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Future Mobile Learning 19

Future Mobile Learning

引用

19th ieee international conference on Emerging eLearning Technologies and Applications, ICETA 2021

作者： Campanella, P. University of Bari 'Aldo Moro' Department of Computer Science Via Orabona Bari4-70126 Italy

ISBN: (纸本)9781665421027

Mobile computing requires new challenges and an approach for solving them by developing information systems. A number of alternative approaches to mobile computing access have emerged during the last two decades. Given the diverse nature of the mobile domain in its entirety, it is obvious that no single technique will be suitable in all circumstances. In the field of architecture solutions, the prominent approach is service oriented. Indeed mobile environments are intrinsically asynchronous, distributed and based on services where one component can supply to whichever other entity on the web can be interested into. We propose a system based on mobile devices and distributed. the web-based architecture allows to query, retrieve, correlate and display heterogeneous informative contents taken from legacy enterprise applications. the enterprise application receives rough data from physical sensors geographically spread around. Design solution will cover only the client application running on the user's mobile device and the application business logic level, while no consideration will be made about enterprise application architectures, types of DBMS used and types and configuration of physical devices. the most appropriate architectural models in this context are: distributed, agent-based, service-oriented, component-based. © 2021 ieee.

关键词： Mobile computing

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distributed Power System Simulation and Monitoring Supported by Cloud Technology : A grid optimization scheduling method based on MADRL

Distributed Power System Simulation and Monitoring Supported...

引用

Energy, Power and Electrical Engineering (EPEE), international conference on

作者： Hui Luo Jie Zhang Yufeng Hu Jingyue Zhang Haiping Guo Libin Huang China Southern Power Grid CSG Key Laboratory for Power System Simulation Electric Power Research Institute Guangzhou China China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

this study explores the optimization of distributed Generation systems (DGS) within a Multi-Agent Deep Reinforcement Learning (MADRL) framework. the focus is on transforming the overall optimization goal of the system into a global reward function and designing local rewards adapted to heterogeneous agents. Using the ieee 33 node system as a case study, the impact of power aggregation and load management from photovoltaic systems, energy storage systems, electric vehicle charging facilities, and HVAC systems is analyzed. the results demonstrate that the proposed edge-cloud collaborative framework optimizes resource allocation and scheduling. the cloud platform is capable of integrating data from diverse sources and managing it in a unified manner, thereby coordinating the operations of multiple heterogeneous agents. the scalability and flexibility of cloud technology allow for the dynamic adjustment of computational resources according to demand, ensuring global optimization and the judicious allocation of resources.

关键词： Photovoltaic systems HVAC Processor scheduling System performance Collaboration Deep reinforcement learning Smart grids Resource management Vehicle dynamics Energy storage

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Mobile Networks and Wireless Communications (ICMNWC), ieee international conference on

作者： Shruthi Bennur Mallikarjuna Channakrishna Raju Department of Computer Science and Engineering SJCE JSS Science and Technological University Mysuru India Department of Artificial Intelligence and Machine Learning Sri Siddhartha Institute of Technology SSAHE Tumakuru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Wireless sensor Networks (WSN) are designed to monitor sensor nodes systems, with a focus on data processing, collection and transmission. Developments in wireless communication have extended the scope of WSNs to various domains such as underwater communication and Internet of things (IoT). However, secure clustering and routing remain challenging due to increased vulnerability to attacks in IoT-WSN, which negatively impact communication and data integrity. this research proposes Similarity Preference Weight-Chimp Optimization Algorithm (SPW-ChOA) for secure clustering and routing in WSN-IoT. Trust, node degree, distance between Cluster Head (CH) and Base Station (BS), energy and distance are employed as fitness functions for Secure CH (SCH) selection. the factors considered for routing in SPW-ChOA include energy and distance. the performance of SPW-ChOA is evaluated in terms of residual energy, Packet Delivery Ratio (PDR), throughput and energy consumption, with the number of nodes varied at 500,1000,1500 and 2000. the proposed SPW-ChOA consumes lesser energy at 0.28 which compared to the existing Butterfly Optimization Algorithm (BOA) and Antlion Optimization Algorithm (AOA).

关键词： Wireless communication Wireless sensor networks Underwater communication Energy consumption Clustering algorithms Routing throughput Internet of things Optimization Monitoring

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U-shaped Dual Attention Transformer: An Efficient Transformer Based on Channel and Spatial Attention

U-shaped Dual Attention Transformer: An Efficient Transforme...

引用

Artificial Intelligence, Robotics, and Communication (ICAIRC), ieee international conference on

作者： Zhaoyuan Zhai Peng Qiao Rongchun Li Zhen Zhou National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Transformer-based methods have demonstrated remarkable performance on image super-resolution tasks. Due to high computational complexity, researchers have been working to achieve a balance between computation costs and performance. Restormer has achieved commendable balance by utilizing global channel attention. However, the performance is limited by insufficient local pixel reconstruction. In this paper, we propose a U-shaped Dual Attention Transformer (UDAT) with local-global receptive field, addressing the limitation of Restormer in local pixel reconstruction. We propose a dense window channel attention to enhance the local feature representation, more efficient in computational complexity. Experiments demonstrate that our UDAT achieve superior performance compared with Restormer on benchmark datasets, surpassing Restormer by 0.57 dB on the Urban100 dataset. On-par with SwinIR, our method reduces computational complexity by 3.2 times and improves inference speed by 2 times, achieving a better balance between computational costs and performance.

关键词： Visualization Superresolution Noise reduction Transform coding Transformers Image restoration Computational complexity Spatial resolution Image reconstruction Robots

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Accurate Prediction Model of Greenhouse sensor Data based on Deep Learning Algorithm

Accurate Prediction Model of Greenhouse Sensor Data based on...

引用

Mobile Networks and Wireless Communications (ICMNWC), ieee international conference on

作者： Haotian Cai Xiaolong Chuai College of Electrical and Information Engineering JiLin Agricultural Science and Technology University Jilin China

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Traditional greenhouse environmental monitoring systems mostly rely on simple algorithms for data processing. When faced with complex environmental factors, these methods often cannot accurately capture the temperature and humidity characteristics in the data, resulting in limited prediction accuracy. this article adopts a precise prediction model for greenhouse sensor data that combines Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) networks, aiming to improve prediction accuracy and the intelligence level of data analysis. the spatial features extracted by CNN are then fed into an LSTM network. the noise increased from 0% to 25%, and the accuracy of temperature prediction decreased from 90% to 72.82%, while the humidity dropped sharply from 88% to 58.62%. the research results indicate that the application of deep learning (DL) technology provides an efficient and innovative method for greenhouse environment monitoring, significantly enhancing the intelligence level and effectiveness of agricultural management.

关键词： Deep learning Accuracy Computational modeling Green products Humidity Predictive models Prediction algorithms Data models Convolutional neural networks Long short term memory

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Study on Aerodynamic Characteristics of distributed Propulsion System (DPS) 4

Study on Aerodynamic Characteristics of Distributed Propulsi...

引用

4th ieee Pune Section international conference, PuneCon 2021

作者： Bhalerao, Tathagat Bhatt, Divyansh Darekar, Ajinkya Bajaj, Dineshkumar MIT School of Engineering Department of Aerospace Engineering Loni Kalbhor Pune412-202 India

ISBN: (纸本)9781665444798

the distributed propulsion system is a recent year concept used to improve Flight performance. distributed propulsion (DP) is a type of propulsion system in which thrust is delivered by multiple propellers arranged on the wings leading edge to improve Aircraft's aerodynamic performance and decrease fuel consumption, emissions, noise, landing distance, and for better maneuverability. Several studies have reported on distributed propulsion systems but it introduces a lot of information regarding electric propulsion and development of advanced propulsion systems, Distortion of flow inside propeller blade and challenges of a Working distributed propulsion mechanism for the future Electric driven passenger and general aviation aircrafts but there was no special study happened on the comparison of DPS aircraft with any typical type of Aircraft. In this project, we compared the aerodynamic characteristics of DPS aircraft with Normal typical Aircraft. the Computational Fluid Dynamics simulation were carried out in Fluent. the meshing is Done in the Hypermesh. the study results are, for Aircraft with distributed propulsion system, the L/D is 2.922, and for Aircraft, without distributed propulsion system, L/D is 1.64 this study compared and shown that DPS aircraft is beneficial over other typical Aircraft in terms of Lift to Drag ratio as aerodynamics characteristic. © 2021 ieee.

关键词： Angle of attack

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distributed and Energy Balanced Routing for Heterogeneous Wireless sensor Network 4th

Distributed and Energy Balanced Routing for Heterogeneous Wi...

引用

4th international conference on Smart computing and Informatics, SCI 2020

作者： Bhasgi, Shivani S. Terdal, Sujatha Poojya Dodappa Appa College of Engineering Gulbarga Karnataka India

ISBN: (纸本)9789811615016

Wireless sensor networks are energy constraint. To minimize the energy usage, data is forwarded through short paths leading to uneven energy usage and causing network partitioning. Hence, energy balancing should also be considered along with energy consumption. Recently, energy harvesting nodes have gained popularity in solving the limited battery life of sensors. Energy harvesting nodes harvest energy from solar, wind, etc. and replenish their battery. But harvesting is sporadic and costly. Hence, in this paper, we propose a hybrid network with both energy harvesting and normal nodes. A distributed routing protocol considering energy usage and balancing is formed along with fault tolerance. Energy density, distance between the nodes and residual energy, is considered to choose relay nodes. Network is rerouted if any node failure is detected in the routing path. the proposed algorithm is compared with existing method. Results show that proposed DEBH performs efficiently when compared with previous method. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy harvesting

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Efficient Real-Time Human Activity Recognition with IoT sensor Data Using Conditionally Parametrized Convolutions

Efficient Real-Time Human Activity Recognition with IoT Sens...

引用

international conference on ICT in Business Industry & Government (ICTBIG)

作者： Mayank Lovanshi Vivek Tiwari Anurag Singh Rajesh Ingle Department of Computer Science Engineering International Institute of Information Technology Naya Raipur India Department of Computer Science Engineering ABV-Indian Institute of Information Technology Gwalior India Department of Electronics Communication Engineering International Institute of Information Technology Naya Raipur India Department of Computer Science Engineering Symbiosis Skills of Professional University Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Human Activity Recognition (HAR) is a trading area in computer vision and deep learning. However, boosting the performance of deep learning models often necessitates increasing their size or capacity, which raises computational demands. the high operational costs present challenges for real-time HAR on sensor-based devices. Shallow learning approaches, while computationally efficient, generally fail to deliver comparable performance. Consequently, there is a critical need for deep learning methods that can effectively balance accuracy and computational cost, a topic that remains largely underexplored. this paper introduces a computationally efficient CNN that utilizes conditionally parametrized convolution, designed specifically for real HAR on sensor-based devices. the model is tested on the OPPORTUNITY dataset, achieving existing accuracy without increasing computational load. through extensive ablation studies, we show that this high-capacity network surpasses baseline models while maintaining a similar level of computational efficiency. the proposed method can directly replace existing deep HAR frameworks and be easily implemented on sensor-based platforms for real-time applications.

关键词： Performance evaluation Deep learning Accuracy Costs Convolution Computational modeling Real-time systems Computational efficiency Human activity recognition Load modeling

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Optimizing Resource Allocation in Edge-distributed Stream Processing 17

Optimizing Resource Allocation in Edge-distributed Stream Pr...

引用

17th international conference on Web Information systems and Technologies (WEBIST)

作者： Rocha Neto, Aluizio Silva, thiago P. Batista, thais, V Lopes, Frederico Delicato, Flavia C. Pires, Paulo E. Fed Univ RN UFRN Dept Informat & Appl Math BR-59078970 Natal RN Brazil Fluminense Fed Univ UFF Comp Sci Dept BR-24220900 Niteroi RJ Brazil

ISBN: (纸本)9789897585364

Emerging Web applications based on distributed IoT sensor systems and machine intelligence, such as in smart city scenarios, have posed many challenges to network and processing infrastructures. For example, environment monitoring cameras generate massive data streams to event-based applications that require fast processing for immediate actions. Finding a missing person in public spaces is an example of these applications, since his/her location is a piece of perishable information. Recently, the integration of edge computing with machine intelligence has been explored as a promising strategy to interpret such massive data near the sensor and reduce the end-to-end latency of processing events. However, due to the limited capacity and heterogeneity of edge resources, the placement of task processing is not trivial, especially when applications have different quality of service (QoS) requirements. In this paper, we develop an algorithm to solve the optimization problem of allocating a set of nodes with sufficient processing capacity to execute a pipeline of tasks while minimizing the operational cost related to latency and energy and maximizing availability. We compare our algorithm with the resource allocation algorithms (first-fit, best-fit, and worst-fit), achieving a lower cost in scenarios with different nodes' heterogeneity. We also demonstrate that distributing processing across multiple edge nodes reduces latency and energy consumption and still improves availability compared to processing only in the cloud.

关键词： Internet of things Technical Infrastructure for Services New Trends in Internet Technology

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Hybrid Quantum-Classical Machine Learning for Near Real-time Space to Ground Communication of ISS Lightning Imaging sensor Data

Hybrid Quantum-Classical Machine Learning for Near Real-time...

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ieee Annual computing and Communication Workshop and conference (CCWC)

作者： Samih Fadli Bharat S Rawal Computer Science Department Capitol Technology University

Near real-time sensor data is growing exponentially faster than our ability to sense and make sense of it as the current classical computing and supercomputing approaches require an enormous volume of computational resources, storage, and training time. Quantum computing is posed to exponentially outperforms today's high-performance computers and accelerate the evolution of information occurring in classical systems and sensor networks. the Lightning Imaging sensor (LIS) mounted on the international Space Station (ISS) locates, senses, and detects lightning activities from low Earth orbit and measures radiant energy at millisecond timing over a broad regional spectrum. In this paper, we are introducing a space-to-ground hybrid quantum-classical machine learning architecture to demonstrate the application potential and the feasibility of Hybrid Quantum Neural Networks using the ISS LIS lightning dataset and a corresponding background dataset as inputs (1) to train a classical deep neural network model (2) where the feature extraction outputs are encoded as quantum states using multiple calibrated quantum encoding patterns (3) then used as part of a quantum feature mapping process, (4) allowing for enhanced hybrid QNN training, optimization, and auto-differentiation, setting the path towards polynomial advantage. the following is how the rest of the paper is structured: the introduction is covered in section I, and the motivation and our contribution are covered in section II. Section III presents related works. Section IV describes the NRT lightning imaging sensor. Section V describes the space- to-ground hybrid quantum architecture. Section VI. Space-to-ground hybrid quantum-classical machine learning architecture. Section VII describes quantum machine learning. Section VIII presents a performance analysis. Finally, section XI concludes the research paper.

关键词： Training Quantum computing Neural networks Lightning Imaging Computer architecture Quantum state

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