IoT Based Smart Agriculture Monitoring System with Predictive Analysis

Bouarourou, Soukaina; Zannou, Abderrahim; Boulaalam, Abdelhak; Nfaoui, El Habib

doi:10.1109/iraset52964.2022.9738367

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Different types of devices provide different data, first requiring different schemes for data cleaning to ensure data integrity and standardization [37]. Further, a wide variety of analysis algorithms are needed for different IoT application scenarios, such as face recognition, defect detection visual models [38,39], log processing natural language models [40], or anomaly detection, predictive analysis structured data processing models [41,42], each with unique development processes, integration methods, and usage. Finally, various intelligent algorithms need to work on the central side or edge side, facing a variety of hardware platforms and resource conditions, requiring complex porting processes [4,34].…”

Section: Intelligencementioning

confidence: 99%

IoT OS Platform: Software Infrastructure for Next-Gen Industrial IoT

Xing,

Lan,

Sun

et al. 2024

Applied Sciences

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With the rapid development of the Internet of Things (IoT), the growth of the Industrial Internet of Things (IIoT) applied in the industrial sector has also been swift. However, in practical applications, there are still issues such as the misalignment between theory and application, the lack of a unified standardization framework, and the frequent occurrence of data silos. These issues limit the maintainability and scalability of IIoT systems and increase the digitalization costs for enterprises. Based on this, drawing from the design principles of classic general-purpose operating systems, we propose the concept of an IoT operating system platform. As a software infrastructure aimed at the next generation of IIoT, the IoT OS platform consists of general-purpose computer operating systems and the platform software running on them. It manages computing resources and entities such as sensors, networks, and ubiquitous artificial intelligence applications or systems, and provides service support for IIoT applications upwards, aiming to improve existing issues and enhance the specificity and scalability of IIoT systems. This paper presents the current status of IIoT systems, the definition and architecture of the IoT OS platform, and validates the theoretical architecture through specific cases.

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Section: Intelligencementioning

confidence: 99%

IoT OS Platform: Software Infrastructure for Next-Gen Industrial IoT

Xing,

Lan,

Sun

et al. 2024

Applied Sciences

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“…Two distinct cases of concept drift are simulated, where the first involves a minor drift by changing the P (x) distribution of the handwritten digit 8 to the P (x ) distribution of the handwritten character B obtained by the EMNIST dataset. 4 The second case, which is more severe, involves changing the P (x) distribution of the handwritten digit 9 to the P (x ) distribution of the handwritten character K. In both cases, the labels are unchanged and the simulated concept drifts are identified as D v 8→B 2 and D v 9→K 2 , respectively. Experimental Setup: We used a Multi-Layer Perception (MLP) classifier consisting of three linear layers and ReLU activations functions.…”

Section: Experimental Scenario Iii: Image Classificationmentioning

confidence: 99%

“…Nowadays, EC applications have proliferated across diverse sectors, spanning an array of domains including healthcare [1], agriculture monitoring [4], energy prediction systems [18] and bike-demand forecasting [29], among others. These applications collectively fall under the expansive umbrella of smart city applications.…”

Section: Introductionmentioning

confidence: 99%

Resilient edge machine learning in smart city environments

Vrachimis,

Gkegka,

Kolomvatsos

2023

SCS

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Distributed Machine Learning (DML) has emerged as a disruptive technology that enables the execution of Machine Learning (ML) and Deep Learning (DL) algorithms in proximity to data generation, facilitating predictive analytics services in Smart City environments. However, the real-time analysis of data generated by Smart City Edge Devices (EDs) poses significant challenges. Concept drift, where the statistical properties of data streams change over time, leads to degraded prediction performance. Moreover, the reliability of each computing node directly impacts the availability of DML systems, making them vulnerable to node failures. To address these challenges, we propose a resilience framework comprising computationally lightweight maintenance strategies that ensure continuous quality of service and availability in DML applications. We conducted a comprehensive experimental evaluation using real datasets, assessing the effectiveness and efficiency of our resilience maintenance strategies across three different scenarios. Our findings demonstrate the significance and practicality of our framework in sustaining predictive performance in smart city edge learning environments. Specifically, our enhanced model exhibited increased generalizability when confronted with concept drift. Furthermore, we achieved a substantial reduction in the amount of data transmitted over the network during the maintenance of the enhanced models, while balancing the trade-off between the quality of analytics and inter-node data communication cost.

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