A Low-Power Wireless Sensor Network for a Smart Irrigation System Powered by Solar Energy

Sohan, Md. Faruk Abdullah Al; Nahar, Afroza

doi:10.1145/3542954.3543031

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Article [41] proposed a low-power WSN for use in agriculture based on the ZigBee protocol for real-time soil moisture, temperature, and humidity monitoring. Finally, [42] proposed a low-power WSN for smart greenhouse monitoring using the ZigBee protocol, designed for real-time monitoring of various environmental parameters, including temperature, humidity, light, and carbon dioxide, and tested it in a real-world application.…”

Section: Background Studymentioning

confidence: 99%

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Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain

Aqeel

Khormi

Bhatia

et al. 2023

Sensors

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The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions.

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Section: Background Studymentioning

confidence: 99%

“…Study [39] prioritizes secure communication using TLS/DTLS, and study [40] has the added benefit of BLE connectivity and a dual-core processor. Study [41] focuses on low-power consumption and onboard Wi-Fi, and study [42] boasts a very low power consumption of only 0.42 milli watts.…”

Section: Background Studymentioning

confidence: 99%

Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain

Aqeel

Khormi

Bhatia

et al. 2023

Sensors

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“…In a large number of measurement signals collected by actual wireless sensor networks, it is inevitable that there will be sudden signal changes caused by signal transmission or storage failures. False alarms and omissions of key measurement signals will even affect the reliability of decision-making of power IoT system, causing safety accidents and bringing huge economic loss [16][17][18][19][20][21][22][23][24]. How to distinguish and extract suddenly changed signals from massive measurement signals collected by wireless sensor networks, and how to detect the working state of electrical equipment combined with the judgment of signal sources, is of positive significance to improve the reliability of system operation and the information management level of electrical equipment.…”

Section: Introductionmentioning

confidence: 99%

Research on State Detection Method of Electrical Equipment Based on Wireless Sensor Network Signal Processing

Liang¹,

Ding²,

Jin-fa³

et al. 2022

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False alarms and omissions of key measurement signals may even affect the reliability of decision-making in power IoT system, lead to safety accidents and bring huge economic losses. In order to improve the reliability of system operation and the information management level of electrical equipment, it is necessary to identify and extract suddenly changed signals from massive measurement signals collected by wireless sensor networks, and to detect the working state of electrical equipment by judging the source of signals. Therefore, this article studies the state detection method of electrical equipment based on wireless sensor network signal processing. In the second chapter, a cluster splitting and merging method is designed to solve the problem that the existing detection methods tend to ignore the imbalance of cluster size. In the third chapter, according to the data characteristics of measurement signals collected by wireless sensor networks, a similarity measurement criterion for composite time series of measurement signals is proposed, and the corresponding distance matrix is generated based on this criterion. In the fourth chapter, wavelet decomposition is used to decompose the initial measurement signals collected by wireless sensor networks, and then the signals are compressed twice based on compressed sensing. Then the abnormal signal data information is imported into support vector machine for training to realize the real-time detection of abnormal signals of electrical equipment state. Experimental results verify the effectiveness of the proposed algorithm.

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A Smart Farm Monitoring and Control System using Wireless Sensor Network

Ndubueze Herbert Otuosorochi

2024

IJARSCT

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Agriculture is one of the most important sector that accounts for economic growth in the developing countries of the world. Many developing countries are now focusing on agricultural development, yet the sector is not without challenges including climate changes, drought, flooding to mention but a few. These result in poor yield of agricultural products. In this work, we l developed a robust smart system to enhance both irrigation and flooding monitoring and control, leveraging on Wireless Sensor Networks (WSNs) to boost agricultural production. In the system’s implementation, Arduino based instrumentation, integrated with temperature, soil moisture and water level sensor shall be adopted to monitor the agricultural environment, while reporting the status wirelessly through the Radio Frequency (RF) modules to the base station. The base station will evaluate the received data and either activates or deactivates the irrigation or drainage pump using specified threshold values. The remote reporting the state of farm shall be done by deploying a 900Mhz transmitter interfaced with the system’s controller. This work shall be validated using an experimental test bed, which shall be used for field experiments, data collection and evaluation. The result of the work shall highlight the potentials of WSN technology in monitoring and control technology of both irrigation and flooding within the farm and in turn boost productivity

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A Low-Power Wireless Sensor Network for a Smart Irrigation System Powered by Solar Energy

Cited by 3 publications

References 24 publications

Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain

Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain

Research on State Detection Method of Electrical Equipment Based on Wireless Sensor Network Signal Processing

A Smart Farm Monitoring and Control System using Wireless Sensor Network

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