Secure Irrigation System for Olive Orchards Using Internet of Things

Massaoudi, Ayman; Berguiga, Abdelwahed; Harchay, Ahlem

doi:10.32604/cmc.2022.026972

Cited by 2 publications

(4 citation statements)

References 25 publications

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“…Third and fourth generation wireless communication technologies are not guaranteed to meet the requirements of smart agriculture applications (particularly precise irrigation), mainly in terms of their delay, speed, scalability, and bandwidth. Therefore, recent studies proposed deploying 5G/6G technology in the agricultural field to overcome the limitations mentioned above (see, for example, [4][5][6][7][8][9][10][11] and the references therein). In fact, 5G/6G networks are characterized by ultra-low delays, super large-scale connections, and high speeds.…”

Section: G/6g In Precision Agriculturementioning

confidence: 99%

“…However, using cables to communicate data in smart farming presents several drawbacks, such as scalability, deployment costs, absence of mobility, and cable corruption. Therefore, adopting wireless communication technologies like LoRaWAN (Long-Range Wide-Area Network), ZigBee, Bluetooth, and the IEEE 802.15.4 protocol presents an attractive alternative to wired networks [5,19]. In [19], Valente et al developed an IoT-based system to monitor the water scarcity of a vineyard, where different types of ground-based sensors were implemented.…”

Section: Precision Irrigationmentioning

confidence: 99%

“…Precision agriculture represents the adoption of new technologies to strengthen the efficiency of a bio-based economy while conserving scarce natural resources. Specifically, smart farming is growing thanks to the adoption of new technologies, including artificial intelligence (AI), the Internet of Things (IoT), and 5G/6G networks [3][4][5]. This can assist farmers in taking appropriate measures at the appropriate moment by monitoring soil moisture, temperature, and other environmental factors, consequently making the production process more scientific in terms of fertilization, pest control, and improving crop yields.…”

Section: Introductionmentioning

confidence: 99%

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Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems

Massaoudi,

Berguiga,

Harchay

et al. 2023

Applied Sciences

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Precision agriculture, also referred to as smart farming, is one of the main pillars of modern society to achieve the Sustainable Development Goals (SDGs). Precision agriculture aims to improve the quality and quantity of production while conserving scarce natural resources. Smart farming has grown in recent years thanks to the adoption of modern technologies, including artificial intelligence (AI) and the Internet of Things (IoT). In this work, we consider an irrigation system for olive orchards based on unmanned aerial vehicles (UAVs). Specifically, UAVs ensure remote sensing (RS), which offers the advantage of collecting vital information on a large temporal and spatial scale (which cannot be achieved with traditional technologies). However, UAV-based irrigation systems face tremendous challenges due to the various requirements of a powerful computing ability, battery capacity, energy efficiency, and spectral efficiency for different connected devices. This paper addresses the energy efficiency and spectral efficiency trade-off problem of UAV-based irrigation systems. We propose to adopt massive multiple input, multiple output (M-MIMO) technology to ensure wireless communication. In fact, this technology plays a significant role in future sixth-generation (6G) wireless mobile networks and has the potential to enhance the energy efficiency as well as the spectral efficiency. We design a network model with a three-layered architecture and analytically compute the achievable spectral efficiency and the energy efficiency of the studied system. Then, we numerically determine the optimal number of ground base station antennas as well as the optimal number of IoT devices that should be used to ensure the maximum energy efficiency while guaranteeing a high spectral efficiency. The numerical results prove that the proposed UAV-based irrigation system outperforms conventional systems and demonstrate that the best spectral and energy efficiency trade-off is obtained by using the M-MMSE combiner.

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Section: G/6g In Precision Agriculturementioning

confidence: 99%

Section: Precision Irrigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems

Massaoudi,

Berguiga,

Harchay

et al. 2023

Applied Sciences

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“…Also, the application of IoT in Agriculture 5.0 ranges from family farming and plays a vital role in changing the overview of conventional agriculture scenarios. For example, the digitalization of agriculture has been brought about by the Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI), where machines and production systems are connected, monitored, and controlled through a network of sensors and actuators [6]. This can assist farmers in taking appropriate measures at the appropriate moment by monitoring soil moisture, temperature, and other environmental factors, consequently making the production process more scientific regarding fertilization, pest control, and improving crop yields.…”

Section: Introductionmentioning

confidence: 99%

GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture

Berguiga,

Harchay,

Massaoudi

et al. 2023

CMC

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Smart Agriculture, also known as Agricultural 5.0, is expected to be an integral part of our human lives to reduce the cost of agricultural inputs, increasing productivity and improving the quality of the final product. Indeed, the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important. To provide more comprehensive protection against potential cyber-attacks, this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture. The proposed Intrusion Detection System IDS, namely GMLP-IDS, combines the feedforward neural network Multilayer Perceptron (MLP) and the Gaussian Mixture Model (GMM) that can better protect the Smart Agriculture system. GMLP-IDS is evaluated with the CIC-DDoS2019 dataset, which contains various Distributed Denial-of-Service (DDoS) attacks. The paper first uses the Pearson's correlation coefficient approach to determine the correlation between the CIC-DDoS2019 dataset characteristics and their corresponding class labels. Then, the CIC-DDoS2019 dataset is divided randomly into two parts, i.e., training and testing. 75% of the data is used for training, and 25% is employed for testing. The performance of the newly proposed IDS has been compared to the traditional MLP model in terms of accuracy rating, loss rating, recall, and F1 score. Comparisons are handled on both binary and multi-class classification problems. The results revealed that the proposed GMLP-IDS system achieved more than 99.99% detection accuracy and a loss of 0.02% compared to traditional MLP. Furthermore, evaluation performance demonstrates that the proposed approach covers a more comprehensive range of security properties for Smart Agriculture and can be a promising solution for detecting unknown DDoS attacks.

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Secure Irrigation System for Olive Orchards Using Internet of Things

Cited by 2 publications

References 25 publications

Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems

Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems

GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture

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