Hybrid Seismic-Electrical Data Acquisition Station Based on Cloud Technology and Green IoT

Qiao, Shuaiqing; Zhang, Qimao; Liu, Denghui; Li, Wenhao

doi:10.1109/access.2020.2966510

Cited by 11 publications

(3 citation statements)

References 36 publications

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“…After nishing the writing, let the teacher present it to the class through the physical booth. Now, how can we save paper and pen through the pad [13] and write directly in the homework mode. After nishing the one-click submission, it can be presented on the big screen.…”

Section: D Printer With the Development Of Technology 3dmentioning

confidence: 99%

Construction of Perceptual Classroom Based on Internet of Things Technology

2022

Mobile Information Systems

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At present, in the traditional classroom teaching process, there are still problems such as differences in students’ original knowledge, low student participation, and single teaching methods. For students, the mobile phone carrying rate is high, but the learning utilization rate is low and has self-control, poor ability, and low classroom participation. To solve the above problems, the research on the construction of perception classrooms based on the Internet of Things technology is particularly important. Perceived classroom refers to the use of existing technology to closely connect students and teachers, fully mobilize students’ enthusiasm, and make the classroom atmosphere. This article aims to study the construction of perceptual classrooms based on the Internet of Things technology to increase students’ interest. Through the investigation of the current situation of the digital campus, relying on the Internet of Things technology, the feasibility of the perception classroom is analyzed, and the perception classroom is constructed through the design of image processing and fuzzy control rules, which provides a perfect hardware environment for the digital campus. The experimental results show that based on the Internet of Things technology, through the perception of classroom interaction and practical activities, learning is no longer a process of mechanical memory of book knowledge, but a process of students constantly discovering and exploring problems and then solving problems in the process of practice. Among 84 classroom teaching behaviors, classroom interaction accounted for 69.05% of the total classroom teaching activities. In the “computer + projection” classroom environment, the proportion of teacher-student interaction in classroom teaching activities is 40.47%. Teachers can focus on the design of teacher-student interaction according to the characteristics of students and learners, avoid long-term theoretical explanations, and intersperse interactive links such as question and answer and request answers.

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Section: D Printer With the Development Of Technology 3dmentioning

confidence: 99%

Construction of Perceptual Classroom Based on Internet of Things Technology

2022

Mobile Information Systems

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“…In recent years, the development scale of service computing model has gradually expanded and has been widely used in various fields. The rapid progress of distributed computing, virtualization, and parallel computing technology has promoted the development of cloud technology [1,2]. At the service model level, cloud computing is usually divided into software as a service (SaaS), platform as a service (PaaS), and IaaS.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Deployment Algorithm for IaaS Cloud Virtual Machines Based on Q Learning Mechanism

Chen

2021

Journal of Sensors

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When deploying infrastructure as a service (IaaS) cloud virtual machines using the existing algorithms, the deployment process cannot be simplified, and the algorithm is difficult to be applied. This leads to the problems of high energy consumption, high number of migrations, and high average service-level agreement (SLA) violation rate. In order to solve the above problems, an adaptive deployment algorithm for IaaS cloud virtual machines based on Q learning mechanism is proposed in this research. Based on the deployment principle, the deployment characteristics of the IaaS cloud virtual machines are analyzed. The virtual machine scheduling problem is replaced with the Markov process. The multistep Q learning algorithm is used to schedule the virtual machines based on the Q learning mechanism to complete the adaptive deployment of the IaaS cloud virtual machines. Experimental results show that the proposed algorithm has low energy consumption, small number of migrations, and low average SLA violation rate.

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“…Real-time communication is also essential for the timely dissemination of earthquake alerts, and various communication technologies, including satellite and wireless networks, are used to transmit sensor data and alerts to processing centers and end-users [155]. In addition, the integration of EEWS systems with IoT and cloud infrastructure can provide scalability, fault-tolerance, and data processing capabilities [156]. IoT devices, such as accelerometers and GPS sensors, can provide additional data sources for earthquake monitoring and analysis.…”

mentioning

confidence: 99%

Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

Abdalzaher,

Krichen,

Yiltas-Kaplan

et al. 2023

Sustainability

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Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems.

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Hybrid Seismic-Electrical Data Acquisition Station Based on Cloud Technology and Green IoT

Cited by 11 publications

References 36 publications

Construction of Perceptual Classroom Based on Internet of Things Technology

Construction of Perceptual Classroom Based on Internet of Things Technology

An Adaptive Deployment Algorithm for IaaS Cloud Virtual Machines Based on Q Learning Mechanism

Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

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