An IoT-Enabled Portable Water Quality Monitoring System With MWCNT/PDMS Multifunctional Sensor for Agricultural Applications

Akhter, Fowzia; Siddiquei, H. R.; Alahi, Eshrat E.; Jayasundera, Krishanthi Padmarani; Mukhopadhyay, Subhas Chandra

doi:10.1109/jiot.2021.3069894

Cited by 55 publications

(20 citation statements)

References 34 publications

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“…The long short-term memory (LSTM) network is an improved and solvable special RNN model [ 24 ]. By introducing a new internal state and gate mechanism, LSTM networks have a good performance in solving the problems of gradient disappearance and long-term dependence, which makes LSTM have selective memory functions [ 25 ]. The construction process of the water-quality-prediction model for river pollution accidents based on LSTM is shown in Figure 3 .…”

Section: Methodsmentioning

confidence: 99%

Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM

Guan

Wang

Yang

et al. 2022

IJERPH

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The openly released and measured data from automatic hydrological and water quality stations in China provide strong data support for water environmental protection management and scientific research. However, current public data on hydrology and water quality only provide real-time data through data tables in a shared page. To excavate the supporting effect of these data on water environmental protection, this paper designs a water-quality-prediction and pollution-risk early-warning system. In this system, crawler technology was used for data collection from public real-time data. Additionally, a modified long short-term memory (LSTM) was adopted to predict the water quality and provide an early warning for pollution risks. According to geographic information technology, this system can show the process of spatial and temporal variations of hydrology and water quality in China. At the same time, the current and future water quality of important monitoring sites can be quickly evaluated and predicted, together with the pollution-risk early warning. The data collected and the water-quality-prediction technique in the system can be shared and used for supporting hydrology and in water quality research and management.

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Section: Methodsmentioning

confidence: 99%

Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM

Guan

Wang

Yang

et al. 2022

IJERPH

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“…Smart sensing systems were created in New Zealand and Australia for monitoring nitrate levels in ground and surfacewater used in agricultural purposes (Alahi et al, 2017a(Alahi et al, , 2017b(Alahi et al, , 2018a(Alahi et al, , 2018b(Alahi et al, , 2018c(Alahi et al, , 2022. In these countries, smart watering systems were developed for monitoring the water quality in aquaculture and agriculture (Akhter et al, 2021a(Akhter et al, , 2021b.…”

Section: Thripsmentioning

confidence: 99%

Using a smart watering system for controlling thrips inside mangosteen canopy in Nakhon Si Thammarat province, Southern Thailand

Malee

Ruang-on

Hussain

et al. 2022

International Journal on Smart Sensing and Intelligent Systems

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Mangosteen (Garcinia mangostana) production is important in Thailand for domestic consumption and export in other countries. High-quality mangosteen is considered when there is no scar on the surface. Thrips infestation is a common cause of scars which reduces the market value of mangosteen. Thrips density is highly affected by humidity; higher humidity decreases thrips numbers. In this study, a smart watering system was designed, developed, and used to increase the humidity and reduce thrips numbers inside mangosteen canopy in Southern Thailand. The system successfully reduced the thrips numbers. This study also shows a negative correlation between humidity and thrips numbers.

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“…The IoT devices of urban pipe network system or Meter reading system are sensitive to battery power supply and power consumption, therefore, the data that the IoT device can send is limited [9]. Due to the occlusion reflection or stray interference of buildings, the wireless signal will be greatly attenuated, hence the battery life is very dependent on the installation environment and data transmission policy [10]. If the installation location and sending policy are not good, the battery will be quickly consumed after the IoT device is started, which cannot support the data collection and transmission business requirements with a period of 6 -10 years [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Layout and Location of Water IoT Device Based on Few-Shot Reinforcement Learning

2022

Teh. vjesn.

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After the traditional water equipment integrates the communication module, IoT (Internet of Things) device is formed. Whether these battery-powered IoT devices can be installed in a certain location depends on whether the power consumption of these IoT devices in these locations can meet the expected life cycle. In this paper, by adopting strategies to save the power consumption of IoT devices when sending data, more locations can be selected to install IoT devices. The process of IoT device sending data packet sequence needs to be aware of the environment, interact with the environment, then make a decision, and then adjust the policy according to the effect of the action. Therefore, in this paper, the process of IoT device sending data packet sequence is modelled as MDP (Markov Sequence Decision Process), and the real-time SINR of channel and the transmission delay of data packet sequence are defined as the state space, and the action space consists of immediate transmission and delayed transmission, with the minimum total power consumption as the objective function. Because IoT devices are very sensitive to power consumption and cannot collect a large amount of data for training, this paper uses the Proximal Policy Optimization algorithm based on prior distribution to conduct few-shot reinforcement learning to quickly obtain the optimal decision sequence of layout and location of IoT devices.

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An IoT-Enabled Portable Water Quality Monitoring System With MWCNT/PDMS Multifunctional Sensor for Agricultural Applications

Cited by 55 publications

References 34 publications

Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM

Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM

Using a smart watering system for controlling thrips inside mangosteen canopy in Nakhon Si Thammarat province, Southern Thailand

Layout and Location of Water IoT Device Based on Few-Shot Reinforcement Learning

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