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

Faculty of Information Technology, Hung Yen University of Technology and Education, Hung Yen 160000, Vietnam
Faculty of Telecommunication 1, Posts and Telecommunications Institute of Technology, Hanoi 100000, Vietnam
GeoloGIS s.r.l., Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze della Terra, Università degli Studi di Messina, Via F. Stagno d’Alcontres, 31-98166 Messina, Italy
Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze della Terra, Università degli Studi di Messina, Via F. Stagno d’Alcontres, 31-98166 Messina, Italy
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(7), 3396;
Submission received: 7 March 2022 / Revised: 21 March 2022 / Accepted: 25 March 2022 / Published: 27 March 2022
(This article belongs to the Special Issue Sustainable Agriculture and Advances of Remote Sensing)


The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector. To achieve this objective, we discuss the vision of IoT-enabled smart agriculture ecosystems by evaluating their architecture (IoT devices, communication technologies, big data storage, and processing), their applications, and research timeline. In addition, we discuss trends and opportunities of IoT applications for smart agriculture and also indicate the open issues and challenges of IoT application in smart agriculture. We hope that the findings of this study will constitute important guidelines in research and promotion of IoT solutions aiming to improve the productivity and quality of the agriculture sector as well as facilitating the transition towards a future sustainable environment with an agroecological approach.

1. Introduction

In order to meet the current global needs of humanity, new solutions and technologies are constantly being proposed and implemented. This has led to the advent of the Internet of Things (IoT) [1,2]. IoT is defined as the network of all objects that are embedded within devices, sensors, machines, software and people through the Internet environment to communicate, exchange information and interact in order to provide a comprehensive solution between the real world and the virtual world [3]. In recent years, IoT has been applied in a series of domains, such as smart homes [4,5], smart cities [6,7], smart energy [8,9], autonomous vehicles [10,11], smart agriculture [12,13,14,15], campus management [16,17], healthcare [18,19], and logistics [20,21]. Series of other IoT applications have been described by Shafique et al. [22]. An illustration of rich and diverse IoT applications for smart agriculture is provided in Figure 1.
According to the United Nations’ (UN 2019) statistics, the world population is estimated to grow to 10 billion by 2050 [23]. As a consequence, the requirements of agricultural products are continually increasing. However, farmlands are declining, natural resources are increasingly depleted, and the rise of unpredictable nature challenges, such as global warming, salinization, and flooding, make food security the most concerning problem for all nations worldwide.
In recent years, with the aim of increasing agricultural production, new solutions and technologies have been introduced in the agriculture sector [24]. An emerging trend is the application of the IoT and big data. A significant number of studies have been focused on research, experiments, and applications [25,26]. According to the Cisco forecast, over 500 billion IoT devices will be connected to the Internet by 2030 [27]. The use of IoT and big data will enable smart agriculture and is expected to enhance efficiency and productivity [28].
Over the years, wireless sensor networks (WSN) have been strongly applied in the agricultural sector, building the foundation for developing smart agriculture [29]. The unique characteristics of WSN, such as the ability to self-organize, self-configure, self-establish, and self-recover, make it suitable for smart agriculture [30]. The sensor device consists of a radio frequency (RF) transceiver, sensor, microcontroller, and battery power [31]. The WSN focuses on applications such as environmental monitoring, machine control automation, and traceability [32,33,34,35].
Along with the development of science and technology, the urgent requirement for breakthrough solutions and technologies aiming at improving productivity and efficiency in the agriculture sector has led to adoption of the IoT. The primary motivation for their applications is the breakthrough progress of smart agriculture and its inevitable role as the future of smart and sustainable environment management. IoT integrates a series of existing solutions and technologies, such as WSN, cognitive radio, ad hoc networks, cloud computing, and end-user applications [36]. In the smart agricultural sector, automation solutions and technologies, mechanical machines, knowledge, decision-making tools, services, and software are integrated seamlessly to help farmers improve productivity, product quality, and profitability [37].
In this work, a comprehensive survey of IoT applications for smart agriculture is conducted. An analysis of 135 relevant works published between 2017 and 2022 was conducted. Firstly, relevant 550 papers published in the period of (2017–2022) were retrieved from major scientific databases, namely IEEE Xplore Digital Library, Science Direct, MDPI, and Springer, by using keywords such as IoT-enabled smart agriculture, smart agriculture, Internet of Things, aquaponics, monitoring forestry based on IoT, tracking and tracing, smart precision farming, greenhouse production, Sigfox, LoRa, Wi-Fi, LoRaWAN, and IoT ecosystems. In the next step, we excluded papers that were published in low-repute conferences and journals, and then we conducted the content analysis for the obtained paper. Finally, 135 papers were selected for the preparation of the present work.
In addition, we analyzed and discussed the benefits and challenges, open issues, trends, and opportunities of IoT in the smart agriculture sector. This work is organized as follows: Section 1 introduces our work, and in Section 2, we present an IoT ecosystem architecture for smart agriculture that consists of three main components: IoT devices, communication technology, and data storage and big data processes. Section 3 presents the IoT applications in agriculture, including (1) monitoring, (2) tracking and traceability, (3) precision agriculture, and (4) greenhouses. Section 4 introduces some open issues and future research challenges of IoT for smart agriculture. Issues are discussed for two main directions: business and technology. In Section 5, we present the main conclusions of this work.

2. IoT Ecosystem Architecture for Smart Agriculture

In this section, we present a common framework of an IoT ecosystem for smart agriculture based on three main components, including (1) IoT devices, (2) communication technologies, and (3) data process and storage solutions. An illustration of the IoT ecosystem for smart agriculture is presented in Figure 2.

2.1. IoT Devices

The common architecture of an IoT device consists of sensors to collect information from the environment, actuators based on wired or wireless connections, and an embedded system that has a processor, memory, communication modules, input–output interfaces, and battery power [38,39]. The common architecture of a typical IoT device for smart agriculture is shown in Figure 3.
Embedded systems are programmable interactive modules, namely FPGAs (field programmable gate arrays). Sensor devices are specially designed to operate in open environments, in nature, in soil, water, and air to measure and collect environmental parameters that affect production, such as soil nutrients, humidity, temperature, etc. Smart farming solutions are agricultural operations that are often deployed on large farmlands, outdoors, so the devices that support solutions need some unique characteristics, such as the ability to withstand the effects of weather, humidity, and temperature instability throughout their service lifecycle. Some of their main features, as shown in Figure 4, make IoT devices suitable for smart agriculture solutions [40,41,42].
Depending on the required operation, there are several typical sensors applied in the smart agriculture sector. Sensors can be divided into several typical categories, such as (1) location sensor, (2) optical sensor, (3) mechanical sensor, (4) electrochemical sensor, and (5) air flow sensor. These sensors are used to collect information such as air temperature, soil temperature, air humidity, soil moisture, leaf moisture, precipitation, wind speed, wind direction, and solar radiation, and barometric pressure [21,24,36].

2.2. Communication Technology

The survey of communication technologies for IoT [43,44] indicated that to integrate IoT into the smart agriculture sector, communication technologies must progressively improve the evolution of IoT devices. They play an important role in the development of IoT systems. The existing communication solutions can be classified as: protocol, spectrum, and topology.
Protocols: many wireless communication protocols have been proposed for the smart agriculture sector. Based on these protocols, devices in a smart agricultural system can interact, exchange information, and make decisions to monitor and control farming conditions and improve yields and production efficiency. The typical, low-power communication protocol numbers commonly used in smart agriculture can be divided into short-range and long-range categories based on the communication range.
Short-range: NFMI (near-field magnetic induction) [45], Bluetooth [46], ZigBee [47], terahertz (Z-Wave) [48,49], and RFID [50].
Long-range: LoRa [51], Sigfox [52], and NB-IoT (Narrowband IoT) [53].
Table 1 presents some typical communication technologies for the smart agriculture sector. The values in Table 1 indicate that short-range communication technologies have a transmission distance of less than 20 (m), high energy efficiency, and low data rate. These protocols are often employed in sensor networks, while long-range communication technologies have transmission distances of up to several tens of kilometres, consume more energy, and are installed for backhaul device-to-device communications. A diverse survey of low-power communication technologies for IoT that presents solutions, challenges, and some open issues is described by Sundaram et al. [54].
Spectrum: Each radio device uses certain frequency bands for communication. The FCC (Federal Communications Commission) has defined unlicensed spectrum bands for unlicensed operations in scientific, industrial, and medical purposes [55]. These spectrum bands are often applied for low-power levels and short-range applications. Consequently, a series of common technologies for the smart agriculture sector, from wireless machine control and UAVs to communication technologies such as Wi-Fi and Bluetooth, use unlicensed spectrum bands [56]. However, the use of unlicensed spectra faces several challenges, such as the quality of service guarantee, the cost of setting up the initial infrastructure, and the interference generated by the huge number of IoT devices [57,58].
A licensed spectrum usually is allocated to mobile networks. It provides more efficient network traffic, more reliability, enhances the quality of service (QoS), offers security, provides extensive coverage, and involves lower initialization infrastructure costs for users. However, the use of licensed spectrum bands has faced some limitations, such as high data transmission costs and the low energy efficiency of IoT devices [59].
Several recent studies have demonstrated the efficiency of unlicensed spectrum bands in the mm wave range. It uses extremely low power but provides large transmission distances and high data rates [60,61]. One limitation of the mm wave spectrum is that the data rate is strongly affected by weather conditions, especially rain [62].
Topology: The establishment of the communication spectrum band and operation protocol of IoT devices depends on the structure that deploys IoT devices for smart agriculture applications. Network structures for smart agriculture usually have two main types of nodes: sensor and backhaul nodes [63]. The common characteristics of IoT sensor nodes are short communication distance, low data rate, and high energy efficiency. In contrast, IoT backhaul nodes often require large transmission distances, high throughput, and data rates. Therefore, based on the role of each IoT network node, the sensor node or backhaul node selects and installs appropriate communication technologies [64]. Figure 5 presents a typical low-power network topology designed for measuring and monitoring factors in a smart farm. The system includes:
IoT sensor nodes collect information from the farming environment, such as soil moisture, air humidity, temperature, nutrient ingredients of soil, pest images, and water quality, then transmit collected data to IoT backhaul devices. Depending on the operation purpose and installation location, IoT sensor nodes can be installed as RFDs (reduced-function devices), which only communicate with FFDs (full-function devices). These nodes cannot communicate with the other RFDs, aiming to save energy and decrease investment costs.
IoT backhaul nodes, besides having the role of an IoT sensor node, also play a role as intermediate nodes to receive information from other IoT nodes and transmit it to the control centre. IoT backhaul nodes are often installed as an FFD device, which can connect to other FDD and RFD devices.

2.3. Data Analytics and Storage Solutions

In the smart agriculture domain, besides the main problems of sensing, collecting data, and controlling devices to respond to the real farming environment, data storage and processing are also important problems and face some challenges [26,28]. In reality, the number of collected data is huge, and traditional data storage, organization, and processing solutions are not feasible. Therefore, big data processing solutions need to be researched and applied for smart agriculture [65,66].
The complexity of data storage and processing is due to the unique characteristics of the smart agriculture field, including unstructured data and various formats, such as text, images, audio and video, economic figures, and market information. Recent solutions and technologies have introduced the use of cloud platforms for storage and data analytics, which are collected from farms [36,67]. In addition, cloud-assisted big data analytic solutions, such as edge computing [68] or fog computing [69], are also proposed to reduce latency and costs and support QoS.
The survey results demonstrate that, in recent years, many management information systems for smart agriculture have been proposed [70,71,72]. Nowadays, possible solutions have been developed and commercialized, providing solutions and services for farmers to manage farms and fields, aiming to increase productivity, reduce human labour, and enhance farming efficiency, as follows:
O n F a r m [73]: It is part of the SWIM family, which specializes in providing solutions and technology for smart agriculture. OnFarm is a technology platform that allows farmers to manage and use data in the simplest way. It is also a comprehensive solution for managing, using, and controlling water on smart farms.
F a r m o b i l e [74]: It is a commercialized online platform to manage smart farms that allows farmers, traders, scientists, and insurance companies to operate and communicate centrally on an online platform.
S i l e n t   H e r d s m a n   P l a t f o r m [75]: It is a platform that allows monitoring of the activities of cow colonies and predicting their milk production.
C r o p X [76]: It is a platform that will enable monitor and control nutrients of farming soil based on a sensor system and big data analytic solutions.
F a r m X [77]: It is an all-in-one platform for tree crops. FarmX provides a series of diverse farming management solutions, including irrigation, fertilizer, cropland management systems, environmental monitoring, and crop production forecasting.
E a s y f a r m [78]: It is a platform that provides software to help farmers manage and account for figures of farms. Easyfarm provides visual figures, including input and output supplies management, production forecasting, and market connectivity, to help farmers fully manage their farms.
K A A [79]: It is a cloud-based IoT platform that aims to provide comprehensive, end-to-end solutions for farmers, including: (1) connecting and managing IoT devices in farms or fields; and (2) monitoring and controlling behaviours of devices based on data analysis results.
F a r m l o g s [80]: It is a platform that provides tools and solutions for: (1) automating the production cost calculation process; (2) managing the day-to-day activities of the farm in real-time; and (3) supporting marketing and increasing sales of products.

3. Typical Applications of IoT in Smart Agriculture

In recent years, a series of IoT applications for agriculture have been introduced. According to survey results, we divided these applications into categories based on their purpose, including monitoring, tracking and traceability, and greenhouse production. The detailed results are presented in the following subsection.

3.1. Monitoring

In the agriculture sector, factors affecting the farming and production process can be monitored and collected, such as soil moisture, air humidity, temperature, pH level, etc. These factors depend on the considered agricultural sector. Some smart agricultural sectors are applying the following monitoring solutions:
Crop Farming: In this sector, some vital factors that affect the farming process and production efficiency include air temperature, precipitation, air humidity, soil moisture, salinity, solar radiation, pest status, soil nutrient ingredients, etc. In [81], the authors designed an IoT device called FarmFox. This device allows real-time collection and analysis of the composition of the farming soil and transmits the information to farmers/owners via the Internet. The results demonstrate the health of the soil is monitored in real time to provide timely recommendations to farmers aiming to increase productivity and farming efficiency.
Furthermore, in [82], the authors proposed an IoT device to allow intelligent control of temperature and humidity factors, called a weather radar. This device will automatically turn on the warning mode using the light signal and send messages to the farmer when the temperature or humidity exceeds a pre-installed threshold. In [83], the authors introduced an IoT system based on Web GIS to monitor pest status and provide early warnings. In addition, this study also proposes a predictive model based on monitoring the habitat of pests and diseases. The efficiency of the proposed system was indicated, based on the predicted figures of the locust epidemic, to have a high accuracy rate (over 87%) in 2019 (China).
Monitoring information, such as soil condition, moisture, and temperature, and the prediction of natural factors, such as rainfall and weather, support the control of growing conditions of crops, helping farmers plan and make irrigation decisions to optimize production and reduce labour costs. In addition, the collected data, combined with big data processing technology, can provide recommendations for implementing preventive and remedial solutions against pests and diseases in farming.
Aquaponics: It is an integration of aquaculture and hydroponics. Aquaponics is a farming technique where fish waste becomes a source of nutrients needed by plants. One of the most important issues in such farms is constantly monitoring water quality, water level, temperature, salinity, pH, sunlight, etc. [84]. According to this research direction, in [85], the authors designed an IoT system to monitor the temperature and pH value of water for aquaponics farms. Moreover, this system is also equipped with a control system of water metrics to keep the fish habitat stable and an automatic fish feeding function to increase the productivity of the fish. The results show that the IoT system had stable operation and provided real-time monitoring parameters. The authors of [86] designed an aquaponics farm for households/urban areas based on IoT. This system recommends the proper ratio of fish and plants.
Consequently, the system decreases feed consumption as well as reduces carbon emissions into the environment. The primary purpose of this proposal aims to balance the self-sustaining ability of the aquaponics system. The experimental results demonstrate the number of fish decreases from 30 to 15, and the number of plants increases from 20 to 30, but the crop production will increase by more than 50%. A detailed and diverse survey of the IoT systems and devices for control and monitoring of aquaponics farms is introduced in [87]. Based on the obtained data, monitoring can improve the production of fish and plants through the control, supplementation, and regulation of nutritional ingredients in the water. The collected data were also used to automate the management of aquaponics farms to reduce labour costs.
Forestry: Humans depend on forests for survival. Moreover, forests play a vital role in the carbon cycle and provide a habitat for more than two-thirds of animal species in the world. Forests also have the effect of protecting watersheds, limiting floods, and mitigating climate change. The main factors that need to be monitored in a forest include soil ingredients, air temperature, humidity, and concentration of several different gases, such as oxygen, methane, ammonia, and hydrogen sulphide. A series of forest control systems and solutions are presented in [88,89] based on IoT and big data analytics.
In [90], the authors designed a peatland forest environmental monitoring system. This forest area plays a very vital role in the rainforest ecosystem of Brunei. However, the peatland forest type is very burnt. This work designed an IoT system to monitor environmental conditions, such as temperature, humidity, wind direction, barometric pressure, and manage possible disasters. For the purpose of enhancing feasibility, IoT devices use the solar-powered system and communicate with the monitoring centre based on the LoRa network. In [91], the authors proposed a solution to control forest changes and vitality by using high-resolution RapidEye satellite imagery. This solution has been deployed commercially in several states in Germany and has detected leaf diseases in a pine forest affected by pests. Survey results indicate that monitoring in forestry focuses on providing early warning systems against forest fires, pest control, or deforestation.
Livestock Farming: It is defined as the process of raising domesticated animals, such as cows, pigs, sheep, and goats, chickens, etc., in an agricultural environment to obtain traction, serve production, and obtain products such as meat, eggs, milk, fur, leather, etc. In this area, the factors to be monitored depend on the type and number of farming animals [92]. In [93], the authors designed a support system for the diagnosis, prevention, and treatment of diseases for livestock called VetLink. This system can provide recommendations for animal health for farmers in rural areas where it is difficult to access veterinary doctors immediately. In [94], the authors proposed a noncontact temperature measurement system and monitoring of animals to ensure early detection of diseases and animal health. This system can be used for remote monitoring of animal health and timely anomaly detection. In [95], the authors introduced a monitoring system for large-scale pig farms based on IoT. The specific solution is to attach an IC tag on each pig to monitor the behaviour of each pig, such as their period of feeding and resting and exercise. Data from sensors are collected and combined with data analytics solutions that can make recommendations for pig health.
The monitoring data of water, feed, and animal health for livestock in the farming process helps farmers set up livestock plans, reduce labour costs, and enhance production efficiency. While a series of solutions has been provided for monitoring large-scale farms, their application in small and medium-sized farms is very limited, especially in developing countries. This can be attributed to the high cost and the lack of knowledge needed to set up, manage, and operate IoT systems. Therefore, effective and low-cost solutions for agricultural IoT have much potential.

3.2. Tracking and Tracing

In order to meet the needs of consumers and increase profit value, in the future, farms need to demonstrate that products offered to the market are clean products and can be tracked and traced conveniently, thereby enhancing the trust of consumers in product safety and health-related issues. In order to solve this problem, a series of tracking- and tracing-based problems for the smart agricultural sector has been proposed, specifically as follows:
In [96], the authors designed an information system that allows tracking and tracing of agricultural products and foods such as dairy and vegetables, called SISTABENE. This system helps suppliers track the production process and errors arising in the supply chain, and helps end-users trace the origin of food. In [97], the authors proposed a food supply chain traceability system based on blockchain technology. It helps to track and trace agrifood supply chains’ production process and trace the origin of agricultural products. This solution has been employed at Shanwei Lvfengyuan Modern Agricultural Development Co., Ltd. (Shanwei, China). Although there are still limitations, the results demonstrate that this solution has successfully supported the tracing of food and agricultural products through QR codes, improving product quality and ensuring the clear traceability of products. In [98,99], the authors proposed smart agricultural solutions to tracking and tracing agricultural products, thereby allowing consumers to know the product’s entire history. These solutions enable tracking and tracing some of the data collected along the supply chain, ensuring that consumers and other stakeholders can identify products’ origin, location, and history.

3.3. Smart Precision Farming

The advent of the GPS (global positioning system) has created breakthrough advances in many fields of science and technology. The GPS provides the most important parameters for locating a device, such as location and time. GPS systems have been successfully deployed in many fields, such as smartphones, vehicles, and IoT ecosystems. However, GPS is only good support for outdoor systems and the sky. Meanwhile, the demand for the locating and navigating systems in the home and on the streets of smart cities is growing rapidly. Aiming to solve this problem, an advanced global navigation satellite system (GNSS) is being deployed [100]. Based on GPS and GNSS systems, suitable farming maps have been established for fields and farms. As a result, agricultural machinery and equipment can be operated autonomously [101]. Figure 6 presents an illustration of the typical cloud-assisted, IoT-based precision agriculture platform.
In smart precision farming, one of the most important applications is the use of drones in monitoring and farming activities. Some common farming tasks using UAVs include spraying pesticides, fertilizing, sowing seeds, evaluating and mapping, and monitoring crop growth. In [102], the authors presented a detailed survey of drone applications for smart agriculture, including applications, control technology, and future trends of the UAV application for smart agriculture. In [103], the authors designed an automatic agricultural product classification system based on camera systems, image processing algorithms, and mechanical actuators. The experimental results for agriculture products such as oranges and tomatoes present a classification success rate of over 95%, and the sorting time for each product is less than 1(s). This solution can be adapted and applied to the classification of different agricultural products. In [104], the authors proposed a solution to estimate grape production. The proposed solution combines an RGB-D camera mounted on a mobile robot platform and size estimation algorithm for a bunch of grapes. The experimental results present an average error in the range of [2.8–3.5] (cm). The results demonstrate this solution is a feasible method for evaluating the productivity of large-scale grape farms.
The survey results show that smart precision agricultural equipment, such as irrigation systems, unmanned aerial vehicles (UAV), and smart agricultural equipment, etc., are configurable in an autonomous-control mode based on certain conditions or can be controlled remotely by the farmer via the Internet [105,106]. Smart precision farming helps to improve productivity and production efficiency and is suitable for large-scale farms. Nowadays, suppliers of precision agricultural equipment have IoT modules built into their machines, allowing machines to operate autonomously and remotely via the Internet [107].

3.4. Greenhouse Production

A greenhouse consists of walls and a roof, which are usually made from transparent materials, such as plastic or glass. In a greenhouse, plants are grown in a controlled environment, including controlling for moisture, nutrient ingredients of the soil, light, temperature, etc. Consequently, greenhouse technology makes it possible for humans to grow any plant, at any time, by providing suitable environmental conditions [108]. Figure 7 illustrates a smart agriculture IoT system for monitoring greenhouse farming factors based on IoT ecosystems.
In [109], the authors introduced an IoT-based greenhouse environmental monitoring system for multipoint monitoring in large greenhouses. Instead of using multiple sensors at different locations, this solution involves a drive system that allows the sensor system to move to different locations in the greenhouse. The experimental results show that the proposed system can effectively monitor multiple points in large greenhouses. In [110], the authors introduced an energy-saving temperature control technology for smart greenhouses. This study proposed two intelligent control methods: active disturbance rejection control and fuzzy active disturbance rejection control. The experimental results demonstrate that the proposed technology saves over 15% of the total energy consumption of the greenhouse. In [111], the authors designed an intelligent IoT system to monitor and control greenhouse temperature for energy efficiency and improve crop productivity. The experimental results for the Kingdom of Saudi Arabia, where daytime temperatures can be above 50 °C, demonstrate the efficiency of the proposed solution, including saving energy and predicting the rate of plant growth.
Recent studies indicated that solutions integrating IoT, big data processing, and artificial intelligence could be applied in greenhouses to reduce labour and energy efficiency. Moreover, it also provides direct connections between the greenhouse farms and the customer [112,113,114,115].

4. Challenges and Open Research Directions

The survey results indicate that IoT components for the smart agriculture sector, including hardware and software, have been focused on research and achieved many breakthrough results. Several IoT solutions have been deployed on large-scale farms/fields. However, the widespread deployment of IoT in the agricultural sector still presents some challenges. We have present two main problems: economic efficiency and technical problems. We consider these issues coupled with policies that will drive the integration of IoT technologies in agriculture.

4.1. Economic Efficiency

In agricultural economics, one of the most important characteristics is a low rate of profit of an investment project, which presents many risks from natural conditions. The benefit–cost of a new technology seeking deployment in agriculture should be carefully calculated to ensure a trade-off between the cost of technology implementation and the profit potential. Therefore, we discuss the economic aspects related to IoT implementation in smart agriculture.
There are several types of costs related to the implementation of IoT in agriculture. We divided them into categories, including (1) the system initialization cost and (2) the system operating cost. The system initialization cost includes hardware purchases (IoT devices, gateways, base station infrastructure). The system operating cost includes service registration cost and the cost of labour to manage IoT devices. Furthermore, additional operating costs include incurred costs from energy consumption, maintenance, data exchange among IoT devices, gateways and cloud servers. According to the opinion of   T urgut and Boloni [116], the successful deployment of the IoT technologies will only happen if the customer benefits (customers need to know the benefits and potential) that IoT systems provide exceed their physical value and privacy costs. The businesses participating in the IoT domain will profit and achieve success. We can describe this process using these two conditions, as follows:
S u c c e s s   o f   I o T   A p p l i c a t i o n s = { V s e r v i c e > C p r i + C h u s e r + C p a y ,   F a r m e r   B e n e f i t s                     ( 1 ) V i n f o + R p a y > C h b u s i n e s s ,   B u s i n e s s e s   B e n e f i t s       ( 2 )
V s e r v i c e   is the expected value received by the IoT service users.
C p r i is the cost of the loss of privacy.
C h u s e r is the equipment and hardware costs the user pays.
C p a y   is the payment for the service fee.
V i n f o is the received information value.
R p a y is the received direct payment.
C h b u s i n e s s is the share of the hardware and maintenance costs of the business.
According to the opinion of the service user (farmers or the owner of the farm), Equation (1) shows that the perceived value of the service for the user ( V s e r v i c e ) must be higher than the total of costs, including: the cost of the loss of privacy ( C p r i ), the equipment and hardware costs the user pays ( C h u s e r ), and the payments for the service fee ( C p a y ), while the opinion of the service provider, as shown in Equation (2), shows that the received information value ( V i n f o ) and the received direct payments ( R p a y ) must be higher than the share of the hardware and maintenance costs of the business ( C h b u s i n e s s ).
There is still a gap between service providers and service users (farmers or the owner of the farm), leading to the slow deployment of IoT applications in smart agriculture. In terms of the economic aspect, the analyzed results show that the need for a support policy from regulatory agencies and governments to allow service providers and service users to use IoT-based smart agriculture applications in their infancy can be met. As discussed in [117], to promote smart agriculture, the European Union has issued supportive economic policies, the so-called the European CAP (Common Agricultural Policy), whose annual budget amounts to approximately EUR 59 billion and is paid for by the nations of the EU.
In our view, to be able to apply IoT in the field of smart agriculture, service costs ( C p a y ) and the operating and system initialization cost of IoT ( C h u s e r ) needs to constantly be improved and optimized to reduce the cost of the IoT services for farmers. In addition, IoT businesses (service providers) also need to maximize the value of information obtained ( V i n f o ) to improve the profitability of the service providers.
In reality, service providers may commercially exploit the information received ( V i n f o ) in the period of providing services for farms, aiming to encourage the deployment of IoT applications in smart agriculture. Nowadays, several IoT platform providers allow free registration and use of services with some limitation conditions regarding services’ functionality and ability processing; the number of connected IoT devices; and the number of data stored while premium functions and services charge users a fee.
In addition, one of the significant factors slowing down IoT adoption in agriculture is farmers’ knowledge and ability to use IoT devices. In developed countries, this issue can be easily solved due to the accessibility of new technologies of farmers. Otherwise, in developing countries, where the majority of farmers in rural areas have very limited access to advanced technologies, this issue is a significant challenge [118,119].

4.2. Technical Problems

Interference: Deploying a huge number of IoT devices for smart agriculture can cause interference to different network systems, especially some IoT networks using short spectrum bands such as ZigBee, Wi-Fi, Sigfox, and LoRa (See Table 1). Interference can degrade system performance as well as reduce the reliability of IoT ecosystems. IoT networks that use cognitive technology to reuse unlicensed spectra increase the cost of the device. In our opinion, the advent of the 6G network [120] will allow a huge number of devices to connect to the Internet with an extremely high access speed and extremely large bandwidth. The full interference problem of IoT networks will be solved.
Security and Privacy: One of the most important problems of applying IoT in smart agriculture is the security problem, including the protection of data and systems from attacks on the Internet. In regard to system security, IoT devices’ limited capacity and ability led to complex encryption algorithms that are impossible to implement on IoT devices. As a result, IoT systems can be attacked using the Internet to gain system control rights; IoT gateways are also attacked via denial of service [121,122,123]. In addition, cloud servers can be attacked by data spoofing to perform unauthorized tasks that affect the autonomous farming processes of farms. Cloud infrastructures can also be controlled by attackers [124,125]. Several issues of detailed IoT data privacy and security measures have been discussed in [126,127,128]. According to Neshenko et al., the IoT data security issue is one of the biggest problems slowing down IoT adoption in smart agriculture [129].
Regarding data security, the obtained information from IoT systems in farms is collected, processed, and commercially exploited by service providers to varying degrees. Therefore, one of the most important problems of policies regards the validity and legal status of farm data [130]. In reality, these data are of great value when aggregated and analyzed for large-scale agricultural activities. Consequently, without policies, the data privacy and security of farms can affect the competitive advantage of farmers/farm owners. In our opinion, using cryptography coupled with access keys is a possible solution to solve this problem. Keys could be made available based on a regional user group and to those who contributed to the database. For further complex cases, secure multiparty computation can be used, where the homomorphic encryption method [131,132], or this method combined with the blockchain [133], can be applied for the purpose of balancing privacy and data utility.
In our opinion, the security problems of IoT systems will be an exciting research topic and garner attention for both academia and industry research. An in-depth survey of threats and solutions to improve robustness, trust, and privacy for future IoT systems is presented in [134].
Reliability: Most IoT devices are expected to be deployed outdoors (in fields and farms). Harsh work environments lead to the rapid degradation of IoT devices’ quality and can lead to unexpected manufacturer failures. The mechanical safety of IoT devices and systems must be ensured so they can withstand extremes of weather, such as temperature, humidity, rainstorms, and floods [135]. In our opinion, new materials and technologies need to continue to be studied to improve the durability of devices.
The open problems and challenges discussed in this section indicate that for IoT to be widely deployed in the smart agriculture sector, there are still many issues to be solved. Service providers need to reduce the service costs, more effectively exploiting the information collected from the farm. On the other hand, farmers need to improve their skills to be able to apply IoT solutions on their farm to enhance productivity and farming efficiency. Researchers need to continually study and propose optimal solutions and technologies to ensure IoT systems’ privacy and security and improve the durability of IoT devices. These are really major challenges and exciting research topics in the future so IoT can be widely applied in the smart agriculture sector.

5. Conclusions

In this study, we presented an overview of IoT and big data for the smart agriculture sector. Several issues related to promoting IoT deployment in the agriculture sector have been discussed in detail. Survey results indicate that many studies have been performed to apply IoT for smart agriculture, aiming to enhance productivity, reduce human labour, and improve production efficiency. The benefits of applying IoT and big data in agriculture were discussed. In addition, we also pointed out the challenges we need to overcome to be able to accelerate the deployment of IoT in smart agriculture. However, there are still some challenges that need to be addressed for IoT solutions to be affordable for the majority of farmers, including small- and medium-scale farm owners. In addition, security technologies need to be continuously improved, but in our opinion, the application of IoT solutions for smart agriculture is inevitable and will enhance productivity, provide clean and green foods, support food traceability, reduce human labour, and improve production efficiency. On the other hand, this survey also points out some interesting research directions for security and communication technologies for IoT. We think that these will be very exciting research directions in the future.

Author Contributions

Conceptualization, V.K.Q., N.V.H., and A.M.; methodology, V.K.Q. and A.M.; validation, N.M.Q., D.V.A., A.M. and N.T.B.; resources, A.M.; writing—original draft preparation, V.K.Q., N.V.H., D.V.A., N.M.Q. and A.M.; writing—review and editing, V.K.Q., G.R., S.L. and A.M.; visualization, V.K.Q., N.V.H., D.V.A., N.M.Q., N.T.B., G.R., S.L. and A.M.; supervision, A.M. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare that they have no conflict of interest.


  1. Quy, V.K.; Nam, V.H.; Linh, D.M.; Ngoc, L.A.; Gwanggil, J. Wireless Communication Technologies for IoT in 5G: Vision, Applications, and Challenges. Wirel. Commun. Mob. Comput. 2022, 2022, 3229294. [Google Scholar] [CrossRef]
  2. Sinche, S.; Raposo, D.; Armando, N.; Rodrigues, A.; Boavida, F.; Pereira, V.; Silva, J.S. A Survey of IoT Management Protocols and Frameworks. IEEE Commun. Surv. Tutor. 2020, 22, 1168–1190. [Google Scholar] [CrossRef]
  3. Elijah, O.; Rahman, T.A.; Orikumhi, I.; Leow, C.Y.; Hindia, M.N. An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges. IEEE Internet Things J. 2018, 5, 3758–3773. [Google Scholar] [CrossRef]
  4. Li, W.; Logenthiran, T.; Phan, V.; Woo, W.L. A Novel Smart Energy Theft System (SETS) for IoT-based Smart Home. IEEE Internet Things J. 2019, 6, 5531–5539. [Google Scholar] [CrossRef]
  5. Shin, D.; Yun, K.; Kim, J.; Astillo, P.V.; Kim, J.-N.; You, I. A Security Protocol for Route Optimization in DMM-Based Smart Home IoT Networks. IEEE Access 2019, 7, 142531–142550. [Google Scholar] [CrossRef]
  6. An, J.G.; Le Gall, F.; Kim, J.; Yun, J.; Hwang, J.; Bauer, M.; Zhao, M.; Song, J.S. Toward Global IoT-Enabled Smart Cities Interworking Using Adaptive Semantic Adapter. IEEE Internet Things J. 2019, 6, 5753–5765. [Google Scholar] [CrossRef]
  7. Cirillo, F.; Gomez, D.; Diez, L.; Maestro, I.E.; Gilbert, T.B.J.; Akhavan, R. Smart City IoT Services Creation through Large-Scale Collaboration. IEEE Internet Things J. 2020, 7, 5267–5275. [Google Scholar] [CrossRef] [Green Version]
  8. Ammad, M.; Shah, M.A.; Islam, S.U.; Maple, C.; Alaulamie, A.A.; Rodrigues, J.J.P.C.; Mussadiq, S.; Tariq, U. A Novel Fog-Based Multi-Level Energy-Efficient Framework for IoT-Enabled Smart Environments. IEEE Access 2020, 8, 150010–150026. [Google Scholar] [CrossRef]
  9. Metallidou, C.K.; Psannis, K.E.; Egyptiadou, E.A. Energy Efficiency in Smart Buildings: IoT Approaches. IEEE Access 2020, 8, 63679–63699. [Google Scholar] [CrossRef]
  10. Quy, V.K.; Nam, V.H.; Linh, D.M.; Ban, N.T.; Han, N.D. Communication Solutions for Vehicle Ad-hoc Network in Smart Cities Environment: A Comprehensive Survey. Wirel. Pers. Commun. 2022, 122, 2791–2815. [Google Scholar] [CrossRef]
  11. Kiani, F.; Seyyedabbasi, A.; Nematzadeh, S.; Candan, F.; Çevik, T.; Anka, F.A.; Randazzo, G.; Lanza, S.; Muzirafuti, A. Adaptive Metaheuristic-Based Methods for Autonomous Robot Path Planning: Sustainable Ag-ricultural Applications. Appl. Sci. 2022, 12, 943. [Google Scholar] [CrossRef]
  12. Patle, K.S.; Saini, R.; Kumar, A.; Palaparthy, V.S. Field Evaluation of Smart Sensor System for Plant Disease Prediction Using LSTM Network. IEEE Sens. J. 2022, 22, 3715–3725. [Google Scholar] [CrossRef]
  13. Vangala, A.; Das, A.K.; Kumar, N.; Alazab, M. Smart Secure Sensing for IoT-Based Agriculture: Blockchain Perspective. IEEE Sens. J. 2020, 21, 17591–17607. [Google Scholar] [CrossRef]
  14. Citoni, B.; Fioranelli, F.; Imran, M.A.; Abbasi, Q.H. Internet of Things and LoRaWAN-Enabled Future Smart Farming. IEEE Internet Things Mag. 2019, 2, 14–19. [Google Scholar] [CrossRef] [Green Version]
  15. Kumar, R.; Mishra, R.; Gupta, H.P.; Dutta, T. Smart Sensing for Agriculture: Applications, Advancements, and Challenges. IEEE Consum. Electron. Mag. 2021, 10, 51–56. [Google Scholar] [CrossRef]
  16. Chang, Y.; Lai, Y. Campus Edge Computing Network Based on IoT Street Lighting Nodes. IEEE Syst. J. 2020, 14, 164–171. [Google Scholar] [CrossRef]
  17. Sutjarittham, T.; Habibi Gharakheili, H.; Kanhere, S.S.; Sivaraman, V. Experiences with IoT and AI in a Smart Campus for Optimizing Classroom Usage. IEEE Internet Things J. 2019, 6, 7595–7607. [Google Scholar] [CrossRef]
  18. Rani, S.; Ahmed, S.H.; Shah, S.C. Smart Health: A Novel Paradigm to Control the Chickungunya Virus. IEEE Internet Things J. 2019, 6, 1306–1311. [Google Scholar] [CrossRef]
  19. Zhou, Z.; Yu, H.; Shi, H. Human Activity Recognition Based on Improved Bayesian Convolution Network to Analyze Health Care Data Using Wearable IoT Device. IEEE Access 2020, 8, 86411–86418. [Google Scholar] [CrossRef]
  20. Humayun, M.; Jhanjhi, N.; Hamid, B.; Ahmed, G. Emerging Smart Logistics and Transportation Using IoT and Blockchain. IEEE Internet Things Mag. 2020, 3, 58–62. [Google Scholar] [CrossRef]
  21. Song, Y.; Yu, F.R.; Zhou, L.; Yang, X.; He, Z. Applications of the Internet of Things (IoT) in Smart Logistics: A Comprehensive Survey. IEEE Internet Things J. 2021, 8, 4250–4274. [Google Scholar] [CrossRef]
  22. Shafique, K.; Khawaja, B.A.; Sabir, F.; Qazi, S.; Mustaqim, M. Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends & Prospects for Emerging 5G-IoT Scenarios. IEEE Access 2020, 8, 23022–23040. [Google Scholar] [CrossRef]
  23. Available online: (accessed on 7 May 2021).
  24. Yang, X.; Shu, L.; Chen, J.; Ferrag, M.A.; Wu, J.; Nurellari, E.; Huang, K. A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges. IEEE/CAA J. Autom. Sin. 2021, 8, 273–302. [Google Scholar] [CrossRef]
  25. Ayaz, M.; Ammad-Uddin, M.; Sharif, Z.; Mansour, A.; Aggoune, E.-H.M. Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk. IEEE Access 2019, 7, 129551–129583. [Google Scholar] [CrossRef]
  26. Alfred, R.; Obit, J.H.; Chin, C.P.-Y.; Haviluddin, H.; Lim, Y. Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks. IEEE Access 2021, 9, 50358–50380. [Google Scholar] [CrossRef]
  27. Zikria, Y.B.; Ali, R.; Afzal, M.K.; Kim, S.W. Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions. Sensors 2021, 21, 1174. [Google Scholar] [CrossRef]
  28. Kour, V.P.; Arora, S. Recent Developments of the Internet of Things in Agriculture: A Survey. IEEE Access 2020, 8, 129924–129957. [Google Scholar] [CrossRef]
  29. Saad, A.; Benyamina, A.E.H.; Gamatié, A. Water Management in Agriculture: A Survey on Current Challenges and Technological Solutions. IEEE Access 2020, 8, 38082–38097. [Google Scholar] [CrossRef]
  30. Tyagi, S.K.S.; Mukherjee, A.; Pokhrel, S.R.; Hiran, K.K. An Intelligent and Optimal Resource Allocation Approach in Sensor Networks for Smart Agri-IoT. IEEE Sens. J. 2020, 21, 17439–17446. [Google Scholar] [CrossRef]
  31. Li, X.; Pu, T.; Li, L.; Ao, J. Enhanced Sensitivity of GaN-Based Temperature Sensor by Using the Series Schottky Barrier Diode Structure. IEEE Electron Device Lett. 2020, 41, 601–604. [Google Scholar] [CrossRef]
  32. Gopalakrishnan, S.; Waimin, J.; Raghunathan, N.; Bagchi, S.; Shakouri, A.; Rahimi, R. Battery-Less Wireless Chipless Sensor Tag for Subsoil Moisture Monitoring. IEEE Sens. J. 2021, 21, 6071–6082. [Google Scholar] [CrossRef]
  33. Udutalapally, V.; Mohanty, S.P.; Pallagani, V.; Khandelwal, V. sCrop: A Novel Device for Sustainable Automatic Disease Prediction, Crop Selection, and Irrigation in Internet-of-Agro-Things for Smart Agriculture. IEEE Sens. J. 2020, 21, 17525–17538. [Google Scholar] [CrossRef]
  34. Spachos, P.; Gregori, S. Integration of Wireless Sensor Networks and Smart UAVs for Precision Viticulture. IEEE Internet Comput. 2019, 23, 8–16. [Google Scholar] [CrossRef]
  35. Abdelnour, A.; Buchin, F.; Kaddour, D.; Tedjini, S. Improved Traceability Solution Based on UHF RFID for Cheese Production Sector. IEEE J. Radio Freq. Identif. 2018, 2, 68–72. [Google Scholar] [CrossRef]
  36. Friha, O.; Ferrag, M.A.; Shu, L.; Maglaras, L.; Wang, X. Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies. IEEE/CAA J. Autom. Sin. 2021, 8, 718–752. [Google Scholar] [CrossRef]
  37. Farooq, M.S.; Riaz, S.; Abid, A.; Abid, K.; Naeem, M.A. A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming. IEEE Access 2019, 7, 156237–156271. [Google Scholar] [CrossRef]
  38. Mishra, D.; Zema, N.R.; Natalizio, E. A High-End IoT Devices Framework to Foster Beyond-Connectivity Capabilities in 5G/B5G Architecture. IEEE Commun. Mag. 2021, 59, 55–61. [Google Scholar] [CrossRef]
  39. Javed, F.; Afzal, M.K.; Sharif, M.; Kim, B. Internet of Things (IoT) Operating Systems Support, Networking Technologies, Applications, and Challenges: A Comparative Review. IEEE Commun. Surv. Tutor. 2018, 20, 2062–2100. [Google Scholar] [CrossRef]
  40. Poyen, F.B.; Ghosh, A.; Kundu, P.; Hazra, S.; Sengupta, N. Prototype Model Design of Automatic Irrigation Controller. IEEE Trans. Instrum. Meas. 2021, 70, 9502217. [Google Scholar] [CrossRef]
  41. Wang, Y.; Rajib, S.M.S.M.; Collins, C.; Grieve, B. Low-Cost Turbidity Sensor for Low-Power Wireless Monitoring of Fresh-Water Courses. IEEE Sens. J. 2018, 18, 4689–4696. [Google Scholar] [CrossRef] [Green Version]
  42. El-Basioni, B.M.M.; El-Kader, S.M.A. Laying the Foundations for an IoT Reference Architecture for Agricultural Application Domain. IEEE Access 2020, 8, 190194–190230. [Google Scholar] [CrossRef]
  43. Alam, M.M.; Malik, H.; Khan, M.I.; Pardy, T.; Kuusik, A.; Le Moullec, Y. A Survey on the Roles of Communication Technologies in IoT-Based Personalized Healthcare Applications. IEEE Access 2018, 6, 36611–36631. [Google Scholar] [CrossRef]
  44. Chettri, L.; Bera, R. A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems. IEEE Internet Things J. 2020, 7, 16–32. [Google Scholar] [CrossRef]
  45. Pal, A.; Kant, K. NFMI: Connectivity for Short-Range IoT Applications. Computer 2019, 52, 63–67. [Google Scholar] [CrossRef]
  46. Collotta, M.; Pau, G.; Talty, T.; Tonguz, O.K. Bluetooth 5: A Concrete Step Forward toward the IoT. IEEE Commun. Mag. 2018, 56, 125–131. [Google Scholar] [CrossRef]
  47. Bacco, M.; Berton, A.; Gotta, A.; Caviglione, L. IEEE 802.15.4 Air-Ground UAV Communications in Smart Farming Scenarios. IEEE Commun. Lett. 2018, 22, 1910–1913. [Google Scholar] [CrossRef]
  48. Gente, R.; Busch, S.F.; Stubling, E.-M.; Schneider, L.M.; Hirschmann, C.B.; Balzer, J.C.; Koch, M. Quality Control of Sugar Beet Seeds With THz Time-Domain Spectroscopy. IEEE Trans. Terahertz Sci. Technol. 2016, 6, 754–756. [Google Scholar] [CrossRef]
  49. Afsharinejad, A.; Davy, A.; Naftaly, M. Variability of Terahertz Transmission Measured in Live Plant Leaves. IEEE Geosci. Remote Sens. Lett. 2017, 14, 636–638. [Google Scholar] [CrossRef]
  50. Wang, X.; Zhang, J.; Yu, Z.; Mao, S.; Periaswamy, S.C.G.; Patton, J. On Remote Temperature Sensing Using Commercial UHF RFID Tags. IEEE Internet Things J. 2019, 6, 10715–10727. [Google Scholar] [CrossRef]
  51. Ali, S.; Glass, T.; Parr, B.; Potgieter, J.; Alam, F. Low Cost Sensor with IoT LoRaWAN Connectivity and Machine Learning-Based Calibration for Air Pollution Monitoring. IEEE Trans. Instrum. Meas. 2021, 70, 5500511. [Google Scholar] [CrossRef]
  52. Joris, L.; Dupont, F.; Laurent, P.; Bellier, P.; Stoukatch, S.; Redouté, J. An Autonomous Sigfox Wireless Sensor Node for Environmental Monitoring. IEEE Sens. Lett. 2019, 3, 5500604. [Google Scholar] [CrossRef]
  53. Popli, S.; Jha, R.K.; Jain, S. A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture, Application & Challenges. IEEE Access 2019, 7, 16739–16776. [Google Scholar] [CrossRef]
  54. Sundaram, J.P.S.; Du, W.; Zhao, Z. A Survey on LoRa Networking: Research Problems, Current Solutions, and Open Issues. IEEE Commun. Surv. Tutor. 2020, 22, 371–388. [Google Scholar] [CrossRef] [Green Version]
  55. Xing, C.; Li, F. Unlicensed Spectrum-Sharing Mechanism Based on Wi-Fi Security Requirements Implemented Using Device to Device Communication Technology. IEEE Access 2020, 8, 135025–135036. [Google Scholar] [CrossRef]
  56. Jiang, X.; Zhang, H.; Yi, E.A.B.; Raghunathan, N.; Mousoulis, C.; Chaterji, S.; Peroulis, D.; Shakouri, A.; Bagchi, S. Hybrid Low-Power Wide-Area Mesh Network for IoT Applications. IEEE Internet Things J. 2021, 8, 901–915. [Google Scholar] [CrossRef]
  57. Lagen, S.; Giupponi, L.; Goyal, S.; Patriciello, N.; Bojovic, B.; Demir, A.; Beluri, M. New Radio Beam-Based Access to Unlicensed Spectrum: Design Challenges and Solutions. IEEE Commun. Surv. Tutor. 2020, 22, 8–37. [Google Scholar] [CrossRef] [Green Version]
  58. Shah, S.W.H.; Mian, A.N.; Crowcroft, J. Statistical QoS Guarantees for Licensed-Unlicensed Spectrum Interoperable D2D Communication. IEEE Access 2020, 8, 27277–27290. [Google Scholar] [CrossRef]
  59. Lu, X.; Petrov, V.; Moltchanov, D.; Andreev, S.; Mahmoodi, T.; Dohler, M. 5G-U: Conceptualizing Integrated Utilization of Licensed and Unlicensed Spectrum for Future IoT. IEEE Commun. Mag. 2019, 57, 92–98. [Google Scholar] [CrossRef]
  60. Razavieh, A.; Chen, Y.; Ethirajan, T.; Gu, M.; Cimino, S.; Shimizu, T.; Hassan, M.K.; Morshed, T.; Singh, J.; Zheng, W.; et al. Extremely-Low Threshold Voltage FinFET for 5G mmWave Applications. IEEE J. Electron Devices Soc. 2021, 9, 165–169. [Google Scholar] [CrossRef]
  61. Patriciello, N.; Lagén, S.; Bojović, B.; Giupponi, L. NR-U and IEEE 802.11 Technologies Coexistence in Unlicensed mmWave Spectrum: Models and Evaluation. IEEE Access 2020, 8, 71254–71271. [Google Scholar] [CrossRef]
  62. Mezzavilla, M.; Polese, M.; Zanella, A.; Dhananjay, A.; Rangan, S.; Kessler, C.; Rappaport, T.S.; Zorzi, M. Public Safety Communications above 6 GHz: Challenges and Opportunities. IEEE Access 2018, 6, 316–329. [Google Scholar] [CrossRef]
  63. Kassim, M.R.M. IoT Applications in Smart Agriculture: Issues and Challenges. In Proceedings of the IEEE Conference on Open Systems (ICOS), Kota Kinabalu, Malaysia, 17–19 November 2020; pp. 19–24. [Google Scholar] [CrossRef]
  64. Boursianis, A.D.; Papadopoulou, M.S.; Gotsis, A.; Wan, S.; Sarigiannidis, P.; Nikolaidis, S.; Goudos, S.K. Smart Irrigation System for Precision Agriculture—The AREThOU5A IoT Platform. IEEE Sens. J. 2020, 21, 17539–17547. [Google Scholar] [CrossRef]
  65. López, I.D.; Figueroa, A.; Corrales, J.C. Multi-Dimensional Data Preparation: A Process to Support Vulnerability Analysis and Climate Change Adaptation. IEEE Access 2020, 8, 87228–87242. [Google Scholar] [CrossRef]
  66. Bhat, S.A.; Huang, N.F. Big Data and AI Revolution in Precision Agriculture: Survey and Challenges. IEEE Access 2021, 9, 110209–110222. [Google Scholar] [CrossRef]
  67. Roopaei, M.; Rad, P.; Choo, K.R. Cloud of Things in Smart Agriculture: Intelligent Irrigation Monitoring by Thermal Imaging. IEEE Cloud Comput. 2017, 4, 10–15. [Google Scholar] [CrossRef]
  68. Zhang, X.; Cao, Z.; Dong, W. Overview of Edge Computing in the Agricultural Internet of Things: Key Technologies, Applications, Challenges. IEEE Access 2020, 8, 141748–141761. [Google Scholar] [CrossRef]
  69. Quy, V.K.; Hau, N.V.; Anh, D.V.; Ngoc, L.A. Smart healthcare IoT applications based on fog computing: Architecture, applications and challenges. Complex Intell. Syst. 2021. [Google Scholar] [CrossRef]
  70. Kopishynska, O.; Utkin, Y.; Galych, O.; Marenych, M.; Sliusar, I. Main Aspects of the Creation of Managing Information System at the Implementation of Precision Farming. In Proceedings of the 2020 IEEE 11th International Conference on Dependable Systems, Services and Technologies (DESSERT), Kyiv, Ukraine, 14–18 May 2020; pp. 404–410. [Google Scholar] [CrossRef]
  71. Soeparno, H.; Perbangsa, A.S.; Pardamean, B. Best Practices of Agricultural Information System in the Context of Knowledge and Innovation. In Proceedings of the 2018 International Conference on Information Management and Technology (ICIMTech), Jakarta, Indonesia, 3–5 September 2018; pp. 489–494. [Google Scholar] [CrossRef]
  72. Zhang, F.; Cao, N. Application and Research Progress of Geographic Information System (GIS) in Agriculture. In Proceedings of the 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Istanbul, Turkey, 16–19 July 2019; pp. 1–5. [Google Scholar] [CrossRef]
  73. Available online: (accessed on 7 May 2021).
  74. Available online: (accessed on 7 May 2021).
  75. Available online: (accessed on 7 May 2021).
  76. Available online: (accessed on 7 May 2021).
  77. Available online: (accessed on 7 May 2021).
  78. Available online: (accessed on 7 May 2021).
  79. Available online: (accessed on 7 May 2021).
  80. Available online: (accessed on 7 May 2021).
  81. Sengupta, A.; Debnath, B.; Das, A.; De, D. FarmFox: A Quad-Sensor based IoT box for Precision Agriculture. IEEE Consum. Electron. Mag. 2021, 10, 63–68. [Google Scholar] [CrossRef]
  82. Kaiyi, L.; Hengyuan, K.; Huansheng, M.; Fan, Z. Design of a New Generation of Weather Radar Intelligent Temperature and Humidity Monitoring System Based on ZigBee. In Proceedings of the 2019 International Conference on Meteorology Observations (ICMO), Chengdu, China, 28–31 December 2019; pp. 1–3. [Google Scholar] [CrossRef]
  83. Dong, Y.; Xu, F.; Liu, L.; Du, X.; Ren, B.; Guo, A.; Geng, Y.; Ruan, C.; Ye, H.; Huang, W.; et al. Automatic System for Crop Pest and Disease Dynamic Monitoring and Early Forecasting. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 4410–4418. [Google Scholar] [CrossRef]
  84. Ghandar, A.; Ahmed, A.; Zulfiqar, S.; Hua, Z.; Hanai, M.; Theodoropoulos, G. A Decision Support System for Urban Agriculture Using Digital Twin: A Case Study with Aquaponics. IEEE Access 2021, 9, 35691–35708. [Google Scholar] [CrossRef]
  85. Pasha, A.K.; Mulyana, E.; Hidayat, C.; Ramdhani, M.A.; Kurahman, O.T.; Adhipradana, M. System Design of Controlling and Monitoring on Aquaponic Based on Internet of Things. In Proceedings of the 2018 4th International Conference on Wireless and Telematics (ICWT), Nusa Dua, Bali, Indonesia, 12–13 July 2018; pp. 1–5. [Google Scholar] [CrossRef]
  86. Lee, C.; Jhang, J. System Design for Internet of Things Assisted Urban Aquaponics Farming. In Proceedings of the 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), Osaka, Japan, 15–18 October 2019; pp. 986–987. [Google Scholar] [CrossRef]
  87. Wei, Y.; Li, W.; An, D.; Li, D.; Jiao, Y.; Wei, Q. Equipment and Intelligent Control System in Aquaponics: A Review. IEEE Access 2019, 7, 169306–169326. [Google Scholar] [CrossRef]
  88. Marcu, A.-E.; Suciu, G.; Olteanu, E.; Miu, D.; Drosu, A.; Marcu, I. IoT System for Forest Monitoring. In Proceedings of the 42nd International Conference on Telecommunications and Signal Processing (TSP), Budapest, Hungary, 1–3 July 2019; pp. 629–632. [Google Scholar] [CrossRef]
  89. Zou, W.; Jing, W.; Chen, G.; Lu, Y.; Song, H. A Survey of Big Data Analytics for Smart Forestry. IEEE Access 2019, 7, 46621–46636. [Google Scholar] [CrossRef]
  90. Essa, S.; Petra, R.; Uddin, M.R.; Suhaili, W.S.H.; Ilmi, N.I. IoT-Based Environmental Monitoring System for Brunei Peat Swamp Forest. In Proceedings of the 2020 International Conference on Computer Science and Its Application in Agriculture (ICOSICA), Bogor, Indonesia, 16–17 September 2020; pp. 1–5. [Google Scholar] [CrossRef]
  91. Marx, A.; Tetteh, G.O. A Forest Vitality and Change Monitoring Tool Based on RapidEye Imagery. IEEE Geosci. Remote Sens. Lett. 2017, 14, 801–805. [Google Scholar] [CrossRef]
  92. Chaudhry, A.A.; Mumtaz, R.; Hassan Zaidi, S.M.; Tahir, M.A.; Muzammil School, S.H. Internet of Things (IoT) and Machine Learning (ML) enabled Livestock Monitoring. In Proceedings of the 2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET), Charlotte, NC, USA, 14–16 December 2020; pp. 151–155. [Google Scholar] [CrossRef]
  93. Yang, Y.; Ren, R.; Johnson, P.M. VetLink: A Livestock Disease-Management System. IEEE Potentials 2020, 39, 28–34. [Google Scholar] [CrossRef]
  94. Ma, S.; Yao, Q.; Masuda, T.; Higaki, S.; Yoshioka, K.; Arai, S.; Takamatsu, S.; Itoh, T. Development of Noncontact Body Temperature Monitoring and Prediction System for Livestock Cattle. IEEE Sens. J. 2021, 21, 9367–9376. [Google Scholar] [CrossRef]
  95. Lee, G.; Kim, M.; Koroki, K.; Ishimoto, A.; Sakamoto, S.H.; Ieiri, S. Wireless IC Tag Based Monitoring System for Individual Pigs in Pig Farm. In Proceedings of the 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech), Osaka, Japan, 12–14 March 2019; pp. 168–170. [Google Scholar] [CrossRef]
  96. Tradigo, G.; Vizza, P.; Veltri, P.; Lambardi, P.; Caligiuri, F.M.; Caligiuri, G.; Guzzi, P.H. SISTABENE: An information system for the traceability of agricultural food production. In Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 18–21 November 2019; pp. 2304–2309. [Google Scholar] [CrossRef]
  97. Wang, L.; Xu, L.; Zheng, Z.; Liu, S.; Li, X.; Cao, L.; Li, J.; Sun, C. Smart Contract-Based Agricultural Food Supply Chain Traceability. IEEE Access 2021, 9, 9296–9307. [Google Scholar] [CrossRef]
  98. Cui, P.; Dixon, J.; Guin, U.; Dimase, D. A Blockchain-Based Framework for Supply Chain Provenance. IEEE Access 2019, 7, 157113–157125. [Google Scholar] [CrossRef]
  99. Abdulhussein, A.B.; Hadi, A.K.; Ilyas, M. Design a Tracing System for a Seed Supply Chain Based on Blockchain. In Proceedings of the 2020 3rd International Conference on Engineering Technology and its Applications (IICETA), Najaf, Iraq, 6–7 September 2020; pp. 209–214. [Google Scholar] [CrossRef]
  100. Kong, S.; López-Salcedo, J.A.; Wu, Y.; Kim, E. IEEE Access Special Section Editorial: GNSS, Localization, and Navigation Technologies. IEEE Access 2019, 7, 131649–131652. [Google Scholar] [CrossRef]
  101. Alatise, M.B.; Hancke, G.P. A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods. IEEE Access 2020, 8, 39830–39846. [Google Scholar] [CrossRef]
  102. Kim, J.; Kim, S.; Ju, C.; Son, H.I. Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications. IEEE Access 2019, 7, 105100–105115. [Google Scholar] [CrossRef]
  103. Zhou, K.; Meng, Z.; He, M.; Hou, J.; Li, T. Design and Test of a Sorting Device Based on Machine Vision. IEEE Access 2020, 8, 27178–27187. [Google Scholar] [CrossRef]
  104. Kurtser, P.; Ringdahl, O.; Rotstein, N.; Berenstein, R.; Edan, Y. In-Field Grape Cluster Size Assessment for Vine Yield Estimation Using a Mobile Robot and a Consumer Level RGB-D Camera. IEEE Robot. Autom. Lett. 2020, 5, 2031–2038. [Google Scholar] [CrossRef]
  105. Zhou, X.; Zhou, J. Data-Driven Driving State Control for Unmanned Agricultural Logistics Vehicle. IEEE Access 2020, 8, 65530–65543. [Google Scholar] [CrossRef]
  106. Favenza, A.; Imam, R.; Dovis, F.; Pini, M. Detecting water using UAV-based GNSS-Reflectometry data and Artificial Intelligence. In Proceedings of the 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Portici, Italy, 24–26 October 2019; pp. 7–12. [Google Scholar] [CrossRef]
  107. Available online: (accessed on 7 May 2021).
  108. Tripathy, P.K.; Tripathy, A.K.; Agarwal, A.; Mohanty, S.P. MyGreen: An IoT-Enabled Smart Greenhouse for Sustainable Agriculture. IEEE Consum. Electron. Mag. 2021, 10, 57–62. [Google Scholar] [CrossRef]
  109. Geng, X.; Zhang, Q.; Wei, Q.; Zhang, T.; Cai, Y.; Liang, Y.; Sun, X. A Mobile Greenhouse Environment Monitoring System Based on the Internet of Things. IEEE Access 2019, 7, 135832–135844. [Google Scholar] [CrossRef]
  110. Fei, X.; Xiao, W.; Yong, X. Development of Energy Saving and Rapid Temperature Control Technology for Intelligent Greenhouses. IEEE Access 2021, 9, 29677–29685. [Google Scholar] [CrossRef]
  111. Subahi, A.F.; Bouazza, K.E. An Intelligent IoT-Based System Design for Controlling and Monitoring Greenhouse Temperature. IEEE Access 2020, 8, 125488–125500. [Google Scholar] [CrossRef]
  112. Misra, N.N.; Dixit, Y.; Al-Mallahi, A.; Bhullar, M.S.; Upadhyay, R.; Martynenko, A. IoT, Big Data and Artificial Intelligence in Agriculture and Food Industry. IEEE Internet Things J. 2020, 1. [Google Scholar] [CrossRef]
  113. Ullah, I.; Fayaz, M.; Naveed, N.; Kim, D. ANN Based Learning to Kalman Filter Algorithm for Indoor Environment Prediction in Smart Greenhouse. IEEE Access 2020, 8, 159371–159388. [Google Scholar] [CrossRef]
  114. Muñoz, M.; Guzmán, J.L.; Sánchez, J.A.; Rodríguez, F.; Torres, M.; Berenguel, M. A New IoT-based Platform for Greenhouse Crop Production. IEEE Internet Things J. 2020, 1. [Google Scholar] [CrossRef]
  115. Bai, X.; Wang, Z.; Sheng, L.; Wang, Z. Reliable Data Fusion of Hierarchical Wireless Sensor Networks with Asynchronous Measurement for Greenhouse Monitoring. IEEE Trans. Control Syst. Technol. 2019, 27, 1036–1046. [Google Scholar] [CrossRef]
  116. Turgut, D.; Boloni, L. Value of Information and Cost of Privacy in the Internet of Things. IEEE Commun. Mag. 2017, 55, 62–66. [Google Scholar] [CrossRef]
  117. Rousi, M.; Sitokonstantinou, V.; Meditskos, G.; Papoutsis, I.; Gialampoukidis, I.; Koukos, A.; Karathanassi, V.; Drivas, T.; Vrochidis, S.; Kontoes, C.; et al. Semantically Enriched Crop Type Classification and Linked Earth Observation Data to Support the Common Agricultural Policy Monitoring. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 529–552. [Google Scholar] [CrossRef]
  118. Ammad Uddin, M.; Ayaz, M.; Aggoune, E.-H.M.; Mansour, A.; Le Jeune, D. Affordable Broad Agile Farming System for Rural and Remote Area. IEEE Access 2019, 7, 127098–127116. [Google Scholar] [CrossRef]
  119. Joshi, A.; Dandekar, I.; Hargude, N.; Shrotri, A.P.; Dandekar, A.R. Application of Internet of the Things(IOT) for the Water Conservation and Entrepreneurship in the Rural Area. In Proceedings of the 2019 IEEE Pune Section International Conference (PuneCon), Pune, India, 18–20 December 2019; pp. 1–4. [Google Scholar] [CrossRef]
  120. De Lima, C.; Belot, D.; Berkvens, R.; Bourdoux, A.; Dardari, D.; Guillaud, M.; Isomursu, M.; Lohan, E.-S.; Miao, Y.; Barreto, A.N.; et al. Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges. IEEE Access 2021, 9, 26902–26925. [Google Scholar] [CrossRef]
  121. Sandal, Y.S.; Pusane, A.E.; Kurt, G.K.; Benedetto, F. Reputation Based Attacker Identification Policy for Multi-Access Edge Computing in Internet of Things. IEEE Trans. Veh. Technol. 2020, 69, 15346–15356. [Google Scholar] [CrossRef]
  122. Wang, J.; Hao, S.; Wen, R.; Zhang, B.; Zhang, L.; Hu, H.; Lu, R. IoT-Praetor: Undesired Behaviors Detection for IoT Devices. IEEE Internet Things J. 2021, 8, 927–940. [Google Scholar] [CrossRef]
  123. Jia, Y.; Zhong, F.; Alrawais, A.; Gong, B.; Cheng, X. FlowGuard: An Intelligent Edge Defense Mechanism against IoT DDoS Attacks. IEEE Internet Things J. 2020, 7, 9552–9562. [Google Scholar] [CrossRef]
  124. Agrawal, N.; Tapaswi, S. Defense Mechanisms against DDoS Attacks in a Cloud Computing Environment: State-of-the-Art and Research Challenges. IEEE Commun. Surv. Tutor. 2019, 21, 3769–3795. [Google Scholar] [CrossRef]
  125. Somani, G.; Gaur, M.S.; Sanghi, D.; Conti, M.; Rajarajan, M.; Buyya, R. Combating DDoS Attacks in the Cloud: Requirements, Trends, and Future Directions. IEEE Cloud Comput. 2017, 4, 22–32. [Google Scholar] [CrossRef] [Green Version]
  126. Wang, J.; Zhu, R.; Liu, S. A Differentially Private Unscented Kalman Filter for Streaming Data in IoT. IEEE Access 2018, 6, 6487–6495. [Google Scholar] [CrossRef]
  127. Yu, Y.; Ding, Y.; Zhao, Y.; Li, Y.; Zhao, Y.; Du, X.; Guizani, M. LRCoin: Leakage-Resilient Cryptocurrency Based on Bitcoin for Data Trading in IoT. IEEE Internet Things J. 2019, 6, 4702–4710. [Google Scholar] [CrossRef] [Green Version]
  128. Ali, I.; Kaur, S.; Khamparia, A.; Gupta, D.; Kumar, S.; Khanna, A.; Al-Turjman, F. Security Challenges and Cyber Forensic Ecosystem in IoT Driven BYOD Environment. IEEE Access 2020, 8, 172770–172782. [Google Scholar] [CrossRef]
  129. Neshenko, N.; Bou-Harb, E.; Crichigno, J.; Kaddoum, G.; Ghani, N. Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations. IEEE Commun. Surv. Tutor. 2019, 21, 2702–2733. [Google Scholar] [CrossRef]
  130. Chaterji, S.; DeLay, N.; Evans, J.; Mosier, N.; Engel, B.; Buckmaster, D.; Ladisch, M.R.; Chandra, R. Lattice: A Vision for Machine Learning, Data Engineering, and Policy Considerations for Digital Agriculture at Scale. IEEE Open J. Comput. Soc. 2021, 2, 227–240. [Google Scholar] [CrossRef]
  131. Jin, B.; Jiang, D.; Xiong, J.; Chen, L.; Li, Q. D2D Data Privacy Protection Mechanism Based on Reliability and Homomorphic Encryption. IEEE Access 2018, 6, 51140–51150. [Google Scholar] [CrossRef]
  132. Harn, L.; Hsu, C.-F.; Xia, Z.; He, Z. He Lightweight Aggregated Data Encryption for Wireless Sensor Networks (WSNs). IEEE Sens. Lett. 2021, 5, 1–4. [Google Scholar] [CrossRef]
  133. Jia, B.; Zhang, X.; Liu, J.; Zhang, Y.; Huang, K.; Liang, Y. Liang Blockchain-Enabled Federated Learning Data Protection Aggregation Scheme With Differential Privacy and Homomorphic Encryption in IioT. IEEE Trans. Ind. Inform. 2022, 18, 4049–4058. [Google Scholar] [CrossRef]
  134. Chen, L.; Thombre, S.; Jarvinen, K.; Lohan, E.S.; Alén-Savikko, A.; Leppakoski, H.; Bhuiyan, M.Z.H.; Bu-Pasha, S.; Ferrara, G.N.; Honkala, S.; et al. Robustness, Security and Privacy in Location-Based Services for Future IoT: A Survey. IEEE Access 2017, 5, 8956–8977. [Google Scholar] [CrossRef]
  135. Ballal, K.D.; Dittmann, L.; Ruepp, S.; Petersen, M.N. IoT Devices Reliability Study: Multi-RAT Communication. In Proceedings of the 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA, 2–16 June 2020; pp. 1–2. [Google Scholar] [CrossRef]
Figure 1. An illustration of IoT applications for smart agriculture.
Figure 1. An illustration of IoT applications for smart agriculture.
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Figure 2. An illustration of IoT ecosystems’ architecture for smart agriculture.
Figure 2. An illustration of IoT ecosystems’ architecture for smart agriculture.
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Figure 3. An illustration of the common architecture of an IoT device.
Figure 3. An illustration of the common architecture of an IoT device.
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Figure 4. The main characteristics of IoT devices.
Figure 4. The main characteristics of IoT devices.
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Figure 5. An illustration of the common IoT-based smart agriculture topology.
Figure 5. An illustration of the common IoT-based smart agriculture topology.
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Figure 6. Cloud-assisted IoT-based precision agriculture platform.
Figure 6. Cloud-assisted IoT-based precision agriculture platform.
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Figure 7. An illustration of IoT application for monitoring farming conditions in a greenhouse.
Figure 7. An illustration of IoT application for monitoring farming conditions in a greenhouse.
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Table 1. Some typical communication technologies for smart agriculture.
Table 1. Some typical communication technologies for smart agriculture.
Type Spectrum Transmission DistanceType of NetworkFrequencyData Rate
802.11a/b/g/n/ac Unlicensed 100 m WLAN 2.4–5 GHz 2–700 Mbps
802.11ah Unlicensed 1000 m WLAN Several Sub-GHz78 Mbps
802.11p Licensed 1 km WLAN 5.9 GHz 3–27 Mbps
802.11afLicensed 1 km WLAN 54–790 25–550 Mbps
SigFox Licensed Rural: 50 km Urban:10 kmLPWA Zwave100–600 bps
LoRaWAN Licensed 20 km LPWA Several Sub-GHz0.3–100 kbps
NB-IoT Licensed35 km LPWA Zwave250 kbps
LTE-3GPPLicensed5 km WWAN 1.4 MHz 200 kbps
EC-GPRS Licensed5 m WWAN GSM bands240 kbps
WiMAX Hybrid50–80 kmWWAN Several Sub-GHz70 Mbps
Bluetooth Unlicensed 100 m WPAN 2.4 GHz 2–26 Mbps
ZigBee Unlicensed 1 km WHAN 2.4 GHz 250 kbps
Z-Wave Unlicensed 100 m WHAN 900 MHz 100 kbps
6LoWPANUnlicensed 30 m WHAN Zwave250 kbps
NFC Unlicensed 20 cm D2D 13.56 MHz 424 kbps
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Quy, V.K.; Hau, N.V.; Anh, D.V.; Quy, N.M.; Ban, N.T.; Lanza, S.; Randazzo, G.; Muzirafuti, A. IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. Appl. Sci. 2022, 12, 3396.

AMA Style

Quy VK, Hau NV, Anh DV, Quy NM, Ban NT, Lanza S, Randazzo G, Muzirafuti A. IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. Applied Sciences. 2022; 12(7):3396.

Chicago/Turabian Style

Quy, Vu Khanh, Nguyen Van Hau, Dang Van Anh, Nguyen Minh Quy, Nguyen Tien Ban, Stefania Lanza, Giovanni Randazzo, and Anselme Muzirafuti. 2022. "IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges" Applied Sciences 12, no. 7: 3396.

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