NeuroRF FarmSense: IoT-fueled precision agriculture transformed for superior crop care

Traditional farming methods, effective for generations, struggle to meet rising global food demands due to limitations in productivity, efficiency, and sustainability amid climate change and resource scarcity. Precision agriculture presents a viable solution by optimizing resource use, enhancing efficiency, and fostering sustainable practices through data-driven decision-making supported by advanced sensors and Internet of Things (IoT) technologies. This review examines various smart sensors used in precision agriculture, including soil sensors for moisture, pH, and plant stress sensors etc. These sensors deliver real-time data that enables informed decision-making, facilitating targeted interventions like optimized irrigation, fertilization, and pest management. Additionally, the review highlights the transformative role of IoT in precision agriculture. The integration of sensor networks with IoT platforms allows for remote monitoring, data analysis via artificial intelligence (AI) and machine learning (ML), and automated control systems, enabling predictive analytics to address challenges such as disease outbreaks and yield forecasting. However, while precision agriculture offers significant benefits, it faces challenges including high initial investment costs, complexities in data management, needs for technical expertise, data security and privacy concerns, and issues with connectivity in remote agricultural areas. Addressing these technological and economic challenges is essential for maximizing the potential of precision agriculture in enhancing global food security and sustainability. Therefore, in this review we explore the latest trends, challenges, and opportunities associated with IoT enabled smart sensors in precision agriculture.

Internet of Things (IoT) has massively adopted the market due to the current era demanding fully intelligent and autonomous services. The IoT industry is flourishing in terms of Internet of healthcare things, Internet of vehicles and autonomous driving, unmanned aerial vehicles, satellite and industrial Internet of things smart homes, etc. IoT can be enabled by employing connected devices such as sensors, continuously communicating, storing, and disseminating data. Data dissemination through energy-constrained low-power IoT sensors suffers from issues such as security and privacy of data. Traditional solutions using single-layer or multi-layer cryptographic algorithms do not suffice the need for security and privacy. Thus, we have proposed a blockchain-based secure onion routing protocol for a trusted and anonymous data dissemination framework for sensor communication in IoT environment. We have employed machine learning (ML) algorithms to classify adversarial and non-adversarial data fed to the onion router to reduce computational time. Our proposed framework uses a Random Forest (RF) classifier outperforming with 93.65% accuracy compared to other ML algorithms. Also, we have achieved low data storage cost, low latency, and high scalability by employing Inter Planetary File System (IPFS) and a 6G network, respectively.

AI-enabled devices, 311 EE and attitude toward, 240 emergence of industry 5. 0 and role of, 21-22 marketing, 197 Attitude toward act/behavior (ATB), 250 Attitude toward artificial intelligence, 233, 235-236 Augmented Dickey-Fuller test (ADF test), 224, 226 Augmented reality (AR), 23, 197 for marketing, 197 Automated Teller Machines (ATM), 40 Automation, 19, 190 strategies to enhance automations in industry 5.0, 20-21 Average variance extracted values (AVE values), 257 Banking and Financial Institutions Act, 183 Banking industry, 40, 294 at age of industry 5.0, 303-304 challenge of rising costs, 302 challenges faced by banks in adoption of AI and blockchain, 301-303 current applications of AI and blockchain in, 296-301 employment challenges, 301 ethical challenges, 302 methodology, 295-296 performance challenges, 301 regulatory challenges, 302-303 security, privacy, and trust challenges, 302 Banking services, 41 Banks, 280, 294 technological know-how within, 43-45 Barclays, 297 Bartlett's test, 109 of sphericity,

Agriculture is central to the economy of the world, with sixty percent of the population depending on it for survival. Farmers are adopting smart farming technics to make agricultural practices more efficient. Smart farming takes advantage of Internet of Things (IoT) technologies for performing tasks such as moisture sensing, weeding, keeping vigilance, spraying, bird and animal scaring, smart irrigation controls, the use of real time field data for intelligent decision making and smart warehouse management which includes theft detection and temperature and humidity maintenance of the warehouse. Smart devices such as watches, computers or cellphones connected to the internet can then be used to control the smart farming system. Smart farming being at nascent stage, its privacy and security needs to be researched and explored as its future partially dependent on the resolution of the privacy and security issues associated. This paper comprehensively reviews various security and privacy issues and challenges associated with IoT deployments in smart farming. Following a structured approach, a framework for smart farming security and privacy was developed in an attempt to address challenges experienced/expected. This framework can also be used for future directions for any IoT related privacy and security initiatives.

Security and privacy of users have become significant concerns due to the involvement of the Internet of Things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this article, we provide a summary of research efforts made in the past few years, from 2008 to 2019, addressing security and privacy issues using ML algorithms and BC techniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past 12 years in the IoT domain. We then classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.

Internet of Things (IoT) technology has been incorporated into the majority of people’s everyday lives and places of employment due to the quick development in information technology. Modern agricultural techniques increasingly use the well-known and superior approach of managing a farm known as “smart farming”. Utilizing a variety of information and agricultural technologies, crops are observed for their general health and productivity. This requires monitoring the condition of field crops and looking at many other indicators. The goal of smart agriculture is to reduce the amount of money spent on agricultural inputs while keeping the quality of the final product constant. The Internet of Things (IoT) has made smart agriculture possible through data collection and storage techniques. For example, modern irrigation systems use effective sensor networks to collect field data for the best plant irrigation. Smart agriculture will become more susceptible to cyber-attacks as its reliance on the IoT ecosystem grows, because IoT networks have a large number of nodes but limited resources, which makes security a difficult issue. Hence, it is crucial to have an intrusion detection system (IDS) that can address such challenges. In this manuscript, an IoT-based privacy-preserving anomaly detection model for smart agriculture has been proposed. The motivation behind this work is twofold. Firstly, ensuring data privacy in IoT-based agriculture is of the utmost importance due to the large volumes of sensitive information collected by IoT devices, including on environmental conditions, crop health, and resource utilization data. Secondly, the timely detection of anomalies in smart agriculture systems is critical to enable proactive interventions, such as preventing crop damage, optimizing resource allocation, and ensuring sustainable farming practices. In this paper, we propose a privacy-encoding-based enhanced deep learning framework for the difficulty of data encryption and intrusion detection. In terms of data encoding, a novel method of a sparse capsule-auto encoder (SCAE) is proposed along with feature selection, feature mapping, and feature normalization. An SCAE is used to convert information into a new encrypted format in order to prevent deduction attacks. An attention-based gated recurrent unit neural network model is proposed to detect the intrusion. An AGRU is an advanced version of a GRU which is enhanced by an attention mechanism. In the results section, the proposed model is compared with existing deep learning models using two public datasets. Parameters such as recall, precision, accuracy, and F1-score are considered. The proposed model has accuracy, recall, precision, and F1-score of 99.9%, 99.7%, 99.9%, and 99.8%, respectively. The proposed method is compared using a variety of machine learning techniques such as the deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM).

ABSTRACTIt has been observed that the agriculture sector has picked exponential growth with the help of the integration of advanced technology like wireless sensor networks (WSNs), the Internet of Things (IoT), and machine learning (ML). They have not only created new opportunities but also boosted the efficiency and productivity in the sector. Out of so many significant hurdles, accurately pinpointing the positions of the sensor nodes and agricultural assets like crops, livestock, and machinery within the field is one major challenge. Merging ML with a WSN‐assisted IoT (WIoT) network has offered a promising solution to overcome the localization challenge for smart agriculture. This integration will greatly benefit agricultural practices, such as precision agriculture, fast decision‐making, accurate positioning of sensor nodes, monitoring, and managing real‐time data effectively. This paper comprehensively discusses state‐of‐the‐art techniques and methodologies employed in localization within WIoT networks for smart agriculture. It explores various ML algorithms, which include reinforcement learning, supervised learning, and unsupervised learning, to perform the localization activity precisely. Moreover, it explores how fusion technologies like WSN, IoT, ML, and sensors will enhance localization accuracy and reliability in various agriculture activities. Additionally, the paper discusses the application of localization in the agriculture sector, such as crop monitoring, livestock management, precision agriculture, environmental conditions, and crop monitoring. It also discusses the challenges and obstacles in the path domain of various evaluation parameters like energy efficiency, scalability, and robustness, along with research and societal implications. Overall, the paper provides valuable guidance and outlines potential directions for future research in ML‐driven localization in WIoT for smart agriculture, offering a clear roadmap for researchers.

Agriculture is an industry that has historically relied on traditional methods for crop production, but with the advent of new technologies, it is now possible to integrate machine learning and Internet of Things (IoT) applications to improve agricultural practices. Machine learning algorithms and IoT devices can be used to analyze data collected from agricultural fields to optimize crop yield, reduce resource consumption, and improve farm management. In this review paper, we explore the various applications of machine learning and IoT in agriculture, specifically focusing on their use in crop monitoring, disease detection, and water management. We examine the challenges associated with implementing these technologies in agriculture, including issues related to data collection, privacy, and security. Finally, we discuss the potential benefits of integrating machine learning and IoT in agriculture and identify future research directions that can help advance this field. Overall, this review highlights the potential of machine learning and IoT technologies to revolutionize agriculture and improve food security in the years to come. The Internet of Things (IoT) network must be integrated with sensors in order for "smart agriculture" to be a reality. At many layers of the IoT system architecture, machine learning (ML) techniques are incorporated to increase usefulness and capabilities. For agricultural systems to properly integrate with information technology, intelligent agricultural systems must be established, and all types of information created by agricultural systems must be integrated and analysed.The agriculture sector might undergo a transformation thanks to the fusion of machine learning (ML) and internet of things (IoT) technology. Precision agriculture and more economical resource usage are made possible by using IoT sensors to collect data on a variety of factors, including soil moisture, temperature, and nutrient levels. Then, using these data, ML algorithms may be used to forecast outcomes and improve decision-making. For example, they can forecast agricultural yields, spot disease or insect infestations, and suggest the best dates for planting and harvesting.

The concept of “smart agriculture” relies on the integration of sensors within an Internet of Things (IoT) network. Machine learning (ML) algorithms are integrated at various levels of the IoT system design to augment its functionality and enhance its capability. This article is a bibliometric review of 42 articles published between 2018 and 2022 using the Web of Science database. The results of the review showed an exponential growth in the use of ML algorithms in IoT systems for different agriculture applications. Additionally, two key research questions are addressed in this article, one being the development of IoT -ML-enabled smart agriculture over the past five years, and the second being the main research gaps for applications of machine learning and IoT in smart agriculture. The article concludes with a discussion of the results and future directions for research in the field.

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

The rise of smart farming, driven by technologies like the Internet of Things (IoT), has opened up new possibilities in agriculture. However, it has also brought about significant security challenges. This study aims to address the need for a comprehensive security system designed specifically for smart farms. The authors developed this system using camera technology, Arduino microcontrollers, vibration sensors, and the SIM900A SIM module. It enhances intrusion detection, provides precise location tracking, and enables real-time incident reporting through Multimedia Messaging Service (MMS) alerts and Short Message Service (SMS). By capturing visual data and leveraging vibration sensors, it offers an effective means of identifying security threats. Importantly, the SIM900A module ensures swift communication, even in remote agricultural areas. This research helps fill the gap in smart farm security, offering a practical and scalable solution to protect assets and data in the evolving landscape of digital agriculture.

Precision agriculture (PA) has gained popularity because it can solve the agricultural industry's problems while reducing its environmental impact. This paper examines precision agriculture's predictive analytics history and possible applications. The report underlines the rising global demand for food and the need for sustainable agriculture. The restrictions and environmental concerns of conventional agriculture have driven precision agriculture adoption. This study analyses the development of precision agricultural technologies from wireless sensor networks (WSN) to the Internet of Things. This article covers the numerous IoT challenges in agriculture. Internet security, power constraints, and storage limits are covered in detail. This study shows that precision agriculture relies on the Internet of Things (IoT). Sensors, communication devices, and embedded systems collect and evaluate crucial agricultural data. This article evaluates LoRa, Bluetooth, and Zigbee in agricultural settings. This research also examines data analytics in agriculture, explaining the concept and emphasising the importance of big data. This article discusses big data in agriculture, covering large data sets, quick data collection, different data kinds, data correctness and dependability, and data value extraction. Machine learning (ML), deep learning (DL), and data mining are essential for predictive analytics, which predicts future outcomes based on previous data. This research shows that precision agriculture can meet global food demand and environmental concerns. In order to exploit precision agriculture, IoT and big data issues must be addressed

Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions

Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city

The Internet of Things (IoT) has been adopted by several areas of society, such as smart transportation systems, smart cities, smart agriculture, smart energy, and smart healthcare. Healthcare is an area that takes a lot of benefits from IoT technology (composing the Internet of Medical Things (IoMT)) since low-cost devices and sensors can be used to create medical assistance systems, reducing the deployment and maintenance costs, and at the same time, improving the patients and their family quality of life. However, only IoT is not able to support the complexity of e-health applications. For instance, sensors can generate a large amount of data, and IoT devices do not have enough computational capabilities to process and store these data. Thus, the cloud and fog technologies emerge to mitigate the IoT limitations, expanding the IoMT applications capacities. Cloud computing provides virtually unlimited computational resources, while fog pushes the resources closest to the end-users, reducing the data transfer latency. Therefore, the IoT, fog, and cloud computing integration provides a robust environment for e-health systems deployment, allowing plenty of different types of IoMT applications. In this paper, we conduct a systematic mapping with the goal to overview the current state-of-the-art in IoMT applications using IoT, fog, and cloud infrastructures.