Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse

Yang, Yehui; Ding, Sheng; Liu, Yuwen; Meng, Shunmei; Chi, Xiaoxiao; Ma, Rui; Yan, Chao

doi:10.1016/j.dcan.2021.11.004

Cited by 25 publications

(9 citation statements)

References 29 publications

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“…Improving the Adaptive Data Rate (ADR) mechanism to enable cellular LoRa increases the performance of long-range wide area (LoRA) connectivity by up to 520% [23]. Finding strange data on wireless sensors using the DLSHiForest method based on Locality-Sensitive Hashing and the time window technique works more accurately and quickly than other methods [24]. To maintain privacy and user device collaboration in the cloud, the implementation of the Hierarchical Data Sandboxing module can maintain hierarchically organized application data [25].…”

Section: Related Workmentioning

confidence: 99%

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

Setyawan,

Marjunus

et al. 2024

ijacsa

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The integration of IoT systems in agriculture has become a very important need amid the high population and increasingly limited farmland, which demands researchers to be more innovative in addressing these issues. Using IoT systems for automatic irrigation, fertilization, and cooling based on sensor values through internet networks. Poor internet connection leads to the failure of automation and sustainability in online conditions, which can be very dangerous for plants. This paper presents a new IoT-based control system divided into two parts: an automation system and an IoT system, which can maintain sustainability in online conditions to ensure that plants in the planting area are always controlled. In addition, the sensors used have undergone calibration processes to determine the increase in precision of the sensor values produced. The research results show that the system can maintain sustainability under online conditions. Mobile apps are available for control when the system is online, but if it goes offline and is unable to reconnect, the Arduino Mega will fully manage control using soil moisture sensor values for irrigation processes if the values fall below a certain threshold. This demonstrates the sustainability of the system in online conditions, allowing continuous control and reducing the risk of plant death in the planting area. The calibration result shows an increase in precision for the air temperature and humidity (DHT 11 sensor) by 7.14 and 6.15, respectively. Additionally, the precision improvement for the soil pH sensor is 1.81, while for the soil moisture sensor and the water flow sensor, it is 0.13 and 0.008, respectively.

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Section: Related Workmentioning

confidence: 99%

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

Setyawan,

Marjunus

et al. 2024

ijacsa

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“…We performed a series of preprocessing steps on images from the NEU-CLS dataset. These steps include resizing the images to a uniform size, converting the images to grayscale to reduce computational complexity, applying histogram equalization to enhance image contrast, and implementing data augmentation techniques to increase the diversity of the dataset [53]. After the preprocessing stage, we constructed a Convolutional Neural Network (CNN) model.…”

Section: Preliminary Work For Encrypted Chunks Classification Modelmentioning

confidence: 99%

Efficient and privacy-preserving image classification using homomorphic encryption and chunk-based convolutional neural network

Jia,

Cai,

Yang

et al. 2023

J Cloud Comp

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Image feature categorization has emerged as a crucial component in many domains, including computer vision, machine learning, and biometrics, in the dynamic environment of big data and cloud computing. It is extremely difficult to guarantee image data security, privacy, and computing efficiency while also lowering storage and transmission costs. This paper introduces a novel method for classifying image features that combines multilevel homomorphic encryption and image data partitioning in an integrated manner. We employ a novel partitioning strategy to reduce computational complexity, significantly reducing computational load and improving classification accuracy. In the quest for increased data security and privacy, we introduce a novel, fully homomorphic encryption approach specialized to partitioned images. To counter the inherent complexity of encryption, we devise a compound encryption strategy that exploits the full potential of homomorphic computation, with an explicit objective to curtail computational and storage overheads. Evidently superior to conventional methods, our methodology showcases pronounced benefits in computational efficiency, storage and transmission cost reduction, and robust security and privacy preservation. Hence, the methodology put forth in this paper presents a pioneering and efficacious resolution to the multifaceted challenges of image feature classification within the intricate milieu of cloud computing and big data.

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“…Multi-type concept drift (CD) detection is widely applied in cloud and security applications [1]. The mining of CD in frequent patterns (FPs) with latent risks, allows the monitoring of changes in potential-risk patterns and related data models in cloud environments [2][3][4]. It is used in cloud computing [5], security information detection [6][7][8][9], blockchain-based [10], healthcare data prediction [11], intelligent data processing in IoT [12][13][14][15], recommendation systems [16,17], and fault container instance seq finding [18].…”

Section: Introductionmentioning

confidence: 99%

Multi-type concept drift detection under a dual-layer variable sliding window in frequent pattern mining with cloud computing

Chen,

Yang,

Gao

et al. 2024

J Cloud Comp

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The detection of different types of concept drift has wide applications in the fields of cloud computing and security information detection. Concept drift detection can indeed assist in promptly identifying instances where model performance deteriorates or when there are changes in data distribution. This paper focuses on the problem of concept drift detection in order to conduct frequent pattern mining. To address the limitation of fixed sliding windows in adapting to evolving data streams, we propose a variable sliding window frequent pattern mining algorithm, which dynamically adjusts the window size to adapt to new concept drifts and detect them in a timely manner. Furthermore, considering the challenge of existing concept drift detection algorithms that struggle to adapt to different types of drifting data simultaneously, we introduce an additional dual-layer embedded variable sliding window. This approach helps differentiate types of concept drift and incorporates a decay model for drift adaptation. The proposed algorithm can effectively detect different types of concept drift in data streams, perform targeted drift adaptation, and exhibit efficiency in terms of time complexity and memory consumption. Additionally, the algorithm maintains stable performance, avoiding abrupt changes due to window size variations and ensuring overall robustness.

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Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse

Cited by 25 publications

References 29 publications

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

Efficient and privacy-preserving image classification using homomorphic encryption and chunk-based convolutional neural network

Multi-type concept drift detection under a dual-layer variable sliding window in frequent pattern mining with cloud computing

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