VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-28T18:00:44ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2021-11-16T04:38:13Z2021-11-16T04:38:13Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4648This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/46482021-11-16T04:38:13ZSimulation of precision feeding systems for swinePrecision livestock farming has become an inevitable trend for agricultural industry in the world. In that field, precision feeding is widely acknowledged because of its potential to reduce feed costs, environmental footprint and to improve animal health and welfare. Precision feeding involves modern multidisciplinary technologies such as information technology, mechanics, electronics, automation, etc. Such a system consists of automatic troughs linked to a computer system to exploit data collected from the individual animals (e.g. body weight, feed intake and feeding behaviour), and/or from ambient sensors. Data is processed and analysed based on mathematical models to make predictions, warnings for farmers or to formulate diets that fit requirements of each individual animal at each production period. However, implementing such a system often requires high investment, which may go beyond the capabilities of smallholders and small/medium laboratories. Furthermore, the risk of implementing by design but not conforming to reality is very high. To avoid this problem, we introduce an agent-based modelling approach to simulate precision feeding systems for swine. Two simulation experiments were conducted to provide predictions about the growth of individual pigs and the usefulness of precision feeding systems over classic feeding models.Manh Linh Phamlinhmp@vnu.edu.vnBa Hieu Nguyennbhieu@vnua.edu.vnHoai Son Nguyensonnh@vnu.edu.vnHuy Hàm Lêlhham@agi.ac.vn2021-10-15T02:47:39Z2021-10-15T02:47:39Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4617This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/46172021-10-15T02:47:39ZTowards a Framework for High-Performance Simulation of Livestock Disease Outbreak: A Case Study of Spread of African Swine Fever in VietnamThe spread of disease in livestock is an important research topic of veterinary epidemiology because it provides warnings or advice to organizations responsible for the protection of animal health in particular and public health in general. Disease transmission simulation programs are often deployed with different species, disease types, or epidemiological models, and each research team manages its own set of parameters relevant to their target diseases and concerns, resulting in limited cooperation and reuse of research results. Furthermore, these simulation and decision support tools often require a large amount of computational power, especially for models involving tens of thousands of herds with millions of individuals spread over a large geographical area such as a region or a country. It is a matter of fact that epidemic simulation programs are often heterogeneous, but they often share some common workflows including processing of input data and execution of simulation, as well as storage, analysis, and visualization of results. In this article, we propose a novel architectural framework for simultaneously deploying any epidemic simulation program both on premises and on the cloud to improve performance and scalability. We also conduct some experiments to evaluate the proposed architectural framework on some aspects when applying it to simulate the spread of African swine fever in Vietnam.Manh Linh Phamlinhmp@vnu.edu.vnNikos Parlavantzasnikos.parlavantzas@irisa.frHuy Hàm Lêlhham@agi.ac.vnQuang Hung Buihungbq@vnu.edu.vn2021-09-15T02:50:14Z2021-09-15T02:50:14Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4605This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/46052021-09-15T02:50:14ZTowards an Elastic Fog Computing Framework for IoT Big Data Analytics ApplicationsIoT applications have been being moved to the cloud during the last decade in order to reduce operating costs and provide more scalable services to users. However, IoT latency-sensitive big data streaming systems (e.g., smart home application) is not suitable with the cloud and needs another model to fit in. Fog computing, aiming at bringing computation, communication, and storage resources from “cloud to ground” closest to smart end-devices, seems to be a complementary appropriate proposal for such type of application. Although there are various research efforts and solutions for deploying and conducting elasticity of IoT big data analytics applications on the cloud, similar work on fog computing is not many. This article firstly introduces AutoFog, a fog-computing framework, which provides holistic deployment and an elasticity solution for fog-based IoT big data analytics applications including a novel mechanism for elasticity provision. Secondly, the article also points out requirements that a framework of IoT big data analytics application on fog environment should support. Finally, through a realistic smart home use case, extensive experiments were conducted to validate typical aspects of our proposed framework.Manh Linh Phamlinhmp@vnu.edu.vnTruong Thang Nguyenntthang@ioit.ac.vnTien Quang Hoanghoangtienquang@hpu2.edu.vn2021-06-28T02:34:13Z2021-06-28T02:34:13Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4536This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/45362021-06-28T02:34:13ZAir pollution in Vietnam during the COVID-19 social isolation, evidence of reduction in human activitiesTruong X. NgoNgoc T.N. DoHieu D.T. PhanVinh T. TranTra T.M. MacAnh H. LeNguyet V. DoHung Q. BuiThanh T.N. Nguyen2021-05-15T02:49:55Z2021-05-15T02:49:55Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4443This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/44432021-05-15T02:49:55ZElasticity for MQTT Brokers in IoT ApplicationsMany domains of human life are more and more impacted by applications of the Internet of Things (i.e., IoT). The embedded devices produce masses of data day after day requiring a strong network infrastructure. The inclusion of messaging protocols like MQTT is important to ensure as few errors as possible in sending millions of IoT messages. This protocol is a great component of the IoT universe due to its lightweight design and low power consumption. Distributed MQTT systems are typically needed in actual application environments because centralized MQTT methods cannot accommodate a massive volume of data. Although being scalable decentralized MQTT systems, they are not suited to traffic workload variability. IoT service providers may incur expense because the computing resources are overestimated. This points to the need for a new approach to adapt workload fluctuation. Through proposing a modular MQTT framework, this article provides such an elasticity approach. In order to guarantee elasticity of MQTT server cluster while maintaining intact IoT implementation, the MQTT framework used off-the-shelf components. The elasticity feature of our framework is verified by various experiments.Manh Linh Phamlinhmp@vnu.edu.vnTien Quang Hoanghoangtienquang@hpu2.edu.vnXuan Truong Nguyennguyenxuantruong@hpu2.edu.vn2021-04-15T01:45:04Z2021-04-15T01:45:04Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4434This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/44342021-04-15T01:45:04ZAn Elasticity Framework for Distributed Message Queuing
Telemetry Transport BrokersInternet of Things (IoT) applications are increasingly making impact in all areas of human life. Day by day, its chatty embedded devices have been generating tons of data requiring effective network infrastructure. To deliver millions of IoT messages back and forth with as few faults as possible, participation of communication protocols like Message Queuing Telemetry Transport (i.e., MQTT) is a must. Lightweight blueprint and battery friendly design are just two of many advantages of this protocol making it become a dominant in IoT world. In real application scenarios, distributed MQTT solutions are usually required since centralized MQTT approach is incapable of dealing with huge amount of data. Although distributed MQTT solutions are scalable, they do not adapt to fluctuations of traffic workload. This might cost IoT service providers because of redundant computation resources. This leads to the need of a novel approach that can adapt its volume changes in workload. This article proposes such an elastic solution by proposing a flexible MQTT framework. Our MQTT framework uses off-the-shelf components to obtain server’s
elasticity while keeping IoT applications intact. Experiments are conducted to validate elasticity function provided by an implementation of our framework.Manh Linh Phamlinhmp@vnu.edu.vnXuan Tung Hoangtunghx@vnu.edu.vn2021-03-15T04:23:03Z2021-03-15T04:23:03Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4374This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/43742021-03-15T04:23:03ZBig Data Analytics and Machine Learning for Industry 4.0: An OverviewThe concept of “Big data” was mentioned for the first time by Roger Mougalas in 2005. Volume hints to the size and/or scale of datasets. Until now, there is no universal threshold for data volume to be considered as big data, because of the time and diversity of datasets. Velocity indicates the speed of processing data. It can fall into three categories: streaming processing, real-time processing, or batch processing. Value alludes to the usefulness of data for decision making. Veracity denotes the quality and trustworthiness of datasets. Parallelization allows one to improve computation time by dividing big problems into smaller instances, distributing smaller tasks across multiple threads and then performing them simultaneously. Feature selection is useful for preparing high scale datasets. Sampling is a method for data reducing that helps to derive patterns in big datasets by generating, manipulating, and analyzing subsets of the original data.Nguyen Tuan Thanh Lethanhlnt@tlu.edu.vnManh Linh Phamlinhmp@vnu.edu.vn