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IoT, Volume 5, Issue 2 (June 2024) – 4 articles

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21 pages, 2334 KiB  
Article
Smart Agriculture Drone for Crop Spraying Using Image-Processing and Machine Learning Techniques: Experimental Validation
by 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
Viewed by 113
Abstract
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 [...] Read more.
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. Full article
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23 pages, 1384 KiB  
Article
FedMon: A Federated Learning Monitoring Toolkit
by Moysis Symeonides, Demetris Trihinas and Fotis Nikolaidis
IoT 2024, 5(2), 227-249; https://doi.org/10.3390/iot5020012 - 11 Apr 2024
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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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Cloud and Edge Computing Systems for IoT)
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15 pages, 1126 KiB  
Article
Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
by 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
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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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Cloud and Edge Computing Systems for IoT)
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25 pages, 9966 KiB  
Article
Development of a Multi-Radio Device for Dry Container Monitoring and Tracking
by Mariano Falcitelli, Misal, Sandro Noto and Paolo Pagano
IoT 2024, 5(2), 187-211; https://doi.org/10.3390/iot5020010 - 2 Apr 2024
Viewed by 762
Abstract
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 [...] Read more.
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. Full article
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