIUrl":"10.1016/j.iot.2024.101376","url":null,"abstract":"<div><div>Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating data from distributed load networks while ensuring data privacy. However, the heterogeneous nature of smart grid load forecasting introduces significant challenges that current methods struggle to address, particularly for resource-constrained devices due to high computational and communication demands. To overcome these challenges, we propose a novel Adaptive Single Layer Aggregation (ASLA) framework tailored for resource-constrained smart grid networks. The ASLA framework mitigates data heterogeneity issues by focusing on local learning and incorporating partial updates from local devices for model aggregation in adaptive manner. It is optimized for resource-constrained environments through the implementation of a stopping criterion during model training and weight quantization. Our evaluation on two distinct datasets demonstrates that quantization results in a minimal loss function degradation of 0.01% for Data 1 and 1.25% for Data 2. Furthermore, local model layer optimization for aggregation achieves substantial communication cost reductions of 829.2-fold for Data 1 and 5522-fold for Data 2. The use of an 8-bit fixed-point representation for neural network weights leads to a 75% reduction in storage/memory requirements and decreases computational costs by replacing complex floating-point units with simpler fixed-point units. By addressing data heterogeneity and reducing storage, computation, and communication overheads, the ASLA framework is well-suited for deployment in resource-constrained smart grid networks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101376"},"PeriodicalIF":6.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524003172/pdfft?md5=d25363c62faa252df41a3050d5c0712d&pid=1-s2.0-S2542660524003172-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing security through continuous biometric authentication using wearable sensors","authors":"Laxmi Divya Chhibbar,&nbsp;Sujay Patni,&nbsp;Siddarth Todi,&nbsp;Ashutosh Bhatia,&nbsp;Kamlesh Tiwari","doi":"10.1016/j.iot.2024.101374","DOIUrl":"10.1016/j.iot.2024.101374","url":null,"abstract":"<div><div>The paper presents a novel approach for biometric continuous driver authentication (CDA) for secure and safe transportation using wearable photoplethysmography (PPG) sensors and deep learning. Conventional one-time authentication (OTA) methods, while effective for initial identity verification, fail to continuously verify the driver’s identity during vehicle operation, potentially leading to safety, security, and accountability issues. To address this, we propose a system that employs Long Short-Term Memory (LSTM) models to predict subsequent PPG values from wrist-worn devices and continuously compare them with real-time sensor data for authentication. Our system calculates a confidence level representing the probability that the current user is the authorized driver, ensuring robust availability to genuine users while detecting impersonation attacks. The raw PPG data is directly fed into the LSTM model without pre-processing, ensuring lightweight processing. We validated our system with PPG data from 15 volunteers driving for 15 min in varied conditions. The system achieves an Equal Error Rate (EER) of 4.8%. Our results demonstrate that the system is a viable solution for CDA in dynamic environments, ensuring transparency, efficiency, accuracy, robust availability, and lightweight processing. Thus, our approach addresses the main challenges of classical driver authentication systems and effectively safeguards passengers and goods with robust driver authentication.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101374"},"PeriodicalIF":6.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"iLocator—A low cost IoT-based hybrid architecture for tracking and locating objects in indoor environments","authors":"Lucas Marquezan,&nbsp;Elmer A. Gamboa Peñaloza,&nbsp;Paulo J.D. de Oliveira Evald,&nbsp;Marlon M. Hernandez Cely,&nbsp;Marcelo L. Rossi,&nbsp;Sigmar de Lima","doi":"10.1016/j.iot.2024.101369","DOIUrl":"10.1016/j.iot.2024.101369","url":null,"abstract":"<div><div>The tracking of objects in external environments is well established in the literature. Various approaches are based on the data provided by satellite navigation systems. However, this type of technology does not offer the accuracy and precision needed for the location and tracking of objects inside buildings, factories, hospitals, or any other place that is not open-air. This paper presents a novel hybrid low-cost architecture for locating and tracking objects in indoor environments. The proposed solution is based on the concepts of the internet of things, modularity, and low energy consumption, aiming to enhance the usability of the tracking system by using only off-the-shelf components that are easily purchasable and inexpensive. The experimental results indicate high accuracy and the robustness in sending and receiving data, making it feasible to locate objects in indoor environments with obstacles.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101369"},"PeriodicalIF":6.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust and efficient three-factor authentication solution for WSN-based industrial IoT deployment","authors":"Khalid Mahmood ,&nbsp;Muhammad Asad Saleem ,&nbsp;Zahid Ghaffar ,&nbsp;Salman Shamshad ,&nbsp;Ashok Kumar Das ,&nbsp;Mohammed J.F. Alenazi","doi":"10.1016/j.iot.2024.101372","DOIUrl":"10.1016/j.iot.2024.101372","url":null,"abstract":"<div><div>The relentless advancements in Cyber-Physical Systems (CPS) and Wireless Sensor Networks (WSN) have paved the way for various practical applications across networking, public safety, smart transportation, and industrial sectors. The Industrial Internet of Things (IIoT) integrates these technologies into complex, interconnected environments where vast amounts of data are transmitted between devices and systems. However, the inherent openness of communication channels in IIoT systems introduces distinctive security and privacy vulnerabilities, where malevolent entities can effortlessly intercept, forge, or delete communication messages. These vulnerabilities are exacerbated by the critical nature of industrial applications, where breaches can lead to significant operational disruptions or safety hazards To address these challenges, several authentication protocols have been proposed. Nevertheless, many of these protocols remain susceptible to various security attacks. To ensure privacy and security in IIoT environments, we introduce a robust and efficient authentication protocol for a WSN-based IIoT environment. This protocol preserves the privacy of information transmitted among all entities and provides an effective solution for securing this sensitive information. Additionally, we present a detailed security analysis of the proposed protocol to formally and informally demonstrate its security strength. The performance analysis is carried out to compare the proposed protocol against existing related protocols, with results unequivocally demonstrating that our protocol offers enhanced privacy and security with reduced costs.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101372"},"PeriodicalIF":6.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Environmental noise monitoring using distributed hierarchical wireless acoustic sensor network","authors":"Bo Peng,&nbsp;Kevin I-Kai Wang,&nbsp;Waleed H. Abdulla","doi":"10.1016/j.iot.2024.101373","DOIUrl":"10.1016/j.iot.2024.101373","url":null,"abstract":"<div><div>Acoustic noise pollution is one of many problems people face as cities grow. Long-term noise exposure can result in a series of physical and mental health diseases that are highly harmful to foetuses and newborns. Hence, many IoT-based wireless sensor network systems have been proposed for automated monitoring for long-term operation. However, these systems suffer from weaknesses in functionality, power consumption, costs, and scalability, which hinder large-scale deployment. In this study, we propose a distributed hierarchical wireless acoustic sensor network for environmental noise monitoring to do sound classification and A-weighted sound-pressure-level measurement to address the shortcomings of existing systems. A series of tests and comparisons are performed in diagnosing the performance with respect to recording continuity, packet loss, recording quality, accuracy on A-weighted sound pressure level calculations, and costs. Results show that this proposed network structure is feasible as a part of hardware implementation in a large-scale, low-cost, and high-scalable environmental noise monitoring system to classify sound.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101373"},"PeriodicalIF":6.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524003147/pdfft?md5=a50fda5582490254b77711226f7044b5&pid=1-s2.0-S2542660524003147-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying impact: Bibliometric examination of IoT's evolution in sustainable development","authors":"Marian Stan ,&nbsp;Adriana Dima ,&nbsp;Dag Øivind Madsen ,&nbsp;Cosmin Dobrin","doi":"10.1016/j.iot.2024.101370","DOIUrl":"10.1016/j.iot.2024.101370","url":null,"abstract":"<div><div>The integration of Internet of Things (IoT) and sustainable development (SD) presents innovative solutions to global challenges, reflecting their pivotal roles in shaping future socio-economic and environmental landscapes. This bibliometric study explores the developing research domain of IoT and SD, focusing on their intersection. The extensive literature review section investigates and critically analyses most relevant papers from Web of Science (WoS) database that explore the IoT and SD domain, highlighting their main findings. Based on a WoS dataset of 908 articles from WoS, bibliometric techniques are applied to identify influential topics, scientific production evolution, key contributors, and thematic evolution. Authors like Singh, Gehlot, Akram, and Bibri have substantial publication records in IoT and SD. China leads in article contributions, followed by India, the USA, Spain, and the UK. The study maps the thematic evolution, and identifies emerging themes focusing on management, big data analytics, and environmental aspects like water and food sustainability. Future research directions should reside in interdisciplinary studies that integrate technology into sustainable practices and focus on energy consumption, safety or supply chain management with an emphasis on empirical assessments to understand their real-world impact.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101370"},"PeriodicalIF":6.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524003111/pdfft?md5=30d7d6a0a3dd0b212de1a69f084c6183&pid=1-s2.0-S2542660524003111-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An IoT-based contactless neonatal respiratory monitoring system for neonatal care assistance in postpartum center","authors":"Yi-Chun Du ,&nbsp;Po-Fan Chen ,&nbsp;Wei-Siang Ciou ,&nbsp;Tsung-Wei Lin ,&nbsp;Tsu-Chi Hsu","doi":"10.1016/j.iot.2024.101371","DOIUrl":"10.1016/j.iot.2024.101371","url":null,"abstract":"<div><div>According to previous studies, one of the major causes of 20 % to 25 % of neonatal deaths is respiratory distress syndrome (RDS). Early identification, progressive monitoring, and treatment and/or management of neonatal RDS can substantially increase the rate of survival in neonates. However, global research indicates frequent shortages and burnout among nursing staff, especially in postpartum units, contributing to the difficulty in early identification of RDS in neonates. Clinicians currently use breathing sounds and frequency as key criteria in the Neonatal Resuscitation Program (NRP) for identifying and treating RDS. In practice, the monitoring of respiratory signal abnormalities relies on sensor patches, which frequently detach from the neonates’ slippery skin, leading to potential skin injuries and unstable signal reception. This paper presents an Internet of Things (IoT)-based contactless neonatal respiratory monitoring system that integrates computer vision (CV), beamforming microphone array (BFMA), and millimeter Wave (mmWave) radar, all connected to a cloud platform. Clinical trials revealed that CV-based neonatal feature identification achieved over 96 % accuracy within 40 cm to 120 cm. The neonatal breathing sound strengthening, utilized CV and BFMA, achieved an average sound-to-noise ratio (SNR) of 5.07 dB, and CV with mmWave radar reduced chest displacement signal error from 0.66 to 0.26 BPM. Additionally, survey results showed that doctors and clinical personnel were satisfied with the system's functionality and usability. This demonstrates the system's ability to assist in monitoring respiratory signals of swaddled neonates and in the early identification of neonatal RDS, with further applications in neonatal care at postpartum centers.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101371"},"PeriodicalIF":6.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524003123/pdfft?md5=cbabcee4ddc20698e720a1837b57d21e&pid=1-s2.0-S2542660524003123-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EdgeBus: Co-Simulation based resource management for heterogeneous mobile edge computing environments","authors":"Babar Ali ,&nbsp;Muhammed Golec ,&nbsp;Sukhpal Singh Gill ,&nbsp;Huaming Wu ,&nbsp;Felix Cuadrado ,&nbsp;Steve Uhlig","doi":"10.1016/j.iot.2024.101368","DOIUrl":"10.1016/j.iot.2024.101368","url":null,"abstract":"<div><p>Kubernetes has revolutionized traditional monolithic Internet of Things (IoT) applications into lightweight, decentralized, and independent microservices, thus becoming the de facto standard in the realm of container orchestration. Intelligent and efficient container placement in Mobile Edge Computing (MEC) is challenging subjected to user mobility, and surplus but heterogeneous computing resources. One solution to constantly altering user location is to relocate containers closer to the user; however, this leads to additional underutilized active nodes and increases migration’s computational overhead. On the contrary, few to no migrations are attributed to higher latency, thus degrading the Quality of Service (QoS). To tackle these challenges, we created a framework named EdgeBus<span><span>¹</span></span>, which enables the co-simulation of container resource management in heterogeneous MEC environments based on Kubernetes. It enables the assessment of the impact of container migrations on resource management, energy, and latency. Further, we propose a mobility and migration cost-aware (MANGO) lightweight scheduler for efficient container management by incorporating migration cost, CPU cores, and memory usage for container scheduling. For user mobility, the Cabspotting dataset is employed, which contains real-world traces of taxi mobility in San Francisco. In the EdgeBus framework, we have created a simulated environment aided with a real-world testbed using Google Kubernetes Engine (GKE) to measure the performance of the MANGO scheduler in comparison to baseline schedulers such as IMPALA-based MobileKube, Latency Greedy, and Binpacking. Finally, extensive experiments have been conducted, which demonstrate the effectiveness of the MANGO in terms of latency and number of migrations.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101368"},"PeriodicalIF":6.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}