Enhancing smart farming through the applications of Agriculture 4.0 technologies

International Journal of Intelligent Networks, Год журнала: 2022, Номер 3, С. 150 - 164

Опубликована: Янв. 1, 2022

Agriculture 4.0 represents the fourth agriculture revolution that uses digital technologies and moves toward a smarter, more efficient, environmentally responsible sector. Agricultural have emerged to enhance sustainability discover effective farm methods. This encompasses all digitalisation automation processes in business our daily lives, including Big Data, Artificial Intelligence (AI), robots, Internet of Things (IoT), virtual augmented reality. These technological advancements are having profound impact on lives. From technical standpoint, it brings us precision agriculture. provides data-driven strategy for efficiently growing maintaining crops cultivable land, enabling farmers use most resources at their disposal. Throughout supply chain, operations create massive volumes data. Most this information was previously untouched, but with help big data technologies, such can be used improve performance production any crop. Depending crop type its growth needs, digitised harvesters handle huge areas various situations, particularly paper is brief about condition. Smart farming, Various key specific domains Exploring Domain discussed detail and, finally, identified significant applications technologies. essential lives since they simplify duties without recognising them. In systems, fleets equipment employ current infrastructures like cloud computing connect, identify processing condition different regions requirement input materials coordinate machinery.

Язык: Английский

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FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things

Journal of Parallel and Distributed Computing, Год журнала: 2022, Номер 165, С. 17 - 31

Опубликована: Март 16, 2022

Язык: Английский

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Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges

Computer Networks, Год журнала: 2021, Номер 203, С. 108661 - 108661

Опубликована: Дек. 14, 2021

The application of Machine Learning (ML) techniques to the well-known intrusion detection systems (IDS) is key cope with increasingly sophisticated cybersecurity attacks through an effective and efficient process. In context Internet Things (IoT), most ML-enabled IDS approaches use centralized where IoT devices share their data centers for further analysis. To mitigate privacy concerns associated approaches, in recent years Federated (FL) has attracted a significant interest different sectors, including healthcare transport systems. However, development FL-enabled its infancy, still requires research efforts from various areas, order identify main challenges deployment real-world scenarios. this direction, our work evaluates approach based on multiclass classifier considering distributions scenario. particular, we three settings that are obtained by partitioning ToN_IoT dataset according devices’ IP address types attack. Furthermore, evaluate impact aggregation functions such setting using IBMFL framework as FL implementation. Additionally, set future directions existing literature analysis evaluation results.

Язык: Английский

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A new DDoS attacks intrusion detection model based on deep learning for cybersecurity

Computers & Security, Год журнала: 2022, Номер 118, С. 102748 - 102748

Опубликована: Май 2, 2022

Язык: Английский

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Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets

Sensors, Год журнала: 2022, Номер 22(10), С. 3744 - 3744

Опубликована: Май 14, 2022

The explosive growth of the Internet Things (IoT) applications has imposed a dramatic increase network data and placed high computation complexity across various connected devices. IoT devices capture valuable information, which allows industries or individual users to make critical live dependent decisions. Most these have resource constraints such as low CPU, limited memory, energy storage. Hence, are vulnerable cyber-attacks due lack capacity run existing general-purpose security software. It creates an inherent risk in networks. multi-access edge computing (MEC) platform emerged mitigate by relocating complex tasks from edge. related works focusing on finding optimized solutions protect We believe distributed leveraging MEC should draw more attention. This paper presents comprehensive review state-of-the-art intrusion detection systems (NIDS) practices for analyzed approaches based platforms utilizing machine learning (ML) techniques. also performs comparative analysis public available datasets, evaluation metrics, deployment strategies employed NIDS design. Finally, we propose framework networks MEC.

Язык: Английский

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Implementation of intrusion detection model for DDoS attacks in Lightweight IoT Networks

Expert Systems with Applications, Год журнала: 2022, Номер 215, С. 119330 - 119330

Опубликована: Ноя. 25, 2022

Язык: Английский

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Soil Quality Prediction in Context Learning Approaches Using Deep Learning and Blockchain for Smart Agriculture

Advances in computational intelligence and robotics book series, Год журнала: 2023, Номер unknown, С. 1 - 26

Опубликована: Сен. 25, 2023

The integration of deep learning and blockchain technologies has the potential to revolutionize soil quality prediction in smart agriculture. Deep models, like neural networks convolutional networks, enable accurate predictions properties by considering intricate relationships within data. Contextual approaches, including embeddings data fusion, enrich process incorporating external factors weather conditions land management practices. Blockchain technology ensures secure storage data, while contracts facilitate automated model execution. This integrated system empowers farmers with for optimal resource allocation fosters collaboration through decentralized sharing. Future directions include advancements algorithms, applications, IoT remote sensing technologies.

Язык: Английский

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Security in IoT-enabled smart agriculture: architecture, security solutions and challenges

Cluster Computing, Год журнала: 2022, Номер 26(2), С. 879 - 902

Опубликована: Апрель 18, 2022

Язык: Английский

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A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks

Network, Год журнала: 2023, Номер 3(1), С. 158 - 179

Опубликована: Янв. 30, 2023

The Internet of Things (IoT) is a network electrical devices that are connected to the wirelessly. This group generates large amount data with information about users, which makes whole system sensitive and prone malicious attacks eventually. rapidly growing IoT-connected under centralized ML could threaten privacy. popular machine learning (ML)-assisted approaches difficult apply due their requirement enormous amounts in central entity. Owing distribution over numerous networks devices, decentralized solutions needed. In this paper, we propose Federated Learning (FL) method for detecting unwanted intrusions guarantee protection IoT networks. ensures privacy security by federated training local device data. Local clients share only parameter updates global server, aggregates them distributes an improved detection algorithm. After each round FL training, receives updated model from server trains dataset, where can keep own intact while optimizing overall model. To evaluate efficiency proposed method, conducted exhaustive experiments on new dataset named Edge-IIoTset. performance evaluation demonstrates reliability effectiveness intrusion achieving accuracy (92.49%) close offered conventional models’ (93.92%) using method.

Язык: Английский

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Smart and sustainable agriculture: Fundamentals, enabling technologies, and future directions

Computers & Electrical Engineering, Год журнала: 2023, Номер 110, С. 108799 - 108799

Опубликована: Июнь 15, 2023

Язык: Английский

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