Machine Learning Modelling-Powered IoT Systems for Smart Applications

Messaoud, Seifeddine; Ahmed, Olfa Ben; Bradai, Abbas; Atri, Mohamed

doi:10.1007/978-3-030-71172-6_8

Cited by 5 publications

(7 citation statements)

References 75 publications

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“…Khan, Su'ud [22] elaborate that transportation systems like Intelligent Transportation Systems (ITS) have catalyzed navigation, route optimization, minimal power consumption, vehicle emissions, and the detection of traffic conditions based on streetlights and innovative parking systems [23]. Concerning car parking, intelligent parking reservation systems, for example, can significantly reduce the time spent searching for a parking space and increase the number of spaces available in parking lots through visual devices, infrared sensors, and magnetic fields [24]. IoT devices can also be hand-held devices that receive information on the road surface from implanted sensors to prevent accidents.…”

Section: Smart Transportationmentioning

confidence: 99%

A Raise of Security Concern in IoT Devices: Measuring IoT Security Through Penetration Testing Framework

Jaafar,

Ismail,

Habir

et al. 2024

ijacsa

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Despite the widespread adoption of IoT devices across different industries to enhance human activities, there is a pressing need to address the vulnerabilities associated with these devices, as they can potentially give rise to a plethora of cyber threats. Cyberattacks targeting IoT devices are predominantly attributed to inadequate patching and security updates. Furthermore, the current atmosphere pertaining to IoT penetration tests primarily focuses on specific devices and sectors while leaving certain fields behind, such as household devices. This study delves into recent penetration testing on IoT devices. Further, it discusses and critically analyzes the significance and issues in conducting IoT penetration tests. The findings of this study reveal a substantial demand for automated IoT penetration testing to serve diverse industries because conducting such testing has the capacity to diminish the consequences of cyber-attacks across numerous industries that utilize IoT devices for various purposes. This study is intended to be a ready reference for the research community to construct effective and innovative solutions in IoT penetration testing, which covers various fields.

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Section: Smart Transportationmentioning

confidence: 99%

A Raise of Security Concern in IoT Devices: Measuring IoT Security Through Penetration Testing Framework

Jaafar,

Ismail,

Habir

et al. 2024

ijacsa

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“…The requirement optimization is performed using time intervals and continuously checking the requirements of the plants. The authors of [11,12] propose an irrigation system using control-based scheduling to manage different factors such as humidity, wind speed, wind velocity, soil moisture, etc. The sensor-based prediction for managing irrigation and soil moisture sensor senses different soil conditions, and mobile applications are used to measure and monitor different activities of the irrigation system.…”

Section: Irrigation Techniquesmentioning

confidence: 99%

“…The time-to-time irrigation requirements are also calculated using logical regression analysis. The authors of [11,12,24] calculate humidity and temperature using a decision tree. The future requirements for the prediction of irrigation systems are calculated using the SVM algorithm, but the requirements for prediction accuracy are very low.…”

Section: Irrigation Techniquesmentioning

confidence: 99%

“…Irrigation models [6,10,11,24,30] A sprinkler-solid, centre pivot, travelling irrigator, and micro-spray are used for spatial-based irrigation. Automatic sensors and evaporation-based models are used to predict the requirements.…”

Section: Previous Models Advantages Limitationsmentioning

confidence: 99%

“…Optimization and machine-learning algorithms [4,5,9,11,12,[22][23][24] Genetic algorithm, KNN, logical regression, SVM and decision-tree algorithms are considered for irrigation requirement predictions.…”

Section: Previous Models Advantages Limitationsmentioning

confidence: 99%

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Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Blessy

Kumar

Prabagaran³

et al. 2023

Sustainability

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Irrigation systems are a crucial research area because it is essential to conserve fresh water and utilize it wisely. As a part of this study, the reliability of predicting the usage of water in the present and future is investigated in order to develop an effective prediction model to communicate demand. In order to improve prediction, we develop a prediction model and share the updated model with nearby farmers. In order to forecast the irrigation requirements, the recommended model utilizes the Internet of Things (IoT), k-nearest neighbours (KNN), cloud storage, long short-term memory (LSTM), and adaptive network fuzzy inference system (ANFIS) techniques. By collecting real-time environmental data, KNN identifies the closest water requirement from the roots and its surrounding. In order to predict short-term requirements, ANFIS is used. To transfer the new requirements for better prediction, transfer learning is used. Time-series-data updates are predicted using LSTM for future forecasting, and the integrated model is shared with other farmers using cloud environments to enhance forecasting and analysis. For implementation, a period of nine to ten months of data was collected from February to December 2021, and banana tree was used to implement the planned strategy. Four farms, with measurements, were considered at varying intervals to determine the minimum and maximum irrigation needs. The requirements of farms were collected over time and compared to the predictions. Future requirements at 8, 16, 24, 32, and 48 h were also anticipated. The results indicated were compared to manual water pouring, and, thus, the entire crop used less water, making our prediction model a real-world option for irrigation. The prediction model was evaluated using R2, MSLE and the average initial prediction value of R2 was 0.945. After using transfer learning, the prediction of the model of Farm-2, 3 and 4 were 0.951, 0.958 and 0.967, respectively.

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