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Evaluation of problems and countermeasures in marketing teaching based on cloud computing data aggregation algorithm

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SN APPLIED SCIENCES 2023年第5期5卷 1-12页

作者： Shen, Weinan Suzhou Vocat Univ Sch Management Suzhou 215000 Jiangsu Peoples R China

In this study, we present a highly practical marketing course. Through learning marketing, we can cultivate students' ability to analyze, solve and practice problems. Therefore, this paper would study the problems encountered in the marketing course through cloud computing technology and propose corresponding countermeasures, so as to better improve students' ability to analyze and solve problems and enhance the effectiveness of the classroom. This paper also analyzed the data aggregation algorithm based on Particle Swarm Optimization (PSO) algorithm. The results showed that when other conditions were the same, only 6 students approve of the traditional marketing teaching mode, accounting for 12%;there were 44 people who did not approve, accounting for 88%. This number was more than half;49 students recognized the optimized marketing teaching mode, accounting for 98%;there was 1 person who did not approve, accounting for 2%. The proportion of people who did not approve after optimization was far less than that of the traditional model, which showed that cloud computing data aggregation algorithm could find out the problems in marketing teaching and provide correct countermeasures to solve them, so that the teaching model could be optimized. (1) The data aggregation algorithm based on cloud computing has a good optimization effect on the evaluation of marketing teaching problems and countermeasures, indicating a new direction for future research. (2) The combination of cloud computing and data aggregation algorithm is not only a novel research perspective, but also a perfect idea. (3) Marketing course is a very important course in today's society. The study of its teaching problems and countermeasures evaluation will help students improve their learning efficiency and enhance their ability to adapt to the society.

关键词： Marketing teaching Cloud computing data aggregation algorithm Analysis of problems and countermeasures Particle swarm optimization algorithm

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Exploration on load balancing data aggregation algorithm in wireless sensor network based on big data artificial intelligence

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INTERNET TECHNOLOGY LETTERS 2024年第2期7卷

作者： Jiang, Huilong Hunan Vocat Coll Sci & Technol Sch Software Changsha 410118 Peoples R China Hunan Vocat Coll Sci & Technol Sch Software Changsha 410004 Hunan Peoples R China

Based on BD-AI(Big data Artificial Intelligence) technology, an efficient LB data collection algorithm was designed and implemented for experimental verification and performance evaluation. The results showed the average load of algorithms A and B tested five times was between 44-48 and 60-64, respectively;the algorithms A and C were tested five times with data transmission speeds between 10-11Mbps and 4-5Mbps, respectively. The LB data aggregation algorithm based on BD-AI in WSN is more efficient than traditional LB algorithms and traditional data aggregation algorithms. It can effectively help solve problems such as imbalanced data exchange, data redundancy, and energy waste among wireless sensor nodes, thereby improving the data collection efficiency and energy conservation of sensor networks, laying the foundation for implementing application scenarios such as intelligent perception and remote monitoring. The load balancing data aggregation algorithm of wireless sensor networks based on Big data artificial intelligence is faced with some challenges and difficulties. For example, wireless sensor networks usually produce a large amount of data, which needs real-time aggregation and processing;Nodes in wireless sensor networks typically have limited computing, storage, and communication resources, among other issues.

关键词： artificial intelligence big data data aggregation algorithm load balancing wireless sensor network

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ADA2 - IoT : An adaptive data aggregation algorithm for IoT infrastructure

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INTERNET OF THINGS 2024年 27卷

作者： Chaudhary, Ajay Peddoju, Sateesh K. Indian Inst Technol Roorkee Dept Comp Sci & Engn Haridwar 24776 UK India

In IoT infrastructure, high-frequency sensing and subsequent transmission of sensed data to computational facilities can lead to redundant data storage and processing, consuming significant storage and processing capacity. As a result, the IoT infrastructure needs more data transmission cycles, leading to data redundancy and low network up-time due to the drainage of limited battery capacity. Conversely, if the data is communicated at a lower rate, it may cause absolute data delivery to the processing unit, which is useless. As a result, a well-designed data aggregation algorithm is required. This paper proposes the ADA2 2 - IoT, , an Adaptive data aggregation algorithm for IoT Infrastructure tailored to optimize parameters such as low data redundancy, limited data communication cycles, and high IoT infrastructure up times. The proposed algorithm consists of two key components: the Route data Aggregator (RDA) performs aggregation during data transit towards the Edge node or gateway, and the Node data Aggregator (NDA) performs data aggregation during capturing or sensing data. The algorithm employs metrics like Age of Information (AoI) and data freshness factor during the node and route data aggregation phase to capture and timely deliver data to the Edge node, where this data is processed for informed decision-making. The proposed algorithm was efficiently tested on a simulation and IoT hardware deployment environment. Both simulation and hardware results demonstrate a substantial improvement in QoS parameters, such as a decrease in data redundancy and packet exchanges, leading to considerable energy savings and prolonging the lifespan of IoT infrastructure.

关键词： data aggregation data aggregation algorithm data aggregation protocol Age of information (AoI) Cluster-based aggregation Internet of Things Quality of service Wireless sensor network IoT infrastructure IoT applications Routing Energy-efficiency

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Spatial feature analysis of rural basic public service supply based on TOPSIS model and data aggregation algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5623-5634页

作者： Cui, Li Zhou, Huiqiu Northeast Agr Univ Harbin 150030 Heilongjiang Peoples R China

The provision of basic public services in rural areas is a necessary condition for promoting farmers, agriculture, and rural development, and has important economic effects. The supply of basic public services in rural areas has an impact on agricultural growth, rural poverty reduction, and farmers' production and investment, and ultimately directly or indirectly affect farmers' income and consumption. In order to effectively improve the effectiveness of providing basic public services in rural areas, this study takes rural basic public services as the research object, and analyzes the economic and geographic perspectives, models, and algorithms of supply effectiveness through a literature review. It was found that the research area level needs to be deepened, and the shortcomings of traditional models and algorithms were also found. This article will make full use of the TOPSIS model's methodological advantages in comprehensive ranking, combine the entropy method with the TOPSIS model, and sort the evaluation objects by approaching the optimal solution, so as to more objectively evaluate the rural basic public service supply in the sample counties. Based on the data aggregation algorithm, with the help of spatial analysis methods, Gini coefficient, and Theil index to further study the spatial distribution characteristics of supply. The research shows that the method has good performance and can be used as a reference for the subsequent related research rural basic services theory.

关键词： TOPSIS model data aggregation algorithm rural basic public services spatial analysis

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Towards Secure and Efficient data aggregation in Blockchain-Driven IoT Environments: A Comprehensive and Systematic Study

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第2期36卷

作者： Tong, Xujun Hamzei, Marzieh Jafari, Nima Anhui Med Coll Publ Basic Coll Hefei Peoples R China Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu Yunlin Taiwan

The rapid evolution of the Internet of Things (IoT) has revolutionized various sectors, fostering seamless intercommunication and real-time monitoring. Central to this transformation is integrating blockchain technology, which ensures data integrity and security in IoT networks. This paper provides a meticulous exploration of data aggregation techniques within the context of blockchain-based IoT systems. The study categorizes data aggregation algorithms into Privacy-Preserving, Machine Learning-Based, Hierarchical, Real-Time, and Custom aggregation algorithms, each tailored to specific IoT requirements. Privacy-Preserving aggregation algorithms focus on safeguarding sensitive data through encryption and secure protocols. Machine Learning-Based aggregation adapts dynamically to data patterns, offering predictive insights and real-time adaptability. Hierarchical aggregation organizes devices into a structured hierarchy, optimizing data processing. Real-Time aggregation processes data instantly, ensuring low latency for time-sensitive applications. Custom aggregation algorithms are bespoke solutions tailored to unique application demands, emphasizing efficiency and security. Through a comparative analysis of these techniques, this paper explores their advantages, disadvantages, and applicability, addressing the challenges and suggesting future research directions. The integration of blockchain-based data aggregation techniques not only enhances IoT network efficiency but also ensures the longevity and security of modern technological infrastructures. This study builds upon prior research in the field of IoT and blockchain technology by extending the exploration of data aggregation techniques and their implications for network efficiency and security. SLR method has been used to investigate each one in terms of influential properties such as the main idea, advantages, disadvantages, and strategies. The results indicate most of the articles were published in 2021 and 202

关键词： blockchain custom aggregation algorithms data aggregation data aggregation algorithm hierarchical aggregation Internet of Things (IoT) machine learning-based privacy-preserving algorithms real-time aggregation

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The algorithm for Classification and Determination of the Spatial Position of Moving Objects

The Algorithm for Classification and Determination of the Sp...

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IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)

作者： Romanov, Alexey M. Volkova, Maria A. MIREA Russian Technol Univ Control Problems Dept Inst Cybernet Moscow Russia

ISBN: (纸本)9781728103396

The paper discusses the task of detecting and determining the spatial position of moving objects according to their dynamic characteristics and with using the aggregation of the sensors of robotic systems. A model of a generalized sensor and classifier according to its indications is proposed. An algorithm for the results of classification processing and determining the spatial position of objects is developed.

关键词： sensors object matching algorithm data aggregation algorithm object position

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Dual data aggregation for Power Quality Assessment

Dual Data Aggregation for Power Quality Assessment

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Albu, Mihaela Univ Politehn Bucuresti Bucharest Romania

ISBN: (纸本)9781467363860

The increased use of distributed energy resources and the emerging active distribution networks require new models for planning and operation of power systems. Microgrids paradigm proposes an optimal energy transfer using dedicated control algorithms and taking advantage from the ability to offer different levels of power quality. In order to achieve a real time control, a detailed model is required together with new algorithms for information retrieval. Although measurements are able to give insight to such details, the information has to be structured in a new way. Presently there are in use aggregation algorithms inherited from power quality standards like the IEC 61000-4-30 set. A discussion of the time-and space- (i.e. dual) aggregation is proposed in the paper, based on an analysis performed on local measurements in weak LV grid in Romania and MV grid with distributed generation. data delivered by PMUs with a reporting rate of 50 frames per second is processed to highlight the effect of aggregation algorithms.

关键词： Statistical analysis power quality phasor measurement units voltage measurements data aggregation algorithm data concentrators

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Measurement data aggregation for Active Distribution Networks

Measurement Data Aggregation for Active Distribution Network...

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4th IEEE International Workshop on Applied Measurements for Power Systems (AMPS)

作者： Calin, Mihai Dumitrescu, Ana Maria Asprou, Markos Kyriakides, Elias Albu, Mihaela Univ Politehn Bucuresti Bucharest Romania Univ Cyprus Nicosia Cyprus

ISBN: (纸本)9781467355711

Power Quality indicators are still conveyed into a classical paradigm of power systems where the steady state operation is governed by quasi-constant frequency. Moreover, distribution grids are considered as network structures with solely load behavior. The standards proposing power quality descriptors are built on models, which seem to limit the possibilities of achieving real time control targeting highly efficient energy transfer, at least in all-electric grids. In this paper the measurement data aggregation algorithms proposed in the well-known standard IEC 61000-4-30 are discussed based on an analysis performed on voltages and frequency measurements. Raw data are obtained from PMUs installed in various nodes of active distribution grids with reporting rate of 50 frames per second. New aggregation algorithms for frequency, rate of change of frequency and unbalance coefficient appear to be needed.

关键词： Statistical analysis power quality phasor measurement units voltage measurements root mean square data aggregation algorithm

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Dynamic Event data aggregation in Wireless Sensor Networks

Dynamic Event Data aggregation in Wireless Sensor Networks

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IEEE International Conference on Mechatronics and Automation

作者： Chen, Wanming Mei, Tao Liu, Yumei Meng, Max Q-H. Liang, Huawei Chinese Acad Sci Inst Intelligent Machines Hefei Anhui Peoples R China Univ Sci & Technol China Dept Automat Hefei Anhui Province Peoples R China Chinese Univ Hong Kong Dept Elect Engn Sha Tin Hong Kong Peoples R China Univ Sci & Technol China Dept Comp Sci & Technol Hefei Peoples R China

ISBN: (纸本)9781424426317

data aggregation is an efficient method to decrease the energy consumption in wireless sensor networks. The structure based data aggregation algorithm is not suitable for the application of dynamic events, because the structured sensor network requires higher communication overheads to capture the event when the data sources are changing dynamically. We propose an energy efficient data aggregation algorithm utilized for dynamic event in wireless sensor networks. In this algorithm, the sensor nodes utilize the advantage of wireless channel that the energy consumption for broadcasting is approximately equivalent to that for sending to just one neighbor. data are aggregated among nodes by one time broadcasting. Geography based routing method is used in data aggregation and transmit the data to the remote sink node. Simulation results show that the proposed data aggregation algorithm can decrease the maximum and mean energy consumption of sensor nodes highly in dynamic events.

关键词： Wireless sensor networks data aggregation algorithm dynamic event

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