Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 145813 - 145852

Published: Jan. 1, 2023

The increasing food scarcity necessitates sustainable agriculture achieved through automation to meet the growing demand. Integrating Internet of Things (IoT) and Wireless Sensor Networks (WSNs) is crucial in enhancing production across various agricultural domains, encompassing irrigation, soil moisture monitoring, fertilizer optimization control, early-stage pest crop disease management, energy conservation. application protocols such as ZigBee, WiFi, SigFox, LoRaWAN are commonly employed collect real-time data for monitoring purposes. Embracing advanced technology imperative ensure efficient annual production. Therefore, this study emphasizes a comprehensive, future-oriented approach, delving into IoT-WSNs, wireless network protocols, their applications since 2019. It thoroughly discusses overview IoT WSNs, architectures summarization protocols. Furthermore, addresses recent issues challenges related IoT-WSNs proposes mitigation strategies. provides clear recommendations future, emphasizing integration aiming contribute future development smart systems.

Language: Английский

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Dependable intrusion detection system using deep convolutional neural network: A Novel framework and performance evaluation approach

Telematics and Informatics Reports, Journal Year: 2023, Volume and Issue: 11, P. 100077 - 100077

Published: July 6, 2023

Intrusion detection systems (IDS) play a critical role in safeguarding computer networks against unauthorized access and malicious activities. However, traditional IDS approaches face challenges accurately detecting complex evolving cyber threats. The proposed framework leverages the power of deep learning to automatically extract meaningful features from network traffic data, enabling more accurate robust intrusion detection. convolutional neural (DCNN) has been trained on large-scale datasets, incorporating both normal traffic, enable effective discrimination between anomalous behavior. To evaluate performance framework, comprehensive evaluation approach is developed, considering key metrics such as accuracy, false positive rate, computational efficiency. Additionally, GPU utilized for boosting model, demonstrating effectiveness superiority CNN-based system over methods. novelty this study lies development dependable that harnesses potential DCNN analysis. evaluated with four publicly available namely ISCX-IDS 2012, DDoS (Kaggle), CICIDS2017, CICIDS2018. Our results demonstrate optimized model improving accuracy. With accuracy levels ranging 99.79% 100%, our underscore model’s efficacy, offering efficient outcomes have significant implications security, providing valuable insights practitioners researchers working towards building intelligent systems.

Language: Английский

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IoT-based agriculture management techniques for sustainable farming: A comprehensive review

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 220, P. 108851 - 108851

Published: March 26, 2024

Language: Английский

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Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO

Mathematics, Journal Year: 2023, Volume and Issue: 11(17), P. 3724 - 3724

Published: Aug. 29, 2023

This study presents a comprehensive exploration of the hyperparameter optimization in one-dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The increasing frequency and complexity cyberattacks have prompted an urgent need effective intrusion-detection systems (IDSs). Herein, we focus on optimizing nine hyperparameters within 1D-CNN model, using two well-established evolutionary computation methods—genetic algorithm (GA) particle swarm (PSO). performances these methods are assessed three major datasets—UNSW-NB15, CIC-IDS2017, NSL-KDD. key performance metrics considered this include accuracy, loss, precision, recall, F1-score. results demonstrate considerable improvements all across datasets, both GA- PSO-optimized models, when compared to those original nonoptimized model. For instance, UNSW-NB15 dataset, GA PSO achieve accuracies 99.31 99.28%, respectively. Both algorithms yield equivalent terms Similarly, vary CIC-IDS2017 NSL-KDD indicating that efficacy is context-specific dependent nature dataset. findings importance effects efficient optimization, greatly contributing field security. serves as crucial step toward developing advanced, robust, adaptable IDSs capable addressing evolving landscape cyber threats.

Language: Английский

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An in-depth evaluation of deep learning-enabled adaptive approaches for detecting obstacles using sensor-fused data in autonomous vehicles

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108550 - 108550

Published: May 9, 2024

Language: Английский

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Botnets Unveiled: A Comprehensive Survey on Evolving Threats and Defense Strategies

Transactions on Emerging Telecommunications Technologies, Journal Year: 2024, Volume and Issue: 35(11)

Published: Oct. 20, 2024

ABSTRACT Botnets have emerged as a significant internet security threat, comprising networks of compromised computers under the control command and (C&C) servers. These malevolent entities enable range malicious activities, from denial service (DoS) attacks to spam distribution phishing. Each bot operates binary code on vulnerable hosts, granting remote attackers who can harness combined processing power these hosts for synchronized, highly destructive while maintaining anonymity. This survey explores botnets their evolution, covering aspects such life cycles, C&C models, botnet communication protocols, detection methods, unique environments operate in, strategies evade tools. It analyzes research challenges future directions related botnets, with particular focus evasion techniques, including methods like encryption use covert channels reinforcement botnets. By reviewing existing research, provides comprehensive overview origins evolving tactics, evaluates how counteract activities. Its primary goal is inform community about changing landscape in combating threats, offering guidance addressing concerns effectively through highlighting methods. The concludes by presenting directions, using strengthen aims guide researchers developing more robust measures combat effectively.

Language: Английский

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Unleashing the potential of IoT, Artificial Intelligence, and UAVs in contemporary agriculture: A comprehensive review

Journal of Terramechanics, Journal Year: 2024, Volume and Issue: 115, P. 100986 - 100986

Published: May 10, 2024

Language: Английский

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TA-YOLO: a lightweight small object detection model based on multi-dimensional trans-attention module for remote sensing images

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 10(4), P. 5459 - 5473

Published: May 8, 2024

Abstract Object detection plays a vital role in remote sensing applications. Although object has achieved proud results natural images, these methods are difficult to be directly applied images. Remote images often have complex backgrounds and small objects, which highly unbalanced distribution of foreground background information. In order solve the above problems, this paper proposes multi-head channel spatial trans-attention (MCSTA) module, performs pixel interaction from dimensions respectively complete attention feature capture function. It is plug-and-play module that can easily embedded any other image convolutional neural network, making it quickly applicable First, reduce computational complexity improve richness, we use special linear convolution obtain three projection features instead simple matrix multiplication transformation Transformer. Second, maps different manner similar self-attention mechanism interrelationships channels spaces. process, perform parallel operations speed. Furthermore, avoid large-scale operations, specially designed an blocking mode computer memory usage increase operation Finally, into YOLOv8, added new head optimized fusion method, thus designing lightweight model named TA-YOLO for fewer parameters than benchmark its mAP on PASCAL VOC VisDrone data sets increased by 1.3% 6.2% respectively. The experimental prove powerful function excellent performance TA-YOLO.

Language: Английский

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Optimizing intrusion detection using intelligent feature selection with machine learning model

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 91, P. 39 - 49

Published: Feb. 6, 2024

Network security is a critical aspect of information technology, targeting to safeguard the confidentiality, integrity, and availability data transmitted across computer networks. Intrusion Detection Systems (IDS) plays an essential role, serving as vigilant sentinels against illegal access, malicious actions, potential threats. IDS operates on analysing network or system activities, analyzing patterns, detecting anomalies that may specify breaches. The enhancement via integration feature selection machine learning, particularly in context IDS. Feature methods enable identification prioritization key attributes, optimizing performance learning algorithms by focusing relevant information. Machine algorithms, such decision trees, support vector machines, neural networks, leverage chosen features dynamically adapt learn from evolving cyber Therefore, this study develops new gravitational search algorithm-based with optimal quantum (GSAFS-OQNN) model for intrusion detection classification. proposed GSAFS-OQNN approach lies effectual intrusions. To accomplish this, method exploits Z-score normalization at preprocessing step. Furthermore, technique designs GSAFS derive optimum subset features. For detection, (QNN) applied. Finally, sandpiper optimization (SPO) used finetune parameters QNN model. experimental analysis implemented benchmark datasets. comprehensive results stated betterment over recent approaches.

Language: Английский

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Anomaly and intrusion detection using deep learning for software-defined networks: A survey

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 256, P. 124982 - 124982

Published: Aug. 5, 2024

Language: Английский

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