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Journal articles on the topic 'Smart data management'

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Author: Grafiati

Published: 14 March 2023

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1

Gonsalves, Sneha Leleat. "Smart Traffic Management Using Data Analysis." International Journal for Research in Applied Science and Engineering Technology V, no. IX (September 30, 2017): 948–53. http://dx.doi.org/10.22214/ijraset.2017.9139.

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Aberer, Karl, Gustavo Alonso, and Donald Kossmann. "Data management for a smart earth." ACM SIGMOD Record 35, no. 4 (December 2006): 40–45. http://dx.doi.org/10.1145/1228268.1228277.

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3

Le Dinh, Thang, Nguyen Anh Khoa Dam, Chan Nam Nguyen, Thi My Hang Vu, and Nguyen Cuong Pham. "From Customer Data to Smart Customer Data: The Smart Data Transformation Process." ITM Web of Conferences 41 (2022): 05002. http://dx.doi.org/10.1051/itmconf/20224105002.

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Nowadays, smart data has emerged as a new trend in creating more business value for enterprises that is defined as the data that is gathered and processed to create new insights to support business decisions. However, the transformation from data into actionable insights is still a real challenge for enterprises. For this reason, this paper presents a smart data transformation process, which aims at transforming customer data into smart customer data in order to offer actionable insights. The purpose of the study is to propose a transformation process that can be used to operate a knowledge structure for a smart service system, which can manage and deliver smart data as a service. The process covers the three dimensions of a service system: Data processing corresponding to the engineering dimension, information processing corresponding to the science dimension, and knowledge processing corresponding to the management dimension for knowledge processing. Accordingly, a case study on the smart data transformation process of a customer journey management system as a smart service system is presented to demonstrate the application of the proposed process.

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Sanakkayala SatyaNarayana, Dr, G. V. Sai Bharath, Katakam Sri Lakshmi Sahithi, and Adusumilli Sai Rutwik. "Data Management in IOT Applications." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 224. http://dx.doi.org/10.14419/ijet.v7i2.32.15572.

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With the technology leaping towards a new phase the next big that is happening is IOT and managing the huge amount of data that is being produced. To apprehend the real Internet of Things in which the entirely is interconnected, direct interactions between sensors and actuators, also known as bindings, are essential. As more and more devices are getting connected to the internet there is a lot of data that is being generated. We need to maintain the quality of data and it should be manageable for future use. Consequently, in evaluation to subsisting studies on smart cities we give a information driven edge depicting the central information administration methodologies employed to check consistency, interoperability, granularity and re-convenience of the information created by strategies for the fundamental Internet of Things( IoT) for smart cities. We try to find the proper communication between the devices and finally try to implement the details for a system. In this paper we are trying to do survey on how the large amount of data is being stored and various strategies for handling the data by using some architectures for the smart traffic system. We are trying to use the SWIFT architecture for analyzing the traffic in smart cities.

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Amović, Mladen, Miro Govedarica, Aleksandra Radulović, and Ivana Janković. "Big Data in Smart City: Management Challenges." Applied Sciences 11, no. 10 (May 17, 2021): 4557. http://dx.doi.org/10.3390/app11104557.

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Smart cities use digital technologies such as cloud computing, Internet of Things, or open data in order to overcome limitations of traditional representation and exchange of geospatial data. This concept ensures a significant increase in the use of data to establish new services that contribute to better sustainable development and monitoring of all phenomena that occur in urban areas. The use of the modern geoinformation technologies, such as sensors for collecting different geospatial and related data, requires adequate storage options for further data analysis. In this paper, we suggest the biG dAta sMart cIty maNagEment SyStem (GAMINESS) that is based on the Apache Spark big data framework. The model of the GAMINESS management system is based on the principles of the big data modeling, which differs greatly from standard databases. This approach provides the ability to store and manage huge amounts of structured, semi-structured, and unstructured data in real time. System performance is increasing to a higher level by using the process parallelization explained through the five V principles of the big data paradigm. The existing solutions based on the five V principles are focused only on the data visualization, not the data themselves. Such solutions are often limited by different storage mechanisms and by the ability to perform complex analyses on large amounts of data with expected performance. The GAMINESS management system overcomes these disadvantages by conversion of smart city data to a big data structure without limitations related to data formats or use standards. The suggested model contains two components: a geospatial component and a sensor component that are based on the CityGML and the SensorThings standards. The developed model has the ability to exchange data regardless of the used standard or the data format into proposed Apache Spark data framework schema. The verification of the proposed model is done within the case study for the part of the city of Novi Sad.

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Mocanu, Mariana, Valentin Cristea, Ciprian Dobre, and Florin Pop. "Smart Data for ICT-based Water Management." Forum geografic XV, Suppl. 2 (December 30, 2016): 73–84. http://dx.doi.org/10.5775/fg.2016.096.s.

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Liu, Xiufeng, Alfred Heller, and Per Sieverts Nielsen. "CITIESData: a smart city data management framework." Knowledge and Information Systems 53, no. 3 (April 12, 2017): 699–722. http://dx.doi.org/10.1007/s10115-017-1051-3.

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Chi, Hao, and Yuyan Chi. "Smart Home Control and Management Based on Big Data Analysis." Computational Intelligence and Neuroscience 2022 (February 10, 2022): 1–14. http://dx.doi.org/10.1155/2022/3784756.

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In order to improve the effect of smart home control and management, a new smart home control and management method based on big data analysis is designed. The basic hardware of smart home control and management is designed, including smoke sensor hardware, temperature and humidity sensor hardware, and infrared sensor hardware, so as to collect smart home data and realize data visualization and buzzer alarm. The collected data are transmitted through the indoor wireless network of smart home gateway equipment, and the data distributed cache architecture based on big data analysis is used to store smart home data. Based on the relevant data, the hybrid particle swarm optimization algorithm is used to schedule the control and management tasks of smart home to complete the control and management of smart home. The experimental results show that the device control and scenario management effect of this method is better, and the communication performance is superior and has high practical application value.

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Montalvo, Isis. "How smart are your data?" Nursing Management (Springhouse) 44, no. 6 (June 2013): 23–24. http://dx.doi.org/10.1097/01.numa.0000430412.80830.e6.

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10

Souifi, Amel, Zohra Cherfi Boulanger, Marc Zolghadri, Maher Barkallah, and Mohamed Haddar. "From Big Data to Smart Data: Application to performance management." IFAC-PapersOnLine 54, no. 1 (2021): 857–62. http://dx.doi.org/10.1016/j.ifacol.2021.08.100.

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Silva, Bhagya, Murad Khan, Changsu Jung, Jihun Seo, Diyan Muhammad, Jihun Han, Yongtak Yoon, and Kijun Han. "Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics." Sensors 18, no. 9 (September 7, 2018): 2994. http://dx.doi.org/10.3390/s18092994.

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The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

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Astill, Jake, Rozita A. Dara, Evan D. G. Fraser, Bruce Roberts, and Shayan Sharif. "Smart poultry management: Smart sensors, big data, and the internet of things." Computers and Electronics in Agriculture 170 (March 2020): 105291. http://dx.doi.org/10.1016/j.compag.2020.105291.

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Yacouba, Yazid Hambally, Amadou Diabagaté, Abdou Maiga, and Adama Coulibaly. "Multi-agent System for Management of Data from Electrical Smart Meters." International Journal of Information Technology and Computer Science 13, no. 1 (February 8, 2021): 18–43. http://dx.doi.org/10.5815/ijitcs.2021.01.02.

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The smart meter can process sensor data in a residential grid. These sensors transmit different parameters or measurement data (index, power, temperature, fluctuation of voltage and electricity, etc.) to the smart meter. All of these measurement data can come in different ways at the smart meter. The sensors transmit each measurement data to the smart meter. In addition, the collection of this data to a central system is a significant concern to ensure data integrity and protect the privacy of residents. The complexity of these data management also lies in their volume, frequency, and scheduling. This work presents a scheduling and a collection mechanism in private power consumption data between both sensors and smart meters on one hand and between smart meters and the central data collection system on other hand. We have found several approaches to intelligent meter data management in scientific researches. We propose another approach in response to this concern for the scheduling and collection of measurement data to a central system from residential areas of sensors’ network connected to smart meters. This work is also an example of a link between data collection and data scheduling in intelligent information management, transmission, and protection. We also propose a modeling of the measurement objects of smart grid and highlight the changes made to these objects throughout the process of data processing. It should be noted that this smart grid system consists of three main active systems namely sensors, smart meters and central system. In addition to these three systems, there are other systems that communicate with the smart meters and the central system. We have identified three implementation models for the smart metering system. We also present an intelligent architecture based on multi-agent systems for the smart grid. Most current electricity management systems are not adapted to the new challenges imposed by social and economic development in Africa. The objectives of this study are to initiate the design of a smart grid system for the management of electricity data.

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Abdalla, Alaa Amin, Yousif Abdelbagi Abdalla, Akarm M. Haddad, Ganga Bhavani, and Eman Zabalawi. "Connections between Big Data and Smart Cities from the Supply Chain Perspective: Understanding the Impact of Big Data." Sustainability 14, no. 23 (December 3, 2022): 16161. http://dx.doi.org/10.3390/su142316161.

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This study explores the impact of Big Data and smart city initiatives on supply chain management. The effects of smart cities and SCM integration on sustainable development are also examined. Big Data, smart cities, and supply chain characteristics have all received a significant amount of attention (supply network structure, governance mechanisms). Based on literature reviews, we created a comprehensive model for supply chains, Big Data, and smart cities. The study concluded that smart cities have various consequences for network architecture and governmental systems. Future research directions in supply chain management and smart cities are also addressed in this paper. A comprehensive model was developed that can be used to undertake empirical research on the implications of smart cities and Big Data on supply chain management and sustainable development in the future. Big Data, smart cities, and supply chains have more than merely causal interactions, and Big Data and smart cities will hugely impact sustainable development and SCM operations. Several studies have recently examined the use of information technology in supply chains, but few have specifically addressed smart cities and Big Data, according to literature analyses.

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15

Le Dinh, Thang, and Nguyen Anh Khoa Dam. "Smart Data as a Service." ITM Web of Conferences 38 (2021): 03001. http://dx.doi.org/10.1051/itmconf/20213803001.

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Nowadays, smart data emerge as a new research direction to create value from business data in an intelligent way. Smart data are defined as the data gathered and processed that can be used to create new insights for smart solutions to support business strategies. This paper aims at proposing a conceptual model for smart data management. In other words, the model can be used for designing a smart service system based on the perspective of service science that can manage and deliver smart data as a service.

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Zhang, Min, Ren Zhang, and Cheng Sheng Liu. "Design of Smart Healthcare Data Management System Based on Hadoop." Advanced Materials Research 998-999 (July 2014): 1121–24. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.1121.

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This paper describes a smart healthcare data management system based on hadoop. Aiming at the disadvantage of Traditional management of medical data such as the increasing cost of consumption and the limited availability of the data, the smart healthcare data management system in this paper introduces a hybrid storage architecture including designs of Structured data storage which supported by RDBMS and Non-structural data storage which supported by Hadoop. This smart healthcare data management system has the advantages of low-cost, high fault tolerance, and scalability, and builds a cloud storage platform applied in the system of smart healthcare.

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17

Kim, Kwang-Hyung, and Junhyuk Lee. "Smart Plant Disease Management Using Agrometeorological Big Data." Research in Plant Disease 26, no. 3 (September 30, 2020): 121–33. http://dx.doi.org/10.5423/rpd.2020.26.3.121.

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18

Giordani, Ilaria, Francesco Archetti, and Antonio Candelieri. "DATA SCIENCE AND ENVIRONMENTAL MANAGEMENT IN SMART CITIES." Environmental Engineering and Management Journal 14, no. 9 (2015): 2095–102. http://dx.doi.org/10.30638/eemj.2015.224.

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19

Chen, Zhenyong, Wei Fan, Zhang Xiong, Pingan Zhang, and Lixin Luo. "Visual data security and management for smart cities." Frontiers of Computer Science in China 4, no. 3 (August 12, 2010): 386–93. http://dx.doi.org/10.1007/s11704-010-0378-7.

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최영환, Kyung-Hee Lee, and Wan-Sup Cho. "Big Data Governance Model for Smart Water Management." Korea Journal of BigData 3, no. 2 (December 2018): 1–10. http://dx.doi.org/10.36498/kbigdt.2018.3.2.1.

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21

Dou, Xiangsheng, and Albert W. K. Tan. "Big data and smart aviation information management system." Cogent Business & Management 7, no. 1 (January 1, 2020): 1766736. http://dx.doi.org/10.1080/23311975.2020.1766736.

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22

Al-Kendi, Wissam Basim, and Huda H. Al-Nayyef. "Data Management via QR Code Using Android Smart Devices." Al-Mustansiriyah Journal of Science 31, no. 3 (August 20, 2020): 95. http://dx.doi.org/10.23851/mjs.v31i3.853.

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Smart devices have become one of the most important facilities in managing the data in the organizations recently. In this research, Android Application has been developed with taking advantage of Quick Response codes technology for storing different types of data (ex: documents, goods or employees) within organizations, and retrieving it on demand on the smart device screen or sharing it to other devices as Excel reports. Discussion of the study assumptions, delimitations, and limitations resulted in developing several approaches for tackling the process of generating unique QR codes representing the items in the organizations, in addition to developing a QR code reader by making use of the camera in the smart device. Office goods have been chosen in this research project as a data sample. Based on the experiments, the findings have proven that QR code technology is highly useful in the data management process, for storing and retrieving information within smart devices, showing a high response speed and accuracy.

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Zhou, Tianqi, Jian Shen, Sai Ji, Yongjun Ren, and Leiming Yan. "Secure and Intelligent Energy Data Management Scheme for Smart IoT Devices." Wireless Communications and Mobile Computing 2020 (September 1, 2020): 1–11. http://dx.doi.org/10.1155/2020/8842885.

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The renewable energy plays an increasingly important role in many fields such as lighting, automobile, and electric power. In order to make full use of the renewable energy, various smart Internet of Thing (IoT) devices are deployed. However, in the field of energy management, the two-way mismatch between the demand and the supply of the renewable energy will greatly affect the efficiency of the renewable energy. In addition, the security threat of the energy data and the privacy leakage of the user may hinder the further development of smart IoT devices. Therefore, how to achieve consistency and balance between the demand and the renewable energy supply and how to guarantee the security and privacy of smart IoT devices become the key problems of the energy-efficient smart environment. In this paper, a secure and intelligent energy data management scheme for smart IoT devices is proposed. It is worth noting that, with the help of artificial intelligence (AI) technologies and secure cryptography primitives, the proposed scheme realizes high-efficient and secure energy utilization in a smart environment. Specifically, the proposed scheme aims at improving the efficiency of the energy utilization in the multidimensions of a smart environment. In order to realize the fine-grain energy management of smart IoT devices, strategies of three different dimensions are considered and realized in the proposed scheme. Moreover, technologies in AI are applied and integrated into the energy management scheme. The analysis shows that the proposed scheme can make full use of the renewable energy in smart IoT devices.

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E. Laxmi Lydia, Dr, B. Prasanna Kumar, and D. Ramya. "Generation of dynamic energy management using data mining techniques basing on big data analytics isssues in smart grids." International Journal of Engineering & Technology 7, no. 2.26 (May 7, 2018): 85. http://dx.doi.org/10.14419/ijet.v7i2.26.12540.

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The Optimal bidirectional flow of the electric power and the communicational data between suppliers and consumers are greatly enabled by the Smart Electricity in Grid. Reliable and Feasible micro energy generated due to Dynamic Energy Management (DEM) and the electricity market by consumers and suppliers. The smart grid features ICCM, aims to bring out the power at reduced cost. Powerful and practical DEM relies on load and sustainable production. Smart meters attain the huge data quantity through practical methods and solutions in this real world working. Smart Grids are enhanced by the operations such as data analytics, giving out high performance estimation, Adequate data network management and cloud computing. This paper aims focusthe issuesin big data and challenges experienced by the Dynamic Energy Management signed in Smart Grid. A detail explanation of data processing techniques that are mostly implemented and It also provides a brief description of the most commonly used data processing methods and recommended proposes a upcoming future directional research in thefield.

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25

Becker, Kurt. "New ways of diabetes management with smart data and genomic data." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 497–500. http://dx.doi.org/10.1515/cdbme-2017-0104.

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AbstractBy 2025, the number of diabetic patients worldwide could rise by more than 50 percent from now 250 million to about 380 million. With about 6 million patients, diabetes mellitus is one of the greatest national diseases in Germany.Type 1 diabetes is a mostly genetically induced autoimmune disease, type 2 diabetes is a civilization disease and arises due to lack of exercise and poor diet. Regardless of the type of diabetes, it is important for those affected to manage their own insulin production of the body and to harmonize these with appropriate possibilities. Because of the harmful side effects of exogenous insulin doses, the major focus should be on a sustainable behavioral change and low-threshold nutritional coaching.The most important side effects of diabetes are damage to the vascular system with possible consequences: myocardial infarction, stroke, kidney weakness, nasal damage and erectile dysfunction. A concept for a knowledge-based expert system for the therapy of diabetes mellitus is presented, in which genetic, anatomical and physiological parameters are recorded, evaluated and visualized by means of a model-based approach to specific therapeutic recommendations. The "user interface" is a digital avatar, which can display the model parameters in various "abstraction levels" as a metamodel.

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Zhou, Caixue, Lihua Wang, and Lingmin Wang. "Lattice-based provable data possession in the standard model for cloud-based smart grid data management systems." International Journal of Distributed Sensor Networks 18, no. 4 (April 2022): 155013292210929. http://dx.doi.org/10.1177/15501329221092940.

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The smart grid is considered to be the next-generation electric power network. In a smart grid, there are massive data to be processed, so cloud computing is introduced into it to form a cloud-based smart grid data management system. However, with data no longer being stored locally, how to ensure the integrity of data stored in the cloud in the smart grid has become an urgent problem awaiting solution. Provable data possession has been proposed to solve this problem. With the development of quantum computer technology, quantum attacks-resistant cryptographic schemes are gradually entering people’s horizons. Lattice cryptography can resist quantum attacks. In this article, a lattice-based provable data possession scheme is proposed for cloud-based smart grid data management systems. The scheme is proved unforgeable under the small integer solution hard assumption in the standard model. Compared with other two efficient lattice-based provable data possession schemes in the standard model, our scheme also shows efficiency.

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Li, Weng Ting, Yan Zheng, Shao Bo Liu, Zhao Zhi Long, and Zhi Cheng Li. "Research of Smart Meter Massive Data Storage Based on Cloud Computing Platform." Applied Mechanics and Materials 341-342 (July 2013): 1434–38. http://dx.doi.org/10.4028/www.scientific.net/amm.341-342.1434.

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With the comprehensive construction of the smart grid, the smart grid operation control and interactive service system will be initially formed. The smart terminal of smart grid are smart meters, and they produce a large number of various data all the time. That how to most effectively manage these massive data storage is an important research point for improving the intelligence service. This paper studies the smart meter massive data storage management based on cloud computing platform. The Hadoop distributed computing platform for smart meter massive data management is reliable, efficient, scalable storage.

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Grané, Aurea, Giancarlo Manzi, and Silvia Salini. "Smart Visualization of Mixed Data." Stats 4, no. 2 (June 1, 2021): 472–85. http://dx.doi.org/10.3390/stats4020029.

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In this work, we propose a new protocol that integrates robust classification and visualization techniques to analyze mixed data. This protocol is based on the combination of the Forward Search Distance-Based (FS-DB) algorithm (Grané, Salini, and Verdolini 2020) and robust clustering. The resulting groups are visualized via MDS maps and characterized through an analysis of several graphical outputs. The methodology is illustrated on a real dataset related to European COVID-19 numerical health data, as well as the policy and restriction measurements of the 2020–2021 COVID-19 pandemic across the EU Member States. The results show similarities among countries in terms of incidence and the management of the emergency across several waves of the disease. With the proposed methodology, new smart visualization tools for analyzing mixed data are provided.

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Majdoubi, Anass, Abdellatif El Abderrahmani, and Rafik Lasri. "Smart environmental data management system into a cattle building." E3S Web of Conferences 234 (2021): 00033. http://dx.doi.org/10.1051/e3sconf/202123400033.

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The climatic atmosphere in which cattle live is an essential parameter of their environment because of its critical role in their productivity. An adapted cattle building must help to mitigate the effects of climatic stress and allow the farmer to properly control the climatic atmosphere during the production cycle. The most important factors influencing the climatic atmosphere inside a cattle building are temperature, humidity, and greenhouse gas emissions. We propose a case study for a wireless sensor network model placed on a cattle farm, in which each measurement node “mote” collects environmental data (temperature, humidity, and emission gas), in order to control the building's climate, this data is stored and managed in a remote database. We will present HBase, a NoSQL database management system, based on the concept of distributed storage, a column-oriented database that provides the read/write access to data on the HADOOP HDFS file system in real-time. The storage results presented in this paper are obtained via a java code that can connect with the HBase database, in order to store the received data at every second from each node constituting the measurement system via HTTP requests.

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Zainab, Ameema, Ali Ghrayeb, Dabeeruddin Syed, Haitham Abu-Rub, Shady S. Refaat, and Othmane Bouhali. "Big Data Management in Smart Grids: Technologies and Challenges." IEEE Access 9 (2021): 73046–59. http://dx.doi.org/10.1109/access.2021.3080433.

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Bumanis, Nikolajs, Irina Arhipova, Liga Paura, Gatis Vitols, and Liga Jankovska. "Data Conceptual Model for Smart Poultry Farm Management System." Procedia Computer Science 200 (2022): 517–26. http://dx.doi.org/10.1016/j.procs.2022.01.249.

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Hudasi, Luca, and Laszlo Ady. "Artificial Intelligence Usage Opportunities in Smart City Data Management." Interdisciplinary Description of Complex Systems 18, no. 3 (2020): 391–97. http://dx.doi.org/10.7906/indecs.18.3.8.

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Quwaider, Muhannad, Mahmoud Al-Alyyoub, and Yaser Jararweh. "Cloud Support Data Management Infrastructure for Upcoming Smart Cities." Procedia Computer Science 83 (2016): 1232–37. http://dx.doi.org/10.1016/j.procs.2016.04.257.

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34

Covic, Filip, and Stefan Voß. "Interoperable smart card data management in public mass transit." Public Transport 11, no. 3 (October 2019): 523–48. http://dx.doi.org/10.1007/s12469-019-00216-x.

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35

Kim, Sunho, Ricardo Perez Del Castillo, Ismael Caballero, Jimwoo Lee, Changsoo Lee, Downgwoo Lee, Sangyub Lee, and Alejandro Mate. "Extending Data Quality Management for Smart Connected Product Operations." IEEE Access 7 (2019): 144663–78. http://dx.doi.org/10.1109/access.2019.2945124.

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Gumz, Jonathan, Diego Castro Fettermann, Enzo Morosini Frazzon, and Mirko Kück. "Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions." Sustainability 14, no. 20 (October 21, 2022): 13642. http://dx.doi.org/10.3390/su142013642.

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Industry 4.0 and its technologies allow advancements in communications, production and management efficiency across several segments. In smart grids, essential parts of smart cities, smart meters act as IoT devices that can gather data and help the management of the sustainable energy matrix, a challenge that is faced worldwide. This work aims to use smart meter data and household features data to seek the most appropriate methods of energy consumption prediction. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, Python Platform, and several prediction methods, prediction experiments were performed with household feature data and past consumption data of over 470 smart meters that gathered data for three years. Support vector machines, random forest regression, and neural networks were the best prediction methods among the ones tested in the sample. The results help utilities (companies that maintain the infrastructure for public services) to offer better contracts to new households and to manage their smart grid infrastructure based on the forecasted demand.

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Doshi, Aayush. "BINTERNET: Smart Waste Management System." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 703–7. http://dx.doi.org/10.22214/ijraset.2021.38882.

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Abstract: To make the cities greener, safer, and more efficient, Internet of Things (IoT) can play an important role. Improvement in safety and quality of life can be achieved by connecting devices, vehicles and infrastructure all around in a city. We present a waste collection management solution based on providing intelligence to waste bins, using an IOT prototype with sensors. It can read, collect, and transmit huge volume of data over the Internet. Such data, when put into a spatial-temporal context and processed by intelligent and optimized algorithms, can be used to dynamically manage waste collection mechanism. Simulations for several cases are carried out to investigate the benefits of such system over a traditional system

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Elvas, Luís B., Bruno Miguel Mataloto, Ana Lúcia Martins, and João C. Ferreira. "Disaster Management in Smart Cities." Smart Cities 4, no. 2 (May 19, 2021): 819–39. http://dx.doi.org/10.3390/smartcities4020042.

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The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures. This data availability opens new research opportunities in the study of the interdependency between those critical infrastructures and cascading effects solutions and focuses on the smart city as a network of critical infrastructures. This paper proposes an integrated resilience system linking interconnected critical infrastructures in a smart city to improve disaster resilience. A data-driven approach is considered, using artificial intelligence and methods to minimize cascading effects and the destruction of failing critical infrastructures and their components (at a city level). The proposed approach allows rapid recovery of infrastructures’ service performance levels after disasters while keeping the coverage of the assessment of risks, prevention, detection, response, and mitigation of consequences. The proposed approach has the originality and the practical implication of providing a decision support system that handles the infrastructures that will support the city disaster management system—make the city prepare, adapt, absorb, respond, and recover from disasters by taking advantage of the interconnections between its various critical infrastructures to increase the overall resilience capacity. The city of Lisbon (Portugal) is used as a case to show the practical application of the approach.

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Soliman, Ahmed, Mohammad Zaher Akkad, and Rima Alloush. "Smart bin monitoring system for smart waste management." Multidiszciplináris tudományok 10, no. 2 (2020): 402–12. http://dx.doi.org/10.35925/j.multi.2020.2.45.

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The fourth industrial revolution offers new technologies and solutions to increase efficiency, availability, flexibility, and decrease the economical footprint of manufacturing and service processes. In smart cities, a wide range of Industry 4.0 technologies can be used in the field of road traffic monitoring, health monitoring, and many operations, like the municipal waste collection. Internet of Things makes it possible to reduce the required material handling solutions of municipal waste collection, like loading and unloading, transportation, and warehousing. With smart waste management, no need for trucks to come every day and check every single dustbin if it is full or not as in the traditional situation, therefore the human power, time, cost, and spreading of toxic gas will be reduced. The amounts of the garbage will be monitored by sensors, pollution level by moisture sensor, and an odor sensor. The collected data can also be used to find the optimal path for the truck’s drivers. Within the frame of this article, the authors describe an on-line smart bin monitoring system.

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Shen, Jian, Xinzhao Jiang, Youngju Cho, Dengzhi Liu, and Tianqi Zhou. "Two-Factor-Based Public Data Protection Scheme in Smart Ocean Management." Sensors 19, no. 1 (January 2, 2019): 129. http://dx.doi.org/10.3390/s19010129.

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Nowadays, two-factor data security protection has become a research hotspot in smart ocean management. With the increasing popularity of smart ocean management, how to achieve the two-factor protection of public data resources in smart ocean management is a serious problem to be tackled. Furthermore, how to achieve both security and revocation is also a challenge for two-factor protection. In this paper, we propose a two-factor-based protection scheme with factor revocation in smart ocean management. The proposed scheme allows data owners (DOs) to send encrypted messages to users through a shipboard server (SS). The DOs are required to formulate access policy and perform attribute-based encryption on messages. In order to decrypt, the users need to possess two factors. The first factor is the user’s secret key. The second factor is security equipment, which is a sensor card in smart ocean system. The ciphertext can be decrypted if and only if the user gathers the key and the security equipment at the same time. What is more, once the security equipment is lost, the equipment can be revoked and a new one is redistributed to the users. The theoretical analysis and experiment results indeed indicate the security, efficiency, and practicality of our scheme.

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Turjo, Manoshi Das, Mohammad Monirujjaman Khan, Manjit Kaur, and Atef Zaguia. "Smart Supply Chain Management Using the Blockchain and Smart Contract." Scientific Programming 2021 (September 28, 2021): 1–12. http://dx.doi.org/10.1155/2021/6092792.

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The manufacture of raw materials to deliver the product to the consumer in a traditional supply chain system is a manual process with insufficient data and transaction security. It also takes a significant amount of time, making the entire procedure lengthy. Overall, the undivided process is ineffective and untrustworthy for consumers. If blockchain and smart contract technologies are integrated into traditional supply chain management systems, data security, authenticity, time management, and transaction processes will all be significantly improved. Blockchain is a revolutionary, decentralized technology that protects data from unauthorized access. The entire supply chain management (SCM) will be satisfied with the consumer once smart contracts are implemented. The plan becomes more trustworthy when the mediator is contracted, which is doable in these ways. The tags employed in the conventional SCM process are costly and have limited possibilities. As a result, it is difficult to maintain product secrecy and accountability in the SCM scheme. It is also a common target for wireless attacks (reply attacks, eavesdropping, etc.). In SCM, the phrase “product confidentiality” is very significant. It means that only those who have been validated have access to the information. This paper emphasizes reducing the involvement of third parties in the supply chain system and improving data security. Traditional supply chain management systems have a number of significant flaws. Lack of traceability, difficulty maintaining product safety and quality, failure to monitor and control inventory in warehouses and shops, rising supply chain expenses, and so on, are some of them. The focus of this paper is on minimizing third-party participation in the supply chain system and enhancing data security. This improves accessibility, efficiency, and timeliness throughout the whole process. The primary advantage is that individuals will feel safer throughout the payment process. However, in this study, a peer-to-peer encrypted system was utilized in conjunction with a smart contract. Additionally, there are a few other features. Because this document makes use of an immutable ledger, the hacker will be unable to get access to it. Even if they get access to the system, they will be unable to modify any data. If the goods are defective, the transaction will be halted, and the customer will be reimbursed, with the seller receiving the merchandise. By using cryptographic methods, transaction security will be a feasible alternative for recasting these issues. Finally, this paper will demonstrate how to maintain the method with the maximum level of safety, transparency, and efficiency.

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Badidi, Elarbi, Zineb Mahrez, and Essaid Sabir. "Fog Computing for Smart Cities’ Big Data Management and Analytics: A Review." Future Internet 12, no. 11 (October 31, 2020): 190. http://dx.doi.org/10.3390/fi12110190.

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Demographic growth in urban areas means that modern cities face challenges in ensuring a steady supply of water and electricity, smart transport, livable space, better health services, and citizens’ safety. Advances in sensing, communication, and digital technologies promise to mitigate these challenges. Hence, many smart cities have taken a new step in moving away from internal information technology (IT) infrastructure to utility-supplied IT delivered over the Internet. The benefit of this move is to manage the vast amounts of data generated by the various city systems, including water and electricity systems, the waste management system, transportation system, public space management systems, health and education systems, and many more. Furthermore, many smart city applications are time-sensitive and need to quickly analyze data to react promptly to the various events occurring in a city. The new and emerging paradigms of edge and fog computing promise to address big data storage and analysis in the field of smart cities. Here, we review existing service delivery models in smart cities and present our perspective on adopting these two emerging paradigms. We specifically describe the design of a fog-based data pipeline to address the issues of latency and network bandwidth required by time-sensitive smart city applications.

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Fugini, Mariagrazia, Jacopo Finocchi, and Paolo Locatelli. "A Big Data Analytics Architecture for Smart Cities and Smart Companies." Big Data Research 24 (May 2021): 100192. http://dx.doi.org/10.1016/j.bdr.2021.100192.

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Jaradat, Manar, Moath Jarrah, Abdelkader Bousselham, Yaser Jararweh, and Mahmoud Al-Ayyoub. "The Internet of Energy: Smart Sensor Networks and Big Data Management for Smart Grid." Procedia Computer Science 56 (2015): 592–97. http://dx.doi.org/10.1016/j.procs.2015.07.250.

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Raptis, Theofanis P., Claudio Cicconetti, Manolis Falelakis, Grigorios Kalogiannis, Tassos Kanellos, and Tomás Pariente Lobo. "Engineering Resource-Efficient Data Management for Smart Cities with Apache Kafka." Future Internet 15, no. 2 (January 22, 2023): 43. http://dx.doi.org/10.3390/fi15020043.

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In terms of the calibre and variety of services offered to end users, smart city management is undergoing a dramatic transformation. The parties involved in delivering pervasive applications can now solve key issues in the big data value chain, including data gathering, analysis, and processing, storage, curation, and real-world data visualisation. This trend is being driven by Industry 4.0, which calls for the servitisation of data and products across all industries, including the field of smart cities, where people, sensors, and technology work closely together. In order to implement reactive services such as situational awareness, video surveillance, and geo-localisation while constantly preserving the safety and privacy of affected persons, the data generated by omnipresent devices needs to be processed fast. This paper proposes a modular architecture to (i) leverage cutting-edge technologies for data acquisition, management, and distribution (such as Apache Kafka and Apache NiFi); (ii) develop a multi-layer engineering solution for revealing valuable and hidden societal knowledge in the context of smart cities processing multi-modal, real-time, and heterogeneous data flows; and (iii) address the key challenges in tasks involving complex data flows and offer general guidelines to solve them. In order to create an effective system for the monitoring and servitisation of smart city assets with a scalable platform that proves its usefulness in numerous smart city use cases with various needs, we deduced some guidelines from an experimental setting performed in collaboration with leading industrial technical departments. Ultimately, when deployed in production, the proposed data platform will contribute toward the goal of revealing valuable and hidden societal knowledge in the context of smart cities.

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Lai, Maotao. "Smart Financial Management System Based on Data Ming and Man-Machine Management." Wireless Communications and Mobile Computing 2022 (January 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/2717982.

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To begin, the architecture of an intelligent financial management system is thoroughly investigated, and a new architecture of an intelligent financial management support system based on data mining is developed. Second, it goes over the definition and structure of a data warehouse and data mining, as well as how to use data mining strategy and technology in financial management. Data mining in relation to technology is being investigated, as is the development of an intelligent data mining algorithm. The flaws of the intelligent data mining algorithm are discovered through an analysis and summary of the algorithm, and an improved algorithm is proposed to address the flaws. Related mining experiments are carried out on the improved algorithm, and the experiment shows that it has certain advantages. Then, using an intelligent forecasting financial management decision as an example, the intelligent financial management based on data mining is thoroughly investigated, the basic design framework for intelligent financial management is established, and the application of a data mining model in decision support system is introduced.

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Patil, Vishal. "Smart Hospital Management System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1878–81. http://dx.doi.org/10.22214/ijraset.2021.35440.

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Information and Communication Technologies (ICTs) are commonly using in healthcare organizations worldwide. There are different kinds of healthcare applications developed in android Smartphone’s which help patients and their caregivers to reduce time and cost efficiency. Hospitals are the largest and most complex organizations where health care is provided. Safe and effective patient care services in hospitals depend on the efficient decisions made by hospital executives. The main task of hospital executives is to ensure the hospital can provide high quality patient care and services. This Android application used for displaying hospital performance metrics on a daily basis. This application allows hospital executives to review and monitor hospital operational data with ease of access and in a portable manner. Thus, reducing the effort of the hospital executives to perform their tasks. In this research work, an application is developed that locates the nearest hospital. The System is designed for Any Hospital to replace their existing manual, paper-based system. The new system is to control the following information; List of Hospitals, bed availability, Book Appointment, List of Doctors, Facilities and Book Ambulance. With the help of this application, a patient can find the nearest hospital according to specialized consultant availability.

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Kalra, Deepak, and Manas Ranjan Pradhan. "Enduring data analytics for reliable data management in handling smart city services." Soft Computing 25, no. 18 (June 10, 2021): 12213–25. http://dx.doi.org/10.1007/s00500-021-05892-1.

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Zhou, Kaile, Chao Fu, and Shanlin Yang. "Big data driven smart energy management: From big data to big insights." Renewable and Sustainable Energy Reviews 56 (April 2016): 215–25. http://dx.doi.org/10.1016/j.rser.2015.11.050.

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Tachizawa, Elcio M., María J. Alvarez-Gil, and María J. Montes-Sancho. "How “smart cities” will change supply chain management." Supply Chain Management: An International Journal 20, no. 3 (May 11, 2015): 237–48. http://dx.doi.org/10.1108/scm-03-2014-0108.

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Purpose – The purpose of this paper is to analyze the impact of smart city initiatives and big data on supply chain management (SCM). More specifically, the connections between smart cities, big data and supply network characteristics (supply network structure and governance mechanisms) are investigated. Design/methodology/approach – An integrative framework is proposed, grounded on a literature review on smart cities, big data and supply networks. Then, the relationships between these constructs are analyzed, using the proposed integrative framework. Findings – Smart cities have different implications to network structure (complexity, density and centralization) and governance mechanisms (formal vs informal). Moreover, this work highlights and discusses the future research directions relating to smart cities and SCM. Research limitations/implications – The relationships between smart cities, big data and supply networks cannot be described simply by using a linear, cause-and-effect framework. Accordingly, an integrative framework that can be used in future empirical studies to analyze smart cities and big data implications on SCM has been proposed. Practical implications – Smart cities and big data alone have limited capacity of improving SCM processes, but combined they can support improvement initiatives. Nevertheless, smart cities and big data can also suppose some novel obstacles to effective SCM. Originality/value – Several studies have analyzed information technology innovation adoption in supply chains, but, to the best of our knowledge, no study has focused on smart cities.

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