Digital Twins Applications in Agriculture and Farming Domain: History
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Contributor:
Nikolaos Peladarinos
,
Dimitrios Piromalis
,
Vasileios Cheimaras
,
Efthymios Tserepas
,
Radu Adrian Munteanu
,
Panagiotis Papageorgas

Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability.

  • digital twins
  • precision farming
  • IoT
  • agriculture 4.0

1. Introduction

Initially, an extended search for literature and bibliography referring to DT in agriculture was conducted in Google Scholar, Scopus, and Web of Science. Researchers used the query item “digital twin” incorporating the logical operation of conjunction with the concepts of “agriculture” including concept association as “crop”, “farm”, “aquaculture”, “animal” and “smart farming” to step on cases of DT in subfields of agriculture. Queries as such provided 144 relevant papers from Google Scholar, 153 papers from Web of Science, and 135 papers from Scopus.
Secondly, researchers narrowed down all references to 105 papers by omitting duplicates, research papers with almost identical titles, with insufficient or less relevant content, and following research individual issues related to the main criteria and title as initially stated in the introduction, i.e., crop modelling, precision agriculture, and predictive maintenance in smart farming via the DT concept.
Diving deeper into the selection criteria, researchers used keywords such as plants, smart agriculture, agriculture 4.0, IoT, aquaculture, food, horticulture, urban farming, DT models, 3D simulation, AR, VR, and Blockchain ledgers. Totally 89 papers, including those for initially referenced material, were chosen as being deemed relevant to the context.
Being familiarized with the relevant literature, researchers achieved insights into the extent of DT penetration in agriculture, addressing methodologies, designs, data analysis techniques, technologies, and use cases utilized in application domains to gain a deeper understanding of the approaches employed by others regarding the DTs in agriculture.

2. The First Categorization of Researched Literature, According to the Main Research Objectives

Based on the breakdown of the 26 papers concerning the DT application domains and target applications related to smart farming as a subset of smart agriculture or agriculture 4.0, highlighting the case studies, researchers gathered a set of related concepts in Table 1. If a relevant text was found, the “√” symbol is included in the cells; otherwise, if not clearly or at all stated, the “-” symbol is used.
Table 1. The main research objectives and content topics of eligible referenced articles are included.
Citation No. Type of
Paper		 Case Study Smart Farming DT Case Application Domains Target Applications
[1] Journal Article √ √ √ Agriculture, farming Precision irrigation
[2] Journal Article √ √ √ Agriculture, farming Precision irrigation for water saving
[3] Journal Article √ √ √ Arable, dairy, and farming livestock farming Greenhouse horticulture, organic vegetable farming
[4] Journal Article √ √ √ Arable farming, dairy farming, greenhouse horticulture Organic vegetable and livestock farming, smart farming
[5] Case Study on Conference Paper √ - √ Virtual nature applications, the digital twin of natural environments Museums, arboretums, field trip experiences, botanical gardens
[6] Conference Paper √ - √ Art realistic and botanically correct plant models Game design, environmental art, GIS, and computer science
[7] Conference paper √ √ √ Precision farming, management Construction and implementation of plant DT
[8] Book √ √ √ Precision farming, management DT development stages and forecasting plant yield
[9] Journal Article √ √ - Automated irrigation via soil water monitoring Greenhouses, vertical farms, or outdoor fields
[10] Systematic Review - - √ Monitoring, modelling, and forecasting natural processes Geoscientific software & code repository
[11] Journal Article √ √ √ Smart farming High-tech data-driven greenhouses
[12] Conference Paper √ √ √ Sustainable agriculture 4.0, vertical arming DT cultivating model in sustainable agriculture
[13] Journal Article √ √ √ Vertical farming-greenhouses and bioeconomy DT requirements for vertical farms
[14] Journal Article √ √ √ DT of greenhouse production flow, energy-efficient DT for the greenhouse production process (WP4)
[15] Journal Article √ - √ DTs in agriculture A digital modelling approach to the food process
[16] Review Paper - √ √ DT adoption in agriculture Data acquisition for automatically controlled actuator systems in agriculture
[17] Review Paper - √ - Digital technologies and techniques in agricultural contexts—food post-harvest processing in the agricultural field A general framework of digital twins in soil, irrigation, robotics, farm machinery
[18] Review Paper Descriptive - Model Digital representation of grain and inventory quality in agriculture Agriculture supply chain management
[19] Review Paper - √ - Controlled farming environment Monitoring activities of livestock, optimization of crops, reduction of emissions to air, soil, and water
[20] Review Paper - √ - Agriculture, farming, crops, livestock New farming methods supported by the DT
[21] Review Paper - √ - Greenhouse horticulture, indoor farming smart agriculture IoT Data-driven food production.
[22] Review Paper - √ - Industry 4.0 approaches to the agricultural sector Virtualization of an agro-food supply chain
[23] Review Paper - √ - Food safety and quality, supply chain Authenticity and traceability of food supply in the agricultural production process
[24] Review Article - √ future study Sustainable and precision agriculture Remote detection and monitoring of vegetation and crop stress in agriculture
[25] Review Conference Paper - √ √ Urban farming, vertical farming, indoor farming, hydroponics, aeroponics, aquaculture, and aquaponics. Monitor, control, coordinate, and execute farm operations at agricultural sites
[26] Review Paper - √ √ DTs applied to precision agriculture. Predictive control, for improving soil quality
Ιn a total of 26 reviewed articles, 3 were relevant case study conference papers, one was a published scientific book, 9 were journal articles, and 12 were review papers. To visually assist in the analysis of the presented information in Table 1 regarding the type of reviewed literature, Figure 1 presents these types in graphical mode.
Figure 1. Review paper type distribution.
Fourteen articles either being descriptive as in [18] or practically using a case study project as in [1][3][4][5][6][7][8][9][11][12][13][14][15][18] address the significance of a DT case study in agriculture. The concept of DTs is mainly addressed in all reviewed papers. In 16 papers though [1][2][3][4][14][15][16], the DT concept is analyzed and utilized in more detail, thus posing the significance of using DTs in smart farming and agriculture.
According to the reviewed literature summarized in Table 1, smart farming as an implementation of agriculture 4.0 [1][2][3][4][7][8][9][11][12][13][14][16][17][19][20][21][22][23][24][25][26] includes smart irrigation strategies by monitoring and controlling various farming stages [1], implementing data acquisition techniques [16] for automatically controlling actuator systems in agriculture or even in high-tech data-driven greenhouses [11]. Furthermore, via simulation, monitoring, controlling, coordinating, and executing farm operations at agricultural sites [25], DTs improve predictive control in precision irrigation [1][2][26], greenhouse horticulture, and organic vegetable and livestock farming. Smart farming enhances remote detection and monitoring of vegetation and crop stress in agriculture [24], developing DT stages and forecasting plant yield in greenhouses, vertical farms, or outdoor fields [8][9], or even in urban and indoor farming, of vertical agriculture utilizing hydroponics, aeroponics, aquaculture, and aquaponics. Smart farming approaches the smart agriculture [22] as well as the sustainable agriculture 4.0 [12] sector to address precision farming and management [7][8] via the adoption of digital technologies and techniques in agriculture [15][16][17], leading the way to digital representations of plants [18], inventory quality in agriculture, greenhouse production flow, and food safety and quality within the food supply chain [23]. Although the DTs remain a major issue, in papers [17][19][20][21][22][23], they are implied as such. In [18], authors propose a functional DT model; in [24], they are proposed as a significant future issue to address and study.

3. The Second Categorization of Researched Literature, Related to Specific Technical Research Aspects

Table 2 below gathers various aspects of the target applications related to sensors, IoT or other smart platforms, smart agriculture, and DT technologies and protocols implemented in the addressed literature for comparison and evaluation. If the topic of a corresponding cell has no relevant data provided in the literature “-” sign is displayed in the corresponding cell. If the topic is merely mentioned, a “√” sign is displayed in the corresponding cell.
Table 2. Various aspects of the target applications concerning sensors, IoT, or other smart platforms relevant to smart agriculture and DT technologies and protocols.
Citation No. Case Study Type of
Paper		 DT
Case		 Target
Applications		 Sensors IoT/Platforms Deployments/
Technologies-
Protocols
[1] √ Journal Article √ Precision irrigation Field probes measure air and soil temperature, humidity, soil moisture, ambient light, geospatial position, (Venus GPS) SWAMP-IoT smart water management,
smartphone application, real-time IoT platform communication.		 I2C serial bus,
RPi-3 module
[2] √ Journal Article √ Precision irrigation for water saving Soil probe SWAMP, OPC UA server Fuzzy Interference System, Programmable Logic Controller (PLC)
[3] √ Journal Article √ Greenhouse horticulture, organic vegetable farming - IoT addressed -
[4] √ Journal Article √ Organic vegetable and livestock farming, smart farming - IoT addressed -
[5] √ Case Study on Conference Paper √ Museums, arboretums, field trip experiences, botanical gardens Drone image cameras, Photographic cameras, - -
[6] √ Conference Paper √ Game design, environmental art, GIS, and computer science Photographic cameras, - Virtual nature construction low-polygon 3D plant models ideal for augmented reality (AR) and virtual reality (VR)
[7] √ Conference paper √ Construction-implementation of plant DT - SWAMP An intelligent digital twin of plant, IDT
[8] √ Book √ DT development stages and forecasting plant yield     Proposal for an intelligent digital twin of plant, IDT
[9] √ Journal Article - Greenhouses, vertical farms, or outdoor fields Infrared (IR) thermometers, mini-LiDAR sensors, multispectral cameras Arduino board, single-board computer Raspberry Pi-3 I2C, serial peripheral interface (SPI), UART
[11] √ Journal Article √ High-tech data-driven greenhouses Handheld controllers, with a button and joystick interaction functionality - BIM model, A Meta Quest2 head-mounted display (HMD) with 2 handheld controllers/VR
[12] √ Conference Paper √ DT cultivating model in sustainable agriculture Mesh of sensors of temperature, humidity, luminosity, and relative CO2 concentration Raspberry Pi -
[13] √ Journal Article √ DT requirements for vertical farms Temperature, humidity, luminosity, and relative CO2, Data Acquisition module Raspberry Pi Digital, PWM, I2C, SPI, Serial, network attached storage, Sampling 42sec
[14] √ Journal Article √ DT for the greenhouse production process (WP4) Mentioned traditional sensor data mentioned -
[15] √ Journal Article √ A digital modelling approach to the food process - - Automatically controlling system actuators
[16] - Review
Paper		 √ Data acquisition for automatically controlled actuator systems in agriculture - - -
[17] - Review Paper - A general framework of digital twins in soil, irrigation, robotics, farm machinery - - Data recording,
artificial
[18] Descriptive Review Paper Model Agriculture supply chain management Bin level, flow, and identification sensors, RFID-DNA tags - -
[19] - Review Paper - Monitoring activities of livestock, optimization of crops, reduction of emissions to air, soil, and water Report - -
[20] - Review Paper - New farming methods supported by the DT Monitoring physical entity crops state, resource optimization, and cultivation support IoT stated -
[21] - Review Paper - IoT Data-driven food production. - Climate control, energy, and lighting management Monitoring, optimization for controlling and autonomy
[22] - Review Paper - Virtualization of an agro-food supply chain - IoT concepts -
[23] - Review Paper - Authenticity and traceability of food supply in the agricultural production process Data acquisition—temperature, humidity, soil conditions, location, RFID - Satellites Robotics technology assisted with AI, ML, and deep-learning techniques
[24] - Review Article Future study Remote detection and monitoring of vegetation and crop stress in agriculture LiDAR-light detection and ranging—stereo-photogrammetry using multi-spectral imagery. Passive microwave remote sensing. Active microwave remote sensing (RADAR). sensors onboard UAVs and satellites - -
[25] - Review-Assessment √ Monitor, control, coordinate, and execute farm operations at agricultural sites IoT sensor nodes IoT sensor nodes IoT sensor nodes acquire and transmit farm data to IoT gateways or edge devices
[26] - Conference
paper		 √ Predictive control, for improving soil quality Soil probes sensors temperature, relative humidity (RH), CO2 concentration, air velocity, and light level sensors. Drones IoT, SWAMP Programmable logic controllers (PLCs) in the irrigation system,
equipment and machines
DTs go along with monitoring systems to gather and analyze information. Sensors pose a critical role in smart agriculture by facilitating the collection of real-time data and offering valuable insights into a wide range of environmental and agricultural parameters.
Field or soil probes that measure air and ground temperature, humidity, soil moisture, and ambient light [1][2][13][14][23][26], added to CO2 sensors measuring relative CO2 concentration [12][13], and infrared (IR) thermometers provide data acquisition modules for automatically controlled actuator systems in agriculture approaching digital modelling to the food process [15] monitoring activities of livestock, optimization of crops, reducing emissions to air, soil, and water. Drone image cameras [26] and high-resolution photographic cameras seem ideal for AR and VR by constructing virtual natural low-polygon 3D plant models as proposed in [5][6]. Building Information Modeling (BIM) models of high-tech data-driven greenhouses are made feasible as in [11] and provide a VR presentation of the farm or field with the aid of a head-mounted display (HMD) with two handheld controllers serving as handy sensors for manual stimulus. Mini light detection and ranging (LiDAR) sensors and multispectral cameras following an Arduino single-board microcontrollers programmable platform or single-board computer Raspberry Pi-3 enhance IoT sensor nodes to acquire and transmit farm data to IoT gateways or edge devices, thus cultivating DT requirements for vertical farms, for the greenhouse production process and finally models in sustainable agriculture.
Remote detection and monitoring of vegetation and crop stress in agriculture seem feasible based on [24] utilizing LiDAR—stereo-photogrammetry technology, using multi-spectral imagery, passive microwave remote sensing, RFID schemes [18][23], active microwave remote sensing (RADAR) and sensors onboard UAVs and satellites [8][23][26]. Location determination in farming via geospatial position (GPS) plays a significant role in the remote detection and monitoring of vegetation and crop conditions in agriculture [26].
IoT deployments in smart farming are based on serial hardware communication protocols such as I2C serial bus [1] and UART that uses asynchronous serial communication with configurable speed and pulse width modulation (PWM) for driving actuators [9][13], in correlation with programmable logic controllers (PLC) for data recording [17], monitoring, and optimization aided occasionally by fuzzy interference systems (FIS) [2].
The decision-making and actions once undertaken by the farmers now have given way to data-driven agricultural decision-making, i.e., assisting them to undertake informed decisions related to planting, fertilization, pest control, harvesting, and overall farm management. Likewise, derived from the smart water management project (SWAMP) [1][2][7] a hands-on approach is being utilized to develop a precision irrigation platform based on IoT technology for smart water management, distributed in Brazil, Italy, and Spain.

4. The Third Categorization of Research Literature That Is Related to Specific Technical Research Aspects

DTs use software to create, operate, and interact with their physical counterparts. The software component of a DT enables the collection, processing, and analysis of data from various sources, including sensors, actuators, and other connected devices.
In [1][9], the high-level programming language Python is used for software development, scripting, and data analysis, serving the purposes of DT applications, implementing the Arduino IDE platform, and exploiting the Debian Buster OS to produce a plant simulation model. Likewise, RPi software for the Raspberry Pi platform is used with goal-oriented requirement language (GRL) for modelling [12]. The use of an open standard file format JavaScript Object Notation (JSON) which is used in [2] for data representation and communication between systems and is widely adopted and supported by various programming languages and platforms. Specific software packages are utilized for constructing 3D plant models, such as Unreal Engine 5, Reality Capture, Photoshop, Mesh Model Construction, Autodesk, Maya [5], Unreal Engine 5 Nanite technology, and Reality [6]. Other specific software packages serving, for example, the construction of a DT flowsheet model as in [15] found in the researched literature are the Aspen Plus and Aspen HYSYS from Aspen Technology, Inc. (Burlington, MA, USA), ChemCAD from Chemstations, Inc. (Houston, TX, USA), UniSim Design from Honeywell (Charlotte, NC, USA), ProSimPlus from ProSim SA (Labege, France) and PRO/II from AVEVA Group plc.
Simulation software was employed in this study [1] to generate a virtual setting for an irrigation system’s digital twin, incorporating a plant simulation model.
As previously mentioned, in [2], Siemens’s industrial plant simulation software was used, and in [5], the Virtual UCF Arboretum Application (V1.0) was developed with virtual plant datasets, plant inventories, VR headsets leading to an AR Holodeck by multiple captured images taken in 3D space and an AR perpetual garden App.
In [7], a linear model of plant growth is proposed utilizing a wheat multi-agent planning module close enough to the implied structure modelling simulation in [17], virtual models are used during the usage phase in [21], an earth system model in [10], a farm is represented in 3D in [13], the concept of the DT model via data-driven modelling is assessed [20]. In [8], the authors imply that a software package was developed containing an ontology editor, a DT editor, multi-agent planning module for creating a prototype of an intelligent plant DT system in Java whilst AR implementations to save BIM model files in Film box (.FBX) in [11] by using Unity game engine to provide 3D modelling software. Further authors in [18] confronted post-harvest models by discrete event simulation such as drying, adding to the simulation models for blending and flow DT models [14][19] that rely heavily on available real-time data a flow for continuous adaption and learning. Finally, DT modelling implies simulation, analysis, and prediction [26] that may assess the modelling and simulation of the fertility of seeds, fertilizers, pesticides, pollution challenges, soil agents (hydrological models, soil data), and crop agents
Table 3 summarises software, 3D, 3D modelling, simulation, and other research aspects addressed regarding the reviewed literature. Suppose the topic of a corresponding cell has no relevant data provided in the literature, a “-” sign is displayed in the corresponding cell. If the topic is merely mentioned, a “√” sign is displayed in the corresponding cell.
Table 3. Various aspects of the target applications concerning communication technologies, IoT, and cloud platforms for data and simulation processes.
Citation No. Communication Technologies Real-Time Data, Visualization, Analytics IoT
Cloud
Services		 Data
Bases		 Software Simulation
Software		 3D,
Modelling,
AR-VR
[1] Ethernet Grafana,
Real-Time Data		 IoT Broker, FIWARΕ,
IoT agent		 Mongo DB, Draco, My-SQL, Python Simulation software to generate a virtual environment for a DT of an irrigation system Plant simulation model
[2] LoRa,
Ethernet		 Grafana,
Real-Time Data		 FIWARE
IoT Agent		 My SQL,
MongoDB		 Fuzzy Inference System (FIS),
Json, FIWARE Cygnus connector, IoT Agent,
OPC UA agent		 Siemens Ind. plant simulation software for the Data model and weather station, -
[3] - - √ - - - Conceptual DT modelling.
[5] Wi-Fi.
Mobile		 AR/VR Software √ - Unreal Engine 5, Reality Capture,
Photoshop,—Mesh Model Construction, Autodesk, Maya		 Virtual UCF Arboretum Application,
ESRI GIS, Plant Datasets, Plant Inventories and Density, VR Headset, AR Holodeck		 Multiple captured images were taken in 3D space, AR Perpetual Garden App
[6] Wi-Fi - √ - Unreal Engine 5 Nanite technology and Reality for 3D plant models - Multiple captured images were taken in 3D space/AR Perpetual Garden App
[7] - - - Knowledge
Base		 Java A linear model of plant growth A descriptively wheat multi-agent planning module
[8] - - - - Java
ontology Editor,
digital twin editor, the multi-agent planning module		 The software package developed claimed an ontology editor, a digital twin editor, a multi-agent planning module Prototype of an intelligent plant DT system in Java
[9] Wi-Fi, Ethernet - - - Debian Buster OS, Python, Arduino IDE - -
[10] - - - - Geo-Soft-Core, a Geoscientific Software & Code Repository, hosted at the archive DIGITAL.CSIC - Model of Earth system
[11] - - - Height value retrieved from CSV files Spreadsheet applications—Microsoft Excel, 3D modelling software. BIM model in Film box (.FBX)
Unity game
engine		 AR
[12] - - Yes SQLite RPi software, Goal-oriented Requirement Language (GRL) for modelling - GRL model
[13] - GUI
prototype		   SQLite - - 3D representation of farm
[14] mentioned Mentioned-Industrial Data Management System
multilayer approach with Developed IoT models		 Big Data only
Mentioned,
cloud-based
enterprise		 - AI, Big Data
analytics		 Mentioned DT modelling relies heavily on available data and a continuous flow of real-time for continuous
adaption and learning
[15] - - - - SuperPro
Designer,
Spreadsheet
applications-Microsoft Excel,		 Aspen Plus/HYSYS ChemCAD (Chemstations, Inc.), UniSim Design (Honeywell), ProSim Plus (ProSim SA), PRO/II (AVEVA Group plc) Flowsheet
model
[17] IoT,
wireless technologies		 Analysis, prediction - - - - Structure modelling simulation
[18] - - - - - - Post-harvest models
Discrete event simulation Drying,
Blending and Flow models
[19] - IoT, wireless technologies - - ML - Simulation models
[20] - - - - ML and DL
algorithms		 - Concept of the DT Model, Data-driven modelling
[21] - - - - - - Virtual models during the usage phase
[22] Bluetooth, RFID, NB-IoT - Farm activities connected to the cloud - Big Data
GPS,		 - -
[23] Wi-Fi, Bluetooth
LoRaWAN Cellular 6G		 LoRaWAN platform Cloud Big Data Analytics Blockchain
technology		 AI - -
[24] - - - - ML
methods		 - Domain radiative transfer models, future multi-Domain radiative transfer models (e.g., SCOPE) with dynamic crop growth models for agroecosystems DTs
[25] Wireless communication technologies,
Wi-Fi,
Cellular		 IoT
dashboards		 Alibaba cloud,
Amazon web services, Microsoft Azure, Google Cloud platform,
IBM Cloud—Cloud computing service providers		 - Blockchain - -
[26] - Predictive analytics. - Big Data ML and AI algorithms to attain surgical control over all operational aspects of production activities.
LED actuators		 Simulation,
analysis and
prediction		 Modelling/Simulating seed fertility, fertilizer, pesticides, pollution challenges/Soil agent (hydrological models, soil data), crop agent.
Predictive control process models (heating, ventilation). Plant development modelling
On the other hand, AI, ML, and DL algorithms seem to be a major issue in [19][20][23][25][26], leading to Big Data analytics [14] to describe DT modelling that relies heavily on real-time available data flow. The immense amount of data is stored in databases such as Mongo DB [1][2], Draco, My-SQL [1][2], and SQLite [12][13], besides using Excel CSV files for retrieving height values [11] or generally mentioned Knowledge bases as in [7] leading to cloud big data manipulation schemes in [13][22][23]. Major cloud computing platforms such as Alibaba Cloud, Amazon web services, Google Cloud Platform, IBM, and Microsoft Azure are examples of cloud computing services provided by leading technology companies that serve the DT idea, as seen in [25].
Cloud IoT service broker agents come into place as a software component or intermediary that facilitates the interaction between cloud service consumers (users or organizations) and cloud service providers like FIWARΕ. In [1][2], FIWARΕ cloud applications are exploited, adding to IoT brοker and IoT agents to facilitate cloud management of context information. The procedure includes converting various communication protocols into a shared base protocol and incorporating intelligent functionality through the processing, analysing, and visualising of contextual data. FIWARE generally is used as an open-source platform to provide a standardized framework and a set of reusable components for building smart applications and services in the context of the IoT and Future Internet (FI) domains. It aims to simplify the development of innovative and interoperable solutions by offering a collection of open APIs, data models, and software tools that facilitate the development of smart applications and services.
A fuzzy inference system enhances adaptive and learning capabilities. It handles nonlinear and complex relationships between variables, as in [2], in addition to Siemens industrial plant simulation software for plant simulation model production with a weather station. In [11][27], the Microsoft Excel spreadsheet application is used instead of other dedicated software such as Geo-Soft-Core, a Geoscientific Software & Code Repository hosted at the archive DIGITAL.CSIC in [10] or Java ontology editor to elaborate a DT editor in [7][8].
Lastly, wireless network communication technologies are addressed in the research literature. Initially, the wired networking technology Ethernet is mentioned in [1][2][9], while the wireless networking technologies such as Wi-Fi are mentioned in [5][6][9][23][25], LoRaWAN in [2][23], mobile or cellular in [5][23][25] and generally mentioned as mandatory in [14][17]. Bluetooth, RFID, and NB-IoT are exploited in [22].

This entry is adapted from the peer-reviewed paper 10.3390/s23167128

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