A Holistic Overview of Anticipatory Learning for the Internet of Moving Things: Research Challenges and Opportunities

Cao, Hung

doi:10.3390/ijgi9040272

Cited by 6 publications

(5 citation statements)

References 142 publications

(79 reference statements)

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“…A standard architecture for IoT scheduling and optimization in smart cities is offered [12]. The author explained IoMT devices present numerous research challenges and opportunities [13]. In [14] the author focused on determining a safe cutting range of input parameters pertaining to stable chatter.The author obtained an optimum range of turning parameters using the merged wavelet denoising and local mean decomposition technique [15] .…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of IoT in 5G Technologies for Fire and Spark Activity Detection

Bhardwaj¹,

Kumar²

2023

Advances in Transdisciplinary Engineering

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Present research is focusing on applications used for Industrial safety using the application of Internet of Things (IoT). With the growing popularity of IoT wireless technologies, 5G technology has emerged as a particularly difficult and exciting research field. With the advent of 5G, this study examines the issues and possibilities that a wide range of communication firms will face. The IoT and the new and evolving technologies that enable it are thoroughly covered in this paper. Low-power networks serve a wider geographic area, and safety issues are taken into account while determining control mechanisms. Fire and spark activity detection and categorization have been performed using an IoT system in this study. Convolution Neural Network (CNN) based deep learning is being used in this research process. The evaluation metrics chosen are Precision, Accuracy and Recall value. A camera is a kind of sensing equipment that captures conventional photos as well as detecting fire and embers.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of IoT in 5G Technologies for Fire and Spark Activity Detection

Bhardwaj¹,

Kumar²

2023

Advances in Transdisciplinary Engineering

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“…Recently, the Internet of ings (IoT) and the Internet of Mobile ings (IOMT) are fundamentally changing the world by allowing multiple (mobile) devices to communicate and exchange data with each other and make decisions without human interventions [14]. erefore, IOMT technology is the key element in the fabrication of intelligent vehicles equipped with safety sensors that allow avoiding many accidents [3].…”

Section: Mobile Technologies For Driving Behavior Improvementmentioning

confidence: 99%

Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study

hafidy

Rachad

Idri

et al. 2021

Mobile Information Systems

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Many research works and official reports approve that irresponsible driving behavior on the road is the main cause of accidents. Consequently, responsible driving behavior can significantly reduce accidents’ number and severity. Therefore, in the research area as well as in the industrial area, mobile technologies are widely exploited in assisting drivers in reducing accident rates and preventing accidents. For instance, several mobile apps are provided to assist drivers in improving their driving behavior. Recently and thanks to mobile cloud computing, smartphones can benefit from the computing power of servers in the cloud for executing machine learning algorithms. Therefore, many mobile applications of driving assistance and control are based on machine learning techniques to adjust their functioning automatically to driver history, context, and profile. Additionally, gamification is a key element in the design of these mobile applications that allow drivers to develop their engagement and motivation to improve their driving behavior. To have an overview concerning existing mobile apps that improve driving behavior, we have chosen to conduct a systematic mapping study about driving behavior mobile apps that exist in the most common mobile apps repositories or that were published as research works in digital libraries. In particular, we should explore their functionalities, the kinds of collected data, the used gamification elements, and the used machine learning techniques and algorithms. We have successfully identified 220 mobile apps that help to improve driving behavior. In this work, we will extract all the data that seem to be useful for the classification and analysis of the functionalities offered by these applications.

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“…By utilizing meta-learning and ensemble techniques, researchers can develop adaptive intrusion detection mechanisms that dynamically adjust decision fusion strategies based on evolving attack patterns and network conditions [ 6 , 7 ]. The combination of machine learning algorithms, deep learning models, and ensemble methods in IDS for IoMT networks not only improves anomaly detection capabilities but also contributes to the creation of lightweight and efficient security solutions tailored to the unique challenges posed by interconnected medical devices and IoT ecosystems [ 8 , 9 ]. As the field of IoT security evolves, the application of ensemble learning in IDS for IoT and IoMT networks holds significant potential in strengthening healthcare systems against cyber threats and ensuring the confidentiality and integrity of patient data [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Internet of Medical Things (IoMT) Security with Meta-Learning: A Performance-Driven Approach for Ensemble Intrusion Detection Systems

Alalhareth,

Hong

2024

Sensors

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This paper investigates the application of ensemble learning techniques, specifically meta-learning, in intrusion detection systems (IDS) for the Internet of Medical Things (IoMT). It underscores the existing challenges posed by the heterogeneous and dynamic nature of IoMT environments, which necessitate adaptive, robust security solutions. By harnessing meta-learning alongside various ensemble strategies such as stacking and bagging, the paper aims to refine IDS mechanisms to effectively counter evolving cyber threats. The study proposes a performance-driven weighted meta-learning technique for dynamic assignment of voting weights to classifiers based on accuracy, loss, and confidence levels. This approach significantly enhances the intrusion detection capabilities for the IoMT by dynamically optimizing ensemble IDS models. Extensive experiments demonstrate the proposed model’s superior performance in terms of accuracy, detection rate, F1 score, and false positive rate compared to existing models, particularly when analyzing various sizes of input features. The findings highlight the potential of integrating meta-learning in ensemble-based IDS to enhance the security and integrity of IoMT networks, suggesting avenues for future research to further advance IDS performance in protecting sensitive medical data and IoT infrastructures.

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A Holistic Overview of Anticipatory Learning for the Internet of Moving Things: Research Challenges and Opportunities

Cited by 6 publications

References 142 publications

Performance Evaluation of IoT in 5G Technologies for Fire and Spark Activity Detection

Performance Evaluation of IoT in 5G Technologies for Fire and Spark Activity Detection

Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study

Enhancing the Internet of Medical Things (IoMT) Security with Meta-Learning: A Performance-Driven Approach for Ensemble Intrusion Detection Systems

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