Journal Description
IoT
IoT
is an international, peer-reviewed, open access journal on Internet of Things (IoT) published quarterly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions
- High Visibility: indexed within ESCI (Web of Science), Scopus, EBSCO, and other databases.
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.9 days after submission; acceptance to publication is undertaken in 4.1 days (median values for papers published in this journal in the first half of 2024).
- Journal Rank: CiteScore - Q1 (Computer Science (miscellaneous))
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually in the journal.
Latest Articles
Home Monitoring Tools to Support Tracking Patients with Cardio–Cerebrovascular Diseases: Scientometric Review
IoT 2024, 5(3), 524-559; https://doi.org/10.3390/iot5030024 (registering DOI) - 22 Aug 2024
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Home care and telemedicine are crucial for physical and mental health. Although there is a lot of information on these topics, it is scattered across various sources, making it difficult to identify key contributions and authors. This study conducts a scientometric analysis to
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Home care and telemedicine are crucial for physical and mental health. Although there is a lot of information on these topics, it is scattered across various sources, making it difficult to identify key contributions and authors. This study conducts a scientometric analysis to consolidate the most relevant information. The methodology is divided into two parts: first, a scientometric mapping that analyzes scientific production by country, journal, and author; second, the identification of prominent contributions using the Tree of Science (ToS) tool. The goal is to identify trends and support decision-making in the health sector by providing guidelines based on the most relevant research.
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Open AccessArticle
Maximal LoRa Range for Unmanned Aerial Vehicle Fleet Service in Different Environmental Conditions
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Lorenzo Felli, Romeo Giuliano, Andrea De Negri, Francesco Terlizzi, Franco Mazzenga and Alessandro Vizzarri
IoT 2024, 5(3), 509-523; https://doi.org/10.3390/iot5030023 - 31 Jul 2024
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This study investigates communication between UAVs using long range (LoRa) devices, focusing on the interaction between a LoRa gateway UAV and other UAVs equipped with LoRa transmitters. By conducting experiments across various geographical regions, this study aims to delineate the fundamental boundary conditions
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This study investigates communication between UAVs using long range (LoRa) devices, focusing on the interaction between a LoRa gateway UAV and other UAVs equipped with LoRa transmitters. By conducting experiments across various geographical regions, this study aims to delineate the fundamental boundary conditions for the efficient control of a UAV fleet. The parameters under analysis encompass inter-device spacing, radio interference effects, and terrain topography. This research yields pivotal insights into communication network design and optimization, thereby enhancing operational efficiency and safety within diverse geographical contexts for UAV operations. Further research insights could involve a weather analysis and implementation of improved solutions in terms of communication systems.
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Open AccessArticle
Advancing XSS Detection in IoT over 5G: A Cutting-Edge Artificial Neural Network Approach
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Rabee Alqura’n, Mahmoud AlJamal, Issa Al-Aiash, Ayoub Alsarhan, Bashar Khassawneh, Mohammad Aljaidi and Rakan Alanazi
IoT 2024, 5(3), 478-508; https://doi.org/10.3390/iot5030022 - 25 Jul 2024
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The rapid expansion of the Internet of Things (IoT) and the advancement of 5G technology require strong cybersecurity measures within IoT frameworks. Traditional security methods are insufficient due to the wide variety and large number of IoT devices and their limited computational capabilities.
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The rapid expansion of the Internet of Things (IoT) and the advancement of 5G technology require strong cybersecurity measures within IoT frameworks. Traditional security methods are insufficient due to the wide variety and large number of IoT devices and their limited computational capabilities. With 5G enabling faster data transmission, security risks have increased, making effective protective measures essential. Cross-Site Scripting (XSS) attacks present a significant threat to IoT security. In response, we have developed a new approach using Artificial Neural Networks (ANNs) to identify and prevent XSS breaches in IoT systems over 5G networks. We significantly improved our model’s predictive performance by using filter and wrapper feature selection methods. We validated our approach using two datasets, NF-ToN-IoT-v2 and Edge-IIoTset, ensuring its strength and adaptability across different IoT environments. For the NF-ToN-IoT-v2 dataset with filter feature selection, our Bilayered Neural Network (2 × 10) achieved the highest accuracy of 99.84%. For the Edge-IIoTset dataset with filtered feature selection, the Trilayered Neural Network (3 × 10) achieved the best accuracy of 99.79%. We used ANOVA tests to address the sensitivity of neural network performance to initial conditions, confirming statistically significant improvements in detection accuracy. The ANOVA results validated the enhancements across different feature selection methods, demonstrating the consistency and reliability of our approach. Our method demonstrates outstanding accuracy and robustness, highlighting its potential as a reliable solution for enhancing IoT security in the era of 5G networks.
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Open AccessArticle
Evaluating the Impact of Controlled Ultraviolet Light Intensities on the Growth of Kale Using IoT-Based Systems
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Suttipong Klongdee, Paniti Netinant and Meennapa Rukhiran
IoT 2024, 5(2), 449-477; https://doi.org/10.3390/iot5020021 - 15 Jun 2024
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Incorporating Internet of Things (IoT) technology into indoor kale cultivation holds significant promise for revolutionizing organic farming methodologies. While numerous studies have investigated the impact of environmental factors on kale growth in IoT-based smart agricultural systems, such as temperature, humidity, and nutrient levels,
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Incorporating Internet of Things (IoT) technology into indoor kale cultivation holds significant promise for revolutionizing organic farming methodologies. While numerous studies have investigated the impact of environmental factors on kale growth in IoT-based smart agricultural systems, such as temperature, humidity, and nutrient levels, indoor ultraviolet (UV) LED light’s operational efficiencies and advantages in organic farming still need to be explored. This study assessed the efficacy of 15 UV light-controlling indoor experiments in three distinct lighting groups: kale cultivated using conventional household LED lights, kale cultivated using specialized indoor UV lights designed for plant cultivation, and kale cultivated using hybrid household and LED grow lights. The real-time IoT-based monitoring of light, soil, humidity, and air conditions, as well as automated irrigation using a water droplet system, was employed throughout the experiment. The experimental setup for air conditioning maintained temperatures at a constant 26 degrees Celsius over the 45-day study period. The results revealed that a combination of daylight household lights and indoor 4000 K grow lights scored the highest, indicating optimal growth conditions. The second group exposed to warm white household and indoor grow red light exhibited slightly lower scores but larger leaf size than the third group grown under indoor grow red light, likely attributable to reduced light intensity or suboptimal nutrient levels. This study highlights the potential of indoor UV LED light farming to address challenges posed by urbanization and climate change, thereby contributing to efforts to mitigate agricultural carbon emissions and enhance food security in urban environments. This research contributes to positioning kale as a sustainable organic superfood by optimizing kale cultivation.
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Open AccessReview
Towards 6G Technology: Insights into Resource Management for Cloud RAN Deployment
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Sura F. Ismail and Dheyaa Jasim Kadhim
IoT 2024, 5(2), 409-448; https://doi.org/10.3390/iot5020020 - 14 Jun 2024
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Rapid advancements in the development of smart terminals and infrastructure, coupled with a wide range of applications with complex requirements, are creating traffic demands that current networks may not be able to fully handle. Accordingly, the study of 6G networks deserves attention from
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Rapid advancements in the development of smart terminals and infrastructure, coupled with a wide range of applications with complex requirements, are creating traffic demands that current networks may not be able to fully handle. Accordingly, the study of 6G networks deserves attention from both industry and academia. Artificial intelligence (AI) has emerged for application in the optimization and design process of new 6G networks. The developmental trend of 6G is towards effective resource management, along with the architectural improvement of the current network and hardware specifications. Cloud RAN (CRAN) is considered one of the major concepts in sixth- and fifth-generation wireless networks, being able to improve latency, capacity, and connectivity to huge numbers of devices. Besides bettering the current set-up in terms of setting the carriers’ network architecture and hardware specifications, among other potential enablers, the developmental trend of 6G also means that there must be effective resource management. As a result, this study covers a thorough analysis of resource management plans in CRAN, optimization, and AI taxonomy, and how AI integration might enhance existing resource management.
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Open AccessArticle
Multi-Hospital Management: Combining Vital Signs IoT Data and the Elasticity Technique to Support Healthcare 4.0
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Gabriel Souto Fischer, Gabriel de Oliveira Ramos, Cristiano André da Costa, Antonio Marcos Alberti, Dalvan Griebler, Dhananjay Singh and Rodrigo da Rosa Righi
IoT 2024, 5(2), 381-408; https://doi.org/10.3390/iot5020019 - 8 Jun 2024
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Smart cities can improve the quality of life of citizens by optimizing the utilization of resources. In an IoT-connected environment, people’s health can be constantly monitored, which can help identify medical problems before they become serious. However, overcrowded hospitals can lead to long
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Smart cities can improve the quality of life of citizens by optimizing the utilization of resources. In an IoT-connected environment, people’s health can be constantly monitored, which can help identify medical problems before they become serious. However, overcrowded hospitals can lead to long waiting times for patients to receive treatment. The literature presents alternatives to address this problem by adjusting care capacity to demand. However, there is still a need for a solution that can adjust human resources in multiple healthcare settings, which is the reality of cities. This work introduces HealCity, a smart-city-focused model that can monitor patients’ use of healthcare settings and adapt the allocation of health professionals to meet their needs. HealCity uses vital signs (IoT) data in prediction techniques to anticipate when the demand for a given environment will exceed its capacity and suggests actions to allocate health professionals accordingly. Additionally, we introduce the concept of multilevel proactive human resources elasticity in smart cities, thus managing human resources at different levels of a smart city. An algorithm is also devised to automatically manage and identify the appropriate hospital for a possible future patient. Furthermore, some IoT deployment considerations are presented based on a hardware implementation for the proposed model. HealCity was evaluated with four hospital settings and obtained promising results: Compared to hospitals with rigid professional allocations, it reduced waiting time for care by up to 87.62%.
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Open AccessArticle
Investigating Radio Frequency Vulnerabilities in the Internet of Things (IoT)
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Eirini Anthi, Lowri Williams, Vasilis Ieropoulos and Theodoros Spyridopoulos
IoT 2024, 5(2), 356-380; https://doi.org/10.3390/iot5020018 - 6 Jun 2024
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With the increase in the adoption of Internet of Things (IoT) devices, the security threat they face has become more pervasive. Recent research has demonstrated that most IoT devices are insecure and vulnerable to a range of cyber attacks. The impact of such
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With the increase in the adoption of Internet of Things (IoT) devices, the security threat they face has become more pervasive. Recent research has demonstrated that most IoT devices are insecure and vulnerable to a range of cyber attacks. The impact of such attacks can vary significantly, from affecting the service of the device itself to putting their owners and their personal information at risk. As a response to improving their security, the focus has been on attacks, specifically on the network layer. However, the importance and impact of other vulnerabilities, such as low-level Radio Frequency (RF) attacks, have been neglected. Such attacks are challenging to detect, and they can be deployed using non-expensive equipment and can cause significant damage. This paper explores security vulnerabilities that target RF communications on popular commercial IoT devices such as Wi-Fi, Zigbee, and 433 Mz. Using software-defined radio, a range of attacks were deployed against the devices, including jamming, replay attacks, packet manipulation, protocol reverse engineering, and harmonic frequency attacks. The results demonstrated that all devices used were susceptible to jamming attacks, and in some cases, they were rendered inoperable and required a hard reset to function correctly again. This finding highlights the lack of protection against both intentional and unintentional jamming. In addition, all devices demonstrated that they were susceptible to replay attacks, which highlights the need for more hardened security measures. Finally, this paper discusses proposals for defence mechanisms for enhancing the security of IoT devices against the aforementioned attacks.
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Open AccessArticle
Adaptive Transmissions for Batteryless Periodic Sensing
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Cheng-Sheng Peng and Chao Wang
IoT 2024, 5(2), 332-355; https://doi.org/10.3390/iot5020017 - 31 May 2024
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Batteryless, self-sustaining embedded sensing devices are key enablers for scalable and long-term operations of Internet of Things (IoT) applications. While advancements in both energy harvesting and intermittent computing have helped pave the way for building such batteryless IoT devices, a present challenge is
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Batteryless, self-sustaining embedded sensing devices are key enablers for scalable and long-term operations of Internet of Things (IoT) applications. While advancements in both energy harvesting and intermittent computing have helped pave the way for building such batteryless IoT devices, a present challenge is a system design that can utilize intermittent energy to meet data requirements from IoT applications. In this paper, we take the requirement of periodic data sensing and describe the hardware and software of a batteryless IoT device with its model, design, implementation, and evaluation. A key finding is that, by estimating the non-linear hardware charging and discharging time, the device software can make scheduling decisions that both maintain the selected sensing period and improve transmission goodput. A hardware–software prototype was implemented using an MSP430 development board and LoRa radio communication technology. The proposed design was empirically compared with one that does not consider the non-linear hardware characteristics. The result of the experiments illustrated the nuances of the batteryless device design and implementation, and it demonstrated that the proposed design can cover a wider range of feasible sensing rates, which reduces the restriction on this parameter choice. It was further demonstrated that, under an intermittent supply of power, the proposed design could still keep the device functioning as required.
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Open AccessArticle
Integration of IoT Technologies for Enhanced Monitoring and Control in Hybrid-Powered Desalination Systems: A Sustainable Approach to Freshwater Production
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Alaa M. Odeh and Isam Ishaq
IoT 2024, 5(2), 311-331; https://doi.org/10.3390/iot5020016 - 31 May 2024
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In the face of our rapidly expanding global population, the necessity of meeting the fundamental needs of every individual is more pressing than ever. Human survival depends upon access to water, making it a vital resource that demands novel solutions to ensure universal
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In the face of our rapidly expanding global population, the necessity of meeting the fundamental needs of every individual is more pressing than ever. Human survival depends upon access to water, making it a vital resource that demands novel solutions to ensure universal availability. Although our planet is abundant in water, 97.5% of it is saltwater, compelling nations to investigate ways to make it suitable for consumption. Seawater desalination is becoming increasingly vital for water sustainability. While seawater desalination offers a solution, existing methods often grapple with high energy consumption and maintaining consistent water quality. This paper proposes a novel hybrid water desalination system that addresses these limitations. Our system leverages solar energy, a readily available renewable resource, to power the desalination process, significantly improving its environmental footprint and operational efficiency. Additionally, we integrated a network of sensors and the Internet of Things (IoT) to enable the real-time monitoring of system performance and water quality. This allows for the immediate detection and improvement in any potential issues, ensuring the consistent production of clean drinking water. By combining solar energy with robust quality control via IoT, our hybrid desalination system offers a sustainable and reliable approach to meet the growing demand for freshwater.
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Open AccessArticle
Addressing Vulnerabilities in CAN-FD: An Exploration and Security Enhancement Approach
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Naseeruddin Lodge, Nahush Tambe and Fareena Saqib
IoT 2024, 5(2), 290-310; https://doi.org/10.3390/iot5020015 - 30 May 2024
Abstract
The rapid advancement of technology, alongside state-of-the-art techniques is at an all-time high. However, this unprecedented growth of technological prowess also brings forth potential threats, as oftentimes the security encompassing these technologies is imperfect. Particularly within the automobile industry, the recent strides in
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The rapid advancement of technology, alongside state-of-the-art techniques is at an all-time high. However, this unprecedented growth of technological prowess also brings forth potential threats, as oftentimes the security encompassing these technologies is imperfect. Particularly within the automobile industry, the recent strides in technology have brought about increased complexity. A notable flaw lies in the CAN-FD protocol, which lacks robust security measures, making it vulnerable to data theft, injection, replay, and flood data attacks. With the rising complexity of in-vehicular networks and the widespread adoption of CAN-FD, the imperative to safeguard the protocol has never been more crucial. This paper aims to provide a comprehensive review of the existing in-vehicle communication protocol, CAN-FD. It explores existing security approaches designed to fortify CAN-FD, demonstrating multiple multi-layer solutions that leverage modern techniques including Physical Unclonable Function (PUF), Elliptical Curve Cryptography (ECC), Ethereum Blockchain, and Smart contracts. The paper highlights existing multi-layer security measures that offer minimal overhead, optimal performance, and robust security. Moreover, it identifies areas where these security measures fall short and discusses ongoing research along with suggestions for implementing software and hardware-level modifications. These proposed changes aim to streamline complexity, reduce overhead while ensuring forward compatibility. In essence, the methods outlined in this study are poised to excel in real-world applications, offering robust protection for the evolving landscape of in-vehicular communication systems.
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(This article belongs to the Special Issue Cloud and Edge Computing Systems for IoT)
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Open AccessArticle
Development, Implementation and Evaluation of An Epidemic Communication System
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Naoki Yamada, Takefumi Hiraguri, Tomotaka Kimura, Hiroyuki Shimizu, Yoshihiro Takemura and Takahiro Matsuda
IoT 2024, 5(2), 271-289; https://doi.org/10.3390/iot5020014 - 24 May 2024
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The purpose of this study is to discuss epidemic communication for drones to share information in flight and to develop a wireless system for implementation. Various theoretical studies have been conducted on epidemic communication, but their applications are not clear, so a system
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The purpose of this study is to discuss epidemic communication for drones to share information in flight and to develop a wireless system for implementation. Various theoretical studies have been conducted on epidemic communication, but their applications are not clear, so a system that assumes practical use is developed. As the main evaluation items, we analyzed the effect of communication interference between drones on the amount of data transmission, and furthermore, proposed an optimal transmission method depending on the flight speed. In these analysis results, we designed functions to be implemented in drones, developed wireless devices, and confirmed their operation through demonstration tests using actual drones. Based on the results of this research, we succeeded in identifying issues to be addressed in order to implement the system on drones and in developing an epidemic communication system based on the results of demonstration experiments, thereby contributing to the realization of inter-drone communication in the future.
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Open AccessArticle
Smart Agriculture Drone for Crop Spraying Using Image-Processing and Machine Learning Techniques: Experimental Validation
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Edward Singh, Aashutosh Pratap, Utkal Mehta and Sheikh Izzal Azid
IoT 2024, 5(2), 250-270; https://doi.org/10.3390/iot5020013 - 22 May 2024
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Smart agricultural drones for crop spraying are becoming popular worldwide. Research institutions, commercial companies, and government agencies are investigating and promoting the use of technologies in the agricultural industry. This study presents a smart agriculture drone integrated with Internet of Things technologies that
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Smart agricultural drones for crop spraying are becoming popular worldwide. Research institutions, commercial companies, and government agencies are investigating and promoting the use of technologies in the agricultural industry. This study presents a smart agriculture drone integrated with Internet of Things technologies that use machine learning techniques such as TensorFlow Lite with an EfficientDetLite1 model to identify objects from a custom dataset trained on three crop classes, namely, pineapple, papaya, and cabbage species, achieving an inference time of 91 ms. The system’s operation is characterised by its adaptability, offering two spray modes, with spray modes A and B corresponding to a 100% spray capacity and a 50% spray capacity based on real-time data, embodying the potential of Internet of Things for real-time monitoring and autonomous decision-making. The drone is operated with an X500 development kit and has a payload of 1.5 kg with a flight time of 25 min, travelling at a velocity of 7.5 m/s at a height of 2.5 m. The drone system aims to improve sustainable farming practices by optimising pesticide application and improving crop health monitoring.
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Open AccessArticle
FedMon: A Federated Learning Monitoring Toolkit
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Moysis Symeonides, Demetris Trihinas and Fotis Nikolaidis
IoT 2024, 5(2), 227-249; https://doi.org/10.3390/iot5020012 - 11 Apr 2024
Abstract
Federated learning (FL) is rapidly shaping into a key enabler for large-scale Artificial Intelligence (AI) where models are trained in a distributed fashion by several clients without sharing local and possibly sensitive data. For edge computing, sharing the computational load across multiple clients
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Federated learning (FL) is rapidly shaping into a key enabler for large-scale Artificial Intelligence (AI) where models are trained in a distributed fashion by several clients without sharing local and possibly sensitive data. For edge computing, sharing the computational load across multiple clients is ideal, especially when the underlying IoT and edge nodes encompass limited resource capacity. Despite its wide applicability, monitoring FL deployments comes with significant challenges. AI practitioners are required to invest a vast amount of time (and labor) in manually configuring state-of-the-art monitoring tools. This entails addressing the unique characteristics of the FL training process, including the extraction of FL-specific and system-level metrics, aligning metrics to training rounds, pinpointing performance inefficiencies, and comparing current to previous deployments. This work introduces FedMon, a toolkit designed to ease the burden of monitoring FL deployments by seamlessly integrating the probing interface with the FL deployment, automating the metric extraction, providing a rich set of system, dataset, model, and experiment-level metrics, and providing the analytic means to assess trade-offs and compare different model and training configurations.
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(This article belongs to the Special Issue Cloud and Edge Computing Systems for IoT)
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Open AccessArticle
Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
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Narges Rashvand, Kenneth Witham, Gabriel Maldonado, Vinit Katariya, Nishanth Marer Prabhu, Gunar Schirner and Hamed Tabkhi
IoT 2024, 5(2), 212-226; https://doi.org/10.3390/iot5020011 - 9 Apr 2024
Cited by 1
Abstract
Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems. This paper presents an innovative approach that leverages Transformer networks, initially designed for natural language processing, to
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Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems. This paper presents an innovative approach that leverages Transformer networks, initially designed for natural language processing, to address the challenges of efficient AMR. Our Transformer network architecture is designed with the mindset of real-time edge computing on IoT devices. Four tokenization techniques are proposed and explored for creating proper embeddings of RF signals, specifically focusing on overcoming the limitations related to the model size often encountered in IoT scenarios. Extensive experiments reveal that our proposed method outperformed advanced deep learning techniques, achieving the highest recognition accuracy. Notably, our model achieved an accuracy of 65.75 on the RML2016 and 65.80 on the CSPB.ML.2018+ dataset.
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(This article belongs to the Special Issue Cloud and Edge Computing Systems for IoT)
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Open AccessArticle
Development of a Multi-Radio Device for Dry Container Monitoring and Tracking
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Mariano Falcitelli, Misal, Sandro Noto and Paolo Pagano
IoT 2024, 5(2), 187-211; https://doi.org/10.3390/iot5020010 - 2 Apr 2024
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Maritime shipping companies have identified continuous tracking of intermodal containers as a key tool for increasing shipment reliability and generating important economies of scale. Equipping all dry containers with an Internet-connected tracking device is a need in the global shipping market that is
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Maritime shipping companies have identified continuous tracking of intermodal containers as a key tool for increasing shipment reliability and generating important economies of scale. Equipping all dry containers with an Internet-connected tracking device is a need in the global shipping market that is still waiting to be met. This paper presents the methods and tools to build and test a prototype of a Container Tracking Device (CTD) that integrates NB-IoT, BLE Mesh telecommunication and low-power consumption technologies for the massive deployment of the IoT. The work was carried out as part of a project to build the so-called “5G Global Tracking System”, enabling several different logistic applications relying on massive IoT, M2M standard platforms, as well as satellite networks to collect data from dry containers when the vessel is in open sea. Starting from a preliminary phase, in which state-of-the-art technologies, research approaches, industrial initiatives and developing standards were investigated, a prototype version of the CTD has been designed, verified and developed as the first fundamental step for subsequent industrial engineering. The results of specific tests are shown: after verifying that the firmware is capable of handling the various functions of the device, a special focus is devoted to the power consumption measurements of the CTD to size the battery pack.
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Open AccessArticle
Integration of Smart Cane with Social Media: Design of a New Step Counter Algorithm for Cane
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Mohamed Dhiaeddine Messaoudi, Bob-Antoine J. Menelas and Hamid Mcheick
IoT 2024, 5(1), 168-186; https://doi.org/10.3390/iot5010009 - 14 Mar 2024
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This research introduces an innovative smart cane architecture designed to empower visually impaired individuals. Integrating advanced sensors and social media connectivity, the smart cane enhances accessibility and encourages physical activity. Three meticulously developed algorithms ensure accurate step counting, swing detection, and proximity measurement.
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This research introduces an innovative smart cane architecture designed to empower visually impaired individuals. Integrating advanced sensors and social media connectivity, the smart cane enhances accessibility and encourages physical activity. Three meticulously developed algorithms ensure accurate step counting, swing detection, and proximity measurement. The smart cane’s architecture comprises the platform, communications, sensors, calculation, and user interface layers, providing comprehensive assistance for visually impaired individuals. Hardware components include an audio–tactile interaction module, input command module, microphone integration, local storage, step count module, cloud integration, and rechargeable battery. Software v1.9.7 components include Facebook Chat API integration, Python Facebook API integration, fbchat library integration, and Speech Recognition library integration. Overall, the proposed smart cane offers a comprehensive solution to enhance mobility, accessibility, and social engagement for visually impaired individuals. This study represents a significant stride toward a more inclusive society, leveraging technology to create meaningful impact in the lives of those with visual impairments. By fostering socialization and independence, our smart cane not only improves mobility but also enhances the overall well-being of the visually impaired community.
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Open AccessArticle
A Wearable Internet of Things Device for Noninvasive Remote Monitoring of Vital Signs Related to Heart Failure
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Sheikh Muhammad Asher Iqbal, Mary Ann Leavitt, Imadeldin Mahgoub and Waseem Asghar
IoT 2024, 5(1), 155-167; https://doi.org/10.3390/iot5010008 - 12 Mar 2024
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Cardiovascular disease is one of the leading causes of death in the world. Heart failure is a cardiovascular disease in which the heart is unable to pump sufficient blood to fulfill the body’s requirements and can lead to fluid overload. Traditional solutions are
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Cardiovascular disease is one of the leading causes of death in the world. Heart failure is a cardiovascular disease in which the heart is unable to pump sufficient blood to fulfill the body’s requirements and can lead to fluid overload. Traditional solutions are not adequate to address the progression of heart failure. Herein, we report a body-mounted wearable sensor to monitor the parameters related to heart failure. These include heart rate, blood oxygen saturation, thoracic impedance, and activity status. The device is compact and wearable and measures the parameters continuously in real time. The device is an Internet of Things (IoT) device connected with a cloud-based database enabling the parameters to be visualized on a mobile application.
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Open AccessReview
Analyzing Threats and Attacks in Edge Data Analytics within IoT Environments
by
Poornima Mahadevappa, Redhwan Al-amri, Gamal Alkawsi, Ammar Ahmed Alkahtani, Mohammed Fahad Alghenaim and Mohammed Alsamman
IoT 2024, 5(1), 123-154; https://doi.org/10.3390/iot5010007 - 5 Mar 2024
Cited by 1
Abstract
Edge data analytics refers to processing near data sources at the edge of the network to reduce delays in data transmission and, consequently, enable real-time interactions. However, data analytics at the edge introduces numerous security risks that can impact the data being processed.
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Edge data analytics refers to processing near data sources at the edge of the network to reduce delays in data transmission and, consequently, enable real-time interactions. However, data analytics at the edge introduces numerous security risks that can impact the data being processed. Thus, safeguarding sensitive data from being exposed to illegitimate users is crucial to avoiding uncertainties and maintaining the overall quality of the service offered. Most existing edge security models have considered attacks during data analysis as an afterthought. In this paper, an overview of edge data analytics in healthcare, traffic management, and smart city use cases is provided, including the possible attacks and their impacts on edge data analytics. Further, existing models are investigated to understand how these attacks are handled and research gaps are identified. Finally, research directions to enhance data analytics at the edge are presented.
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(This article belongs to the Special Issue Cloud and Edge Computing Systems for IoT)
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Open AccessArticle
A Reference Design Model to Manage Consent in Data Subjects-Centered Internet of Things Devices
by
Pankaj Khatiwada, Bian Yang, Jia-Chun Lin, Godfrey Mugurusi and Stian Underbekken
IoT 2024, 5(1), 100-122; https://doi.org/10.3390/iot5010006 - 6 Feb 2024
Abstract
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Internet of Things (IoT) devices have changed how billions of people in the world connect and interact with each other. But, as more people use IoT devices, many questions arise about how these devices handle private data and whether they properly ask for
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Internet of Things (IoT) devices have changed how billions of people in the world connect and interact with each other. But, as more people use IoT devices, many questions arise about how these devices handle private data and whether they properly ask for permission when using it. Due to information privacy regulations such as the EU’s General Data Protection Regulation (GDPR), which requires companies to seek permission from data subjects (DS) before using their data, it is crucial for IoT companies to obtain this permission correctly. However, this can be really challenging in the IoT world because people often find it difficult to interact with and manage multiple IoT devices under their control. Also, the rules about privacy are not always clear. As such, this paper proposes a new model to improve how consent is managed in the world of IoT. The model seeks to minimize “consent fatigue” (when people get tired of always being asked for permission) and give DS more control over how their data are shared. This includes having default permission settings, being able to compare similar devices, and, in the future, using AI to give personalized advice. The model allows users to easily review and change their IoT device permissions if previous conditions are not met. It also emphasizes the need for easily understandable privacy rules, clear communication with users, and robust tracking of consent for data usage. By using this model, companies that provide IoT services can do a better job of protecting user privacy and managing DS consent. In addition, companies can more easily comply with data protection laws and build stronger relationships with their customers.
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Open AccessArticle
Development and Assessment of Internet of Things-Driven Smart Home Security and Automation with Voice Commands
by
Paniti Netinant, Thitipong Utsanok, Meennapa Rukhiran and Suttipong Klongdee
IoT 2024, 5(1), 79-99; https://doi.org/10.3390/iot5010005 - 1 Feb 2024
Cited by 2
Abstract
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With the rapid rise of digitalization in the global economy, home security systems have become increasingly important for personal comfort and property protection. The collaboration between humans, the Internet of Things (IoT), and smart homes can be highly efficient. Interaction considers convenience, efficiency,
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With the rapid rise of digitalization in the global economy, home security systems have become increasingly important for personal comfort and property protection. The collaboration between humans, the Internet of Things (IoT), and smart homes can be highly efficient. Interaction considers convenience, efficiency, security, responsiveness, and automation. This study aims to develop and assess IoT-based home security systems utilizing passive infrared (PIR) sensors to improve user interface, security, and automation controls using voice commands and buttons across different communication protocols. The proposed system incorporates controls for lighting and intrusion monitoring, as well as assessing both the functionality of voice commands and the precision of intruder detection via the PIR sensors. Intelligent light control and PIR intruder detection with a variable delay time for response detection are unified into the research methodology. The test outcomes examine the average effective response time in-depth, revealing performance distinctions among wireless fidelity (Wi-Fi) and fourth- and fifth-generation mobile connections. The outcomes illustrate the reliability of voice-activated light control via Google Assistant, with response accuracy rates of 83 percent for Thai voice commands and 91.50 percent for English voice commands. Moreover, the Blynk mobile application provided exceptional precision regarding operating light-button commands. The PIR motion detectors have a one hundred percent detection accuracy, and a 2.5 s delay is advised for PIR detection. Extended PIR detection delays result in prolonged system response times. This study examines the intricacies of response times across various environmental conditions, considering different degrees of mobile communication quality. This study ultimately advances the field by developing an IoT system prepared for efficient integration into everyday life, holding the potential to provide improved convenience, time-saving effectiveness, cost-efficiency, and enhanced home security protocols.
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