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IoT, Volume 6, Issue 1 (March 2025) – 16 articles

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14 pages, 2295 KiB  
Article
Fog-Enabled IoT Robotic System for Efficient Date Palm Monitoring in Moroccan Oases
by Baghrous Mohamed and Ezzouhairi Abdellatif
IoT 2025, 6(1), 16; https://doi.org/10.3390/iot6010016 - 14 Feb 2025
Viewed by 254
Abstract
Date palms are a vital resource in the oases of Morocco, providing economic and ecological benefits to local communities. However, monitoring these trees at scale presents challenges, particularly in rural areas where traditional IoT-based agricultural systems rely on extensive sensor networks. These systems [...] Read more.
Date palms are a vital resource in the oases of Morocco, providing economic and ecological benefits to local communities. However, monitoring these trees at scale presents challenges, particularly in rural areas where traditional IoT-based agricultural systems rely on extensive sensor networks. These systems face significant limitations, including high deployment costs, network coverage issues, and energy constraints. To address these challenges, we propose an agricultural robot equipped with multiple IoT sensors to monitor date palm health and environmental parameters, reducing the need for widespread fixed sensors. To overcome the latency, bandwidth, and energy inefficiencies associated with cloud-based solutions, the system integrates the fog computing paradigm. Fog computing brings computational resources closer to the data source, enabling real-time processing and decision-making while reducing network congestion. Using iFogSim, we evaluate the proposed system on key metrics, including latency, energy consumption, and network performance. The results demonstrate significant improvements over traditional approaches, highlighting the potential of IoT-based robotic systems combined with fog computing to revolutionize date palm monitoring in remote oases. Full article
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18 pages, 4490 KiB  
Article
Smart Monitoring System for Temperature and Relative Humidity Adapted to the Specific Needs of the Colombian Pharmaceutical Service
by Maria Paula Cabezas, Juan David Carvajal, Fulvio Yesid Vivas and Diego Mauricio Lopez
IoT 2025, 6(1), 15; https://doi.org/10.3390/iot6010015 - 13 Feb 2025
Viewed by 354
Abstract
Patient safety (PS) is essential in medical care, and preventing medication errors (MEs) is key to guaranteeing it. In Colombia, pharmaceutical services must comply with regulations that require adequate environmental monitoring to ensure medication quality. This study aims to propose an IoT-based smart [...] Read more.
Patient safety (PS) is essential in medical care, and preventing medication errors (MEs) is key to guaranteeing it. In Colombia, pharmaceutical services must comply with regulations that require adequate environmental monitoring to ensure medication quality. This study aims to propose an IoT-based smart system that automatizes temperature and relative humidity monitoring in the Colombian pharmaceutical service (CPS). Using the model for IoT platform design as a methodology, an efficient and flexible architecture that integrates data quality management (DQM) dimensions to improve the accuracy and reliability of the system was designed. In addition, tests based on the agile quadrant methodology demonstrate, as a result, its effectiveness, highlighting its ability to optimize environmental monitoring, prevent MEs, and improve PS. The successful implementation of this IoT-based smart system shows its potential in the pharmaceutical sector, offering an innovative solution that reduces risks and improves the quality of drug storage. Full article
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21 pages, 1742 KiB  
Article
Evaluating the Performance of LoRa Networks: A Study on Disaster Monitoring Scenarios
by Isadora Rezende Lopes, Paulo Rodolfo da Silva Leite Coelho, Rafael Pasquini and Rodrigo Sanches Miani
IoT 2025, 6(1), 14; https://doi.org/10.3390/iot6010014 - 12 Feb 2025
Viewed by 284
Abstract
The development of technologies using the Internet of Things (IoT) concept evolves daily. These numerous technologies, such as LoRa (Long Range) transceivers, find applications in various domains, including monitoring natural disasters and those caused by human error. Security vulnerabilities arise concurrently with the [...] Read more.
The development of technologies using the Internet of Things (IoT) concept evolves daily. These numerous technologies, such as LoRa (Long Range) transceivers, find applications in various domains, including monitoring natural disasters and those caused by human error. Security vulnerabilities arise concurrently with the advancement of these new technologies. Cyberattacks seeking to disrupt device availability, such as denial-of-service (DoS) attacks, can effectively exploit vulnerabilities in LoRa devices, hindering disaster monitoring efforts. Therefore, our goal is to assess the network parameters that impact the development of a disaster monitoring environment using LoRaWAN. Specifically, we aim to identify the parameters that could result in network availability issues, whether caused by malicious actors or configuration errors. Our results indicate that certain LoRa network parameters (collision checks, packet size, and the number of nodes) can significantly affect network performance, potentially rendering this technology unsuitable for building robust disaster monitoring systems. Full article
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42 pages, 2801 KiB  
Review
Emerging Developments in Real-Time Edge AIoT for Agricultural Image Classification
by Maurizio Pintus, Felice Colucci and Fabio Maggio
IoT 2025, 6(1), 13; https://doi.org/10.3390/iot6010013 - 10 Feb 2025
Viewed by 485
Abstract
Advances in deep learning (DL) models and next-generation edge devices enable real-time image classification, driving a transition from the traditional, purely cloud-centric IoT approach to edge-based AIoT, with cloud resources reserved for long-term data storage and in-depth analysis. This innovation is transformative for [...] Read more.
Advances in deep learning (DL) models and next-generation edge devices enable real-time image classification, driving a transition from the traditional, purely cloud-centric IoT approach to edge-based AIoT, with cloud resources reserved for long-term data storage and in-depth analysis. This innovation is transformative for agriculture, enabling autonomous monitoring, localized decision making, early emergency detection, and precise chemical application, thereby reducing costs and minimizing environmental and health impacts. The workflow of an edge-based AIoT system for agricultural monitoring involves two main steps: optimal training and tuning of DL models through extensive experiments on high-performance AI-specialized computers, followed by effective customization for deployment on advanced edge devices. This review highlights key challenges in practical applications, including: (i) the limited availability of agricultural data, particularly due to seasonality, addressed through public datasets and synthetic image generation; (ii) the selection of state-of-the-art computer vision algorithms that balance high accuracy with compatibility for resource-constrained devices; (iii) the deployment of models through algorithm optimization and integration of next-generation hardware accelerators for DL inference; and (iv) recent advancements in AI models for image classification that, while not yet fully deployable, offer promising near-term improvements in performance and functionality. Full article
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11 pages, 653 KiB  
Article
Routing Protocols Performance on 6LoWPAN IoT Networks
by Pei Siang Chia, Noor Hisham Kamis, Siti Fatimah Abdul Razak, Sumendra Yogarayan, Warusia Yassin and Mohd Faizal Abdollah
IoT 2025, 6(1), 12; https://doi.org/10.3390/iot6010012 - 10 Feb 2025
Viewed by 329
Abstract
IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) are specifically designed for applications that require lower data rates and reduced power consumption in wireless internet connectivity. In the context of 6LoWPAN, Internet of Things (IoT) devices with limited resources can now seamlessly connect [...] Read more.
IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) are specifically designed for applications that require lower data rates and reduced power consumption in wireless internet connectivity. In the context of 6LoWPAN, Internet of Things (IoT) devices with limited resources can now seamlessly connect to the network using IPv6. This study focuses on examining the performance and power consumption of routing protocols in the context of 6LoWPAN, drawing insights from prior research and utilizing simulation techniques. The simulation involves the application of routing protocols, namely Routing Protocol for Low-power and Lossy (RPL) Networks, Ad hoc On-demand Distance Vector (AODV), Lightweight On-demand Ad hoc Distance-vector Next Generation (LOADng), implemented through the Cooja simulator. The simulation also runs in different network topologies to gain an insight into the performance of the protocols in the specific topology including random, linear, and eclipse topology. The raw data gathered from the tools including Powertrace and Collect-View were then analyzed with Python code to transfer into useful information and visualize the graph. The results demonstrate that the power consumption, specifically CPU power, Listen Power, and Total Consumption Power, will increase with the incremental of motes. The result also shows that RPL is the most power-efficient protocol among the scenarios compared to LOADng and AODV. The result is helpful because it brings insights into the performance, specifically power consumption in the 6LoWPAN network. This result is valuable to further implement these protocols in the testbed as well as provide an idea of the algorithmic enhancements. Full article
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22 pages, 9855 KiB  
Article
Shower–IoT: An Internet of Things System for Monitoring Electric Showers
by Helder Holanda Prezotto, Natássya Barlate Floro da Silva, Lucas Dias Hiera Sampaio and Rogério Santos Pozza
IoT 2025, 6(1), 11; https://doi.org/10.3390/iot6010011 - 8 Feb 2025
Viewed by 234
Abstract
The electric shower is the main form of heating water for bathing in Brazilian homes and one of the significant appliances related to electricity and water consumption. Internet of Things (IoT) projects make it possible to connect objects to the Internet and collect [...] Read more.
The electric shower is the main form of heating water for bathing in Brazilian homes and one of the significant appliances related to electricity and water consumption. Internet of Things (IoT) projects make it possible to connect objects to the Internet and collect data from machines remotely. In this work, we developed an Internet of Things system for monitoring an electric shower, called Shower–IoT, whose sensor data are water temperature, electric tension, electric current, and water flow. To implement the software infrastructure, we used the services present in cloud computing, such as a broker, processing, and storage, in which the information about the electric shower was made available through an Android application. The results demonstrate that our system can monitor an electric shower integrated with cloud services, allowing the users to visualize its behavior in real time and detect possible failures by comparing sensor data from previous evaluations. Full article
(This article belongs to the Special Issue Advances in IoT and Machine Learning for Smart Homes)
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27 pages, 2913 KiB  
Article
Healthcare Monitoring Using an Internet of Things-Based Cardio System
by Galya Georgieva-Tsaneva, Krasimir Cheshmedzhiev, Yoan-Aleksandar Tsanev, Miroslav Dechev and Ekaterina Popovska
IoT 2025, 6(1), 10; https://doi.org/10.3390/iot6010010 - 6 Feb 2025
Viewed by 551
Abstract
This study describes an IoT-based health monitoring system designed to notify attending physicians when necessary. The developed IoT system incorporates sensors for ECG, PPG, and temperature; a gyroscope/accelerometer; and a microcontroller. A critical analysis of existing components in these areas was conducted to [...] Read more.
This study describes an IoT-based health monitoring system designed to notify attending physicians when necessary. The developed IoT system incorporates sensors for ECG, PPG, and temperature; a gyroscope/accelerometer; and a microcontroller. A critical analysis of existing components in these areas was conducted to ensure the IoT system’s good performance, reliability, and suitability for continuous cardiac monitoring and data processing. This study addresses the challenge of monitoring cardiac activity in patients with arrhythmias, focusing on the differences in heart rate variability (HRV) parameters between healthy individuals and those with extrasystolic arrhythmia. The purpose of this research is to evaluate the effectiveness of IoT-based systems using PPG and ECG sensors for cardiac data registration and HRV analysis. The system leverages time domain and frequency domain methods for HRV analysis to assess the states of the autonomic nervous system. Significant differences were observed in HRV parameters, such as the SDNN, SDANN, RMSSD, and the LF/HF ratio. The results demonstrated that both the PPG and ECG methods provide comparable HRV measurements, despite PPG’s higher susceptibility to noise. This study concludes that IoT-based monitoring systems with PPG and ECG integration can reliably detect arrhythmias and offer real-time data for cardiac care. Full article
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41 pages, 2872 KiB  
Review
A Comprehensive Survey on the Requirements, Applications, and Future Challenges for Access Control Models in IoT: The State of the Art
by Mohammad Shamim Ahsan and Al-Sakib Khan Pathan
IoT 2025, 6(1), 9; https://doi.org/10.3390/iot6010009 - 24 Jan 2025
Viewed by 764
Abstract
The Internet of Things (IoT) is a technologyof connecting billions of devices with heterogeneous types and capabilities. Even though it is an attractive environment that could change the way we interact with the devices, the real-life and large-scale implementation of it is greatly [...] Read more.
The Internet of Things (IoT) is a technologyof connecting billions of devices with heterogeneous types and capabilities. Even though it is an attractive environment that could change the way we interact with the devices, the real-life and large-scale implementation of it is greatly impeded by the potential security risks that it is susceptible to. While the potential of IoT is significant, the security challenges it faces are equally formidable. IoT security can be addressed from different angles, but one of the key issues is the access control model because among the many challenges, access control is a pivotal concern that determines the overall security of IoT systems. This eventually determines which device is given access to the IoT systems and which is denied access. In this work, we conduct a systematic and thorough survey on the state-of-the-art access control models in IoT. This study includes more than 100 related articles, including 77 best-quartile journal papers. We cover conventional as well as advanced access control models, taking the crucial period of various studies in this particular area. In addition, a number of critical questions are answered and key works are summarized. Furthermore, we identify significant gaps in existing models and propose new considerations and prospects for future developments. Since no existing survey explores both conventional and sophisticated access control models with essential challenges, trends and application domains analysis, and requirements analysis, our study significantly contributes to the literature, especially in the IoT security field. Full article
(This article belongs to the Special Issue Advances in IoT and Machine Learning for Smart Homes)
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23 pages, 7373 KiB  
Article
Computer Model of an IoT Decision-Making Network for Detecting the Probability of Crop Diseases
by Grygorii Diachenko, Ivan Laktionov, Oleksandr Vovna, Oleksii Aleksieiev and Dmytro Moroz
IoT 2025, 6(1), 8; https://doi.org/10.3390/iot6010008 - 21 Jan 2025
Viewed by 704
Abstract
This article is devoted to the development and testing of a computer model of an IoT system that combines wireless network technologies for the online monitoring of climatic and soil conditions in agriculture. The system supports decision-making by predicting the probability of crop [...] Read more.
This article is devoted to the development and testing of a computer model of an IoT system that combines wireless network technologies for the online monitoring of climatic and soil conditions in agriculture. The system supports decision-making by predicting the probability of crop diseases. This study focuses on the processes of aggregation, wireless transmission, and processing of soil and climatic measurement data within infocommunication software and hardware solutions. This research makes both scientific and practical contributions. Specifically, it presents a computer model based on wireless sensor networks and edge-computing technologies. This model aggregates and intelligently processes agricultural monitoring data to predict crop diseases. The software component, developed using an adaptive neuro-fuzzy inference system (ANFIS), was integrated into the microcontroller unit of IoT systems for agricultural applications. This approach enabled the substantiation of an optimised algorithmic and structural organisation of the IoT system, enabling its use in designing reliable architectures for agricultural monitoring systems in open fields with decision-making support. Full article
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19 pages, 2816 KiB  
Article
An LDDoS Attack Detection Method Based on Behavioral Characteristics and Stacking Mechanism
by Junwei Ye, Zhixuan Wang, Jichen Yang, Chunan Wang and Chunyu Zhang
IoT 2025, 6(1), 7; https://doi.org/10.3390/iot6010007 - 21 Jan 2025
Viewed by 416
Abstract
Today, the development of the Internet of Things has grown, and the number of related IoT devices has reached the order of tens of billions. Most IoT devices are vulnerable to attacks, especially DdoS (Distributed Denial of Service attack) attacks. DDoS attacks can [...] Read more.
Today, the development of the Internet of Things has grown, and the number of related IoT devices has reached the order of tens of billions. Most IoT devices are vulnerable to attacks, especially DdoS (Distributed Denial of Service attack) attacks. DDoS attacks can easily cause damage to IoT devices, and LDDoS is an attack launched against hardware resources through a small string of very slow traffic. Compared with traditional large-scale DDoS, their attacks require less bandwidth and generate traffic similar to that of normal users, making them difficult to distinguish when identifying them. This article uses the CICIoT2023 dataset combined with behavioral features and stacking mechanisms to extract information from the attack behavior of low-rate attacks as features and uses the stacking mechanism to improve the recognition effect. A method of behavioral characteristics and stacking mechanism is proposed to detect DDoS attacks. This method can accurately detect LDDoS. Experimental results show that the recognition rate of low-rate attacks of this scheme reaches 0.99, and other indicators such as accuracy, recall, and F1 score are all better than other LDDoS detection methods. Thus, the method model proposed in this paper can effectively detect LDDoS attacks. At present, DDoS attacks are relatively mature, and there are many related results, but there is less research on LDDoS detection alone. This paper focuses on the investigation and analysis of LDDoS attacks in DDoS attacks and deduces feasible LDDoS detection methods. Full article
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21 pages, 1568 KiB  
Article
Efficient State Synchronization in Distributed Electrical Grid Systems Using Conflict-Free Replicated Data Types
by Arsentii Prymushko, Ivan Puchko, Mykola Yaroshynskyi, Dmytro Sinko, Hryhoriy Kravtsov and Volodymyr Artemchuk
IoT 2025, 6(1), 6; https://doi.org/10.3390/iot6010006 - 11 Jan 2025
Viewed by 601
Abstract
Modern electrical grids are evolving towards distributed architectures, necessitating efficient and reliable state synchronization mechanisms to maintain structural and functional consistency. This paper investigates the application of conflict-free replicated data types (CRDTs) for representing and synchronizing the states of distributed electrical grid systems [...] Read more.
Modern electrical grids are evolving towards distributed architectures, necessitating efficient and reliable state synchronization mechanisms to maintain structural and functional consistency. This paper investigates the application of conflict-free replicated data types (CRDTs) for representing and synchronizing the states of distributed electrical grid systems (DEGSs). We present a general structure for DEGSs based on CRDTs, focusing on the Convergent Replicated Data Type (CvRDT) model with delta state propagation to optimize the communication overhead. The Observed Remove Set (ORSet) and Last-Writer-Wins Register (LWW-Register) are utilized to handle concurrent updates and ensure that only the most recent state changes are retained. An actor-based framework, “Vigilant Hawk”, leveraging the Akka toolkit, was developed to simulate the asynchronous and concurrent nature of DEGSs. Each electrical grid node is modelled as an independent actor with isolated state management, facilitating scalability and fault tolerance. Through a series of experiments involving 100 nodes under varying latency degradation coefficients (LDK), we examined the impact of network conditions on the state synchronization efficiency. The simulation results demonstrate that CRDTs effectively maintain consistency and deterministic behavior in DEGSs, even with increased network latency and node disturbances. An effective LDK range was identified (LDK effective = 2 or 4), where the network remains stable without significant delays in state propagation. The linear relationship between the full state distribution time (FSDT) and LDK indicates that the system can scale horizontally without introducing complex communication overhead. The findings affirm that using CRDTs for state synchronization enhances the resilience and operational efficiency of distributed electrical grids. The deterministic and conflict-free properties of CRDTs eliminate the need for complex concurrency control mechanisms, making them suitable for real-time monitoring and control applications. Future work will focus on addressing identified limitations, such as optimizing message routing based on the network topology and incorporating security measures to protect state information in critical infrastructure systems. Full article
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30 pages, 4500 KiB  
Article
A Deep Learning-Based Gunshot Detection IoT System with Enhanced Security Features and Testing Using Blank Guns
by Tareq Khan
IoT 2025, 6(1), 5; https://doi.org/10.3390/iot6010005 - 3 Jan 2025
Viewed by 2551
Abstract
Although the U.S. makes up only 5% of the global population, it accounts for approximately 31% of public mass shootings. Gun violence and mass shootings not only result in loss of life and injury but also inflict lasting psychological trauma, cause property damage, [...] Read more.
Although the U.S. makes up only 5% of the global population, it accounts for approximately 31% of public mass shootings. Gun violence and mass shootings not only result in loss of life and injury but also inflict lasting psychological trauma, cause property damage, and lead to significant economic losses. We recently developed and published an embedded system prototype for detecting gunshots in an indoor environment. The proposed device can be attached to the walls or ceilings of schools, offices, clubs, places of worship, etc., similar to smoke detectors or night lights, and they can notify the first responders as soon as a gunshot is fired. The proposed system will help to stop the shooter early and the injured people can be taken to the hospital quickly, thus more lives can be saved. In this project, a new custom dataset of blank gunshot sounds is recorded, and a deep learning model using both time and frequency domain features is trained to classify gunshot and non-gunshot sounds with 99% accuracy. The previously developed system suffered from several security and privacy vulnerabilities. In this research, those vulnerabilities are addressed by implementing secure Message Queuing Telemetry Transport (MQTT) communication protocols for IoT systems, better authentication methods, Wi-Fi provisioning without Bluetooth, and over-the-air (OTA) firmware update features. The prototype is implemented in a Raspberry Pi Zero 2W embedded system platform and successfully tested with blank gunshots and possible false alarms. Full article
(This article belongs to the Special Issue Advances in IoT and Machine Learning for Smart Homes)
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22 pages, 1666 KiB  
Article
CoAP/DTLS Protocols in IoT Based on Blockchain Light Certificate
by David Khoury, Samir Haddad, Patrick Sondi, Patrick Balian, Hassan Harb, Kassem Danach, Joseph Merhej and Jinane Sayah
IoT 2025, 6(1), 4; https://doi.org/10.3390/iot6010004 - 2 Jan 2025
Viewed by 628
Abstract
The Internet of Things (IoT) is expanding rapidly, but the security of IoT devices remains a noteworthy concern due to resource limitations and existing security conventions. This research investigates and proposes the use of a Light certificate with the Constrained Application Protocol (CoAP) [...] Read more.
The Internet of Things (IoT) is expanding rapidly, but the security of IoT devices remains a noteworthy concern due to resource limitations and existing security conventions. This research investigates and proposes the use of a Light certificate with the Constrained Application Protocol (CoAP) instead of the X509 certificate based on traditional PKI/CA. We start by analyzing the impediments of current CoAP security over DTLS with the certificate mode based on CA root in the constrained IoT device and suggest the implementation of LightCert4IoT for CoAP over DTLS. The paper also describes a new modified handshake protocol in DTLS applied for IoT devices and Application server certificate authentication verification by relying on a blockchain without the complication of the signed certificate and certificate chain. This approach streamlines the DTLS handshake process and reduces cryptographic overhead, making it particularly suitable for resource-constrained environments. Our proposed solution leverages blockchain to reinforce IoT gadget security through immutable device characters, secure device registration, and data integrity. The LightCert4IoT is smaller in size and requires less power consumption. Continuous research and advancement are pivotal to balancing security and effectiveness. This paper examines security challenges and demonstrates the effectiveness of giving potential solutions, guaranteeing the security of IoT networks by applying LightCert4IoT and using the CoAP over DTLS with a new security mode based on blockchain. Full article
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23 pages, 4496 KiB  
Article
LoRa Technology Enhanced with a Custom-Designed High-Gain Yagi-Uda Antenna for Data Transmission from Misti Volcano Monitoring to Arequipa City
by Flor de Milagro Yesit Arana Medina and Jorge Rendulich
IoT 2025, 6(1), 3; https://doi.org/10.3390/iot6010003 - 29 Dec 2024
Viewed by 695
Abstract
This study details the design and implementation of a high-gain Yagi-Uda antenna network for the transmission of real-time monitoring data from the Misti Volcano to the city of Arequipa. As Misti is classified as a high-risk volcano due to its active volcanic nature [...] Read more.
This study details the design and implementation of a high-gain Yagi-Uda antenna network for the transmission of real-time monitoring data from the Misti Volcano to the city of Arequipa. As Misti is classified as a high-risk volcano due to its active volcanic nature and the close proximity of nearly one million inhabitants, the current monitoring infrastructure is insufficient to meet the demands of effective surveillance. In response, this project integrates Internet of Things (IoT) technology, the LoRa (Long Range) network, and an optimized seven-element Yagi-Uda antenna, developed using advanced optimization algorithms to enhance transmission efficiency. The primary objective is to facilitate the reliable collection and transmission of critical sensor data for subsequent analysis by volcanological experts, thereby supporting improved prediction and mitigation of potential volcanic hazards. Field tests have demonstrated that the Yagi-Uda antenna, when coupled with LoRa technology, achieved uninterrupted data transmission over a distance of 16 km. The integration of IoT, LoRa, and the optimized antenna design offers a scalable and resilient solution for the continuous monitoring and risk assessment of Misti, enabling the incorporation of advanced high-precision sensors for enhanced surveillance capabilities. Full article
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15 pages, 3380 KiB  
Article
Vegetation Effects on LoRa-Based Wireless Sensor Communication for Remote Monitoring of Automatic Orchard Irrigation Status
by Shahriar Ahmed, Md Nasim Reza, Samsuzzaman, Md Rejaul Karim, Hongbin Jin, Heetae Kim and Sun-Ok Chung
IoT 2025, 6(1), 2; https://doi.org/10.3390/iot6010002 - 26 Dec 2024
Viewed by 1279
Abstract
LoRa-based sensor nodes may provide a reliable solution for wireless communication in orchard cultivation and smart farming, facilitating real-time environmental monitoring. However, the signal strength and data integrity can be affected by several factors, such as trees, terrain, weather, and nearby electrical devices. [...] Read more.
LoRa-based sensor nodes may provide a reliable solution for wireless communication in orchard cultivation and smart farming, facilitating real-time environmental monitoring. However, the signal strength and data integrity can be affected by several factors, such as trees, terrain, weather, and nearby electrical devices. The objective of this study is to evaluate the impact of orchard trees on the performance of a LoRa sensor node under orchard conditions. A sensor node, built with a commercial LoRa transceiver and microcontroller unit (MCU), was paired with a single-channel gateway linked to an orchard irrigation system. Performance metrics such as the packet delivery ratio (PDR), received signal strength indicator (RSSI), and signal-to-noise ratio (SNR) were measured over a range of 20 to 120 m under open field conditions and in an orchard with trees averaging 3.12 and 4.36 m in height. Data were sent every 20 s using three spreading factors (SF8, SF10, and SF12) and stored as a CSV file in the MCU via a Python program. The results showed that the PDR remained consistently high (100%) under non-vegetative (open field) conditions. In the orchard under vegetative conditions, the PDR dropped significantly, with SF12 maintaining 100% only up to 120 m. For SF10, the packet delivery rates dropped to 45% at 80 m, while SF8 achieved 100% at 20 m but decreased to 52% at 40 m. SNR values also declined with an increase in distance, becoming largely undetectable beyond 40 m for SF8. These findings indicate that vegetation greatly impacts LoRa sensor node performance, reducing packet delivery and signal quality in orchards. Full article
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17 pages, 2945 KiB  
Article
K-Nearest Neighbors with Third-Order Distance for Flooding Attack Classification in Optical Burst Switching Networks
by Hilal H. Nuha, Satria Akbar Mugitama, Ahmed Abo Absa and Sutiyo
IoT 2025, 6(1), 1; https://doi.org/10.3390/iot6010001 - 25 Dec 2024
Viewed by 436
Abstract
Optical burst switching (OBS) is a network architecture that combines the advantages of packet and circuit switching techniques. However, OBS networks are susceptible to cyber-attacks, such as flooding attacks, which can degrade their performance and security. This paper introduces a novel machine learning [...] Read more.
Optical burst switching (OBS) is a network architecture that combines the advantages of packet and circuit switching techniques. However, OBS networks are susceptible to cyber-attacks, such as flooding attacks, which can degrade their performance and security. This paper introduces a novel machine learning method for flooding attack detection in OBS networks, based on a third-order distance function for k-nearest neighbors (KNN3O). The proposed distance is expected to improve detection accuracy due to higher sensitivity with respect to the distance difference between two points. The developed method is compared with seven other machine learning methods, namely standard KNN, KNN with cosine distance (KNNC), multi-layer perceptron (MLP), naive Bayes classifier (NBC), support vector machine (SVM), decision tree (DT), and discriminant analysis classifier (DAC). The methods are further assessed using five metrics: accuracy, precision, recall, F1-score, and specificity. The proposed method achieved an accuracy of 99.3%, outperforming the original KNN, MLP, and SVM, which achieved accuracies of 99%, 76.4%, and 94.7%, respectively. The results show that KNN3O is the best method for flooding attack detection in OBS networks, as it achieves the highest scores in all five metrics. Full article
(This article belongs to the Special Issue 6G Optical Internet of Things (OIoT) for Sustainable Smart Cities)
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