IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges

Quy, Vu Khanh; Nguyen, Van-Hau; Anh, Dang Van; Quý, Nguyễn Minh; Ban, Nguyễn Tiến; Lanza, Stefania; Randazzo, Giovanni; Muzirafuti, Anselme

doi:10.3390/app12073396

Cited by 223 publications

(77 citation statements)

References 125 publications

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“…Akhter et al stated that [ 50 ] secondhand dossier data and machine intelligence in an IoT system to present an indicator model for Apple ailment in the sphere gardens of Kashmir basin. Quy et al [ 51 ] judge the architecture (IoT designs, communication sciences, ample data conversion, and transform), applications, and research schedule of IoT-enabled brainy agriculture environments. The IoT ecosystem is described by Elijah et al [ 52 ], who show how the integration of IoT and DA is enabling intelligent agriculture.…”

Section: Literature Reviewmentioning

confidence: 99%

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Rokade

Singh

Arora

et al. 2022

Computational and Mathematical Methods in Medicine

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In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods.

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Section: Literature Reviewmentioning

confidence: 99%

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Rokade

Singh

Arora

et al. 2022

Computational and Mathematical Methods in Medicine

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“…As the distance between sensor nodes increases, so does the SAR level, which necessitates more power and raising the temperature of the nodes. The 𝑺𝑨𝑹 is heat produced on tissue conductivity (𝝈) by the induced electromagnetic field radiation (𝑬𝑴𝑭) and the density of tissue (𝝆) can be estimated in (5).…”

Section: ) Energy Modelmentioning

confidence: 99%

“…The IoT vision extends traditional compute-based centralized systems to a dispersed environment by providing a seamless platform for connecting people and objects. It provides a wide range of everyday smart applications in industries [1], education [2], homes [3], cities [4], agriculture [5], and health [6]. The health sector is the most important, with everincreasing demands providing greater insight into the various platforms for conducting advanced research in this dynamic domain.…”

Section: Introductionmentioning

confidence: 99%

Optimized Distributive Cross-Layer and Thermal-Aware Convergecast Protocol for Wireless Body Area Network

et al. 2022

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The Internet of Healthcare Things has significantly altered traditional patient-doctor relationships by allowing essential healthcare treatment from the comfort of one's home. The wireless body area network is an IEEE 802.15.6 standard that focuses on healthcare data, necessitating various cross-layer and thermal-aware protocols. However, most cross-layer protocols have long convergence delays and a single failure point. Moreover, these protocols exploit excessive broadcasts and handshake acknowledgments, causing communication and processing overheads. Furthermore, thermal-aware protocols focus on thermal variations, disperse data collection, and do not support cross-layer techniques. To address these limitations, this study proposes an optimal distributive cross-layer and thermal-aware convergecast protocol. The proposed protocol enforces a novel hybrid convergecast using probability and both minimum attenuation strategies to collect data from leaf nodes to the root to improve data flow and adaptability in the network. In addition, it accelerates the convergence process by reducing recurrent broadcasts and unnecessary acknowledgments, resulting in improved energy efficiency and thermal control. The proposed protocol supports a distributive hierarchy by establishing multiple parent-child relationships to avoid a single root point failure. A multi-parameter maximum benefit-cost function calculates the next hop according to the extracted weights. Packet loss probability validates the number and sequence of packets received at the sink node. The simulation results demonstrate that cross-layer and thermal-aware protocols can coexist effectively. The proposed protocol reduces delays to 19.4%, improves throughput from 8% to 13.75%, and retains a packet loss probability of 0.3% by keeping the thermal rise within bounds.

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“…For instance, in a smart irrigation system, the conditioned unit value for the soil moisture sensor is 1000; thus, if the sensor provides less than 1000 soil moisture values, the actuation will start. Some of the communication technologies namely 802.11*, Sigfox, LoRaWAN, NBIoT, LTE-3GPP, EC-GPRS, WiMAX, Bluetooth, ZigBee, Z-Wave, 6LoWPAN and NFC used in IoTA are categorised based on distinct parameters like licenced spectrum, transmission distance, type of network used, frequency, and data rate [41] [38] [32] [42] [34]. The communication technology allows the hardware (in the physical layer such as sensors, actuators and the controller) to communicate by wired or wireless medium [43].…”

Section: Includes People and Business Processesmentioning

confidence: 99%

IoT based Agriculture (IoTA): Architecture, Cyber Attack, Cyber Crime and Digital Forensics Challenges

Rudrakar¹,

Rughani²

2022

Preprint

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The Internet of Things (IoT) is a thriving technology in almost all fields of human life to make their lives easy. Smart Agriculture is one of the IoT applications on which farmers are widely switching to improve productivity and reduce labor costs. As IoT involves the Internet, data transmission technology creates the possibility of privacy/security breaches concerning users’ data. The cybercrime rate may also increase in IoT-based agriculture (IoTA). Thus there must be ready concerning digital forensics in IoTA. Various review articles are available regarding the emerging cyber attack, cybercrime, and forensics challenges in IoT applications like smart home, smart city, smart healthcare, smart grid, smart retail, smart supply chain management, and smart agriculture, while a few review articles on IoTA security and forensic challenges. This review paper is focused on IoTA solutions and their security and forensics challenges. The systematic literature review method has been used for this study which includes articles published from 2011 - 2021. This article has brought the limelight on IoT architecture and its application in agricluture (IoTA) to emphasize on its introduction, architecture, challenges and issues related emerging cybercrimes and digital forensics. At the end, this review paper discusses various challenges in securing IoTA systems and presents future directions for research.

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IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges

Cited by 223 publications

References 125 publications

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Optimized Distributive Cross-Layer and Thermal-Aware Convergecast Protocol for Wireless Body Area Network

IoT based Agriculture (IoTA): Architecture, Cyber Attack, Cyber Crime and Digital Forensics Challenges

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