An Open-Source Smart Sensor Architecture for Edge Computing in IoT Applications

Cordelli, Ermanno; Pennazza, Giorgio; Sabatini, Marco; Santonico, Marco; Vollero, Luca

doi:10.3390/proceedings2130955

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…While trying to determine which form of IoT would work for a certain application while also fulfilling the needs of the end user, it is critical to have a thorough understanding of all of these features. In [26], the use of edge computing was proposed as a method for creating efficient IoT applications. In [20][21][22][23], various sensor networks used for deploying sensors and IoT-based applications are described.…”

Section: Literature Reviewmentioning

confidence: 99%

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Remote Sensing and Decision Support System Applications in Precision Agriculture: Challenges and Possibilities

Mehedi,

Hanif,

Bilal

et al. 2024

IEEE Access

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As the world's population rises, there will be a greater need for food, which will have repercussions on the environment and on crop yields. Increased production, efficient resource allocation, climate change adaptation, and diminished food waste are the four cornerstones of Agriculture 4.0's vision for the future of farming. Agriculture 4.0 makes use of cutting-edge data systems and Internet technology to acquire, analyze, and organize massive amounts of farming facts such as weather reports, soil conditions, market demands, and land usage to better guide farmers' decisions and boost their bottom lines. As a result, research on agricultural decision support systems for Agriculture 4.0 has gained significant momentum. Crop monitoring and yield forecasting are two applications where remote sensing has proven useful, and these two areas are intrinsically linked to variations in soil, weather, and biophysical and biochemical factors. Multi-and hyper-spectral data, radar, and lidar imaging are just some of the remote tools that could be employed for crop monitoring and yield forecasting. This paper's goal is to examine some of the difficulties that can arise in the future while using agricultural decision-support platforms in the context of Agriculture 4.0. Addressing these identified obstacles may help future researchers create better decisionassistance systems. This research examines the possibilities, benefits, and drawbacks of each method, as well as how well they work in various agricultural settings. Furthermore, these methods are demonstrated in a variety of strategies that can be effectively employed. In this research, we take a look at some remote sensing techniques developed to increase farm profits while minimizing their impact on the natural world. This research shows how remote sensing information can be used to predict crop yields, evaluate plant nutrient needs and soil nutrient levels, calculate plant moisture levels, and manage weed populations, among other applications.

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Section: Literature Reviewmentioning

confidence: 99%

“…There is also talk of agricultural and distant sensing sensors. In [25][26][27][28][29][30][31], the authors provide a thorough overview of IoT and its applications, with a particular focus on how to best use IoT in the field of remote sensing.…”

Section: Literature Reviewmentioning

confidence: 99%

Remote Sensing and Decision Support System Applications in Precision Agriculture: Challenges and Possibilities

Mehedi,

Hanif,

Bilal

et al. 2024

IEEE Access

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“…Transduction and processing of raw signals have been embedded within a smart sensor architectural model [21]. This model has the purpose of modularizing the electronic and digital conversion functionalities of raw data, interfacing them to typical IoT sensor functionalities: (i) the local data processing functionality; (ii) the data transmission functionality.…”

Section: Sensor Connectivitymentioning

confidence: 99%

Proof of Concept Study of an Electrochemical Sensor for Inland Water Monitoring with a Network Approach

et al. 2021

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The technologies most suitable for monitoring the ecosystem of inland waters are image spectrometry and electrochemical sensors. The reason is that these instruments are able to ensure accuracy in the surveillance of very large areas through reliable and frequent measurements performed remotely. Electrochemical systems provide low-cost, miniaturized, reliable sensors that can be organized, when equipped with commercial on the shelf (COTS) low-power radio components implementing LoRaWAN, Sigfox or NB-IoT communications, in a dense network of sensors achieving the aforementioned requirements. In this work, a low-cost, low-size and low-noise electrochemical sensor endowed with protocols for network configuration, management and monitoring is presented. The electronic interface of the sensor allows high reproducible responses. As proof of concept for its utilization in inland water monitoring, the device has been tested for water composition analysis, bacteria identification and frequent pollutant detection: atrazine, dichloromethane and tetrachloroethene. The results are promising, and future investigations will be oriented to unlock the true potential of a general-purpose approach exploiting the continuous fusion of distributed data in each of the three considered application scenarios. A new device, with reduced power consumption and size, has been also developed and tested; this new device should be a node of a large network for inland water monitoring.

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Network Data Processing Methods Based on Edge Computing

Jiang

Wu²

2021

Advances in Intelligent Systems and Computing

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An Open-Source Smart Sensor Architecture for Edge Computing in IoT Applications

Cited by 7 publications

References 10 publications

Remote Sensing and Decision Support System Applications in Precision Agriculture: Challenges and Possibilities

Remote Sensing and Decision Support System Applications in Precision Agriculture: Challenges and Possibilities

Proof of Concept Study of an Electrochemical Sensor for Inland Water Monitoring with a Network Approach

Network Data Processing Methods Based on Edge Computing

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