Relevant bibliographies by topics / IoT analytics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'IoT analytics'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "IoT analytics"

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Chahal, Ayushi, Preeti Gulia, and Nasib Singh Gill. "Different analytical frameworks and bigdata model for internet of things." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1159. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1159-1166.

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Sensor devices used in internet of things (IoT) enabled environment produce large amount of data. This data plays a major role in bigdata landscape. In recent years, correlation, and implementation of bigdata and IoT is being extrapolated. Nowadays, predictive analytics is gaining attention of many researchers for big IoT data analytics. This paper summarizes different sort of IoT analytical platforms which consist in-built features for further use in machine learning, MATLAB, and data security. It emphasizes on different machine learning algorithms that plays important role in big IoT data analytics. Besides different analytical frameworks, this paper highlights the proposed model for bigdata in IoT domain and elaborates different forms of data analytical methods. Proposed model comprises different phases i.e., data storing, data cleaning, data analytics, and data visualization. These phases cover the basic characteristics of bigdata V’s model and most important phase is data analytics or big IoT analytics. This model is implemented using an IoT dataset and results are presented in graphical and tabular form using different machine learning techniques. This study enhances researchers’ knowledge about various IoT analytical platforms and usability of these platforms in their respective problem domains.
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Chahal, Ayushi, Preeti Gulia, and Nasib Singh Gill. "Different analytical frameworks and bigdata model for internet of things." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1159–66. https://doi.org/10.11591/ijeecs.v25.i2.pp1159-1166.

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Sensor devices used in internet of things (IoT) enabled environment produce large amount of data. This data plays a major role in bigdata landscape. In recent years, correlation, and implementation of bigdata and IoT is being extrapolated. Nowadays, predictive analytics is gaining attention of many researchers for big IoT data analytics. This paper summarizes different sort of IoT analytical platforms which consist in-built features for further use in machine learning, MATLAB, and data security. It emphasizes on different machine learning algorithms that plays important role in big IoT data analytics. Besides different analytical frameworks, this paper highlights the proposed model for bigdata in IoT domain and elaborates different forms of data analytical methods. Proposed model comprises different phases i.e., data storing, data cleaning, data analytics, and data visualization. These phases cover the basic characteristics of bigdata V’s model and most important phase is data analytics or big IoT analytics. This model is implemented using an IoT dataset and results are presented in graphical and tabular form using different machine learning techniques. This study enhances researchers’ knowledge about various IoT analytical platforms and usability of these platforms in their respective problem domains.
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C V, Pooja Suresh, and Dr S. K. Manju Bargavi. "IoT for Healthcare Data Analytics." International Journal of Research Publication and Reviews 5, no. 5 (2024): 6365–69. http://dx.doi.org/10.55248/gengpi.5.0524.1421.

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Dost, Muhammad Khan. "Data Streaming of Healthcare from Internet of Things (IoTs) using Big Data Analytics." Global Social Sciences Review 4, no. 1 (2019): 287–95. https://doi.org/10.5281/zenodo.4362047.

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The present study aims at the concept of the IoTs (IoT) and its relation with the healthcare sector. Nowadays, IoT is the main focus of researchers and scientists while this concept illustrates the data stream generated from IoT devices in massive amounts like big data with a continuous stream that requires its proper handling. This study aims at the analytical processing of big datasets having a medical history of patients and their diseases. The data cleansing is applied before going through the analytics phase due to the existence of some noisy and missing data. The analytics of data identified that what events are happening while the mining approaches identified why and how events are happening. Together, both phases help in data analytics and mining. Finally, the analytics and visualization led to the decision making and its results depict the effectiveness and efficiency of the proposed framework for data analytics in IoT
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Vishal Jariwala. "Optimizing IoT Analytics: Energy-Efficient Approaches with Automated Machine Learning in Dynamic Contexts." Journal of Information Systems Engineering and Management 10, no. 54s (2025): 408–13. https://doi.org/10.52783/jisem.v10i54s.11127.

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The proliferation of the Internet of Things (IoT) has led to the generation of massive amounts of data, necessitating efficient analytics for effective decision making. This paper presents innovative energy-efficient approaches for optimizing IoT analytics, leveraging Automated Machine Learning (AutoML) within dynamic contexts. The proposed methodologies focus on minimizing energy consumption while maintaining high performance and accuracy in IoT data processing. By integrating adaptive algorithms that respond to varying conditions and data streams, our approach ensures real-time analytics with reduced computational overhead. Extensive experiments demonstrate the effectiveness of our solutions in diverse IoT environments, highlighting significant improvements in energy efficiency without compromi- sing analytical precision. This work provides a robust framework for sustainable IoT deployments, promoting intelligent, context aware data analytics that align with the growing demand for energy conservation in IoT ecosystems.
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الهادی, محمد. "IoT analytics: Reaping value from IoT data." مجلة الجمعیة المصریة لنظم المعلومات وتکنولوجیا الحاسبات 24, no. 24 (2021): 32–36. http://dx.doi.org/10.21608/jstc.2021.165204.

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Choudhury, Salimur, Qiang Ye, Mianxiong Dong, and Qingchen Zhang. "IoT Big Data Analytics." Wireless Communications and Mobile Computing 2019 (July 30, 2019): 1. http://dx.doi.org/10.1155/2019/9245392.

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Bifet, Albert, and João Gama. "IoT data stream analytics." Annals of Telecommunications 75, no. 9-10 (2020): 491–92. http://dx.doi.org/10.1007/s12243-020-00811-1.

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Khan, Dost Muhammad, Muhammad Jameel Sumra, and Faisal Shahzad. "Data Streaming of Healthcare from Internet of Things (IoTs) using Big Data Analytics." Global Social Sciences Review IV, no. I (2019): 287–95. http://dx.doi.org/10.31703/gssr.2019(iv-i).38.

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The present study aims at the concept of the IoTs (IoT) and its relation with the healthcare sector. Nowadays, IoT is the main focus of researchers and scientists while this concept illustrates the data stream generated from IoT devices in massive amounts like big data with a continuous stream that requires its proper handling. This study aims at the analytical processing of big datasets having a medical history of patients and their diseases. The data cleansing is applied before going through the analytics phase due to the existence of some noisy and missing data. The analytics of data identified that what events are happening while the mining approaches identified why and how events are happening. Together, both phases help in data analytics and mining. Finally, the analytics and visualization led to the decision making and its results depict the effectiveness and efficiency of the proposed framework for data analytics in IoT
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Sai, Sandeep Ogety. "Enhancing Cloud Security Governance with AI and Data Analytics." European Journal of Advances in Engineering and Technology 8, no. 7 (2024): 132–42. https://doi.org/10.5281/zenodo.14274546.

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The group of real-world physical devices like sensors, machines, vehicles and various “things” connected to Internet is called as Internet of things (IoT). The major challenge in IoT is that  it is fully dependent on the cloud for all kinds of computation, which leads to high latency in the IoT devices. To overcome this latency issue, the Serverless edge computing and AI approaches were introduced newline. Serverless edge computing allows moving the data goverence and managing closer to the Serverless edge of the device. ICT’s three pillars namely computing, network and storage faces some challenges in terms of goverence and structuring the data while using formal Cloud computing methods. To propose a framework on IoT devices data by combining two things which is mainly focused on IoT data goverence and data security goverence goverence. To design modified auto-encoder algorithms (AI) for goverence of data in Serverless edge computing architecture. To investigate the present scenario of the data accessing techniques, then to design an effective auto-encoder model to process the huge amount of raw data generated from IoT devices time-to- time (Transforming data to Serverless edge) in the Serverless edge Computing. To consider different types of attacks on IoT data, to investigate the different policies of security and to design a model for Access Control for IoT data by considering the above important processes which can solve the current problems in IoT data access and security. In the performance analysis, Latency minimization, Network Management, Cost Optimization, Data Management, Energy Management, and Resource Management are analysed at the service level and Serverless edge computing based IoT security challenges and self-protection system for IoT specifically in detection, prediction and response mechanisms discussed.
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Dissertations / Theses on the topic "IoT analytics"

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Bahri, Maroua. "Improving IoT data stream analytics using summarization techniques." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT017.

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Face à cette évolution technologique vertigineuse, l’utilisation des dispositifs de l'Internet des Objets (IdO), les capteurs, et les réseaux sociaux, d'énormes flux de données IdO sont générées quotidiennement de différentes applications pourront être transformées en connaissances à travers l’apprentissage automatique. En pratique, de multiples problèmes se posent afin d’extraire des connaissances utiles de ces flux qui doivent être gérés et traités efficacement. Dans ce contexte, cette thèse vise à améliorer les performances (en termes de mémoire et de temps) des algorithmes de l'apprentissage supervisé, principalement la classification à partir de flux de données en évolution. En plus de leur nature infinie, la dimensionnalité élevée et croissante de ces flux données dans certains domaines rendent la tâche de classification plus difficile. La première partie de la thèse étudie l’état de l’art des techniques de classification et de réduction de dimension pour les flux de données, tout en présentant les travaux les plus récents dans ce cadre.La deuxième partie de la thèse détaille nos contributions en classification pour les flux de données. Il s’agit de nouvelles approches basées sur les techniques de réduction de données visant à réduire les ressources de calcul des classificateurs actuels, presque sans perte en précision. Pour traiter les flux de données de haute dimension efficacement, nous incorporons une étape de prétraitement qui consiste à réduire la dimension de chaque donnée (dès son arrivée) de manière incrémentale avant de passer à l’apprentissage. Dans ce contexte, nous présentons plusieurs approches basées sur: Bayesien naïf amélioré par les résumés minimalistes et hashing trick, k-NN qui utilise compressed sensing et UMAP, et l’utilisation d’ensembles d’apprentissage également<br>With the evolution of technology, the use of smart Internet-of-Things (IoT) devices, sensors, and social networks result in an overwhelming volume of IoT data streams, generated daily from several applications, that can be transformed into valuable information through machine learning tasks. In practice, multiple critical issues arise in order to extract useful knowledge from these evolving data streams, mainly that the stream needs to be efficiently handled and processed. In this context, this thesis aims to improve the performance (in terms of memory and time) of existing data mining algorithms on streams. We focus on the classification task in the streaming framework. The task is challenging on streams, principally due to the high -- and increasing -- data dimensionality, in addition to the potentially infinite amount of data. The two aspects make the classification task harder.The first part of the thesis surveys the current state-of-the-art of the classification and dimensionality reduction techniques as applied to the stream setting, by providing an updated view of the most recent works in this vibrant area.In the second part, we detail our contributions to the field of classification in streams, by developing novel approaches based on summarization techniques aiming to reduce the computational resource of existing classifiers with no -- or minor -- loss of classification accuracy. To address high-dimensional data streams and make classifiers efficient, we incorporate an internal preprocessing step that consists in reducing the dimensionality of input data incrementally before feeding them to the learning stage. We present several approaches applied to several classifications tasks: Naive Bayes which is enhanced with sketches and hashing trick, k-NN by using compressed sensing and UMAP, and also integrate them in ensemble methods
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Lunardi, Willian Tesaro. "Decision support IoT framework : device discovery and stream analytics." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2016. http://tede2.pucrs.br/tede2/handle/tede/6929.

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Submitted by Setor de Tratamento da Informa??o - BC/PUCRS (tede2@pucrs.br) on 2016-08-29T13:47:15Z No. of bitstreams: 1 DIS_WILLIAN_TESSARO_LUNARDI_COMPLETO.pdf: 1857452 bytes, checksum: 335f6fe3c020d0f7f19050cdd006cca5 (MD5)<br>Made available in DSpace on 2016-08-29T13:47:15Z (GMT). No. of bitstreams: 1 DIS_WILLIAN_TESSARO_LUNARDI_COMPLETO.pdf: 1857452 bytes, checksum: 335f6fe3c020d0f7f19050cdd006cca5 (MD5) Previous issue date: 2016-03-23<br>Durante os ?ltimos anos, como r?pido desenvolvimento e prolifera??o da Internet das Coisas (IoT), muitas ?reas de aplica??o come?aram a explorar este novo paradigma de computa??o. O n?mero de dispositivos computacionais ativos tem crescido em um ritmo acelerado ao redor do mundo. Consequentemente, um mecanismo para lidar com estes diferentes dispositivos tornou-se necess?rio. Middlewares para a IoT t?m sido desenvolvidos tanto em ambientes industriais como de pesquisa para suprir esta necessidade, no entanto, a descoberta e a sele??o de dispositivos, bem como o suporte a tomada de decis?o baseada no fluxo de dados destes dispositivos continuam sendo um desafio cr?tico. Neste trabalho apresentamos o Decision Support IoT Framework, composto pelo sistema COBASEN, um motor de busca de dispositivos da IoT, e o sistema DMS, o qual atua sobre dados de dispositivo em movimento, extra indo informa??es valiosas para dar suporte a tomada de decis?es. O sistema COBASEN opera com base nas caracter?sticas textuais dos perfis dos dispositivos. O sistema DMS utiliza processamento de eventos complexos para analisar e reagir sobre os dados de fluxo cont?nuo, permitindo, por exemplo, disparar um alerta quando um erro ou condi??o espec?fica aparece no fluxo de dados do dispositivo. O objetivo principal deste trabalho ? destacar a import?ncia de um motor de busca de dispositivos para a Internet das Coisas e um sistema de apoio ? tomada de decis?o baseado na an?lise de fluxo cont?nuo dos dispositivos IoT. Foi desenvolvido dois sistemas que implementam conceitos COBASEN e DMS. No entanto, em testes preliminares, realizado uma avalia??o funcional de ambos os sistemas em termos de desempenho. Resultados iniciais sugerem que o Decision Support IoT Framework fornece abordagens importantes que facilitam o desenvolvimento de aplica??es da Internet das Coisas, podendo executar fun??es essenciais para melhorar os processos de ambientes que fazem uso deste paradigma.<br>During the past few years, with the fast development and proliferation of the Internet of Things (IoT), many application areas have started to exploit this new computing paradigm. The number of active computing devices has been growing at a rapid pace in IoT environments around the world. Consequently, a mechanism to deal with this different devices has become necessary. Middleware systems solutions for IoT have been developed in both research and industrial environments to supply this need. However, device discovery and selection, as well decision analytics remain a critical challenge. In this work we present the Decision Support IoT Framework composed of COBASEN, an IoT search engine to address the research challenge regarding the discovery and selection of IoT devices when large number of devices with over lapping and sometimes redundant functionality are available in IoT middleware systems, and DMS, which allows to setup analytic computations on device data when it is still in motion, extracting valuable information from it for decision management. COBASEN operates based on textual characteristics of devices. The DMS uses Complex Event Processing to analyze and react over streaming data, allowing for example, to triggers an alert when a specific error or condition appears in the stream. The main goal of this work is to high light the importance of an IoT search engine for devices and a decision support system for stream analytics in the IoT paradigm. We developed two systems that implements COBASEN and DMS concepts. However, for preliminarily tests, we made a functional evaluation of both systems in terms of performance. Our initial findings suggest that the Decision Support IoT Framework provides important approaches that facilitate the development of IoT applications, which may perform essential roles to improve IoT processes.
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Winberg, André, and Ramin Alberto Golrang. "Analytics as a Service : Analysis of services in Microsoft Azure." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-47655.

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Mendula, Matteo. "Interaction and Behaviour Evaluation for Smart Homes: Data Collection and Analytics in the ScaledHome Project." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20151/.

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Nowadays more and more devices are becoming "smart", in fact they can take autonomous decision and interact proactively with the surrounding environment. Smart home is just one of the most popular terms related with this relevant change we are witnessing and its relevance in this project is mainly due to the fact that the residential sector account an important percentage in terms of energy consumption. New ways to share and save energy have to be taken into account in order to optimize the usage of the devices needed by houses to make the environment cozy and comfortable for their inhabitants. The work done with Professor Turgut's team has improved the knowledge in the smart home system area providing a scalable and reliable architecture, a new dataset and an example of application of these data useful to save energy while satisfying the demands of its inhabitants.
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Zamam, Mohamad. "A unified framework for real-time streaming and processing of IoT data." Thesis, Linnéuniversitetet, Institutionen för medieteknik (ME), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-66057.

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The emergence of the Internet of Things (IoT) is introducing a new era to the realm of computing and technology. The proliferation of sensors and actuators that are embedded in things enables these devices to understand the environments and respond accordingly more than ever before. Additionally, it opens the space to unlimited possibilities for building applications that turn this sensation into big benefits, and within various domains. From smart cities to smart transportation and smart environment and the list is quite long. However, this revolutionary spread of IoT devices and technologies rises big challenges. One major challenge is the diversity in IoT vendors that results in data heterogeneity. This research tackles this problem by developing a data management tool that normalizes IoT data. Another important challenge is the lack of practical IoT technology with low cost and low maintenance. That has often limited large-scale deployments and mainstream adoption. This work utilizes open-source data analytics in one unified IoT framework in order to address this challenge. What is more, billions of connected things are generating unprecedented amounts of data from which intelligence must be derived in real-time. This unified framework processes real-time streams of data from IoT. A questionnaire that involved participants with background knowledge in IoT was conducted in order to collect feedback about the proposed framework. The aspects of the framework were presented to the participants in a form of demonstration video describing the work that has been done. Finally, using the participants’ feedback, the contribution of the developed framework to the IoT was discussed and presented.
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MALAVISI, MARZIA. "Structural Health Monitoring Framework for Automatic Damage Detection based on IoT and Big Data Analytics: Application to a network of structures." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2840369.

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Laricchia, Luigi. "Monitoraggio ambientale tramite tecnologia LoRaWAN: misurazioni sperimentali e piattaforma di data analytics." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17312/.

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I requisiti di molte applicazioni IoT necessitano di trasmettere dati su lunghe distanze, con basso data rate e con il minor impatto possibile sul consumo energetico. Le tecnologie LPWAN (Low Power Wide Area Network) sono state progettate per complementare ed in alcuni casi sostituire le soluzioni offerte dalla reti cellulari e dalle reti di sensori a corto/medio raggio. Nonostante la pletora di standards LPWAN disponibili sul mercato, la tecnologia LoRa/LoRaWAN sta riscuotendo notevole successo grazie alle performance che riesce a garantire. L’imponente mole di dati generata dalle applicazioni IoT richiede soluzioni in grado di poter archiviare e gestire in maniera efficiente il ciclo di vita delle informazioni. L’utilizzo di piattaforme di data analytics basate su sistemi NoSQL permettono una gestione più agile dei Big Data. In questa tesi è stata progettata ed implementata un’infrastruttura per il monitoraggio ambientale tramite LoRaWAN e la relativa piattaforma di data analytics adoperata per lo studio delle metriche relative alla trasmissione radio LoRa. I risultati ottenuti dalla sperimentazione possono essere usati per fare tuning delle configurazioni per il deploy in contesti reali.
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Coimbra, Rafael Melo. "Framework based on lambda architecture applied to IoT: case scenario." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/21739.

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Mestrado em Sistemas de Informação<br>Desde o início da primeira década do presente milénio, tem-se testemunhado um aumento exponencial da quantidade de dados produzidos de dia para dia. Numa primeira instância, o aumento foi atribuído aos dados gerados pelos dispositivos GPS; numa segunda fase, à rápida expansão das redes sociais, agora não devido a um fator específico, mas devido ao surgimento de um novo conceito denominado de Internet das Coisas. Este novo conceito, com resultados já mensuráveis, nasceu da premissa de facilitar o dia-a-dia das pessoas fazendo com que os dispositivos eletrónicos comunicassem entre si com o objetivo de sugerir e assistir a pequenas decisões dado os comportamentos observados no passado. Com o objetivo de manter o conceito possível e o estender para além das já existentes aplicações, os dados gerados pelos dispositivos necessitam não apenas de serem armazenados, mas igualmente processados. Adicionando ao volume de dados a sua variedade e velocidade de produção, estes são igualmente fatores que quando não ultrapassados da maneira correta podem apresentar diversas dificuldades, ao ponto de inviabilizarem a criação de novas aplicações baseadas neste novo conceito. Os mecanismos e tecnologias existentes não acompanharam a evolução das novas necessidades, e para que o conceito possa evoluir, novas soluções são obrigatórias. A liderar a lista das novas tecnologias preparadas para este novo tipo de desafios, composto por um sistema de ficheiros distribuído e uma plataforma de processamento distribuída, está o Hadoop. O Hadoop é uma referência para a resolução desta nova gama de problemas, e já comprovou ser capaz de processar enormes quantidades de dados de maneira económica. No entanto, dadas as suas características, tem alguma dificuldade em processar menores quantidades de dados e tem como desvantagem a grande latência necessária para a iniciação do processamento de dados. Num mercado volátil, ser capaz de processar grandes quantidades de dados baseadas em dados passados não é o suficiente. Tecnologias capazes de processar dados em tempo real são igualmente necessárias para complementar as necessidades de processamento de dados anteriores. No panorama atual, as tecnologias existentes não se demonstram à prova de tão distintas necessidades e, quando postas à prova, diferentes produtos tecnológicos necessitam ser combinados. Resultado de um ambiente com as características descritas é o ambiente que servirá de contexto para a execução do trabalho que se segue. Tendo com base as necessidades impostas por um caso de uso pertencente a IoT, através da arquitetura Lambda, diferentes tecnologias serão combinadas com o objetivo de que no final todos os requisitos impostos possam ser ultrapassados. No final, a solução apresentada será avaliada sobre um ambiente real como forma de prova de conceito.<br>Since the beginning of the first decade of current millennium, it has been witnessed an exponential grow of data being produced every day. First, the increase was given to the amount of data generated by GPS devices, then, the quickly arise of social networks, and now because a new trend as emerged named Internet of Things. This new concept, which is already a reality, was born from the premise of facilitating people's lives by having small electronic devices communicating with each other with the goal to suggest small daily decisions based on the behaviours experienced in the past. With the goal to keep this concept alive and extended further to other applications, the data produced by the target electronic devices is however need to be process and storage. The data volume, velocity and variety are the main variables which when not over planned on the correct way, a wall is created at the point of enviabilize the leverage of the true potential of this new group of applications. Traditional mechanisms and technologies did not follow the actual needs and with the goal to keep the concept alive the address of new technologies are now mandatory. On top of the line, leading the resolution of this new set of challenges, composed by a distributed file system and a parallel processing Framework is Hadoop. Hadoop have proven to fit under the new imposed challenges being capable of process and storage high volumes of data on a cost-effective batch-oriented way. However, given its characteristics on other hand it presents some drawbacks when faced with small amounts of data. In order to gain leverage on market, the companies need not only to be capable of process the data, but process it in a profitable way. Real time processing technologies are needed to complement batch oriented technologies. There is no one size fits all system and with the goal to address the multiples requirements, different technologies are required to be combined. Result of the demanding requirements imposed by the IoT concepts, is the environment which on will be relied the address of the business use case under analyses. Based on the needs imposed by a use case belonging to IoT, through the Lambda architecture, different technologies will be combined with the goal that in the end all the imposed requirements can be accomplished and exceeded. In the end, the solution presented will be evaluated on a real environment as proof of concept.
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Hvizdák, Lukáš. "Systém sběru dat v průmyslu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413269.

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The master thesis focuses on the design and implementation of data collection from production using a PLC into an SQL database located in the cloud and subsequent visualization. The work describes the applicable communication protocols MQTT and OPC UA with the fact that the protocol MQTT was selected. It deals with securing data transfer from the line to the cloud using the TLS protocol. The individual cloud services and their possibilities for data collection are described here. The work deals with the possibilities of data visualization using existing open source solutions and the differences between them. I describe the possibilities of modifying the open source environment of the Grafany project. Real dashboards from production are presented. The data collection system was deployed in two plants for testing.
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Nemrow, Andrew Craig. "Implementing an IIoT Core System for Simulated Intelligent Manufacturing in an Educational Environment." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8822.

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In this new digital age, efficiency, quality and competition are all increasing rapidly as companies leverage the Industrial Internet of Things (IIoT). However, while industrial innovation moves at a faster and faster pace, educational institutions have lagged in the development of the curriculum and environment needed to support further development of the IIoT. To fully realize the potential of the IIoT in the manufacturing sector educational institutions must support the technological training and education rigor demanded to instill the skills and thought leadership to move the industry forward. The purpose of this research is to provide an IIoT core system in an educational factory environment. This system will assist in teaching basic principles of IIoT in the factory while simultaneously allowing for students to envision the manufacturing journey of any facility by implementing principles of IIoT. This will be accomplished by providing all the following capabilities together in a single data system: unified connectivity, role-based data display, real-time issue identification, data analytics, and augmented reality.
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Books on the topic "IoT analytics"

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Perros, Harry G. An Introduction to IoT Analytics. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003139041.

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Pattnaik, Prasant Kumar, Raghvendra Kumar, Souvik Pal, and S. N. Panda, eds. IoT and Analytics for Agriculture. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9177-4.

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Soldatos, John. Building Blocks for IoT Analytics Internet-of-Things Analytics. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337423.

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Nayak, Padmalaya, Souvik Pal, and Sheng-Lung Peng, eds. IoT and Analytics for Sensor Networks. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-2919-8.

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Tanwar, Sudeep, ed. Fog Data Analytics for IoT Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6044-6.

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Alam, Bashir, and Mansaf Alam. Intelligent Data Analytics, IoT, and Blockchain. Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781003371380.

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Pattnaik, Prasant Kumar, Suneeta Mohanty, and Satarupa Mohanty, eds. Smart Healthcare Analytics in IoT Enabled Environment. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37551-5.

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Venkataramani, Guru Prasadh, Karthik Sankaranarayanan, Saswati Mukherjee, Kannan Arputharaj, and Swamynathan Sankara Narayanan, eds. Smart Secure Systems – IoT and Analytics Perspective. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7635-0.

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Rajendran, Sathiyaraj, Munish Sabharwal, Gheorghita Ghinea, Rajesh Kumar Dhanaraj, and Balamurugan Balusamy. IoT and Big Data Analytics for Smart Cities. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003217404.

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Gupta, Govind P., Rakesh Tripathi, Brij B. Gupta, and Kwok Tai Chui. Big Data Analytics in Fog-Enabled IoT Networks. CRC Press, 2023. http://dx.doi.org/10.1201/9781003264545.

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Book chapters on the topic "IoT analytics"

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Soldatos, John. "Introducing IoT Analytics." In Building Blocks for IoT Analytics Internet-of-Things Analytics. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337423-2.

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Klein, Scott. "Azure Stream Analytics." In IoT Solutions in Microsoft's Azure IoT Suite. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2143-3_5.

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Vuppalapati, Chandrasekar. "Edge Analytics." In Building Enterprise IoT Applications. CRC Press, 2019. http://dx.doi.org/10.1201/9780429056437-8.

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Perarasi, T., R. Gayathri, M. Leeban Moses, and B. Vinoth. "IoT Analytics/Data Science for IoT." In Data Science and Data Analytics. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003111290-2-3.

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Vashisht, Priyanka, Vijay Kumar, and Meghna Sharma. "IoT, Big Data, and Analytics." In Predictive Analytics. CRC Press, 2020. http://dx.doi.org/10.1201/9781003083177-10.

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Biswas, Abdur Rahim, Corentin Dupont, and Congduc Pham. "IoT, Cloud and BigData Integration for IoT Analytics." In Building Blocks for IoT Analytics Internet-of-Things Analytics. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337423-3.

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Klein, Scott. "Azure Data Lake Analytics." In IoT Solutions in Microsoft's Azure IoT Suite. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2143-3_10.

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Chowdhery, Aakanksha, Marco Levorato, Igor Burago, and Sabur Baidya. "Urban IoT Edge Analytics." In Fog Computing in the Internet of Things. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57639-8_6.

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Srivastav, Alok Kumar, Priyanka Das, and Ashish Kumar Srivastava. "Bioinformatics and Cloud Analytics." In Biotech and IoT. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0527-1_9.

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Mohapatra, Subasish, Amlan Sahoo, Subhadarshini Mohanty, and Munesh Singh. "Healthcare IoT." In Intelligent Analytics for Industry 4.0 Applications. CRC Press, 2023. http://dx.doi.org/10.1201/9781003321149-10.

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Conference papers on the topic "IoT analytics"

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Sekar, G., John T. Mesia Dhas, Anandaraj B, M. Nalini, V. Sathya, and Siva Subramanian R. "IoT and Data Analytics in Healthcare." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026330.

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Cyril, B. Rex, P. Ramesh, M. Anjankumar, V. Sathiya, Kalai Priya V, and Ashok Kumar. "Big Data Analytics in IoT Ecosystems." In 2025 International Conference on Frontier Technologies and Solutions (ICFTS). IEEE, 2025. https://doi.org/10.1109/icfts62006.2025.11031548.

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P, Siva K., Shri Suriya P, Praveen R, Nimesh Rajan D, and Santhosh Kumar G. "Crop Productivity Enhancement Through Data Analytics and IOT." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717032.

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Zamare, Rupali Anantrao. "Elderly Healthcare IoT through Data Analytics and Artificial Intelligence." In 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG). IEEE, 2024. https://doi.org/10.1109/ictbig64922.2024.10911206.

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Suthiv, Diya, Aditi Kanojia, Shritama Sengupta, Rajashree Jain, and Jatinderkumar R. Saini. "Water Quality Monitoring and Prediction Using IOT and Analytics." In 2024 2nd International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC). IEEE, 2024. https://doi.org/10.1109/icmacc62921.2024.10894063.

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Jayalakshmi, D., V. R. Vimal, S. Loganayagi, Lakshmi Kanthan Narayanan, and R. Hemavathi. "Enhancing Supply Chain Efficiency with IoT and Data Analytics." In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET). IEEE, 2024. https://doi.org/10.1109/icraset63057.2024.10894860.

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G, Rathanasabhapathy, Sharmila S, Swathi R, and Sivananthini V. "Cloud-based ECG Monitoring and Analytics with IoT Technology." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM). IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10987912.

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Wani, Priyanka, Sujit Bankar, and Karan Baral. "IOT Based Accident Detection and Alert System." In 2024 International Conference on Big Data Analytics in Bioinformatics (DABCon). IEEE, 2024. https://doi.org/10.1109/dabcon63472.2024.10919354.

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Dhakshinamoorthy, JaiAmudhan, Vishwa Varshini Viswanathan, Yamuneswar Annadurai, and V. P. Dhivya. "Parking Reservation and Smart Allocation using IoT." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961083.

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Taneja, Tushar, Aman Jatain, and Shalini Bhaskar Bajaj. "Predictive analytics on IoT." In 2017 International Conference on Computing, Communication and Automation (ICCCA). IEEE, 2017. http://dx.doi.org/10.1109/ccaa.2017.8230000.

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Reports on the topic "IoT analytics"

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Pasupuleti, Murali Krishna. AI-Driven Marketing Innovations: Personalization and Ethics in the Digital Era. National Education Services, 2025. https://doi.org/10.62311/nesx/rr625.

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Abstract: This article explores the transformative impact of artificial intelligence (AI) on digital marketing, focusing on strategies for delivering personalized content and ensuring ethical advertising. By leveraging AI, marketers can now analyze consumer behavior with precision, enabling targeted content, automated ad placement, and real-time adjustments that enhance user engagement and conversions. The Article examines foundational AI techniques, such as recommendation engines, predictive analytics, and natural language processing, which drive personalization at scale. Additionally, it addresses critical ethical considerations, including data privacy, transparency in AI-driven decisions, and reducing algorithmic bias to ensure fair, trustworthy, and responsible marketing practices. Looking ahead, this Article highlights emerging trends like hyper-personalization, ethical AI frameworks, and the integration of AI with technologies like AR/VR and IoT, offering a forward-looking perspective on AI's role in shaping consumer-centric and ethical digital marketing. Keywords: Artificial intelligence, digital marketing, personalized content, ethical advertising, consumer behavior, recommendation engines, predictive analytics, natural language processing, data privacy, transparency, algorithmic bias, hyper-personalization, responsible marketing, AR/VR, IoT, consumer engagement.
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Cimene, Dr Francis Thaise A. Emerging Technological Trends and Business Process Management: Preparing the Philippines for the Future. Asian Productivity Organization, 2024. https://doi.org/10.61145/dktv2301.

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The Philippine IT-BPM sector plays a vital role in driving economic growth and global competitiveness. This mini-report highlights how emerging technologies such as cloud computing, IoT, and big data analytics are transforming traditional business processes. Grounded in endogenous growth theory, the report emphasizes the impact of innovation and human capital on productivity. Policy recommendations are provided to bolster the nation’s position as a leading outsourcing hub and prepare for future technological advancements.
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Ruvinsky, Alicia, Timothy Garton, Daniel Chausse, Rajeev Agrawal, Harland Yu, and Ernest Miller. Accelerating the tactical decision process with High-Performance Computing (HPC) on the edge : motivation, framework, and use cases. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42169.

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Managing the ever-growing volume and velocity of data across the battlefield is a critical problem for warfighters. Solving this problem will require a fundamental change in how battlefield analyses are performed. A new approach to making decisions on the battlefield will eliminate data transport delays by moving the analytical capabilities closer to data sources. Decision cycles depend on the speed at which data can be captured and converted to actionable information for decision making. Real-time situational awareness is achieved by locating computational assets at the tactical edge. Accelerating the tactical decision process leverages capabilities in three technology areas: (1) High-Performance Computing (HPC), (2) Machine Learning (ML), and (3) Internet of Things (IoT). Exploiting these areas can reduce network traffic and shorten the time required to transform data into actionable information. Faster decision cycles may revolutionize battlefield operations. Presented is an overview of an artificial intelligence (AI) system design for near-real-time analytics in a tactical operational environment executing on co-located, mobile HPC hardware. The report contains the following sections, (1) an introduction describing motivation, background, and state of technology, (2) descriptions of tactical decision process leveraging HPC problem definition and use case, and (3) HPC tactical data analytics framework design enabling data to decisions.
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Rihm, Alfredo, Carolina Piamonte, Eduardo Antonio Restrepo Lagos, Magda Correal, and Paula Gabriela Guerra Morán. Digital Transformation of Solid Waste Management: Waste Collection Innovation, Business Intelligence, and Digital Technologies to Transition Waste Management Towards Circularity in Latin America and the Caribbean. Edited by Claudia M. Pasquetti. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013169.

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If you are interested in technology and innovation, and have been wondering what are the new trends in technological and digital innovation in the solid waste sector in Latin America and the Caribbean, this publication is for you! The transition to the circular economy, climate action, the fourth industrial revolution bring new challenges to operators in the sector. Key challenges highlighted include the need for robust data and the digitization of waste management systems to meet the objectives of the circular economy. The text details the efforts of organizations such as the IDB to develop data generation and analysis tools through digital innovations. It also explores the role of smart waste technologies (SWT), such as Artificial Intelligence (AI), Internet of Things (IoT) and data analytics, in transforming integrated solid waste management (ISWM), improving operational efficiency and supporting sustainable practices. The publication delves into various technological tools used in ISMS, including business intelligence (BI), enterprise resource planning (ERP) and fleet management software. Case studies from countries such as Argentina, Colombia and Ecuador illustrate the successful application of these tools, highlighting their benefits in improving decision making, operational efficiency and overall service quality. The text concludes with recommendations for implementing smart waste technologies in the LAC region to foster digital transformation and support a circular economy model effectively.
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Calderon, Marta, and Luis Jugo. Leveraging Data Analytics Beyond Assurance. Inter-American Development Bank, 2014. http://dx.doi.org/10.18235/0006992.

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This presentation gives an overview of the importance of creating a data analytics strategy in an internal audit department. It describes reasons for having a data analytics strategy, where to start (steps) when formulating a strategy, typical challenges to be addressed, what should be the focus of the strategy, and how to analyze the data analytics capability and usage maturity. Finally, it presents the IDB experience implementing a data analytics strategy for its internal audit activities.
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Walker, Alex, Brian MacKenna, Peter Inglesby, et al. Clinical coding of long COVID in English primary care: a federated analysis of 58 million patient records in situ using OpenSAFELY. OpenSAFELY, 2021. http://dx.doi.org/10.53764/rpt.3917ab5ac5.

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This OpenSAFELY report is a routine update of our peer-review paper published in the British Journal of General Practice on the Clinical coding of long COVID in English primary care: a federated analysis of 58 million patient records in situ using OpenSAFELY. It is a routine update of the analysis described in the paper. The data requires careful interpretation and there are a number of caveats. Please read the full detail about our methods and discussionis and the full analytical methods on this routine report are available on GitHub. OpenSAFELY is a new secure analytics platform for electronic patient records built on behalf of NHS England to deliver urgent academic and operational research during the pandemic. You can read more about OpenSAFELY on our website.
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Hussey, T. W., and S. S. Payne. Analytic theory of the Rayleigh-Taylor instability in a uniform density plasma-filled ion diode. Office of Scientific and Technical Information (OSTI), 1987. http://dx.doi.org/10.2172/6488379.

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Los, Josyp. TOP ANALYTICS OF OPINION JOURNALISM: HISTORY AND MODERNITY. Ivan Franko National University of Lviv, 2022. http://dx.doi.org/10.30970/vjo.2022.51.11405.

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The article investigates the immortality of books, collections, including those, translated into foreign languages, composed of the publications of publications of worldview journalism. It deals with top analytics on simulated training of journalists, the study of events and phenomena at the macro level, which enables the qualitative forecast of world development trends in the appropriate contexts for a long time.
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Cheung, Mike. Meta-Analytic SEM in R. Instats Inc., 2023. http://dx.doi.org/10.61700/2sgaqfuzkt040469.

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This seminar introduces the logic of meta-analytic structural equation modeling (MASEM) and illustrates how to conduct the analyses with R. Meta-analytic SEM is an incredibly powerful tool for hypothesis and theory testing, relying on pooled correlation matrices from primary studies, and this seminar will teach you the basics of MASEM and how to apply it in your own research, using many hands-on examples with Professor Cheung's R package for MASEM. When purchasing the MASEM seminar you will be freely enrolled in two on-demand seminars that introduce the logic of path analysis and CFA/SEM in R by Professor Zyphur, offering a substantial value. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Morrison, John F. Analyzing Interviews with Terrorists. RESOLVE Network, 2020. http://dx.doi.org/10.37805/rve2020.7.

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For years the dominant narrative has been that there is a dearth of primary sources in terrorism studies. This is now changing. The talk about the scarcity of data is gradually being replaced by discussions of a “data revolution” and a “golden age” of terrorism research. We are now publishing more research based on the analysis of primary source data than ever before. Included in this has been some ground-breaking interview research with recent and former terrorists—research that could define how we think about terrorist involvement for years to come. With this increased access to data, if our research is to have any analytical value and concurrently respected both within and outside of academia, we need to actively consider how we analyze it. This chapter discusses some of the issues that need to be taken into consideration when analyzing first-hand interviews, including the importance of specificity, different available analytic techniques, the role of triangulation, and ethical practices.
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