Academic literature on the topic 'IoT Data Management'
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Journal articles on the topic "IoT Data Management"
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Byun, Siwoo. "Replicated Data Management Using Scaled Segment Chain in Unstable IoT Environments." Webology 19, no. 1 (2022): 4286–98. http://dx.doi.org/10.14704/web/v19i1/web19282.
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IoT edge gateway reduces cloud computing's overload that redirect sensor data to remote servers. For reliable and efficient IoT gateway, column-based flash memory has become a reasonable storage due to its space efficiency and compression performance. This paper introduces recent IoT network and edge computing technology. It proposes efficient replication management called Context-mapped Segment Submirroring to support stable data services for sensor data in the edge-based IoT environment. Sensor context scaling and chained segment submirroring schemes are presented to improve the reliability and performance using IoT edge gateway. In the chained submirroring scheme, the sensor data are kept in the space-efficient storage of IoT edge. Consequently, sensor data transmission and mirroring storage cost can be minimized. The simulation results show that the proposed scheme outperforms the traditional scheme in respect of operation throughput and its response time.
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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 (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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Huang, Xiaohui, Junqing Fan, Ze Deng, Jining Yan, Jiabao Li, and Lizhe Wang. "Efficient IoT Data Management for Geological Disasters Based on Big Data-Turbocharged Data Lake Architecture." ISPRS International Journal of Geo-Information 10, no. 11 (2021): 743. http://dx.doi.org/10.3390/ijgi10110743.
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Multi-source Internet of Things (IoT) data, archived in institutions’ repositories, are becoming more and more widely open-sourced to make them publicly accessed by scientists, developers, and decision makers via web services to promote researches on geohazards prevention. In this paper, we design and implement a big data-turbocharged system for effective IoT data management following the data lake architecture. We first propose a multi-threading parallel data ingestion method to ingest IoT data from institutions’ data repositories in parallel. Next, we design storage strategies for both ingested IoT data and processed IoT data to store them in a scalable, reliable storage environment. We also build a distributed cache layer to enable fast access to IoT data. Then, we provide users with a unified, SQL-based interactive environment to enable IoT data exploration by leveraging the processing ability of Apache Spark. In addition, we design a standard-based metadata model to describe ingested IoT data and thus support IoT dataset discovery. Finally, we implement a prototype system and conduct experiments on real IoT data repositories to evaluate the efficiency of the proposed system.
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Jerome Oswald Ebenezer J and Calduwel Newton P. "Data Management in IoT: A Detailed Survey." International Journal of Information Technology, Research and Applications 2, no. 2 (2023): 18–32. http://dx.doi.org/10.59461/ijitra.v2i2.49.
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This paper surveys the current state of data management in the Internet of Things (IoT). It begins by outlining the challenges and opportunities that data management in IoT presents. Firstly, data management deals with the technical challenges and solutions related to data management in IoT, including data acquisition, storage, and integration. The paper concludes with a set of recommendations for the development of effective data management strategies in the context of IoT. Secondly, the requirement of IoT for data management extends offline storage, query processing, and transaction management activities into online-offline communication and storage dual operations, and the idea of data management is broadened. This is accomplished by IPv6, as well as IoT-specific capabilities and protocols including CoAP, HTTP, and WebSocket. Users may track, monitor, and manage devices with Internet of Things (IoT) device management, ensuring that they operate effectively and securely after deployment. Finally, the paper discusses the various applications of IoT based on the concept of data management in IoT. Numerous more objects, including wearables, medical equipment, houses, cities, farms, industries, and workplaces, are being interacted with by billions of sensors. The IoT platforms assist in establishing and maintaining criteria to enhance and preserve data appropriately. The paper concludes with a set of recommendations for the development of effective data management strategies in the context of IoT. Smart gadgets automate processes so we may save time by controlling the environment. The most valuable data is protected by edge devices for data management, which also lowers bandwidth costs. These also offer excellent performance, data ownership, and cheap maintenance costs.
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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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AYMAN, N. MAJED, and MUAYAD MOHAMMED MAHMOOD. "IOT ENHANCING THE EFFICIENT OF SENSORS DATA COLLECTION FROM REAL WORLD: AN OVERVIEW." Seybold Report V16, no. 12 (2021): 23–36. https://doi.org/10.5281/zenodo.6553802.
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<strong>Abstract</strong> The current industry era 4.0 dramatically increase productivity of technology through real-time data collection and analysis. It combines the uniqueness of Internet of Things (IoT) with the capabilities of cloud computing to create insights that help optimize the decision-making process. With this excellent technology which provide sensor management system in a cloud server that used for the data collection. Through this method, the data are easily control and manage in anywhere at any place through Information Communication Technology (ICT) devices as a remote. This paper provides a cross review related to IoT.
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Zhang, Lina, Dongwon Jeong, and Sukhoon Lee. "Data Quality Management in the Internet of Things." Sensors 21, no. 17 (2021): 5834. http://dx.doi.org/10.3390/s21175834.
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Nowadays, IoT is being used in more and more application areas and the importance of IoT data quality is widely recognized by practitioners and researchers. The requirements for data and its quality vary from application to application or organization in different contexts. Many methodologies and frameworks include techniques for defining, assessing, and improving data quality. However, due to the diversity of requirements, it can be a challenge to choose the appropriate technique for the IoT system. This paper surveys data quality frameworks and methodologies for IoT data, and related international standards, comparing them in terms of data types, data quality definitions, dimensions and metrics, and the choice of assessment dimensions. The survey is intended to help narrow down the possible choices of IoT data quality management technique.
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Dorage, Sachin Vasant. "Cybersecurity in Industrial Management within the Internet of Things." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–16. http://dx.doi.org/10.55041/ijsrem37081.
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Recently, it has become important to integrate cybersecurity threat management policies in the management of any organization that uses information systems, whether large, medium or even small organizations. People live in the center of the field of smart homes, business opportunities of smart industrial cities and healthcare. Even as IoT security issues become increasingly ubiquitousin the industrial realm, they face the added challenge of evolving networks. Architecture towards the integration of information technology (IT) and operational technology (OT) networks. This article analyzes the underlying cybersecurity risks, attack landscape in the Industrial IoT (IIoT), and suggests possible countermeasures for future hybrid IoT applications, based on lessons learned from IIoT projects. Security is essential for IoT systems to protect sensitive data and infrastructure, while security issues are becoming increasingly costly, especially in the industrial sector. The domains of the Internet of Things (IIoT). With this in mind, the issue of cybersecurity has become paramount for the Internet of Things (IoT) and the Industrial Internet of Things (IIoT) to mitigate cybersecurity risks for organizations and end users. New cybersecurity technologies and applications have improved IoT security management. However, there is a disparity in the effectiveness of solutions for IoT cyber risks. Key Words: Cybersecurity; Computer security; IT security; Internet of things (IoT); Safety; Industrial internet of things (IIoT); Blockchain and SDN (Software Defined Networking); 5G
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Izonin, Ivan. "AIoT Data Management, Analytics and Decision Making (Artificial Intelligence of Things Data Management, Analytics and Decision Making)." International Journal of Sensors, Wireless Communications and Control 11, no. 5 (2021): 496–97. http://dx.doi.org/10.2174/221032791105210401153652.
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Nowadays, the fast development of hardware for IoT-based systems creates appropriate conditions for the development of services for different application areas. As we know, the large number of multifunctional devices, which are connected to the Internet is constantly increasing. Today, most of the IoT devices just only collect and transmit data. The huge amount of data produced by these devices requires efficient and fast approaches to its analysis. This task can be solved by combining Artificial Intelligence and IoT tools. Essentially, AI accelerators can be used as a universal sensor in IoT systems, that is, we can create Artificial Intelligence of Things (AIoT). AIoT can be considered like a movement from data collection to knowledge aggregation. AIoT-based systems are being widely implemented in many high-tech industrial and infrastructure systems. Such systems are capable of providing not only the ability to collect but also analyse various aspects of data for identification, planning, diagnostics, evaluation, monitoring, optimization, etc., at the lower level in the entire system's hierarchy. That is, they are able to work more efficiently and effectively by generating the knowledge that is needed for real-time analytics and decision-making in some application areas.
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Tulasi.B* and J. Vemulkar Girish. "BLENDING IOT AND BIG DATA ANALYTICS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 4 (2016): 192–96. https://doi.org/10.5281/zenodo.48868.
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Internet is continuously evolving and changing. Internet of Things (IoT) can be considered as the future of Internet applications which involves machine to machine learning (M2M). The actionable intelligence can be derived through fusion of Big Data and real time analytics with IoT. Big Data and IoT can be viewed as two sides of a coin. With the connection between Big Data and the objects on Internet benefits of IoT can be easily reaped. The applications of IoT spread across various domains like health care, retail management and disaster management. Despite the benefits associated with the synthesis of Big Data analytics and IoT there are inherent complexities and challenges involved which needs to be catered. This paper focuses on the combination of Big Data analytics and IoT.
Dissertations / Theses on the topic "IoT Data Management"
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Laamech, Nouha. "Towards a secure data sharing management approach for IoT environments." Electronic Thesis or Diss., Pau, 2024. http://www.theses.fr/2024PAUU3031.
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Les environnements connectés promettent de nous apporter de nouveaux services : meilleure gestion de l'énergie, optimisation des transports, ciblage de l'information diffusée, etc. La grande valeur ajoutée de ces architectures à base d'objets connectés (IoT) est la donnée dont la collecte et le traitement impliquent un très grand nombre de systèmes informatiques opérés par des acteurs différents. Chaque acteur pouvant avoir ses propres objectifs, contraintes et enjeux, un des défis en terme de sécurité est de garder la maîtrise des informations échangées afin d'assurer ce que l'on appelle l'autodétermination informationnelle. Cette notion signifie par exemple que chaque acteur peut contrôler qui utilise ses données, où et pour quelle finalité.Cette thèse décrit notre approche pour la gestion du partage des données dans les environnements connectés. L'exploitation des données générées par les ressources de l'internet des objets soulève des risques de sécurité en raison du manque de transparence entre les différents acteurs de l'environnement. Ainsi, nous proposons tout d'abord une ontologie de gestion du partage des données IoT qui prend en considération les contextes, appelée IdSM-O, afin d'établir un vocabulaire de sécurité partagé et de gérer l'interopérabilité des environnements IoT. Ensuite, nous introduisons un gestionnaire de règles sémantiques automatique à trois niveaux, qui recueille les exigences des politiques de sécurité des fournisseurs de données et les traduit automatiquement en règles sémantiques prêtes pour le processus du raisonnement. Ces contributions constituent la base d'IdSM, un framework de sécurité de bout en bout pour la gestion du partage des données, qui répondent aux exigences de sécurité de l'information et au respect des obligations entre les différentes parties. Enfin, nous développons un prototype de la proposition afin de prouver sa faisabilité et d'analyser ses performances<br>Internet of Things (IoT) generates, connects and shares collected data from smart devices with various independent parties. With the increasing number of connected devices, its wide deployment is revolutionizing the modern world by covering almost every aspect of an individual's life. In this context, it is in the best interest of the community to successfully motivate users to share their IoT data with the rest of the environment, to allow the emergence of new services in different fields such as healthcare, education, or industrial manufacturing. However, requesting data to be able to extract valuable information from it can be a sensitive matter to approach. Therefore, framing requests and providing clarity on how this information will be used is necessary for building trust and credibility in connected environments. More precisely, when data providers decide to share their data with the community, they have little control over how their information are being used and in which context. In parallel, data consumers don't have the ability to trace back the different nodes by which the available data went through and its processing history to determine, for example, if it meets the technical and legal requirements of a given activity.Our research focus on three main challenges: (i) the definition of a semantic layer that handles the security requirements in the context of IoT data sharing, (ii) the enforcement of a context-aware security policy that matches both the data provider's preferences and the data consumer's usage, and (iii) the establishment of an end-to-end security solution that manage the sharing of IoT data in a decentralized architecture while eliminating the need to trust any involved IoT parties.To address these issues, we first present a context-aware IoT data Sharing Management ontology called IdSM-O, to establish a shared security vocabulary and handle the interoperability of IoT environments. Following that, we introduce a three-layer automatic semantic rule manager, that collects data provider's security policies requirements and automatically translate them to semantic rules ready for reasoning. Those contributions are the basement of IdSM, an end-to-end security framework for data sharing management during the phases of collection, transmission, and processing. Using this framework, we aim at addressing user's control enforcement over the owned smart devices, information security requirements, and obligation compliance between various parties in the IoT environment. Finally, we design, implement, and develop a prototype of the the proposal in order to prove its feasibility and analyze its performances
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Sellami, Youssef. "Secure data management in an IoT-Fog/Edge computing architecture." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2024. https://ged.uphf.fr/nuxeo/site/esupversions/14bb8a1d-7fbb-4d10-a7e7-99650617c232.
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L'internet des objets vise à intégrer les mondes physique et numérique dans un écosystème unique en interconnectant un grand nombre d'objets intelligents (capteurs, smartphones, véhicules autonomes, etc.) à l'internet. Cependant, la quantité massive de données est l'une des conséquences inévitables de la croissance du nombre d'objets connectés. L'évolution de l'IoT et de ses applications dans les années à venir (industrie 4.0, villes intelligentes) nécessite une gestion des données adaptée aux capacités limitées des objets connectés. De nouveaux paradigmes de traitement et de communication, tels que le fog/edge computing, sont à l'étude pour répondre aux attentes des applications et de leurs utilisateurs. Ces architectures utilisent des composants (routeurs, stations de base, machines utilisateurs, etc.) situés à proximité des objets et de l'utilisateur final. Cependant, ce couplage de l'IoT-Fog/Edge n'intègre pas encore de mécanismes de sécurité suffisamment robustes au regard des environnements de déploiement visés et des applications critiques qu'ils devront supporter. Cette thèse explore tout d'abord les architectures émergentes de l'IoT-edge/fog et met en évidence les différents défis et problèmes de sécurité posés par ce paradigme. L'un des problèmes critiques identifiés est la garantie de l'intégrité des données dans le fog. Malheureusement, les auditeurs tiers centralisés traditionnels sont inefficaces en raison de la latence élevée du réseau et des contraintes associées. Par conséquent, nous proposons un protocole de vérification publique efficace qui s'appuie sur le problème Short Integer Solution et sur des signatures basées sur l'identité. Ce nouveau protocole garantit l'intégrité et l'authenticité des données, autorise les modifications légitimes des données et permet une vérification distribuée de l'intégrité des données sans dépendre d'un tiers de confiance. Nous abordons également dans cette thèse la question de la fiabilité des données dans le fog, qui est cruciale pour la fiabilité des événements partagés entre les nœuds de fog et les sources de données. Une nouvelle solution basée sur la blockchain est présentée pour créer un environnement transparent et traçable permettant d'évaluer la fiabilité des données, de préserver les scores de confiance et d'encourager la responsabilité. Notre modèle calcule les scores de confiance sur la base de facteurs tels que la plausibilité de l'événement, la pertinence temporelle et la pertinence de la distance afin d'identifier efficacement les entités malveillantes et d'encourager un comportement digne de confiance. Enfin, nous avons mis l'accent dans cette thèse sur la protection de la confidentialité des données contre la menace quantique dans le contexte Edge/IoT. En outre, plusieurs schémas cryptographiques post-quantiques ont été proposés dans la littérature, visant à développer des techniques de cryptage résistantes à de telles attaques. En raison de ses propriétés de sécurité prometteuses, NTRU a été sélectionné comme candidat lors de la phase finale du concours du NIST sur la cryptographie post-quantique. Toutefois, cette méthode pose des problèmes pour les objets IoT en raison de ses exigences potentiellement plus élevées en matière de calcul et de mémoire. Motivés par la nécessité d'augmenter la durée de vie des objets IoT tout en étant capables de résister aux attaques quantiques, nous proposons un nouveau schéma collaboratif basé sur NTRU. Notre schéma préserve la confidentialité des données échangées entre les objets IoT déployés dans l'architecture edge. En outre, notre schéma répartit la charge des opérations cryptographiques entre les nœuds du edge et les objets IoT. Cette approche collaborative permet aux objets IoT de réduire considérablement leurs coûts de calcul tout en garantissant la confidentialité des données. En outre, la répartition proposée du calcul permet l'évolutivité de l'architecture et améliore la durabilité des environnements IoT<br>The Internet of Things (IoT) aims to integrate the physical and digital worlds into a single ecosystem by interconnecting a large number of intelligent objects (sensors/actuators, smartphones, autonomous vehicles, etc.), to the internet. However, the massive amount of data is one of the inevitable consequences of the exponential growth in the number of connected objects. The evolution of the IoT and its applications in the years to come (industry 4.0, smart cities, intelligent transport) requires data management adapted to the limited capacities of connected objects. New processing and communication paradigms, such as fog or edge computing, are being studied to meet the expectations of applications and their users.These architectures use components (such as routers, base stations, user machines, etc.) located in close proximity to objects and end-user. However, this technological coupling of IoT and Fog/Edge computing does not yet incorporate sufficiently robust security mechanisms in view of the targeted deployment environments and the critical applications they will have to support.This thesis first explores the emerging IoT-edge and fog computing architectures and highlights the various security challenges and issues posed by this new paradigm. One of the critical problems identified is guaranteeing data integrity in the highly dynamic and distributed environment of fog computing. Unfortunately, the traditional centralized third-party auditors are ineffective due to high network latency and associated constraints. Therefore, to solve this issue, we propose an efficient public verification protocol leveraging the Short Integer Solution (SIS) problem and identity-based signatures. This new protocol ensures data integrity and authenticity, allows for legitimate data modifications, and enables distributed data integrity verification without relying on a trusted third party.Furthermore, we address in this thesis the data trustworthiness in fog computing systems, which is crucial for the reliability of events shared between fog nodes and data sources. A novel Blockchain-based solution is presented to create a transparent, traceable environment for evaluating event trustworthiness, preserving trust scores, and fostering accountability. Our model calculates trust scores based on factors such as event plausibility, temporal relevance and distance relevance to effectively identify malicious entities and encourage trustworthy behavior.Finally, we focused in this thesis on the protection of data confidentiality against the quantum threat in the Edge/IoT context. In addition, several post-quantum cryptographic schemes have been proposed in the literature, aiming to develop encryption techniques resistant to such attacks.Due to its promising security properties and efficiency against quantum attacks, NTRU was selected as a candidate in the final round of the NIST competition on post-quantum cryptography. However, this method poses challenges for constrained devices due to its potentially higher computational and memory requirements. Motivated by the necessity to increase the lifetime of resource-constrained IoT devices while being able to resist quantum attacks, we propose a new NTRU-based collaborative scheme. Our scheme preserves the confidentiality of sensitive information exchanged among constrained IoT devices deployed in an edge computing architecture. Moreover, it distributes the workload of the cryptographic operations across edge nodes and IoT devices within the same network. This collaborative approach allows IoT devices to significantly reduce their computational costs while guaranteeing data confidentiality. Furthermore, the proposed distribution of computing enables scalability of the architecture and improves the sustainability of IoT environments.Keywords: Fog computing, Edge computing, IoT, Data integrity, Security, Lattice-based cryptography, SIS problem, NTRU, Trust management, Confidentiality
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Kandi, Mohamed Ali. "Lightweight key management solutions for heterogeneous IoT." Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2575.
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L'Internet des objets (IdO) est une technologie émergente ayant le potentiel d'améliorer notre quotidien de différentes façons. Elle consiste à étendre la connectivité au-delà des appareils standards (tels que les ordinateurs, les tablettes et les smartphones) à tous les objets du quotidien. Ces appareils, également appelés objets intelligents, peuvent alors collecter des données de leur entourage, collaborer pour les traiter puis agir sur leur environnement. Cela augmente leurs fonctionnalités et leur permet d'offrir divers services au profit de la société. Cela dit, de nombreux défis ralentissent le développement de l'IdO. La sécurisation des communications entre ces appareils est l'un des problèmes les plus difficiles qui empêche cette technologie de révéler tout son potentiel. La cryptographie fournit un ensemble de mécanismes permettant de sécuriser les données. Pour leur bon fonctionnement, ces derniers ont besoin de paramètres secrets appelés clés. La gestion des clés est une branche de la cryptographie qui englobe toutes les opérations impliquant la manipulation de ces clés : génération, stockage, distribution et remplacement. Par ailleurs, la cryptographie légère consiste à étendre les mécanismes conventionnels (la gestion des clés comprise) aux appareils à ressources limitées. Afin d'être efficaces dans l'IdO, les nouveaux mécanismes doivent offrir un bon compromis entre sécurité, performance et consommation de ressources. La gestion légère des clés est donc l'essence de la communication sécurisée dans l'IdO et le cœur de notre travail. Dans cette thèse, nous proposons un nouveau protocole léger de gestion des clés pour sécuriser la communication entre les appareils hétérogènes et dynamiques de l'IdO. Pour concevoir notre solution, nous considérons trois modes de communication : d'appareil à appareil, de groupe et de multi-groupes. Alors que la plupart des travaux connexes se concentrent uniquement sur l'un de ces modes de communication, notre solution sécurise efficacement les trois. Aussi, elle équilibre automatiquement les charges entre les appareils hétérogènes en fonction de leurs capacités. Nous prouvons alors que cela rend notre protocole plus adapté à l'IdO étant donné qu'il est efficace et hautement évolutif. De plus, nous proposons une décentralisation de notre protocole basée sur la technologie blockchain et les contrats intelligents. Ainsi, nous montrons qu'en permettant à plusieurs participants de gérer les clés cryptographiques, la décentralisation résout les problèmes de confiance, réduit le risque de défaillance du système et améliorer la sécurité. Nous implémentons enfin notre solution sur des plateformes IoT à ressources limitées qui sont basées sur le système d'exploitation Contiki. L'objectif est d'évaluer expérimentalement les performances de notre solution et de compléter nos analyses théoriques<br>The Internet of Things (IoT) is an emerging technology that has the potential to improveour daily lives in a number of ways. It consists of extending connectivity beyond standard devices (such as computers, tablets and smartphones) to all everyday objects. The IoT devices, also called smart objects, can collect data from their surroundings, collaborate to process them and then act on their environment. This increases their functionalities and allow them to offer various services for the benefit of society. However, many challenges are slowing down the development of the IoT. Securing communication between its devices is one of the hardest issue that prevents this technology from revealing its full potential. Cryptography provides a set of mechanisms to secure data. For their proper functioning, these mechanisms require secret parameters called keys. The Key Management is a branch of cryptography that encompasses all operations involving the handling of these of extending the conventional mechanisms (including the Key Management) to the resource-limited devices. To be efficient in the IoT, the new mechanisms must offer a good compromise between security, performance and resource requirements. Lightweight Key Management is the essence of secure communication in the IoT and the core of our work. In this thesis, we propose a novel lightweight Key Management protocol to secure communication between the heterogeneous and dynamic IoT devices. To design our solution, we consider three modes of communication: device-to-device, group and multi-group communication. While most of the related works focus only on one of these modes of communication, our solution efficiently secures all three of them. It also automatically balances the loads between the heterogeneous devices according to their capabilities. We then prove that this makes our protocol more suitable for the IoT as it is e_cient and highly scalable. Furthermore, we propose a decentralization of our protocol based on the blockchain technology and smart contracts. We show that, by empowering multiple participants to manage the cryptographic keys, decentralization solves trust issues, lowers risk of system failure and improves security. We finally implement our solution on resource-constrained IoT motes that are based on the Contiki operating system. The objective is to experimentally evaluate the performance of our solution and to complete our theoretical analyses
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Sridharan, Vaikunth. "Sensor Data Streams Correlation Platform for Asthma Management." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1527546937956439.
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Klasson, Anders, and Johan Rosengren. "Industrial IoT Management Systemfor Tubes with Integrated Sensors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237412.
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Sandvik har utvecklat en teknik för att placera sensorer i rör. Denna teknik har stor marknadspotential och kan effektivisera många industriprocesser. Den färdiga tjänsten ska kunna strömma sensordata till molntjänster för analys och avläsning.Deras nuvarande system kräver idag manuell konfiguration på plats och är komplicerad att installera. Denna uppsats undersöker hur systemets utrustning kan konfigureras automatiskt och hur ett system för underliggande IT-tjänster skulle kunna fungera.En lösning presenteras där många delar av installationsprocessen har automatiserats, samt en skiss för ett underliggande system.Lösningen utvärderas genom att utföra en mätning av konfigureringskomplexitet. Slutsatsen av utvärderingen var att det utvecklade system hade utökad funktionalitet, jämfört med dagens manuella tillvägagångssätt, och var inte mer komplex att konfigurera. I många avseenden mindre komplex.<br>Sandvik has developed a technique to place sensors inside tubes. This technology has great market potential and can optimize many industrial processes. The finished product should be able to stream sensor data to cloudservices for analysis and reading.The current system requires manual configuration on-site and the installation is labor intensive. This thesis investigates how the system’s hardware can be configured atomically, and how a supporting IT-system could function.A solution is presented where large portion of the installation process has been automated, along with an outline for a supporting system.The solution is evaluated by performing a measurement of the configuration complexity. The evaluation shows that the developed system had increased functionality compared to today’s manual configuration, configuration complexity was not increased. In many aspects, the configuration complexity was reduced.
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Minardi, Sara. "Processamento ed analisi di open data IoT mediante algoritmi di classificazione." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019.
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Il continuo sviluppo delle tecnologie degli ultimi anni ha permesso ad un insieme sempre più ampio di dispositivi di connettersi e scambiare informazioni tra loro e con l’ambiente esterno. I collegamenti sono costruiti attraverso l’utilizzo della rete, dando forma ad un nuovo paradigma tecnologico che prende il nome Internet of Things.
Gli oggetti materiali che interagiscono nel sistema sono definiti smart objects, ovvero oggetti intelligenti perché in grado di interagire con altri dispositivi e con il mondo esterno.
In rete esistono spazi che raccolgono le grandi quantità
di dati che si generano, e a seconda della tipologia di accesso possono essere identificati come Open Data.
Un dato per essere aperto deve essere facilmente accessibile ed utilizzabile da tutti.
Un dato aperto è un dato a cui si può accedere facilmente in maniera gratuita ed è impostato per essere potenzialmente utilizzabile da tutti.
Grazie all’implementazione di tecniche di processamento e analisi è possibile trasformare i dati grezzi in dati di valore.
Il progetto di questa tesi consiste nella realizzazione e valutazione degli algoritmi di diverse strutture basati sulle tecniche del Data Mining, al fine trovare un modello di classificazione per dati eterogenei e quindi applicabile ad un dataset di Open Data.
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Mezghani, Emna. "Towards Autonomic and Cognitive IoT Systems, Application to Patients’ Treatments Management." Thesis, Toulouse, INSA, 2016. http://www.theses.fr/2016ISAT0016/document.
Full textAbstract:
Dans cette thèse, nous proposons une méthodologie basée sur les modèles pour gérer la complexité de la conception des systèmes autonomiques cognitifs intégrant des objets connectés. Cette méthodologie englobe un ensemble de patrons de conception dont nous avons défini pour modéliser la coordination dynamique des processus autonomiques pour gérer l’évolution des besoins du système, et pour enrichir les systèmes avec des propriétés cognitives qui permettent de comprendre les données et de générer des nouvelles connaissances. De plus, pour gérer les problèmes reliés à la gestion des big data et à la scalabilité du système lors du déploiement des processus, nous proposons une plate-forme sémantique supportant le traitement des grandes quantités de données afin d’intégrer des sources de données distribuées et hétérogènes déployées sur le cloud pour générer des connaissances qui seront exposées en tant que service (KaaS). Comme application de nos contributions, nous proposons un système cognitif prescriptif pour la gestion du plan de traitement du patient. Ainsi, nous élaborons des modèles ontologiques décrivant les capteurs et le contexte du patient, ainsi que la connaissance médicale pour la prise de décision. Le système proposé est évalué de point de vue clinique en collaborant avec des experts médicaux, et de point de vue performance en proposant des différentes configurations dans le KaaS<br>In this thesis, we propose a collaborative model driven methodology for designing Autonomic Cognitive IoT systems to deal with IoT design complexity. We defined within this methodology a set of autonomic cognitive design patterns that aim at (1) delineating the dynamic coordination of the autonomic processes to deal with the system's context changeability and requirements evolution at run-time, and (2) adding cognitive abilities to IoT systems to understand big data and generate new insights. To address challenges related to big data and scalability, we propose a generic semantic big data platform that aims at integrating heterogeneous distributed data sources deployed on the cloud and generating knowledge that will be exposed as a service (Knowledge as a Service--KaaS). As an application of the proposed contributions, we instantiated and combined a set of patterns for the development of prescriptive cognitive system for the patient treatment management. Thus, we elaborated two ontological models describing the wearable devices and the patient context as well as the medical knowledge for decision-making. The proposed system is evaluated from the clinical prescriptive through collaborating with medical experts, and from the performance perspective through deploying the system within the KaaS following different configurations
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Pisanò, Lorenzo. "IoT e Smart Irrigation: gestione dei Big Data attraverso un sistema di notifica intelligente." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23531/.
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Con questo lavoro di tesi ho potuto approfondire anche un altro tema di grande attualità collegato ad IoT, la 'Smart Irrigation', conosciuta anche come 'Irrigazione di precisione'. Considerando la necessità sempre più evidente di migliorare la gestione della distribuzione irrigua ed energetica nel campo dell'agricoltura e, tenendo presente le indicazione meteoclimatiche e, l'importanza di avere informazioni tempestive ed aggiornate per migliorare le attività in campo, la Smart Irrigation assume un ruolo rilevante
nel risparmio idrico ed energentico, evitando sprechi ed usi impropri di queste preziose risorse.
Il software che ho realizzato è stato sviluppato nell'ambito di un programma europeo più vasto, il progetto SWAMP (Smart WAter Management Platform), che ha come obiettivo quello di determinare una svolta decisiva nell'utilizzo moderato e privo di
sprechi dell' acqua dolce ad uso irriguo, proponendo un sistema efficiente per la gestione della distribuzione di questo bene in vari contesti. L'area di competenza del progetto fa parte di quella amministrata dal Consorzio di Boni�ca dell'Emilia Centrale (CBEC),
responsabile delle irrigazioni e del drenaggio d'acqua di un'area di 1200 km2 suddivisi in circa 5400 terreni proprietari.
Il software di seguito descritto genera un sistema di acquisizione dati provenienti da alcuni pluviometri dislocati nel comune di Bologna. Successivamente, li elabora e classi�fica la quantità di pioggia che cade nell'area di studio in 5 differenti livelli di rischio.
Queste informazioni vengono poi noti�cate all'utente attraverso la piattaforma WDA, permettendo di ovviare ad eventi di inondazione e alluvione anche nelle aree adiacenti a quelle classi�cate 'a rischio'.
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Ismaili-Alaoui, Abir. "Methodology for an Augmented Business Process Management in IoT Environment." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0232.
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Les processus métiers peuvent être vu comme une collection d'activités exécutées et coordonnées afin de produire un résultat bien spécifique, répondant aux besoins d'un client (interne et/ou externe). La gestion des processus métiers (Business process management - BPM) est un domaine de recherche très actif dans la discipline des systèmes d'informations. Il a pour objectif l'amélioration continue de l'efficacité et la performance des processus d'une entreprise, par le biais des méthodes, des techniques et des outils qu'il offre afin d'appuyer la conception, la mise en œuvre, la gestion, l'analyse, et l'automatisation, dans la mesure du possible, des processus métier, et donc gagner en termes d'agilité,de flexibilité et de performance. Même si plusieurs méthodes d'amélioration des processus métier (Business Process Improvement- BPI) sont disponibles dans la littérature, les organisations rencontrent toujours des difficultés pour les appliquer efficacement. Ces difficultés peuvent être justifiées par le fait que les méthodes BPI existantes ne répondent pas à toutes les exigences récentes des organisations et ne s'adaptent pas aux progrès réalisés, ces dernières années, dans plusieurs domaines tels que l'intelligence artificielle, les techniques d'analyse des données, l'apprentissage automatique, le process mining et le traitement des (flux) événements, etc. En outre, avec cette nouvelle ère de digitalisation et l'essor de plusieurs nouvelles technologies telles que le Big Data, l'Internet des objets (IoT), le Cloud Computing, etc, les organisations sont confrontées à de nouveaux facteurs et défis redéfinissant le marché et qui génèrent de réels changements dans le BPM traditionnel. Parmi ces nouveaux défis on trouve la quantité de données et d'événements, provenant, avec une très grande vélocité,de différentes sources hétérogènes (des interactions internes ou externes de l'entreprise,IoT, etc). Ces données doivent être bien analysées et exploitées afin d'en extraire, des résultats à forte valeur ajoutée qui peuvent aider l'entreprise dans son processus de prise de décision. Cependant, les outils traditionnels proposés par la méthode du management des processus métiers présentent différentes limites concernant le traitement, la fouille et l'analyse des données et l'exploitation des résultats de ces analyses en temps réel. La nature interdisciplinaire du BPM est un facteur clé qui favorise les perspectives d'amélioration dans ce domaine. L'objectif de ce travail de thèse est de proposer de nouvelles approches pour augmenter les processus métier, en s'appuyant principalement sur l'analyse des données, les algorithmes d'apprentissage automatique et le traitement des événements complexes, afin d'exploiter les données et événements générés par l'exécution des processus métier et de trouver des moyens d'améliorer ces processus sous différents angles tels que l'ordonnancement des instances et la gestion des événements dans un environnement IoT. L'loT est en train de devenir une zone d'innovations technologiques et de promesses de développement économique pour de nombreuses industries et services. Ce nouveau changement de paradigme affecte toutes les couches de l'architecture d'entreprise, de l'infrastructure au métier. Le Business Process Management (BPM) est un domaine parmi d'autres qui est affecté par cette nouvelle technologie. Pour faire face à l'explosion des données et des événements résultant, entre autres, de l'loT, les processus d'analyse de données combinés aux techniques de traitement des événements, examinent de grands ensembles de données pour découvrir des modèles cachés, des corrélations inconnues entre les événements collectés, soit à un niveau très technique (détection des incidents/anomalies, maintenance prédictive), soit au niveau métier (préférences des clients, tendances du marché, opportunités de revenus) pour fournir une meilleure efficacité opérationnelle, un meilleur service client et des avantages [...]<br>Business Processes (BP) can be seen as a collection of activities executed and coordinated in order to produce a specific result and to meet the needs of a customer (internal and/or external). Business process management (BPM) is a very active research area, its objective is to provide a comprehensive and insightful analysis of the product flow and to identify inefficiencies and potential improvement areas in the process to achieve better decision making and results. Although we can find several Business Process Improvement (BPI) methods in the literature, organizations are still facing some difficulties to apply these methods effectively. These difficulties can be justified by the fact that the existing BPI methods do not fi twith all the recent requirements of the organizations and the progress that the world has achieved, in the past few years, in several domains such as Artificial Intelligence, Data Analytics techniques, Machine Learning, Process Mining, and Event (Stream) Processing, etc. Besides, with this new digitized era and the rise of several new technologies such as Big Data, Internet of things, Cloud computing, etc, organizations are faced with many factors and challenges that generate real changes in the traditional BPM. Among these challenges, we have the huge amount of data and event data that are continuously gathered within the organization. These data represent a real engine of growth for organizations, and must be adequately exploited to extract high added value that can assist the organization in its decision making process. Furthermore, enterprises are looking for advanced technologies that optimize time and resources and increase agility, productivity and most importantly, proactivity. However, traditional BPM systems present different limits, as they do not facilitate the use of knowledge extracted from this data, by business processes, because they do not benefit from statistical functionalities and data analysis and manipulation techniques in real time.The interdisciplinary nature of BPM is a key factor that fosters opportunities for improvement in this domain. The objective of this thesis work is to propose new approaches for augmenting business processes, by relying mainly on data analysis, machine learning algorithms, and complex event processing, to exploit the data generated by business process execution (event data, event logs) and find ways to improve these processes from different perspectives such as instances scheduling and event management in an IoT environment
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Sgarbi, Andrea. "Machine Cloud Connectivity: a robust communication architecture for Industrial IoT." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
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Industry 4.0 springs from the fourth industrial revolution, which is bringing innovation to fully automated and interconnected industrial production. This movement is composed of macro areas to expand the technological horizon starting from the tools used to date. The use of data, computing power and connectivity are the fundamental concepts on which the study of this thesis is based and are declined in big data, open data, Internet of Things (IoT), machine-to-machine and cloud computing for the centralization of information and its storage. Once the data has been collected, it is necessary to derive value from it in order to obtain advantages from ”machine learning”, i.e. machines that improve their performance by ”learning” from the data collected and analyzed.
The advent of the Internet of Things can be seen in all respects as the greatest technological revolution of recent years, which will bring a huge amount of information into the hands of users. The latter can offer countless advantages in daily life and in the diagnostics of the production process.
Industrial IoT (IIoT) enables manufacturing organizations to create a communication path through the automation pyramid, obtaining a real data stream in order to improve the machine performances. From an information security point of view, the importance of the information transmitted should not be underestimated and this also concerns an important aspect of industry 4.0. Protocols and authentication systems are constantly updated to ensure the privacy and security the customer needs.
Through this thesis project, the implementation requirements will be dealt with in order to study and analyze different vendor technologies and to construct a cloud architecture. The focus is concentrated on the cybersecurity and on the information losses avoidance in order to get a robust transfer.
Books on the topic "IoT Data Management"
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García Márquez, Fausto Pedro, Isaac Segovia Ramírez, Pedro José Bernalte Sánchez, and Alba Muñoz del Río, eds. IoT and Data Science in Engineering Management. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27915-7.
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Al-Turjman, Fadi, Satya Prakash Yadav, Manoj Kumar, Vibhash Yadav, and Thompson Stephan, eds. Transforming Management with AI, Big-Data, and IoT. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-86749-2.
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Ur Rehman, Masood, and Ahmed Zoha, eds. Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95593-9.
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Mucchi, Lorenzo, Matti Hämäläinen, Sara Jayousi, and Simone Morosi, eds. Body Area Networks: Smart IoT and Big Data for Intelligent Health Management. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34833-5.
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Mizmizi, Marouan, Maurizio Magarini, Prabhat Kumar Upadhyay, and Massimiliano Pierobon, eds. Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-72524-1.
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Murray, Fergus. Organisation studies & I.T. strategies: Some critical considerations. Manchester School of Management, University of Manchester Institute of Science and Technology, 1989.
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Liu, Kailong, Yujie Wang, and Xin Lai. Data Science-Based Full-Lifespan Management of Lithium-Ion Battery. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01340-9.
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Nalchigar, Sorush. E-Resources for Information Technology Students. IRAN Ministry of Science, Research and Technology Press, 2007.
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Paul, Suman, Sara Paiva, and Bo Fu, eds. Frontiers of Data and Knowledge Management for Convergence of ICT, Healthcare, and Telecommunication Services. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-77558-2.
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Dietrich, Seibt, and Informationskreis Organisation und Datenverarbeitung, eds. Informatik in Wirtschaft und Verwaltung: Gestern, heute, morgen : Symposium anlässlich des 25-jährigen Bestehens des Informationskreises Organisation und Datenverarbeitung (IOD). Vieweg, 1992.
Find full textBook chapters on the topic "IoT Data Management"
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Srivastav, Alok Kumar, Priyanka Das, and Ashish Kumar Srivastava. "Data Management, Security, and Ethical Considerations." In Biotech and IoT. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0527-1_6.
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Lalitha, K., and M. Pounambal. "IoT-Based Traffic Management." In Emerging Research in Data Engineering Systems and Computer Communications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0135-7_14.
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Joshva Devadas, T., S. Thayammal, and A. Ramprakash. "IoT Data Management, Data Aggregation and Dissemination." In Intelligent Systems Reference Library. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33596-0_16.
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Balali, Farhad, Jessie Nouri, Adel Nasiri, and Tian Zhao. "Implementation Tools of IoT Systems." In Data Intensive Industrial Asset Management. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35930-0_9.
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Jahnavi, Kattu, Addanki Snehitha Naga Sai Lalitha, and Manas Kumar Yogi. "Current Trends in IoT Data Management." In Internet of Things Applications and Technology. Auerbach Publications, 2024. http://dx.doi.org/10.1201/9781003458401-3.
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Sahani, Amrit, Ranjit Kumar, Suchismita Chinara, Anjali Kumari, and Bina Patro. "Data Management for the Internet of Things." In Internet of Things (IoT). Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37468-6_5.
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Balali, Farhad, Jessie Nouri, Adel Nasiri, and Tian Zhao. "Internet of Things (IoT): Principles and Framework." In Data Intensive Industrial Asset Management. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35930-0_1.
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Empl, Philip, Henric Hager, and Günther Pernul. "Digital Twins for IoT Security Management." In Data and Applications Security and Privacy XXXVII. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37586-6_9.
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Anjali, Yeole, and D. R. Kalbande. "MAC-Based Group Management Protocol for IoT [MAC GMP-IoT]." In Lecture Notes on Data Engineering and Communications Technologies. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8339-6_3.
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Singh, Gurwinder, Munish Rattan, and Gurjot Kaur Walia. "Cloud Security Risk Management Quantifications." In Big Data, Cloud Computing and IoT. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003298335-9.
Full textConference papers on the topic "IoT Data Management"
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R, Kanishka, Saravanan V, Dinesh S, and Rajasekaran N. "IoT based Fish Farming Management." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914802.
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Cerbulescu, Catalin Constantin, Marius Marian, and Eugen Ganea. "IoT Data Management Architectures to Detect Critical Data Evolution." In 2024 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). IEEE, 2024. http://dx.doi.org/10.1109/inista62901.2024.10683827.
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Pruthvi, C. N., K. Sandhya, J. Shreyas, T. Arpitha, S. Amutha, and D. R. Ramesh Babu. "ADMT: ACO-based Data Management Technique for IoT." In 2024 Third International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON). IEEE, 2024. https://doi.org/10.1109/teeccon64024.2024.10939931.
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S, Muthulakshimi, Malathy Sathyamoorthy, Akash S, Charumathi P, and Sri Suvathi N M. "IoT Based Water Management and Monitoring System." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10915161.
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Liu, Fangqi. "Leveraging Mobility to Enhance IoT Applications." In 2025 26th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2025. https://doi.org/10.1109/mdm65600.2025.00042.
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Huseynov, Khayal, Lal Verda Cakir, Sarah Al-Shareeda, Mehmet Özdem, and Berk Canberk. "A Data Serialization-based Framework for Efficient IoT Management." In 2024 IEEE 10th World Forum on Internet of Things (WF-IoT). IEEE, 2024. https://doi.org/10.1109/wf-iot62078.2024.10811147.
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G, Shanmugavadivel, Nathan S, Raguladhithiya S, and Rakesh C. "IoT Based EV Multiple Fault Detection and Battery Management." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961827.
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Anuradha, Ch, Manoj Pothuraju, Niveditha Manam, and Chandana Chigurupati. "IoT-Based Bin Level Monitoring for Efficient Waste Management." In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). IEEE, 2024. https://doi.org/10.1109/icdici62993.2024.10810966.
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Tiwari, Ashish, Vaishali Raut, Manisha G. Waje, Janhvi Chevale, Bhagyashree Nikam, and Aamir Pathan. "Smart waste management system using AR and IoT with AI." In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE). IEEE, 2025. https://doi.org/10.1109/aide64228.2025.10987435.
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Garah, Abdelhamid, Nader Mbarek, and Sergey Kirgizov. "IoT Data Confidentiality Self-Management." In 2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). IEEE, 2023. http://dx.doi.org/10.1109/dasc/picom/cbdcom/cy59711.2023.10361375.
Full textReports on the topic "IoT Data Management"
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Yaseen, Lama, Nourah Al-Hosain, Ibrahem Shatnawi, and Abdelrahman Muhsen. Impact of Urban Traffic on Fuel Consumption Leveraging IoT Data: Case Study of Riyadh City. King Abdullah Petroleum Studies and Research Center, 2024. https://doi.org/10.30573/ks--2024-dp72.
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This study explores the rising trend of traffic congestion in Riyadh and its impact on fuel consumption for passenger cars amid the challenges of rapid urbanization and increasing vehicle use. By utilizing real-time floating car data (FCD) collected by vehicles equipped with Global Positioning System (GPS) technology and communication systems, this study illustrates the potential of the Internet of Things (IoT) and smart city technologies in developing intelligent transportation systems and improving urban mobility management. A spatial analysis of the traffic flow dynamics in Riyadh, focusing on selected primary highways, reveals that driving on high-capacity roads tends to increase fuel consumption. We conducted an analysis at a mesoscopic level, representing traffic congestion in Riyadh on high-capacity roads. It shows that traffic congestion leads to up to a 29% increase in fuel consumption, primarily due to frequent stop-and-go driving behavior, reducing overall fuel efficiency. This study enhances our understanding of urban traffic patterns, providing policymakers with data-driven insights to help them create more sustainable road planning strategies to address the specific needs and challenges of urban mobility in cities.
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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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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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Phillips, Paul. The Application of Satellite-based Internet of Things for New Mobility. SAE International, 2024. http://dx.doi.org/10.4271/epr2024001.
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<div class="section abstract"><div class="htmlview paragraph">With the increased use of devices requiring the Internet of Things (IoT) to enable “New Mobility,” the demand for satellite-enabled IoT is growing steadily, owing to the extensive coverage provided by satellites (over existing ground-based infrastructure). Satellite-based IoT provides precise and real-time vehicle location and tracking services, large-scale geographical vehicle and/or infrastructure monitoring, and increased coverage for remote locations where it may not be possible to install ground-based solutions.</div><div class="htmlview paragraph"><b>The Application of Satellite-based Internet of Things for New Mobility</b> discusses satellite-based IoT topics that still need addressing, which can be broadly classifieds into two areas: (1) affordable technology and (2) network connectivity and data management. While recent innovations are driving down the cost of satellite-based IoT, it remains relatively expensive, and widespread adoption is still not as high as terrestrial, ground-based systems. Security concerns over data and privacy also create significant barriers to entry and need to be addressed along with issues such as intermittent connectivity, latency and bandwidth limitations, and data storage and processing restrictions.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>
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Pasupuleti, Murali Krishna. Empathetic AI in Action: Transforming Customer Service with Emotional Intelligence. National Education Services, 2025. https://doi.org/10.62311/nesx/rr725.
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Abstract: This article explores the transformative impact of Emotionally Intelligent AI on customer service, focusing on how AI systems are designed to understand and respond to human emotions with empathy and precision. It delves into the core technologies, such as sentiment analysis, emotion recognition models, and reinforcement learning, that enable AI to provide emotionally aware interactions. Practical applications are discussed, including AI-powered customer support, personalized experiences, and crisis management solutions. The Article also covers the psychological foundations of AI-driven empathy, ethical and privacy considerations, and future trends in affective computing and integration with technologies like AR/VR and IoT. The potential business advantages of adopting Emotionally Intelligent AI for enhanced customer satisfaction and long-term relationship management are highlighted, emphasizing the balance between technology and the human touch. Keywords: Emotionally Intelligent AI, customer service, empathy, sentiment analysis, emotion recognition, reinforcement learning, affective computing, personalized interactions, ethical AI, data privacy, AR/VR, IoT, human-AI interaction, future trends, business impact.
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Latorre, Lucia, Valeria Lovaisa, Lorenzo De Leo, Alexander Riobó, Eduardo Riego, and Mariana Gutierrez. Tech Report: Edge Computing. Inter-American Development Bank, 2024. https://doi.org/10.18235/0013021.
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The adoption of edge computing in Latin America and the Caribbean (LAC) underscores its ability to address the regions unique challenges, including limited connectivity and the need to process data in remote locations. Innovative projects in smart public lighting, urban security, and sustainable resource management highlight the transformative impact of edge computing in improving quality of life and operational efficiency. Looking ahead, edge computing stands at the forefront of technological innovation. Deeper integration with artificial intelligence and the rise of 5G will unlock fresh opportunities for Internet of Things (IoT) applications. Moreover, the evolution of Edge as-a-Service promises flexibility and efficiency for businesses of all scales. Data security will continue to be a focal point, with novel solutions aimed at safeguarding information in this decentralized setting.
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Leathers, Emily, Clayton Thurmer, and Kendall Niles. Encryption for edge computing applications. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48596.
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As smart sensors and the Internet of Things (IoT) exponentially expand, there is an increased need for effective processing solutions for sensor node data located in the operational arena where it can be leveraged for immediate decision support. Current developments reveal that edge computing, where processing and storage are performed close to data generation locations, can meet this need (Ahmed and Ahmed 2016). Edge computing imparts greater flexibility than that experienced in cloud computing architectures (Khan et al. 2019). Despite these benefits, the literature highlights open security issues in edge computing, particularly in the realm of encryption. A prominent limitation of edge devices is the hardware’s ability to support the computational complexity of traditional encryption methodologies (Alwarafy et al. 2020). Furthermore, encryption on the edge poses challenges in key management, the process by which cryptographic keys are transferred and stored among devices (Zeyu et al. 2020). Though edge computing provides reduced latency in data processing, encryption mechanism utilization reintroduces delay and can hinder achieving real-time results (Yu et al. 2018). The IoT is composed of a wide range of devices with a diverse set of computational capabilities, rendering a homogeneous solution for encryption impractical (Dar et al. 2019). Edge devices are often deployed in operational locations that are vulnerable to physical tampering and attacks. Sensitive data may be compromised if not sufficiently encrypted or if keys are not managed properly. Furthermore, the distributed nature and quantity of edge devices create a vast attack surface that can be compromised in other ways (Xiao et al. 2019). Understanding established mechanisms and exploring emerging methodologies for encryption reveals potential solutions for developing a robust solution for edge computing applications. The purpose of this document is to detail the current research for encryption methods in the edge computing space and highlight the major challenges associated with executing successful encryption on the edge.
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Paglialonga, Lisa, and Carsten Schirnick. Data management plan. OceanNETs, 2022. http://dx.doi.org/10.3289/oceannets_d8.1.
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This is the data management plan for the research project OceanNETs. It compiles OceanNETs research data output and describes the data handling during and after the projects duration with the aim to make OceanNETs research data FAIR – sustainably available for the scientific community. This data management plan is a living document; it will be continuously developed in close cooperation with the consortium members throughout the project duration
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García-Espinosa, J., and C. Soriano. Data management plan. Scipedia, 2021. http://dx.doi.org/10.23967/prodphd.2021.9.003.
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This document presents the deliverable D8.1 – the Data Management Plan (DMP) of work package 8 of the prodPhD project. It aims to present the plan for the management, generation, collection, security, preservation and sharing of data generated through the prodPhD project. The DMP is a key element for organizing the project’s data. It provides an analysis of the data, which will be collected, processed and published by the prodPhD consortium. The project embraces the initiatives of the European Commission to promote the open access to research data, aiming to improve and maximize access to and reuse of research data generated by Horizon 2020 projects. In this sense prodPhD will adhere to the Open Research Data Pilot (ORD Pilot) fostered by the European Commission, and this DMP will be developed following the standards of data storage, access and management. This plan will detail what data will be generated through the project, whether and how it will be made accessible for the verification and reuse and how it will be curated and preserved. In this context, the term data applies to the information generated during the different experimental campaigns carried out in the project, and specifically to the data, including associated metadata, to be used to validate the computational models and the technical solutions to be developed in the project. This document is the first version of the DMP and may be updated throughout the project, if significant changes (new data, changes in consortium policies, changes in consortium composition, etc.) arise.
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Paglialonga, Lisa, and Carsten Schirnick. OceanNETs Data Management Plan. OceanNETs, 2020. http://dx.doi.org/10.3289/oceannets_dmp_v1.
Full textAbstract:
This is the data management plan for the research project OceanNETs. It compiles OceanNETs research data output and describes the data handling during and after the projects duration with the aim to make OceanNETs research data FAIR – sustainably available for the scientific community. This data management plan is a living document; it will be continously developed in close cooperation with the consortium members throughout the project duration.