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Smart farming is a new concept that can be described as applying different kinds of modern technology to make farming more efficient. One such technology is the Internet of Things, an extension of the Internet and other connections into mundane devices. This paper uses dedicated sensors and cloud computing technologies to model a smart farming system that helps farmers keep track of state of the crops and livestock. It also covers implementing and testing an Android tracker app that is supposed to become the first component of the mentioned system, a client application that communicates with the cloud computing services yet to be implemented. The end product provides its users with an easy way of keeping track of the state of their farms, simplifying the farmers' work and enhancing productivity. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet of Things (IoT) environments are evolving and becoming popular. The number of devices connected to the Internet continues to raise. IoT is an interrelated network of numerous devices in which data is automatically gathered from the environment by the sensors and transferred over the internet without human support and intervention. The IoT eases individuals interacting with real-world applications over the internet in the IoT environment. Modern innovations in IoT have added computers, sensors, streets, buildings, and even communities to the impression of smartness. IoT appliances function in distinct environments to fulfill several purposes; result in the variety of computational devices and communication technologies employed in healthcare, education, military, agriculture, and commerce. Thus, IoT holds a lot of promise for enhancing social and corporate life. Nevertheless, IoT equipment are a soft target and prone to attacks due substantially to their resource limitations, and the nature of their networks. There are many approaches and technologies utilized to preclude IoT from varied attacks and assaults, Intrusion Detection System (IDS) and Intrusion Preventions System (IPS) are some of them, which can ensure the security, privacy, and reliability of the IoT. In this paper, we provide a deep study of many recent and pertinent IDS/IPS proposed between 2019 and 2022 for IoT networks, giving their key specifics, strengths, shortcomings, and challenges in order to spot the issues that still require to be handled. The paper also lines the mainstream research direction and opens the way for new avenues of research for forthcoming researchers. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has fundamentally transformed our interaction with technology by connecting a diverse array of devices and objects through the internet, presenting boundless possibilities in various domains such as healthcare, agriculture, transportation, and industrial automation. However, this interconnected network of smart devices also brings forth complex security and privacy challenges, encompassing the potential for infringements on privacy, data breaches, and unauthorized access. To address these concerns and ensure successful adoption, this paper proposes a novel mechanism that involves an Audit Manager for transparency and accountability. This comprehensive approach aims to safeguard user data, promote trust, and strengthen the security of IoT environments, facilitating their seamless integration into various aspects of our lives. The security analysis of the proposed mechanism has proved it to be secure and reliable. [ABSTRACT FROM AUTHOR]

Agriculture must overcome escalating problems in order to feed a growing population while preserving the environment and natural resources. Recently, it has become clear that sensors and the Internet of Things (IoT) are effective tools for boosting agricultural sustainability and food security. This study provides insights into the global market size for smart agriculture in future years from 2021 to 2030, In addition, this research offered four levels of the IoT architecture for smart agriculture: the perception or sensing and actuator layer, the network layer, the cloud layer, and the application layer. The state of the art in IoT and sensor technologies for agriculture is examined in this review paper, along with some of their potential uses, including 1) irrigation monitoring systems, 2) fertilizer administration, 3) crop disease detection, 4) monitoring (yield monitoring, quality monitoring, processing monitoring logistic monotoring), forecasting, and harvesting, 5) climate conditions monitoring, and 6) fire detection. Additionally, this review offers a number of sensors for agriculture that can detect parameters like soil NPK, moisture, nitrate, pH, electrical conductivity, CO2, temperature, humidity, light, weather station, water level, livestock, plant disease, smoke, flame, flexible wearable. Subsequently, this study highlights the advantages of IoT in smart agriculture, including superior efficiency, expansion, reduced resources, cleaner method, agility, and product quality improvement. However, there are still issues that need to be resolved in order for IoT technology to be used in agriculture where covered in this paper, and also provide insights into future research directions and opportunities. This study will contribute to helping future readers and researchers to better understand the state of academic achievement in this subject. [ABSTRACT FROM AUTHOR]

Urbanisation is leading to a concentration of growing city populations that contribute significantly to economic growth, while becoming epicentres of waste generation, greenhouse gas emissions, and food consumption. Nutrient smart city circular economy is currently an understudied intersection of growing city populations of food consumers, nutrient recovery technologies, Internet of Things (IoT), and agriculture. Meanwhile, machine learning has exploded with popularity over the years, with many circular economy literatures examining its usefulness in its predictive qualities to support management, optimisation, and recovery of useful resources from organic waste. This review paper examines advancements in machine learning for macronutrient recovery in city organic waste systems for a circular economy. The use of ML will greatly improve the scalability, transparency, productivity and accuracy of nutrient: recovery technologies, logistics, dissemination, and reuse. ML can also be combined with hardware to automate tedious waste separation, recovery and agricultural tasks using drones, hydroponics and satellites. Meanwhile, crop yields, nutrient demand-supply efficiencies, food security, environmental soil monitoring, and prosumer involvement could all increase. However, ML applications for urine, anaerobic digestion and prosumer economics are lacking. [Display omitted] • Lack of ML urine, membrane-AD, and prosumer studies for nutrient recovery. • IoT hardware, drones, democratised data sharing to address ML underfitting. • Lack of digital twins; CE BIM; good technologies and economics were barriers. • ML could increase transparency and food security for building consumer trust. • ML IoT to predict nutrients for soil, hydroponics, water treatment and farming. [ABSTRACT FROM AUTHOR]

As climate change and population growth intensify, the agricultural sector's need for sustainable solutions is paramount. This paper presents an overview of smart agriculture, a strategy leveraging technology and data analytics, and its potential to advance sustainability socially, economically, and environmentally. Drawing on recent research, we examine the integration of smart technologies in agriculture, their impact on sustainability, and the architectural design and key factors influencing their adoption. We critically review the literature on the effectiveness of smart agriculture, illuminating how smart farming strategies can curb environmental harm, stimulate economic growth, and promote social inclusivity. We identify existing research gaps and propose directions for future investigation, emphasising the need for rigorous studies on implementation strategies and long-term impacts of smart agriculture on rural landscapes. Our comprehensive overview aims to engage and guide diverse stakeholders, enhancing understanding of sustainable agriculture practices and encouraging more sustainable farming implementations. This paper contributes to global efforts towards harvesting a sustainable future. • Climate change and population growth drive the need for sustainable solutions in agriculture. • Smart tech in agriculture lessens environmental impact and boosts economic and social inclusivity. • Existing literature reveals the effectiveness of smart agriculture in achieving sustainability goals. • Guides stakeholders towards more sustainable farming practices and contributes to global sustainability efforts. [ABSTRACT FROM AUTHOR]

Agriculture automation is a primary issue and a rapidly developing field for the nation. The global population is growing swiftly, so there is a severe need to fulfill food demand. Traditional farming methods are insufficient to meet the rising demand, so they pressure using fertilizers to increase crop productivity. That fall impacts agricultural activity; sometimes, land stays barren and lacks fertility. This paper is focused on the deep analysis of smart farming-related components such as the Internet of Things (IoT), wireless communication technology, sensors, and hardware. There is serious concern about selecting technology, sensors, and hardware in the different agriculture practices. This may result in an increase in mechanization among various agricultural practices in an easy way. This paper provides a systematic extensive review of the implication of automation in agriculture. And addressed how agricultural operations may benefit from modern sensors, wireless communication technologies, and hardware. Although, major challenges and components have been discussed. Moreover, future applications for crop health, human health, and machine health are also addressed in this study. [Display omitted] [ABSTRACT FROM AUTHOR]

Agriculture is the major occupation and the main origin of income in India, and most of the population depends on agriculture for their living. The smart farming system's foremost goal is to monitor farmlands by automatically rinsing the lands with appropriate water and a pest detection framework. The conventional agricultural methods lack higher crop productivity with increased human efforts. Hence, the paper proposes an IoT-based solution for smart farming. The proposed system focuses on automatic irrigation of water and plant disease detection. It uses machine learning algorithms to accurately predict adequate water required by the fields and automatic pest identification based on the requirements of the farmland. K-Nearest Neighbour and support vector machine learning algorithms in the pest detection module are used to precisely predict plant diseases. The convenient features from the leaves of the plants were obtained. Then the obtained features are used for classification purposes. Extracting the suitable features and classifying them helps detect whether the plant is pest infected or not. The proposed system monitors, analyses, assesses and controls agricultural fields for the automatic irrigation of the water and disease identification of the plants. The numerical analysis of the machine learning algorithms and the significance of the classification with accuracy are investigated. From the numerical results, it is demonstrated that an accuracy of 84 % is achieved. [ABSTRACT FROM AUTHOR]

• Agriculture is crucial for human development and depends heavily on irrigation. This paper presents an autonomous irrigation system using AI and 6G-IoT. • The system uses a prediction algorithm based on weather history data to anticipate rainfall patterns and climate changes. • It creates an intelligent system that irrigates the fields according to environmental conditions. • The developed system achieved an accuracy of 86.34%, sensitivity of 89.28%, and precision of 91%. Agriculture plays a vital role in the growth of humankind. The total world depends upon the agricultural contribution, and wherein the agriculture purely depends upon the necessity of irrigation. This paper develops an autonomous irrigation system using Artificial Intelligence (AI) and the 6G enabled Internet of Things (6G-IoT) transforming to create a hassle-free smart agriculture model. The developed autonomous irrigation system is purely based on prediction algorithm which is mount in a microprocessor, which uses weather history data using IoT to identify and predict rainfall patterns and any climatic changes; hence it creates an intelligent system which irrigates the field depending upon the outer environment. The developed system not only checks the environmental condition, but it also measures the moisture content of the soil to supply the required amount of water to the crops. The developed autonomous AI and 6G-IoT based smart agricultural irrigation system has been tested under control environment with an accuracy of 86.34%, sensitivity of 89.28% and precision of 91%. Hence, the proposed methodology could be the possible solution for autonomous irrigation of crops in future. [ABSTRACT FROM AUTHOR]

• An unequal grouping algorithm is introduced in this work to address the energy-hole problem in Internet-of-things (IoT) and wireless sensor networks (WSNs). • The work introduced an energy level-based group head selection model that considers multi-objective parameters such as hop count, energy, and packet loss ratio. • An energy and delay aware routing optimization mechanism is introduced to reduce delay and minimize energy consumption. • Finally, a multipath-based route is established, and effective load balancing is done for transmitting packets with QoS (quality of service) and without QoS. • The existing methods' performance is generally studied considering total node death; however, the EDAR model is studied considering different scenarios such as first node death and total node death. • The simulation results show the EDAR is very efficient in reducing communication overhead, delay, enhanced lifetime, and improved throughput in comparison with the recent state-of-art routing mechanisms such as low energy adaptive clustering hierarchy (LEACH), unequal clustering algorithm (UCA), and reliable and energy efficient routing (REER). The adoption of modern technology into farming has seen significant expansion to enhance the quality and quantity of crop production. In this research work, wireless sensor networks (WSNs) and the Internet of Things (IoTs) are integrated to provide a real-time crop monitoring system to farmers. Usually, battery-powered IoT devices equipped with sensors like temperature, humidity, motion sensors, etc. are placed across agriculture areas for real-time monitoring of crops and cattle. In improving energy efficiency and reducing delay clustering multi-hop routing has been used with good effect; However, the existing methods failed to enhance network lifetime and target coverage due to failure in addressing load-balancing and hotspot problems. In addressing research problems this paper presents an Energy and Delay Aware Routing (EDAR) scheme for heterogenous WSN-IoTs leveraging unequal grouping of networks and adoption of multipath routing for provisioning IoT applications. The simulation outcome shows the EDAR model achieves very good performance in terms of throughput and lifetime performance with minimal delay and communication overhead. Graphical abstract of energy and delay aware routing design. [Display omitted] [ABSTRACT FROM AUTHOR]

• Comprehensive review of IoT-based agriculture management techniques for sustainable farming. • Detailed discussion on the introduction of AI and IoT-based intelligent techniques in achieving the UN SDGs. • Comparative discussions on manual versus technology-driven smart agriculture practices. • Identifying the research gaps that are essential for the sustainable implementation of IoT-based smart farming. The sustainable development goals (SDGs) of the United Nations seek to promote an agriculture sector that can lead change on both a local and global scale. By helping farmers to produce more food sustainably, Internet of Things (IoT)-based agriculture management solutions are significantly contributing to the achievement of these SDGs. By 2050, the world's population is expected to reach 9.7 billion, necessitating a 60% increase in food production efficiency and security in order to meet the growing population's demands. Smart farming powered by the IoT and utilizing artificial intelligence (AI) and machine learning (ML) algorithms is becoming a viable and sustainable method for increasing agricultural productivity and reducing environmental impact. The newest research and development initiatives in IoT-based smart farming are summarized in this comprehensive review article, along with how they might change the agricultural sector. The study gives a general overview of the machine abilities that smart farming is supported by, including perception, reasoning and learning, communication, task planning and execution, and systems integration. The use of robots and unmanned aerial vehicles (UAVs) in diverse agricultural contexts is also demonstrated in the paper, indicating the potential for improved productivity and diminished environmental effect. The report concludes by highlighting the significance of utilizing wireless communication LoraWan technology in the creation of smart systems that can further enable sustainable agriculture management. [ABSTRACT FROM AUTHOR]

In smart agriculture, the accurate prediction of evapotranspiration plays a crucial role in optimizing water usage and maximizing crop yield. However, the increasing adoption of IoT sensor technologies has resulted in the accumulation of large amounts of data, which are frequently contaminated by noise and pose a significant challenge to extract reliable knowledge through data modeling. This research addresses the problem of noisy IoT sensor data and its impact on evapotranspiration prediction, an essential aspect of agricultural practices. The effect of noise on sensor variables and evapotranspiration is extensively analyzed by simulating different noise levels in evapotranspiration datasets collected from various agricultural areas in Spain, enabling a comprehensive evaluation of its impact on the performance of data science models. Despite the potential consequences of this type of errors, a noise preprocessing stage is often overlooked in existing literature in this field, which is necessary to improve data quality prior to modeling. In order to address this challenge, this paper proposes the usage of regression noise filters as approach to mitigate the detrimental effects of noisy IoT sensor data on evapotranspiration prediction. Additionally, we introduce the rgnoisefilt R package, which offers a practical and efficient implementation of noise filtering techniques for regression datasets, providing a valuable solution for handling noisy data in smart agriculture applications. The experimental results obtained emphasize the negative impacts of noise on evapotranspiration prediction performance and highlight the importance of an appropriate data treatment to mitigate system deterioration. Furthermore, the findings of this research emphasize the efficacy of the regression noise filters implemented in the rgnoisefilt software, enhancing the performance of the models built and providing a valuable tool for improving data quality in smart agriculture. • Noisy IoT sensor data in evapotranspiration prediction are addressed. • This research analyzes the impact of different types of noise in the data. • Regression noise filters are proposed as a tool to improve data quality. • The rgnoisefilt R package provides efficient solutions for noisy sensor data. • The effectiveness of filters with noisy evapotranspiration data is shown. [ABSTRACT FROM AUTHOR]

• Technical properties of various wireless communication protocols are compared. • Inconsistent information about the properties in different sources are underlined. • Utilization of the protocols in six major application areas are investigated in detail. • Trending protocols and applications are discussed by analyzing recent papers. IoT based smart agriculture systems are important for efficient usage of lands, water, and energy resources. Wireless communication protocols constitute a critical part of smart agriculture systems because the fields, in general, cover a large area requiring system components to be placed at distant locations. There are various communication protocols with different features that can be utilized in smart agriculture applications. When designing a smart agriculture system, it is required to carefully consider the features of possible protocols to make a suitable and optimal selection. Therefore, this review paper aims to underline the specifications of the wireless communication protocols that are widely used in smart agriculture applications. Furthermore, application-specific requirements, which may be useful during the design stage of the smart agriculture systems, are highlighted. To accomplish these aims, this paper compares the technical properties and investigates the practical applications of five different wireless communication protocols that are commonly used in IoT applications: ZigBee, Wi-Fi, Sigfox, NB-IoT, and LoRaWAN. In particular, the inconsistencies in the technical properties of these protocols reported in different resources have been highlighted and the reason for this situation has been discussed. Considering the features offered by the protocols and the requirements of smart agriculture applications, the appropriateness of a particular protocol to a particular smart agriculture application is examined. In addition, issues about cost, communication quality, and hardware of the five protocols have been mentioned. The trending technologies with high potential for the future applications of smart agriculture have been introduced. In this context, the relation of the technologies like aerial systems, cellular communication, and big data analytics with wireless have been specified. Finally, the leading protocol and the smart agriculture application area have been highlighted through observing the year-based distribution of the recent publications. It has been shown that usage of LoRaWAN protocol has become more widespread in recent years. [ABSTRACT FROM AUTHOR]

Due to the increasing world population, demand for food products have created the need to modernize and intensify agricultural operations through precision agriculture. The Internet of Things offers a wide variety of solutions for precision agriculture, but implementing it in the agriculture field imposes challenges to hardware and data communication in the network. Importantly, the sensor nodes have to be reliable for long periods with the limited available battery power. In this connection, the goal of this paper is to propose an efficient data collection algorithm which improves the network lifetime of the wireless sensor network (WSN), decreases the energy hole issue and reduces the data collection delay. In the proposed work, the rendezvous points (RPs) in the network are selected based on the energy level of the nodes, data packet density and the distance to the RPs. Then the mobile sink (MS) moves to the RPs in the shortest possible path to collect the data from the data-powered points. A hybrid meta-heuristic algorithm called WOAXGWO is proposed to find the shortest path for the MS. To assess the performance, the network's lifetime, energy consumption, and latency are considered. Simulation findings show the effectiveness of the proposed algorithm and are validated on a coconut farm to demonstrate its reliability. • WOAXGWO algorithm is proposed to alleviate premature convergence. • Proposed data collection algorithm first determines the optimal set of RPs. • Mobile sink determines the shortest path to visit all the RPs and reduces the delay. • The proposed work is implemented in real-time coconut farm to prove its reliability. [ABSTRACT FROM AUTHOR]

Agriculture farms such as crop, aquaculture and livestock have begun the implementation of Internet of Things (IoT) and artificial intelligence (AI) technology in improving their productivity and product quality. However, microalgae farming which requires precise monitoring, controlling and predicting the growth of microalgae biomass has yet to incorporate with IoT and AI technology, as it is still in its infancy phase. Particularly, the cultivation stage of microalgae involves many essential parameters (i.e. biomass concentration, pH, light intensity, temperature and tank level) which require precise monitoring as these parameters are important to ensure an effective biomass productivity in the microalgae farming. Besides, the conventional practices in the current process equipment are still powered by electricity, thus further development by integrating IoT into these processes can ease the production process. Further to that, many researchers has studied the machine learning approach for the identification and classification of microalgae. However, there are still limited studies reported on applying machine learning for the application of microalgae industry such as optimising microalgae cultivation for higher biomass productivity. Therefore, the implementation of IoT and AI in microalgae farming can contribute to the development of the global microalgae industry. The purpose of this current review paper focuses on the overview microalgae biomass production process along with the implementation of IoT toward the future of smart farming. To bridge the gap between the conventional and microalgae smart farming, this paper also highlights the insights on the implementation phases of microalgae smart farming starting from the infant stage that involves the installation and programming of IoT hardware. Then, it is followed by the application of machine learning to predict and auto-optimise the microalgae smart farming process. Furthermore, the process setup and detailed overview of microalgae farming with the integration of IoT have been discussed critically. This review paper would provide a new vision of microalgae farming for microalgae researchers and bio-processing industries into the digitalisation industrial era. • Implementation of IoT technology helps to monitor microalgae farming remotely. • Microalgae industries should collaborate with local companies to implement IoT. • Lack of machine learning studies on the application of microalgae farming • Machine learning support IoT in data optimization and predict production forecast • Challenges are internet access, lack of profession, cybersecurity, industrial scale. [ABSTRACT FROM AUTHOR]

With the increase in the number of IoT farming datasets, it has become so difficult to identify the right data for IoT agriculture applications. Therefore, a meaningful structure is needed to well understand, interpret and index IoT farming datasets. This paper proposes a new IoT farming ontology that allows the organization, the understanding, and the classification of IoT agriculture datasets knowledge as well as meta-data storage. For this, we have developed a new IoT agriculture taxonomy that helps to identify an IoT agriculture application based on the combination of various IoT agriculture sensors. The evaluation of the semantic IoT agriculture datasets classification, based on the background knowledge provided by the proposed ontology, was achieved using Machine Learning algorithms, including Logistic Regression, Decision Tree Classifier, K-Neighbors Classifier, Linear Discriminant Analysis, Gaussian NB, SVM, and Random Forest Regressor. The obtained results clearly show the effectiveness of the proposed ontology to classify IoT agriculture datasets with high performances and accuracy (0.98), (0.99) using Decision tree classifier and SVM respectively. [ABSTRACT FROM AUTHOR]

Monitoring system based on internet of things (IoT) for the environment-friendly and energy-efficient greenhouse building. [Display omitted] • Review on environmental monitoring system of agricultural greenhouse building. • Essential parameters analysis including greenhouse building environment, soil, and crop. • Comparison and evaluation on components and technologies of the monitoring system. • Discussion of trends and valuable research directions in monitoring system. As a controllable environment, greenhouse has less resource consumption and emission than field crop production and reduced greenhouse gas emissions from agricultural production. Besides, the greenhouse with an intelligent monitoring system has better energy-saving and emission reduction effects. Simultaneously, the intelligent monitoring system can predict the extreme greenhouse environment in advance, reduce diseases and insect pests, reduce the use of pesticides and fertilizers, and provide high-quality food. Researchers are becoming more and more interested in greenhouse monitoring systems, and how to put them into production correctly and effectively is a major challenge. This paper aims to review the intelligent greenhouse monitoring system systematically, serve the data transmission and server processing subsystems by identifying, listing and further explaining the greenhouse environmental parameters and studying the overall design of the greenhouse monitoring system. According to the characteristics of each component of the system, the paper makes a comparative study and obtains its development trend, summarizes the current popular technology and the future development trend of the intelligent monitoring system, and provides support for the research of greenhouse monitoring system. It was found that multi-parameter monitoring is beneficial to achieve effective greenhouse control, and wireless technology has gradually replaced wired mode for data transmission in the environment both inside and outside the greenhouse. Notably, deep learning, big data, and other advanced technologies used in greenhouse monitoring are considered valuable developments, further refine unmanned greenhouse management, and further improve greenhouse construction's energy utilization. [ABSTRACT FROM AUTHOR]

Highlights • Major sensor data collected from IoT weather stations show spatiotemporal data patterns. • Transform-based lossy compressions can significantly reduce the volume of agricultural IoT data. • Agricultural IoT data exhibit a strong correlation between energy compaction and data fidelity. • Sensor sampling granularities have a significant impact on the data fidelity. Abstract As the volume of data collected by various IoT sensors used in smart farm applications increases, the storing and processing of big data for agricultural applications become a huge challenge. The insight of this paper is that lossy compression can unleash the power of compression to IoT because, as compared with its counterpart (a lossless one), it can significantly reduce the data volume when the spatiotemporal characteristics of IoT sensor data are properly exploited. However, lossy compression faces the challenge of compressing too much data thus losing data fidelity, which might affect the quality of the data and potential analytics outcomes. To understand the impact of lossy compression on IoT data management and analytics, we evaluated four classification algorithms with reconstructed agricultural sensor data based on various energy concentration. Specifically, we applied three transformation-based lossy compression mechanisms to five real-world weather datasets collected at different sampling granularities from IoT weather stations. Our experimental results indicate that there is a strong positive correlation between the concentrated energy of the transformed coefficients and the compression ratio as well as the data quality. While we observed a general trend where much higher compression ratios can be achieved at the cost of a decrease in quality, we also observed that the impact on the classification accuracy varies among the data sets and algorithms we evaluated. Lastly, we show that the sampling granularity also influences the data fidelity in terms of the prediction performance and compression ratio. [ABSTRACT FROM AUTHOR]

Critical infrastructures such as healthcare, intelligent transportation, and home automation adapt billions of smart IoT devices to handle real-time data effectively in the digital era. The latest development in sensory and communication technologies collaborate directly with modern applications to bridge networking services without human intervention. Most information systems enable the extension of digital technology to demonstrate the potential use of next-generation network services and management. However, recent advancements open various opportunities, including repudiation, data tampering, and security threats in digital security and privacy. Thus, this survey article chooses a systematic literature review to present a comprehensive survey on authentication and key management in IoT. To discuss the major issues involved in IoT environments, this survey categorizes the work progress into four modules: 1. Discuss the broad aspects of convergence technologies, including healthcare, smart farming, intelligent transportation, etc., to address challenges such as security and privacy in IoT; 2. Conduct a comprehensive review of state-of-the-art technologies to identify security requirements, services, and challenges associated with modern IoT applications; 3. Present a systematic approach to review various key agreement schemes based on network models and performance analysis to examine potential vulnerabilities. 4. Show a thematic analysis to determine suitable corrective measurements dealing with various security and privacy issues. Finally, this paper rationalizes significant progress, including research challenges and future directions in IoT to promote new insights. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has been a major influence in Agriculture since its application to the sector. This paper provides an extensive review of the use of smart technologies in agriculture and elaborates the state-of-the-art technologies for smart agriculture including, Internet of Things, cloud computing, machine learning, and artificial intelligence. The application of smart farming to crop and animal production and post-harvesting is discussed. The impact of climate change on agriculture is also considered. This paper contributes to knowledge by iterating the challenges of smart technology to agriculture while highlighting the issues identified from existing framework of smart agriculture. The authors identify many gaps in existing research affecting the application of IoT in smart farming, and suggest further research to improve the current food production globally, to provide better food management and sustainability measures across the globe. [ABSTRACT FROM AUTHOR]

The terms Internet of Things and Big Data are currently subject to much attention, though the specific impact of these terms in our practical lives are difficult to apprehend. Data-driven approaches do lead to new possibilities, and significant improvements within a broad range of domains can be achieved through a cloud-based infrastructure. In the agricultural sector, data-driven precision agriculture shows great potential in facilitating the increase in food production demanded by the increasing world population. However, the adoption rate of precision agriculture technology has been slow, and information and communications technology needed to promote the implementation of precision agriculture is limited by proprietary integrations and non-standardized data formats and connections. In this paper, an open geospatial data infrastructure is presented, based on standards defined by the Open Geospatial Consortium (OGC). The emphasis in the design was on improved interoperability, with the capability of using sensors, performing cloud processing, carrying out regional statistics, and provide seamless connectivity to machine terminals. The infrastructure was implemented through open source software, and was complemented by open data from governmental offices along with ESA satellite imagery. Four use cases are presented, covering analysis of nearly 50 000 crop fields and providing seamless interaction with an emulated machine terminal. They act to showcase both for how the infrastructure enables modularity and interoperability, and for the new possibilities which arise from this new approach to data within the agricultural domain. [ABSTRACT FROM AUTHOR]

This paper reviews agro-industrial and environmental applications that are using Internet of Things (IoT). It is motivated by the need to identify application areas, trends, architectures and open challenges in these two fields. The underlying survey was developed following a systematic literature review using academic documents written in English and published in peer-reviewed venues from 2006 to 2016. Selected references were clustered into four application domains corresponding to: monitoring, control, logistics, and prediction. Implementation-specific details from each selected reference were compiled to create usage distributions of sensors, actuators, power sources, edge computing modules, communication technologies, storage solutions, and visualization strategies. Finally, the results from the review were compiled into an IoT architecture that represents a wide range of current solutions in agro-industrial and environmental fields. [ABSTRACT FROM AUTHOR]

World agriculture faces the challenge of increasing its agricultural production by 50 % from 2012 to 2050, while reducing water consumption, as agriculture accounts for 69 % of all fresh water used on the planet. The use of technologies such as the internet of things, artificial intelligence, digital twins, among others, in the agricultural environment is increasing. Digital twin applied to agriculture is in its early stages of development. A digital twin is one in which data flows automatically and in both directions between a physical object and a virtual object. Withing this context, this paper presents a digital twin of a smart irrigation system in an application scenario. A digital twin for an irrigation system is one in which the physical components of the system, such as its sensors and actuators, are connected with their virtual representations. The system is composed of a FIWARE-based internet of things platform and a discrete event simulation model in Siemens Plant Simulation software. The internet of things platform is used to collect, aggregate and process soil, weather and crop data to calculate daily irrigation prescriptions. The simulation model is used to simulate the behavior of an irrigation system defined in an application scenario. The communication between the platform and the simulation model happens in real-time and is intermediate by an OPC UA server. A application scenario is considered to evaluate the behavior of the system and to evaluate, in future research, different irrigation strategies. The benefits of the systems proposed are twofold. First, evaluate the behavior of the Internet of Things (IoT) platform and an irrigation system before implementing both in the field. Second, enable the evaluation of different irrigation strategies in parallel with current farm practices. Within the system proposed, farmers can evaluate the behavior of the system before implementing in their farms and allowing the system to operate automatically. It is possible to infer that the system can improve farm operations and reduce water usage by allowing farmers to gather soil, weather and crop information and evaluate multiple irrigation strategies. [Display omitted] • Developing a Digital Twin for an irrigation system. • Integrating a IoT platform with a simulation model. • Evaluating the IoT platform communication with a simulation model. • Evaluating irrigation prescriptions calculated by different methods. • Improving water usage efficiency in irrigation. [ABSTRACT FROM AUTHOR]

A technology in smart farming, the Internet of Things (IoT), is predicted to continue altering farm life by introducing opportunities and obstacles. However, there are limited studies on how farmers' views of IoT influence their decision-making regarding technology adoption. To understand what characterises farmers' experiences with IoT, we conducted a discourse analysis of 32 interviews with farmers in Ontario. Discourse analysis was used to understand the range of meanings associated with IoT by farmers. We find that two main discourses are present (1) the extent to which IoT was viewed as useful/helpful vs not useful/unhelpful and (2) the extent to which IoT was viewed as being their choice. The results indicate that farmers respond to IoT in four categories: embrace, accept, ignore, and caution. This paper contributes to the literature by categorising the farmers' responses to IoT implementation and highlighting why farmers adopt these categories. Current literature recognizes that diagnosing the current readiness and use of innovations is a proxy for their readiness to scale. Understanding how farmers view opportunities enabled by IoT and how they experience the diffusion of IoT is a foundation for suggesting recommendations for technology improvement and development in agriculture. [Display omitted] • Internet of Things is predicted to continue to alter farm life. • There are limited studies on how farmer's views of IoT influence adoption. • We conducted a discourse analysis of 32 interviews with farmers in Ontario. • Two main discourses were identified. The results indicate that farmers respond to IoT in four categories. • This work contributes by categorising the farmers response to perceived pressures and why farmers adopt these categories. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology can be used to enhance traditional approaches by combining advanced technologies with sophisticated methodologies aiming to boost agricultural production quality and quantity. The global population is rapidly growing, and the demand for food is rising accordingly. Thus, traditional agriculture will be unable to fulfill the demands of the crops. This paper aims to review the state-of-the-art contributions of smart IoT-based agriculture system design including IoT technology which presents the core framework of present and future agricultural development. Also, the basic structure of the IoT agricultural system is highlighted including the hardware and the software along with the data processing components. The potential issues systems are also investigated. The results of previous works show that the developed agricultural based-IoT systems can provide higher accuracy for making the best decision to structure a healthy farm environment. Consequently, crop production can be increased many folds. [Display omitted] [ABSTRACT FROM AUTHOR]

• Compatibility, PU, and PEoU impact the adoption of smart home technology. • Females and males have different views on smart home technology. • Consumer perceived innovativeness effects the adoption of smart home technology. • Trialability has no direct effect on the intention, but it has through PU and PEo. • Perceived cost has a significant negative effect on the intention to use. Smart home technology, an application of Internet of Things (IoT), provides households with e.g., comfort, control and convenience. The technology has been around for sometimes, but its prevalence is not yet widespread, and thus the potentials have largely been underestimated. The purpose of this paper is to develop a comprehensive model that can explain a large percentage of variance in the intention to use smart home technology. To do so, this paper, based on technology acceptance model, diffusion of innovation theory and consumer perceived innovativeness, proposes an integrated model and validates it through Structural Equation Modelling on data collected from 156 respondents. The results reveal that compatibility, perceived usefulness and perceived ease of use are important determinants affecting the adoption of smart home technology. Moreover, trialability while show no direct effect on the intention to use, its affect is realized indirectly via constructs of PU and PEoU. The SEM results also show that the perceived cost negatively impacts the intention to use. This paper is one of the first empirical attempt that includes consumer perceived innovativeness in the model. This paper contributes to the literature of IoT, in particular to smart home technology research. Some theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]

• Practical approach to measure & monitor the contribution of IoT to sustainability. • We developed SDG-based typology to measure impact using key performance indicators. • The novel approach enables actors to test and evaluate IoT i.r.t. sustainability. • We show evidence how IoT can contribute to sustainable agricultural production. • Supports communication between multiple stakeholders incl. policy makers. Sustainability is a major challenge in agri-food systems. Digital technologies, such as Internet of Things (IoT) hold substantial promises for attaining the sustainability goals of the economy, environment and society at large. However, in practice it is difficult to evaluate to which extent these technologies contribute to sustainable development raising doubts about their impact. This paper demonstrates a stepwise approach that allows for measuring and monitoring IoT contribution to sustainability in a real-life context. The UN sustainable development goals (SDGs) underpin the principles of the approach by a typology and by framing the sustainability impact in terms of business opportunities. The approach has been developed and evaluated by 33 use cases in the EU-funded IoF2020 project. The research illustrates how the measurement and monitoring tool is applied in 5 of these use cases from different agricultural subsectors showing how the approach is applied and validated. The results indicate an overall positive impact of IoT on improving sustainability, although these results are also partly determined by other influential external factors that cannot be easily discerned in a practical situation. The main contribution of this approach is the set of instruments for practitioners to measure and monitor the impact of fast-changing technologies such as IoT to sustainability in a real-life context. This set of instruments can also be used by other stakeholders in large IoT projects where strategic sustainability objectives should be supported by IoT solutions. The stepwise approach is easy to communicate and supports stakeholders such as farmers in decision-making, but also policy makers and investors in funding projects. [ABSTRACT FROM AUTHOR]

• A prototype was designed using IoT components for agricultural monitoring. • Benefits of using IoT devices with energy harvesting for agricultural applications. • Experiments were conducted throughout a year and in different seasons. • LoRaWAN is the optimal wireless technology to use for agricultural monitoring. Technological advances in the Internet of Things (IoT) have paved the way for wireless technologies to be used in new areas. Agricultural monitoring is an example where IoT can help to increase productivity, efficiency, and output yield. However, powering these devices is a concern as batteries are often required due to devices being located where electricity is not readily available. In this paper, an experimental comparison is performed between IoT devices with energy harvesting capabilities that use three wireless technologies: IEEE 802.11 g (WiFi 2.4 GHz), IEEE 802.15.4 (Zigbee), and Long Range Wireless Area Network (LoRaWAN), for agricultural monitoring. Four experiments were conducted to examine the performance of each technology under different environmental conditions. According to the results, LoRaWAN is the optimal wireless technology to be used in an agricultural monitoring system, when the power consumption and the network lifetime are a priority. [ABSTRACT FROM AUTHOR]