Survey, comparison and research challenges of IoT application protocols for smart farming

Glaroudis, Dimitrios P.; Iossifides, Athanassios C.; Chatzimisios, Periklis

doi:10.1016/j.comnet.2019.107037

Cited by 176 publications

(99 citation statements)

References 24 publications

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“…IoT are mainly used in monitoring crop, soil and weather, forecasting disease and crop yields, controlling irrigation machinery and autonomous vehicles and robots [22]. Based on several reviews, it can be concluded that incorporating precision livestock farming technologies (Sensors), big data analytics, and the IoT in smart farming practices puts forth a possible solution to assist us to improve agriculture productivity and meet projected global agricultural product demands [23]- [25], [28]. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the smart farming strategy new abilities to predict changes and identify opportunities.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities. The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. These three elements (e.g., BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture. In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production. We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture. Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture.

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Section: Literature Reviewmentioning

confidence: 99%

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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“…Countable IoT application layer protocols were introduced, but still, HTTP and MQTT protocols are the developer's choice to implement IoT applications. IoT application layer protocols like CoAP, AMQP, and XMPP protocols are infrequent in real-time IoT deployments [217]. Tanin Sultana et al [218] provided a detailed analysis of MQTT, AMQP, DDS, HTTP, CoAP, and DDS protocols using IoVT framework.…”

Section: E Restful Apis/ Httpmentioning

confidence: 99%

An Empirical Study on System Level Aspects of Internet of Things (IoT)

2020

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Internet of Things (IoT) is an integration of the Sensor, Embedded, Computing, and Communication technologies. The purpose of the IoT is to provide seamless services to anything, anytime at any place. IoT technologies play a crucial role everywhere, which brings the fourth revolution of disruptive technologies after the internet and Information and Communication Technology (ICT). The Research & Development community has predicted that the impact of IoT will be more than the internet and ICT on society, which improves the wellbeing of society and industries. Addressing the predominant system-level design aspects like energy efficiency, robustness, scalability, interoperability, and security issues result in the use of a potential IoT system. This paper presents the current state of art of the functional pillars of IoT and its emerging applications to motivate academicians and researches to develop real-time, energy-efficient, scalable, reliable, and secure IoT applications. This paper summarizes the architecture of IoT, with the contemporary status of IoT architectures. Highlights of the IoT system-level issues to develop more advanced real-time IoT applications have been discussed. Millions of devices exchange information using different communication standards, and interoperability between them is a significant issue. This paper provides the current status of the communication standards and application layer protocols used in IoT with the detailed analysis. The computing paradigms like Cloud, Cloudlet, Fog, and Edge computing facilitate IoT with various services like data offloading, resource and device management, etc. In this paper, an exhaustive analysis of Edge Computing in IoT with different edge computing architectures and existing status are deliberated. The widespread adoption of IoT in society has resulted in privacy and security issues. This paper emphasizes on analyzing the security challenges, privacy and security threats, conventional mitigation techniques, and further scope for IoT security. The features like fewer memory footprints, scheduling, real-time task execution, fewer interrupt, and thread switching latency of Real-Time Operating Systems (RTOS) enables the development of time critical IoT applications. Also, this review offers the analysis of the RTOS's suitable for IoT with the current status and networking stack. Finally, open research issues in IoT system development are discussed.

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“…Being a key technology of smart farming, IoT adds value to obtained data by automatic processing, analysis and access by ensuring data flow between different devices such as sensors, relays and gateways. Hence more cost-efficient and timely production and management practices are enabled in smart farms [3]. Furthermore, IoT reduces the inherent climate impact by enabling real-time reactions to infestations such as weed, pest or disease detection, monitoring and predicting weather conditions, soil conditions, etc.…”

Section: Introductionmentioning

confidence: 99%

A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming

et al. 2021

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To reach the goal of sustainable agriculture, smart farming is taking advantage of the Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) paradigm. These smart farms are designed to be run by interconnected devices and vehicles. Some enormous potentials can be achieved by the integration of different IoT technologies to achieve automated operations with minimum supervision. This paper outlines some major applications of IoT and UAV in smart farming, explores the communication technologies, network functionalities and connectivity requirements for Smart farming. The connectivity limitations of smart agriculture and it’s solutions are analysed with two case studies. In case study-1, we propose and evaluate meshed Long Range Wide Area Network (LoRaWAN) gateways to address connectivity limitations of Smart Farming. While in case study-2, we explore satellite communication systems to provide connectivity to smart farms in remote areas of Australia. Finally, we conclude the paper by identifying future research challenges on this topic and outlining directions to address those challenges.

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Survey, comparison and research challenges of IoT application protocols for smart farming

Cited by 176 publications

References 24 publications

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

An Empirical Study on System Level Aspects of Internet of Things (IoT)

A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming

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