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The wetland that known as "the kidney of the earth" is an ecological system with many resources. Monitoring of wetland environment includes the monitoring of water quality, air and soil. The parameters of temperature, pH value, turbidity, dissolved oxygen (DO), water level, conductivity of water, illuminance, PM2.5, harmful gas, and soil moisture i...
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... It has the potential to address the current challenges by making the agricultural value chain more efficient, equitable and environmentally sustainable (Naik andSuresh, 2018, Schroeder et al., 2021). Agriculture 4.0 signifies the digital transformation of food and agricultural systems through the utilization of technologies such as artificial intelligence (Gallordo et al., 2020), the Internet of Things (IoTs) (Kakani et al., 2020), drones (Dayana et al., 2021), robots (Lottes et al., 2017), as well as machine and deep learning algorithms (Sonka, 2015;Kamath et al., 2019), along with sensors (Jia, 2020). These advancements work in tandem to establish an intricately connected network encompassing farms, machinery, and factories, ultimately leading to the optimization of both food production and consumption. ...
Escalating global population and their demands for food, water and energy is exploiting the available resources. The intensive agricultural practices results into higher greenhouse gas emissions, deforestation and land degradation. This demand for reformation in traditional agricultural systems and "Digital Agriculture" could be a possible solution. Agriculture 4.0 has revolutionary potential of growing more food on lesser land, feed numerous people and improve farmers' livelihood. This not only meets the growing demand but also help mitigate the adversities of climate change. Artificial intelligence, Internet of Things, drones, robots, machine and deep learning algorithms, sensors, etc., generate a hyper connected network of farms, machines and factories that optimizes both food production and consumption. It ensures need based, precise application of inputs and aids in adoption of best management strategies, thereby, making agriculture environment friendly, profitable and sustainable in the long run. Thus, this chapter presents the potential of digital agriculture in enhancing crop health and productivity for a sustainable future.
... Jia [167] developed a WSN with two types of multisensory combination modules (MSCM): one for water, and another for air. Each MSCM had a GPS sensor attached to identify the problem location. ...
Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water.
... The monitoring of water quality involves the tracking of physical, biochemical, and other indicators and data analysis. The STM32 microcontroller [12][13][14][15][16][17][18] combined with a range of sensors is extensively utilized in monitoring systems, which facilitate the acquisition of environmental data. The International Atomic Energy Agency (IAEA) set up a network of monitoring stations as cited in literature [19] to collect continuous, long-term data from river water. ...
The detection of water quality indicators such as Temperature, pH, Turbidity, Conductivity, and TDS involves five national standard methods. Chemically based measurement techniques may generate liquid residue, causing secondary pollution. The water quality monitoring and data analysis system can effectively address the issues that conventional methods require multiple pieces of equipment and repeated measurements. This paper analyzes the distribution characteristics of the historical data from five sensors at a specific time, displays them graphically in real time, and provides an early warning of exceeding the standard; It selects four water samples from different sections of the Li River, based on the national standard method, the average measurement errors of Temperature, PH, TDS, Conductivity and Turbidity are 0.98%, 2.23%, 2.92%, 3.05% and 3.98%.;It further uses the quartile method to analyze the outlier data over 100,000 records and five historical periods are selected. Experiment results show the system is relatively stable in measuring Temperature, PH and TDS, and the proportion of outlier is 0.42%, 0.84% and 1.24%. When Turbidity and Conductivity are measured, the proportion is 3.11% and 2.92%. In the experiment of using 7 methods to fill outlier, K nearest neighbor algorithm is better than others. The analysis of data trends, outliers, means, and extreme values assists in making decisions, such as updating and maintaining equipment, addressing extreme water quality situations, and enhancing regional water quality oversight.
... Monitoring, tracking, and locating objects are applications of LoRaWAN technology in maritime environments. Whether monitoring natural disasters, water quality, or vessels, the technology delivers significant results in cost-effectiveness and real-time collection [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. ...
Designing and deploying telecommunications and broadcasting networks in the challenging terrain of the Amazon region pose significant obstacles due to its unique morphological characteristics. Within low-power wide-area networks (LPWANs), this research study introduces a comprehensive approach to modeling large-scale propagation loss channels specific to the LoRaWAN protocol operating at 915 MHz. The objective of this study is to facilitate the planning of Internet of Things (IoT) networks in riverside communities while accounting for the mobility of end nodes. We conducted extensive measurement campaigns along the banks of Universidade Federal do Pará, capturing received signal strength indication (RSSI), signal-to-noise ratio (SNR), and geolocated point data across various spreading factors. We fitted the empirical close-in (CI) and floating intercept (FI) propagation models for uplink path loss prediction and compared them with the Okumura–Hata model. We also present a new model for path loss with dense vegetation. Furthermore, we calculated received packet rate statistics between communication links to assess channel quality for the LoRa physical layer (PHY). Remarkably, both CI and FI models exhibited similar behaviors, with the newly proposed model demonstrating enhanced accuracy in estimating radio loss within densely vegetated scenarios, boasting lower root mean square error (RMSE) values than the Okumura–Hata model, particularly for spreading factor 9 (SF9). The radius coverage threshold, accounting for node mobility, was 945 m. This comprehensive analysis contributes valuable insights for the effective deployment and optimization of LoRa-based IoT networks in the intricate environmental conditions of the Amazon region.
... Water, soil, air monitoring and evaluation [58] Water4Cities [47] projects were developed by mapping stakeholder needs to ensure that the CPS delivered value to diverse stakeholders, including water treatment plant managers, conservation agencies and the public. As CPS integration becomes more complex, systems may look to incorporate increasing levels of ML functionality into their data flows and management. ...
Water governance is facing rapid transformations as cyber-physical systems (CPS) are deployed across water-related sectors and river basins. These CPS-often considered as artificial intelligence-enabled, automated or 'smart' technological systems-are promoted for improving monitoring, management and governance of hydrological systems. We review recent applications of CPS, highlighting their diverse functions across the water cycle, including in rural, urban and coastal settings. We then focus on how smart technologies connect to people, policy and ecosystems. Key to our argument is that integrating the social and ecosystem dimensions into CPS research and design will be vital for sustainable transformations in water management and governance, as per a cybernetic approach. This includes consideration of social data requirements, end-user experience, sociopolitical and environmental impacts, as well as acceptability, of CPS.
... There is also no specific definition of the technology that can be used to transmit data to a base station. However, common techniques include Wi-Fi, GSM, Bluetooth and LoRa [16][17][18][19][20][21][22][23]. ...
... However, work on the direct measurement of soil-specific CO2 with a WSN is much more limited. Most research in this sector lacks direct soil CO2 measurements, using only air CO2 measurements along with ancillary soil sensors, such as temperature and humidity [16,21,22,25]. For in situ soil CO2 concentration measurements, there are two main commercial systems. ...
This article outlines the design and implementation of an internet-of-things (IoT) platform for the monitoring of soil carbon dioxide (CO2) concentrations. As atmospheric CO2 continues to rise, accurate accounting of major carbon sources, such as soil, is essential to inform land management and government policy. Thus, a batch of IoT-connected CO2 sensor probes were developed for soil measurement. These sensors were designed to capture spatial distribution of CO2 concentrations across a site and communicate to a central gateway using LoRa. CO2 concentration and other environmental parameters, including temperature, humidity and volatile organic compound concentration, were logged locally and communicated to the user through a mobile (GSM) connection to a hosted website. Following three field deployments in summer and autumn, we observed clear depth and diurnal variation of soil CO2 concentration within woodland systems. We determined that the unit had the capacity to log data continuously for a maximum of 14 days. These low-cost systems have great potential for better accounting of soil CO2 sources over temporal and spatial gradients and possibly flux estimations. Future testing will focus on divergent landscapes and soil conditions.
... Researchers have studied the design of protocols that can address these challenges in a variety of application domains, taking into account the challenges of the application [9][10][11][12][13]. The protocols are intended to operate at the sensor network protocol stack (physical, link, network, and transport layers). ...
... Another implementation involves real-time monitoring using a multisensor combination module (MSCM) and LoRa. In the implementation, wetland parameters include Journal of Sensors water temperature, pH, conductivity, turbidity, dissolved oxygen, and water level [11]. A recent work by authors considers the inability of the elderly and disabled farmers to monitor and oversee agricultural tasks on farms. ...
In recent years, communication technology has improved exponentially, partly owing to the locations and nature of the deployment of sensor nodes. Wireless sensor networks (WSNs) comprise these sensor nodes and can provide real-time physical and environmental measurements. The sensor nodes have limited power, which reduces their lifespan, especially when placed in human-inaccessible locations. This paper reviews energy-efficient protocols for environmental monitoring applications and energy harvesting-wireless sensor networks. The dynamic deployment and communication challenges associated with environmental monitoring applications (EMAs) make this paper take into account the WSN protocol stack, focusing on the physical layer, network layer (routing), and medium access control (MAC). The paper will delve deeper into the security challenges of deploying sensor nodes for environmental monitoring applications (EMAs). The paper further describes scientific approaches that churn out innovative and engineering applications that must be followed to improve environmental monitoring applications.
... Some authors like Jia [19] presents a system to monitor the quality of water and air in wetlands. The system uses LoRa technology to send the collected data to the base station and a data fusion algorithm to reduce the amount of data that is sent. ...
Agriculture Farming activity near to rivers and coastal areas sometimes imply spills of chemical and fertilizers products in aquifers and rivers. These spill highly affect the water quality in rivers’ mouths and beaches close to those rivers. The presence of these elements can worse the quality for its normal use, even for its enjoying. When this polluted water reaches the sea can also have problematic consequences for fauna and flora. For this reason, it is important to rapidly detect where these spills are taking place and where the water does not have the minimum of quality to be used. In this article we propose the design and implementation of a LoRa (Long Range) based wireless sensor network for monitoring the quality of water in coastal areas, rivers and ditches with the aim to generate an observatory of water quality of the monitored areas. This network is composed by several wireless sensor nodes endowed with several sensors to physically measure parameters of water quality, such as turbidity, temperature, etc., and weather conditions such as temperature and relative humidity. The data collected by the sensors is sent to a gateway that forwards them to our storage database. The database is used to create an observatory that will permit the monitoring of the environment where the network is deployed. We test different devices to select the one that presents the best performance. Finally, the final solution is tested in a real environment for checking its correct operation. Two different tests will be carried out. The first test checks the correct operation of sensors and the network architecture while the second test show us the devices performance in terms of coverage.
... Bluetooth Low Energy is the version of the Bluetooth protocol is intended for the IoT.Several fields of application such as monitoring the health of people and patients have used BLE for its low cost and ease of use [20][21]. ...
... Y Jia. and others studied methods and technologies for ultralow power wireless sensor networks. According to the particularity of wireless sensor network communication protocols, new routing protocols based on negotiation protocols, directional release protocols, energy sensitive protocols, multipath protocols, and propagation routing protocols are proposed [16]. Watt, A. J. and others believe that wireless sensor network system can also play a great role in traffic. ...
In order to effectively solve the problem of traditional environmental monitoring system due to high sensor cost, difficult deployment, and high maintenance cost, the node design and implementation of a wireless sensor network-based environmental monitoring system are proposed. Simulation experiments show that the time-consuming running time is 14.210361 s. After adding the action force of the grid point on the node, the running time is 11.257740 s, and the operation efficiency of the algorithm is significantly improved. The improved virtual force algorithm optimization improved node coverage by 5.2%.The system is easy to deploy, reduces the development and maintenance cost, and can obtain data or monitor through wireless communication. It is convenient to use and maintain.
