Design and development of an IoT-enabled portable phosphate detection system in water for smart agriculture

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

doi:10.1016/j.sna.2021.112861

Cited by 33 publications

(16 citation statements)

References 35 publications

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“…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]. Because the installed device is unattended, when the device needs to replace the battery, it is generally necessary to manually go to the site for operation [12].…”

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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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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“…Step 7: Estimate piled correlation coefficient between diversified features as in equation (5) Step 8: Evaluate classified correlation coefficient as in equation (6) Step 9: Estimate the Neyman-Pearson correlated probability distribution as in equation (7) Step 10: Return (feature selected 'FS')…”

Section: (7)mentioning

confidence: 99%

“…A low-cost smart agriculture framework for smart agriculture employing IoT featuring sensor for an extensive range of temperature and phosphate detection was presented in [6]. However, the energy consumption involved in the analysis was not discussed.…”

Section: Introductionmentioning

confidence: 99%

Big Data Application in the Neyman-Pearson Regression and Deep Bernoulli and Boltzmann for Iot Based Soil Quality Prediction

Balaji¹,

Vijayakumar²

2022

SJLSA

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Purpose. The aim of the work is to enhance the soil quality prediction accuracy and time by using feature selection and classification-based model. Background. Soil quality analysis was handled based on the farmer’s first-hand data mining competence and with the world population expected to increase exponentially (i.e., big data), erratic changes in climate have started influencing soil capitulates incorrectly. Big data is employed used to examine the large amount of dataset for soil quality analysis. It is helps to address a lot of new and significant farming decisions and issue. Soil quality analysis depends on fertility of the soil. By soil quality analysis accuracy prediction is very crucial for practical utilization of resources. But, the existing data mining techniques failed to select the correct crop based on the soil and environmental features. Material and methods. The study presents the data mining techniques based on smart and efficient soil quality prediction for agriculture development. In our work, first, linear regression and the Neyman-Pearson correlation-based feature selection model is employed to obtain the computationally efficient and relevant features. Next, an enhanced deep learning model called deep Bernoulli and Boltzmann IoT-based soil quality prediction is designed to classify the complex soil features with better sensitivity and specificity. Results. Experimental results obtained confirmed the performance and reliability of the proposed method. The result evaluations are carried out on the basis of the prediction accuracy, prediction time, sensitivity and specificity. Conclusion. The result shows that the NPR-DBB method achieves better results than the state-of-the-art methods.

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“…T HE DRIVE toward building pervasive intelligence encompassing urban as well as rural environments has paved the way for the Internet of Things (IoT) [1], which has reshaped our regular lifestyle alleviating the dependence on wired communication systems since its inception [2]. The inexorable advancement in low to ultralow-power electronics have steered the rapid growth of the IoT platform expanding into several application fields, including disaster management [3], agriculture [4], industrial monitoring [5], wearable motion sensing [6], and condition monitoring [7], to name a few. With the ongoing implementation of fifth generation (5G) and the emergence of sixth generation (6G) wireless technology on the horizon, the explosive growth of IoT-connected devices reinforces the requirement of a robust and reliable power solution for the deployed wireless communication platforms.…”

Section: Introductionmentioning

confidence: 99%

A Concertina-Shaped Vibration Energy Harvester-Assisted NFC Sensor With Improved Wireless Communication Range

Paul¹,

Gawade²,

Simorangkir³

et al. 2022

IEEE Internet Things J.

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The explosive growth of wireless sensor platforms and their emerging wide range of application areas make the development of a sustainable and robust power source, an essential requirement to enable widespread deployment of these wireless devices. As a solution to this cardinal issue, this article reports the design and fabrication of a resonant vibration energy harvester (VEH) that comprises interleaved springs, manifesting a concertina-shaped structure that can enable large mechanical amplitudes of oscillation. Within a relatively small footprint (9 cm 3 ), this concertina-VEH yields a large power density of 455.6 µW/cm 3 g 2 while operating at a resonant frequency of 75 Hz. Additionally, the feasibility of the implemented VEH to support near field communication (NFC)-based wireless sensor platform, that is yet uncharted, is also investigated in this work. A very low-power consumption NFC wireless sensor node has been designed and developed for this purpose. The developed concertina VEH has been employed to power the electronics interface of this NFC sensor. Using mechanical energy derived from as low as 0.2-g excitation, our study shows that the VEH can enhance the electromagnetic interaction between the transmitting antenna and the reader, resulting in a 120% increase in wireless communication range for the NFC sensor node. Such a high-performance energy harvester-assisted NFC sensor node has the potential to be used in a wide range of Internet of Things (IoT) platforms as a reliable and sustainable power solution.

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Design and development of an IoT-enabled portable phosphate detection system in water for smart agriculture

Cited by 33 publications

References 35 publications

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

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

Big Data Application in the Neyman-Pearson Regression and Deep Bernoulli and Boltzmann for Iot Based Soil Quality Prediction

A Concertina-Shaped Vibration Energy Harvester-Assisted NFC Sensor With Improved Wireless Communication Range

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