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Schemes of the sensors and network topology of the studied WSNs: (a) ZigBee; (b) DigiMesh; and (c) WiFi.
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In recent years, attention has been paid to wireless sensor networks (WSNs) applied to precision agriculture. However, few studies have compared the technologies of different communication standards in terms of topology and energy efficiency. This paper presents the design and implementation of the hardware and software of three WSNs with different...
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Context 1
... identify the performance of the ZigBee and WiFi standards for monitoring greenhouses, three WSNs were implemented. The number of network nodes, the type of installed sensors, and the configured topology are shown in the block diagrams of Figure 1. In this section, we provide the system design and implementation to monitor environmental variables inside the greenhouse. We describe next the methods and elements used for the analyzed WSNs. First, the suitable ranges of the variables that influence tomato growth are described, as well as the sensing, packaging, and processing techniques that were used. Second, the relevant hardware features and configuration parameters of the ZigBee, DigiMesh, and WiFi networks are described. Third, the design of the HMI and web application for variables visualization are explained. Finally, the physical characteristics of the greenhouses, the location of the elements of the networks, and the startup of the monitoring system are presented. ...
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... this Appendix, we show the flow diagrams applied to nodes for the ZigBee and DigiMesh networks ( Figures A1, A2, A3), and the mobile application ( Figure A4). The programming logic of the WiFi network nodes is not shown, because the sensor node was programmed very similarly to the ZigBee network, and the coordinating node (Meshlium TM router) was configured using the device own ...
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... XBEE communication modules were chosen to send and receive packages, since their cost and energy consumption are low. The ZigBee network was implemented with XBEE ZB S2 PRO modules, whose firmware allows creating networks with tree topology. In the case of the DigiMesh network, the XBEE ZB S1 PRO modules were used, because the firmware facilitates the configuration of networks with mesh topology. The XBEE modules were configured with the X-CTU software, which provides a friendly graphical user interface. For the communication between nodes to be successful, the XBEE modules were configured with the consideration that all modules operate in the same network group PAN ID, channel CH, and transmission BD. The parameters that were configured in each module are shown in Table 4. The source and destination addresses were assigned based on the serial number printed on the modules. Since the type of communication was broadcast, the destination address was the same on all nodes and corresponded to DH = 13A200 and DL = FFFF. The transmission speeds BD were configured to different values for analysis of energy consumption and according to the operating rates of the Waspmote modules. Each network was configured in a different channel CH to avoid electromagnetic interference affecting the signals. The programming code of the sensor nodes was developed in the ID PRO software of Libelium TM . The logic that was used in the sensor nodes of both networks was similar, except for the routing process for sending data. Appendix A shows some technical details of the flowcharts of the programming of the sensor and coordinator nodes for the ZigBee and DigiMesh networks. The programming flowchart that was created for the sensor nodes of the ZigBee network is shown in Figure A1 (Appendix A). The flow diagram that was implemented for the DigiMesh network is shown in Figure A2 (Appendix A). The coordinating node structure shown in Figure 4 was implemented for the ZigBee and DigiMesh networks, and they differed only in the type of XBEE wireless communication module that they incorporated. This modules were also configured with the X-CTU software and they used the same parameters as in Table 3. The programming of the coordinating nodes of the ZigBee and DigiMesh networks were the same, and the flow diagram is shown in Figure A3 (Appendix A). ...
Citations
... WSNs have attracted global attention since their inception [6][7]. In 2003, the US National Science Foundation launched a research program on sensor networks, and Germany, Japan, France, and other developed countries in science and technology subsequently launched their own research programs to begin specific research on wireless sensor networks, in addition to national institutions to carry out related research projects, some multinational companies such as Microsoft, Intel, for example, has launched the CAST project, which is mainly used to provide services for the aging population [8][9][10]. ...
Among the various WSN technologies, node localization technology is one of the core parts of WSN applications and an important component and key technology, which is also a hot spot and focus of research at this stage. In this paper, firstly, we propose a classical ion swarm localization algorithm for wireless sensor network localization, randomly assigning appropriate velocity and position to each particle in the population in order to find the global optimum in the iterative process. Based on this, a network node localization model is established to convert the functional optimization problem with constraints into an unconstrained optimization problem to solve. At the same time, the space is searched using a chaotic search strategy, which greatly improves the search efficiency. Next, the particle swarm algorithm is further optimized, and simulation experiments are set up using MATLAB software to do comparison experiments on the PSOAPF algorithm and other algorithms. The experimental results show that when the density of beacon nodes is 40%, the average localization error of the PSOAPF algorithm is 14.26%, with the smoothest decreasing trend. When the percentage of anchor nodes is 10%, the localization error is reduced by 6.89%, and the localization accuracy of the PSOAPF algorithm is also higher than other models. This study shows that the improved particle swarm algorithm can effectively improve the localization accuracy, reduce error and accelerate the convergence speed in wireless sensing network localization.
... As a result of the analysis of current internationally recognized regulations ASABE and FAO [2,3] on ensuring compliance with the requirements of complexity and consistency of functional transfor-mations of measurement data by means of information and communication technologies in greenhouse conditions, it was found that temporal instability and spatial heterogeneity of processes, the need to take into account the relationship of a significant number of physical and chemical soil and climatic parameters, as well as the scale of adaptation of information and measurement procedures to different types and periods of vegetation, have not allowed developing a theory of integrated monitoring and adaptive hardware and software control of agrotechnical processes. A review of current trends in the development of instrumental information and computer-oriented technologies has allowed us to identify a set of those that can be used as a basis for increasing the level of intellectualization and digitalization of agricultural procedures [4,5]: artificial neural networks (ANN); evolutionary computing and genetic algorithms (ECGA); fuzzy logic (FL); evolutionary robotics (ER); the Internet of Things (IoT); wireless sensor networks (WSN). ...
Nowadays, applied computer-oriented and information digitalization technologies are developing very dynamically and are widely used in various industries. One of the highest priority sectors of the economy of Ukraine and other countries around the world, the needs of which require intensive implementation of high-performance information technologies, is agriculture. The purpose of the article is to synthesise scientific and practical provisions to improve the information technology of the comprehensive monitoring and control of microclimate in industrial greenhouses. The object of research is non-stationary processes of aggregation and transformation of measurement data on soil and climatic conditions of the greenhouse microclimate. The subject of research is methods and models of computer-oriented analysis of measurement data on the soil and climatic state of the greenhouse microclimate. The main scientific and practical effect of the article is the development of the theory of intelligent information technologies for monitoring and control of greenhouse microclimate through the development of methods and models of distributed aggregation and intellectualised transformation of measurement data based on fuzzy logic.
... Additionally, opportunistic computing has been employed to collect data from distant crop fields using tractors fitted with sensors as agricultural equipment has grown more mobile [62,63]. The environment's impact on sensor information interchange can be brought on by sensor node distance [59], a breakdown in communication in farmlands [63], or even the impact of vegetation on signal transmission. Each agricultural scenario also provides specific obstacles for productivity. ...
... As shown by [62], who examined the effects of 2.4 GHz and 433 MHz signal transmission in expansive estates and an orchard, vegetation itself may act as an obstruction to sensor contact in addition to the distance between sensor devices, gateways, and other network equipment. The abundance of detectors, which could cause wireless signal interruption owing to their close proximity, is another disadvantage of greenhouses [59]. Two examples of wired connections that can be used to solve this problem are Ethernet [78] and CAN [76]. ...
... Networks of stars, for instance, consist of a centre unit and numerous end nodes. Data is transmitted from peripheral nodes to the central node in this arrangement [59]. The distance between the peripheral nodes and the main node is in this case constrained by the physical layer communication standard. ...
This paper's objective is to evaluate the use of an Internet of Things (IoT)-based system in the field of precision agriculture. Because of bug infestations and a lack of tools to operate the farm efficiently, farmers experience significant losses every year. The chosen article provides a summary of the IoT in smart farming equipment and methodology that is advised. The goal of this analysis is to highlight and explain key hardware, cloud platforms, communication standards, and data processing techniques. By updating every aspect, including crop field data and application use, this evaluation emphasizes an updated technology for agricultural smart management. Agriculture stakeholders may improve environmental protection and enhance food production in a way that will meet future global demand by tailoring their technology spending decisions. The application of IoT in agriculture helps to improve sensing and monitoring of production, including farm resource utilization, animal behavior, crop development, and food processing, which is the final contribution of this research. Additionally, it gives a greater grasp of the specific agricultural situations, including environmental and climatic factors, weed, pest, and disease growth.
... A realtime operating system can reliably execute programs with specific timing requirements, which is critical for the greenhouse system. WSNs solved the problem of sensors being difficult to deploy in different space angles (Xing et al., 2017), and they have an appropriate level of stability (Erazo-Rodas et al., 2018;Liu and Bi, 2017), reliability (Gomes et al., 2015;Jahnavi and Ahamed, 2015;Li et al., 2010;Xing et al., 2017), and flexibility (Loukatos et al., 2021;Shi et al., 2013). Furthermore, once installed, the cabled measurement points are difficult to relocate. ...
Today, advancements in greenhouse technology and modifications have pushed science-based solutions for optimal plant production in all seasons worldwide by adjusting internal climate growing factors such as temperature, humidity, light intensity, and CO2 concentration. Solar greenhouses increase crop yield and quality, addressing global food security concerns. This paper presents an overview of current design trends in construction, current development technology for controlling and monitoring greenhouse microclimates, and the various systems available for managing greenhouse environments. First, it discusses different processes of the greenhouse geometry, orientation, and cladding material for different climates. This paper also examines the various strategies in the greenhouse control environment, sensing networks, different wireless gateway used in monitoring systems, and the many control approaches. The last section of this review presented the system for managing climate in the greenhouse. The results of this research are the best selection of geometry, orientation, and covering material of the greenhouse also achieves a suitable environment, as well as the strategy of control and management of climate, plays a vital role in achieving high crop production and decreasing the cost and the energy consumption.
... As an illustration, different sensors ranging from simple (temperature, humidity, pH, illumination, pressure, UV, CO 2 , wind speed, solar radiation (Jahnavi and Ahamed 2015;Thirukkuralkani et al. 2018;Jiang et al. 2016;Erazo-Rodas et al. 2018) to complex [cameras (Hwang and Yoe 2016), mid-infrared spectroscopy ] have been used, and their measurements were communicated wirelessly to the host system using several technologies such as Zigbee (Chen et al. 2016a(Chen et al. , 2017Xing et al. 2017;Raheemah et al. 2016;Pascual et al. 2015;Luo et al. 2016;Liu and Bi 2017;Aiello et al. 2018;Rao et al. 2016;Ismail et al. 2016;Ibayashi et al. 2016;Erazo, et al. 2015;Çaylı et al. 2017), radio (Liang-Ying and Zhao-Wei, 2015;Mahbub 2020), GSM/GPRS (Liang-Ying and Zhao-Wei, 2015;Liu and Zhang 2017;Navarro-Hellín et al. 2015;Mat et al. 2016), 3G/4G (Zhou and Duan 2016;Chung et al. 2015;Zhang et al. 2015), Wi-Fi (Aiello et al. 2018;Chung et al. 2015;Mohapatra and Lenka 2016;Liang et al. 2018a) and Bluetooth (Hong and Hsieh 2016;Taşkın et al. 2018). However, to keep the greenhouse automation trouble-free, a secure protocol for wireless sensor networks has been proposed in the automated agricultural environment and has allowed more reliability (Sivamani et al. 2018). ...
Greenhouse farming is essential in increasing domestic crop production in countries with limited resources and a harsh climate like Qatar. Smart greenhouse development is even more important to overcome these limitations and achieve high levels of food security. While the main aim of greenhouses is to offer an appropriate environment for high-yield production while protecting crops from adverse climate conditions, smart greenhouses provide precise regulation and control of the microclimate variables by utilizing the latest control techniques, advanced metering and communication infrastructures, and smart management systems thus providing the optimal environment for crop development. However, due to the development of information technology, greenhouses are undergoing a big transformation. In fact, the new generation of greenhouses has gone from simple constructions to sophisticated factories that drive agricultural production at the minimum possible cost. The main objective of this paper is to present a comprehensive understanding framework of the actual greenhouse development in Qatar, so as to be able to support the transition to sustainable precision agriculture. Qatar’s greenhouse market is a dynamic sector, and it is expected to mark double-digit growth by 2025. Thus, this study may offer effective supporting information to decision and policy makers, professionals, and end-users in introducing new technologies and taking advantage of monitoring techniques, artificial intelligence, and communication infrastructure in the agriculture sector by adopting smart greenhouses, consequently enhancing the Food-Energy-Water Nexus resilience and sustainable development. Furthermore, an analysis of the actual agriculture situation in Qatar is provided by examining its potential development regarding the existing drivers and barriers. Finally, the study presents the policy measures already implemented in Qatar and analyses the future development of the local greenhouse sector in terms of sustainability and resource-saving perspective and its penetration into Qatar’s economy.
... Further, as agricultural equipment has become more mobile, opportunistic computing has been used to collect data from remote crop fields via tractors equipped with sensors [62,63]. Each agricultural condition in Table 1 poses unique challenges for production, along with the environment's influence on sensor information exchange, which can be caused by sensor node distance [59], a breakdown in communication in farmlands [63], or even the effect of vegetation on signal transmission. Additionally, meteorological variables such as snow, fog, or solar irradiance have an impact on both the sensor network and the planting. ...
... Apart from the range between sensor devices, gateways, as well as other network equipment, vegetation itself may operate as an obstruction to sensor contact, as demonstrated by [62], who evaluated the impact of 2.4 GHz and 433 MHz signal transmission in large estates as well as an orchard. Another drawback with greenhouses is the amount of detectors, which might result in wireless signal interruption due to their close proximity [59]. To address this issue, Ethernet [78] and CAN [76] are two examples of wired connections that can be utilized. ...
... For instance, networks of stars are made up of a central unit and a large number of end nodes. In this design, data is sent from peripheral nodes to the center node [59]. In this case, the physical layer communication standard limits how far the peripheral nodes can be from the main node. ...
The goal of this paper is to review the implementation of an Internet of Things (IoT)-based system in the precision agriculture sector. Each year, farmers suffer enormous losses as a result of insect infestations and a lack of equipment to manage the farm effectively. The selected article summarises the recommended systematic equipment and approach for implementing an IoT in smart farming. This review's purpose is to identify and discuss the significant devices, cloud platforms, communication protocols, and data processing methodologies. This review highlights an updated technology for agricultural smart management by revising every area, such as crop field data and application utilization. By customizing their technology spending decisions, agriculture stakeholders can better protect the environment and increase food production in a way that meets future global demand. Last but not least, the contribution of this research is that the use of IoT in the agricultural sector helps to improve sensing and monitoring of production, including farm resource usage, animal behavior, crop growth, and food processing. Also, it provides a better understanding of the individual agricultural circumstances, such as environmental and weather conditions, the growth of weeds, pests, and diseases.
... The later-emerging wireless sensor networks (WSNs) have become very popular for such projects, with the advantages of modularity, low power consumption, etc. [80]. For this reason, they have been used in greenhouses for monitoring [81] and precision agriculture [82,83] in many studies. However, in general, such automatic systems do not fully meet the requirements for precise temperature, humidity, etc., settings; therefore, yield losses in greenhouses occur [84]. ...
... Although greenhouse solutions with WSNs have been presented in many studies, their application in the greenhouse has been experimentally limited, usually because WSN applications can be used in small-sized areas. For example, Erazo-Rodas, Sandoval-Moreno, Muñoz-Romero, Huerta, Rivas-Lalaleo, Naranjo and Rojo-Álvarez [81] and Rodríguez, Gualotuña and Grilo [82] implemented WSNs in small greenhouses. Jiang et al. [85] conducted a WSN-based monitoring study in a larger greenhouse but by adding a large number of nodes (wireless sensors). ...
The increasing world population makes it necessary to fight challenges such as climate
change and to realize production efficiently and quickly. However, the minimum cost, maximum income, environmental pollution protection and the ability to save water and energy are all factors that should be taken into account in this process. The use of information and communication technologies (ICTs) in agriculture to meet all of these criteria serves the purpose of precision agriculture. As unmanned aerial vehicles (UAVs) can easily obtain real-time data, they have a great potential to address and optimize solutions to the problems faced by agriculture. Despite some limitations, such as the battery, load, weather conditions, etc., UAVs will be used frequently in agriculture in
the future because of the valuable data that they obtain and their efficient applications. According to the known literature, UAVs have been carrying out tasks such as spraying, monitoring, yield estimation, weed detection, etc. In recent years, articles related to agricultural UAVs have been presented in journals with high impact factors. Most precision agriculture applications with UAVs occur in outdoor environments where GPS access is available, which provides more reliable control of the UAV in both manual and autonomous flights. On the other hand, there are almost no UAV-based applications in greenhouses where all-season crop production is available. This paper emphasizes this deficiency and provides a comprehensive review of the use of UAVs for agricultural tasks and highlights the importance of simultaneous localization and mapping (SLAM) for a UAV solution in the greenhouse.
... This scientific and applied problem can be solved by expanding the functionality of classical information and measurement systems through the introduction of modern methodological approaches to the Internet of Things and wireless sensor networks, as well as methods of intelligent analysis of physical quantities, which is proven and tested in articles [8][9][10]. ...
... During the research, the described software components have been used sequentially, as shown in Figure 5. Organization of the polling queue of measuring channels 5 End of the list of network modules? 4 End of the list of measuring channels? 6 Analogue processing (optional) 7 Analogue to digital conversion (optional) 8 Primary digital processing 9 The first level of secondary digital processing Taking into account the characteristics of the hardware and software components used in the design of the computerized greenhouse prototype and the unit for monitoring external climatic factors, it can be stated that the development meets the basic scientific and practical provisions of IoT and WSN technologies and can be used as software and hardware base in researching information models of wireless monitoring of the integrated state of the microclimate of industrial agricultural greenhouses. ...
... Allowing interrupts for wireless modules 6 7 8 End of the first averaging interval? 9 Is the sensor of analogue type? 10 Calculation of the arithmetic mean α-truncated filter averaging 11 12 Writing results to a local database Sending data to a cloud server Figure 14 shows a block diagram of the algorithm of one iteration of the proposed methods to improve the accuracy of processing and reliability of transmission of measurement information. Such procedures and functions, which form the algorithmic basis of the developed information model, are performed in the main subprogram of the computer-integrated technology for monitoring the state of the greenhouse microclimate an infinite number of times before the forced stop. ...
... We deploy an endto-end TVWS network between the farm office and the fields, and perform an extensive measurement study of TVWS channels by taking into consideration antennas' placement, variations of temperature and humidity, crop diversity (soybean vs. corn), height and density of corn fields. Some past studies analyzed the impact of crops [7], [8], [9] and the impact of weather [10], [11], [12] : for ground antenna height, the RSS during daytime is 5 dBm lower than that in the morning, the RSS for soybean is 1.5 dBm higher than that for corn, the RSS for dense crop density is 6 dBm lower than that for sparse crop density, the RSS for dense tree foliage is 9 dBm lower than that for sparse tree foliage, etc. study are summarized in Table I. • We investigate which path loss model is most applicable to TVWS in agricultural environments according to the empirical path loss computed for different antenna heights. We find out that, when antennas are close to the ground, the path loss is better modeled by the Okumura-Hata (suburban) path loss model, and, for higher antenna heights, it is better modeled by the Plane Earth path loss model. ...
Operating at lower frequencies than systems such as Wi-Fi, TVWS wireless communication can enable long-range communication in rural communities and can more easily penetrate obstacles (vegetation, terrains). Thus, it is appealing to scenarios where line-of-sight is not always guaranteed. In particular, TVWS communication is a good candidate for supporting precision agriculture such as camera-based plant phenotyping and sensor-based analysis of plant behaviour. Yet there lacks in-depth real-world measurement data on the behavior of TVWS wireless channels in agriculture farms. To fill this gap, we use the field-deployed TVWS network of CyNet to measure TVWS channel behaviour in the Curtiss Research Farm in Ames, Iowa, where the landscape is predominantly composed of soybean and corn fields. We investigate the impact that crop diversity (soybean vs. corn), height and density of corn fields, antennas' placement and variations of temperature and humidity have on the spatiotemporal behaviour of TVWS channels.This study also helps identify path loss models that best reflect radio propagation characteristics of TVWS systems in corn farms for different antenna heights.
... Different monitoring systems have been developed for remote health monitoring, daily life monitoring, and conserving quality of life of elder adults. The WSNs and IoT based monitoring systems have been presented in many different areas; an IoT and smart sensor technology based wind turbine gear box monitoring system to prevent serious problems occurring in wind mills [1], a WSN to decrease the negative impact of noise pollution in urban environment [2], a chloride sensors based on distributed sensor networks to measure and monitor chloride concentration in both fluid and wet soil environments [3] and a WSN including sensors for solar radiation, UV radiation, wind direction, wind speed, carbon dioxide, air temperature, air relative humidity, luminosity in order to track environmental conditions on efficient tomato growth [4] and an IoT-cloud based temperature and relative humidity monitoring and crop comfort levels assessment system to reach the best environment condition for agriculture crop [5] and a WSN/GPRS based automated irrigation system using distributed wireless network of soil-moisture and temperature sensors placed in the root zone of the plants to optimize water use for agricultural crops [6] in agriculture, an IoT based low-cost, stand-alone sensory platform using photovoltaic irradiance sensor to monitor and identify overirradiance and extreme overirradiance [7], a low cost distributed structural crack monitoring system using displacement sensor with tens of micrometer resolution to track monumental structures and foresee possible damage [8]- [9], an IoT-cloud based traffic monitoring system using a GSM/GPRS/GPS TK103 tracker device installed in vehicles to track traffic flow and to prevent possible accidents by sending alert notification in necessary situations [10], a WSN based landslide monitoring system using wireless inertial measurement unit sensor device for effective, reliable, and efficient monitoring of landslides [11]. ...
ISO/IEC 17025, general requirements for the competence of testing and calibration laboratories, requires the control, monitoring, and recording of laboratory environmental conditions. For a ventilated environment with a simple air conditioner, it is necessary to take measurements from various points to show that a homogeneous temperature and humidity distribution exists. The sensor nodes having the capability of measuring temperature and humidity can be a solution for such a laboratory environment. In this study, an IoT-based monitoring system designed and applied for tracking temperature and humidity in the laboratory ventilated with a simple air conditioner is presented in accordance with ISO/IEC 17025 standards. The system consists of three layers including the slave nodes which sleep under normal conditions but send the temperature and humidity values to the receiver module when they are awakened, the master module that collects data from the sensor nodes and the internal sensor, saves them to SD memory, display current data on a LCD screen and sends average temperature and humidity values to the cloud environment and the IoT-cloud storage platform where data is stored and followed by laboratory workers and laboratory service users.
