Letteratura scientifica selezionata sul tema "IoT Internet des objets"
Cita una fonte nei formati APA, MLA, Chicago, Harvard e in molti altri stili
Consulta la lista di attuali articoli, libri, tesi, atti di convegni e altre fonti scientifiche attinenti al tema "IoT Internet des objets".
Accanto a ogni fonte nell'elenco di riferimenti c'è un pulsante "Aggiungi alla bibliografia". Premilo e genereremo automaticamente la citazione bibliografica dell'opera scelta nello stile citazionale di cui hai bisogno: APA, MLA, Harvard, Chicago, Vancouver ecc.
Puoi anche scaricare il testo completo della pubblicazione scientifica nel formato .pdf e leggere online l'abstract (il sommario) dell'opera se è presente nei metadati.
Articoli di riviste sul tema "IoT Internet des objets":
1
Milivojević, Sanja, e Elizabeth Radulski. "The 'Future Internet' and crime: Towards a criminology of the Internet of Things". Crimen 11, n. 3 (2020): 255–71. http://dx.doi.org/10.5937/crimen2003255m.
Abstract (sommario):
The Internet of Things (IoT) is poised to revolutionise the way we live and communicate, and the manner in which we engage with our social and natural world. In the IoT, objects such as household items, vending machines and cars have the ability to sense and share data with other things, via wireless, Bluetooth, or Radio Frequency IDentification (RFID) technology. "Smart things" have the capability to control their performance, as well as our experiences and decisions. In this exploratory paper, we overview recent developments in the IoT technology, and their relevance for criminology. Our aim is to partially fill the gap in the literature, by flagging emerging issues criminologists and social scientists ought to engage with in the future. The focus is exclusively on the IoT while other advances, such as facial recognition technology, are only lightly touched upon. This paper, thus, serves as a starting point in the conversation, as we invite scholars to join us in forecasting-if not preventing-the unwanted consequences of the "future Internet".
2
C. Sailaja. "Industrial Internet of Things – An Overview". December 2022 4, n. 4 (30 dicembre 2022): 257–71. http://dx.doi.org/10.36548/jismac.2022.4.003.
Abstract (sommario):
Internet of Things (IoT) is a network of intelligent objects (smart sensors) that are connected and accessible to the Internet through devices such as routers to exchange data. Many useful applications can be developed using IoT. IoT has brought a change in such a way that everything around us can be turned into smart devices e.g., smart cars, smart homes, smart parking, smart cities etc., thereby connecting human, machine & things seamlessly. The advent of cheaper processing power, communication cost and some marketing have created the IoT of today. When the principles of IoT are applied to Industries, it is termed as Industrial IoT. Smart sensors and Industrial Internet of Things (IIoT) are modern tools which drive Industry 4.0. The use of smart sensors and IIoT have a positive impact in enhancing the quality & value of products and improve efficiency. The combination of smart sensors with its processing & networking capabilities help in transferring the process data to the centralized database for further analysis. Thus, digitalization has helped industry to reimage the business with the transformation brought about by IIoT. This paper discusses some of the applications of IIoT such as Remote Monitoring & Service, AI & ML based predictive analysis and Closed loop Digital twin using IoT sensors.
3
Suryani, Vera, Selo Sulistyo e Widyawan Widyawan. "Trust-Based Privacy for Internet of Things". International Journal of Electrical and Computer Engineering (IJECE) 6, n. 5 (1 ottobre 2016): 2396. http://dx.doi.org/10.11591/ijece.v6i5.9678.
Abstract (sommario):
<p>Internet of Things or widely known as IOT makes smart objects become active participants in the communication process between objects and their environment. IoT services that utilize Internet connection require solutions to a new problem: security and privacy. Smart objects and machine-to-machine communications in IOT now become interesting research, including that related to security. Privacy, which is a safe condition in which object is free from interference from other objects, is one of the important aspects in IOT. Privacy can be implemented using various ways for examples by applying encryption algorithms, restrictions on access to data or users, as well as implementing rules or specific policy. Trustable object selection is one technique to improve privacy. The process of selecting a trustable object can be done based on past activities or trust history of the object, also by applying a threshold value to determine whether an object is "trusted" or not. Some researchers have studied this approach. In this study, the selection processes of trustable objects are calculated using Modified Ant Colony algorithm. The simulation was performed and resulted in declining graphic trend but stabilized in certain trust value.</p>
4
Suryani, Vera, Selo Sulistyo e Widyawan Widyawan. "Trust-Based Privacy for Internet of Things". International Journal of Electrical and Computer Engineering (IJECE) 6, n. 5 (1 ottobre 2016): 2396. http://dx.doi.org/10.11591/ijece.v6i5.pp2396-2402.
Abstract (sommario):
<p>Internet of Things or widely known as IOT makes smart objects become active participants in the communication process between objects and their environment. IoT services that utilize Internet connection require solutions to a new problem: security and privacy. Smart objects and machine-to-machine communications in IOT now become interesting research, including that related to security. Privacy, which is a safe condition in which object is free from interference from other objects, is one of the important aspects in IOT. Privacy can be implemented using various ways for examples by applying encryption algorithms, restrictions on access to data or users, as well as implementing rules or specific policy. Trustable object selection is one technique to improve privacy. The process of selecting a trustable object can be done based on past activities or trust history of the object, also by applying a threshold value to determine whether an object is "trusted" or not. Some researchers have studied this approach. In this study, the selection processes of trustable objects are calculated using Modified Ant Colony algorithm. The simulation was performed and resulted in declining graphic trend but stabilized in certain trust value.</p>
5
Park, Jong. "Advances in Future Internet and the Industrial Internet of Things". Symmetry 11, n. 2 (16 febbraio 2019): 244. http://dx.doi.org/10.3390/sym11020244.
Abstract (sommario):
After the emergence of the Internet and mobile communication networks, the IoT has been considered as the third wave of information technology. The Industrial Internet of Things (IIoT) is the use of Internet of Things (IoT) technologies in manufacturing. IIoT incorporates machine learning and big data technology, sensor data, and machine-to-machine (M2M) communications that have existed in industrial areas for years. In the future, people and objects will be connected at any time, any place, with anything and anyone and will utilize any network and services. IIoT is creating a new world in which people and businesses can manage their assets in more informed ways and can make more opportune and better-informed decisions. Many advanced IIoT and 5G technologies have been successfully applied in everyday life, but there are still many practical problems tackled by traditional methods which are generally difficult to experimentally solve in the advanced Industrial Internet of Things. Therefore, in this special issue, we accepted five articles in three different dimensions: communication networks, optimized resource provisioning and data forwarding, privacy and security.
6
Raimundo, Ricardo Jorge, e Albérico Travassos Rosário. "Cybersecurity in the Internet of Things in Industrial Management". Applied Sciences 12, n. 3 (2 febbraio 2022): 1598. http://dx.doi.org/10.3390/app12031598.
Abstract (sommario):
Nowadays, people live amidst the smart home domain, while there are business opportunities in industrial smart cities and healthcare. However, there are concerns about security. Security is central for IoT systems to protect sensitive data and infrastructure, whilst security issues have become increasingly expensive, in particular in Industrial Internet of Things (IIoT) domains. Nonetheless, there are some key challenges for dealing with those security issues in IoT domains: Applications operate in distributed environments such as Blockchain, varied smart objects are used, and sensors are limited, as far as machine resources are concerned. In this way, traditional security does not fit in IoT systems. The issue of cybersecurity has become paramount to the Internet of Things (IoT) and the Industrial Internet of Things (IIoT) in mitigating cybersecurity risk for organizations and end users. New cybersecurity technologies/applications present improvements for IoT security management. Nevertheless, there is a gap in the effectiveness of IoT cyber risk solutions. This review article discusses the literature trends around opportunities and threats in cybersecurity for IIoT, by reviewing 70 key articles discovered from a profound Scopus literature survey. It aims to present the current debate around the issue of IIoT rather than suggesting any particular technical solutions to solve network security problems.
7
Birje, Mahantesh N., Arun A. Kumbi e Ashok V. Sutagundar. "Internet of Things". International Journal of Hyperconnectivity and the Internet of Things 1, n. 2 (luglio 2017): 45–71. http://dx.doi.org/10.4018/ijhiot.2017070104.
Abstract (sommario):
This article describes how the Internet of Things (IoT) enables interaction and integration of real world things or objects—such as cars, electronic devices, homes, etc.—using various inter-operable communication protocols. This leads to a digital society of billions of devices that can sense, process, share, track, shop, chat and work in cooperative manner. There exist many challenges in the realization of IoT based distributed systems. This article intends to introduce various aspects of IoT such as views/visions, Architecture and protocols of IoT, communication technology, general requirements and various application domains. This article intends to disseminate the state-of-the-art multi-fold information on IoT to researchers, academicians, and student community.
8
Long, Rong, Xiaohui Fan, Kai Wei, Junxuan Bai e Shanpeng Xiao. "Internet-of-Things object model". Digital Twin 2 (12 aprile 2022): 5. http://dx.doi.org/10.12688/digitaltwin.17562.1.
Abstract (sommario):
Background: With the advancement of communication technology and advanced sensors, there are massive demands for Internet-of-Things (IoT) applications in buildings, communities, factories, parks, etc. Accessing IoT devices provides convenience for scene management and monitoring, ameliorating production and life intelligently. However, due to the lack of a unified model for IoT devices, data is often skipped over IoT platforms and transmitted to applications directly. This leads to the fact that each manufacturer needs to produce its devices and develop its customized software, which hugely increases the development cycle. On the other hand, it is difficult to convey information between different systems, limiting cross- system control. Moreover, digital twin relies on large amounts of heterogeneous data, and it is impracticable to provide enough data without a unified model for device description. Methods: First, we illustrate the motivation, design goals, and design principles for creating the Internet-of-Things Object Model (IoT-OM). Then we propose a unified description to define IoT devices. The proposed concept has been accepted by several companies, and we analyse one platform that adopts the model. To demonstrate the effectiveness of the model, we introduce two projects based on the platform. One project is an intelligent fire protection system, and another project is an intelligent air quality monitoring system. Results: We measured the time taken by five companies when developing IoT devices and their applications, including the development cycle duration without utilizing the proposed model and the duration using the model at China Mobile’s OneNET platform. The results prove that the proposed model can significantly shorten the development cycle. Conclusions: This paper proposes a model for IoT devices, which helps to unify heterogeneous data among different manufacturers and helps to shorten the development cycles for developers.
9
E, Umamaheswari, e Ajay Dm. "SCOPE OF INTERNET OF THINGS: A SURVEY". Asian Journal of Pharmaceutical and Clinical Research 10, n. 13 (1 aprile 2017): 187. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19633.
Abstract (sommario):
The advent of internet of things (IoT) has influenced and revolutionized the information systems and computing technologies. A computing conceptwhere physical objects used in daily life, will identify themselves by getting connected to the internet is called IoT. Physical objects embedded withelectronic, software, radio-frequency identification, sensors, actuators and smart objects converge with the internet to accumulate and share datain IoT. IoT is expected to bring in drastic changes and solutions to most of the daily problems in the real world. This paper explains in detail the keyconcepts of IoT, the applications of IoT, the challenges in establishing IoT. This work also explains in detail the future scope of IoT in different fields.
10
Susanto, Fredy, Ni Komang Prasiani e Putu Darmawan. "IMPLEMENTASI INTERNET OF THINGS DALAM KEHIDUPAN SEHARI-HARI". Jurnal Imagine 2, n. 1 (21 aprile 2022): 35–40. http://dx.doi.org/10.35886/imagine.v2i1.329.
Abstract (sommario):
ABSTRACT
Internet of Things or abbreviated as IoT is a technology that innovates surrounding objects with the internet so that daily activities become easier and more efficient. The Internet of Things is a technology that allows objects around us to be connected to the Internet. The importance of the Internet of Things can be seen with the increasing number of applications in various lines of life today. IoT gives us many ideas to participate in various aspects of development ranging from micro to macro around the world. The internet of things has made it a separate field of research since the development of internet technology (IT) and other communication media. The methodology used in the development of IoT is of various kinds. From the real time system to the use of the prototype flow. The more human needs about technology are developing, the more research will be present, the internet of things is one of the thoughts of researchers who optimize several tools such as sensor media, Radio Frequency Identification (RFID), Wireless Sensor Networks and other Smart Objects that allow humans easy to interact with all equipment connected to the internet network.
Keywords: IoT, RFID, Wireless Sensor Network, Smart Object
ABSTRAK
Internet of Things atau disingkat dengan istilah IoT merupakan teknologi yang menginovasi benda-benda sekitar dengan internet agar aktivitas sehari-hari menjadi lebih mudah dan efisien. Internet of Things adalah teknologi yang memungkinkan benda-benda di sekitar kita terhubung dengan Internet. Pentingnya Internet of Things dapat dilihat dengan semakin banyaknya diterapkan dalam berbagai lini kehidupan saat ini. IoT memberikan kita banyak gagasan untuk turut berperan serta dalam berbagai segi perkembangan mulai dari hal mikro hingga makro di seluruh dunia. Internet of things menjadikannya sebuah bidang penelitian tersendiri sejak berkembangnya teknologi internet (IT) dan media komunikasi lain. Metodology yang yang digunakan dalam pengembangan IoT ini adalah berbagai macam. Dari yang real time system hingga penggunaan alur prototype. Semakin berkembang keperluan manusia tentang teknologi, maka semakin banyak penelitian yang akan hadir, internet of things adalah salah satu hasil pemikiran para peneliti yang mengoptimasi beberapa alat seperti media sensor, Radio Frequency Identification (RFID), Wireless Sensor Network serta Smart Object lain yang memungkinkan manusia mudah berinteraksi dengan semua peralatan yang terhubung dengan jaringan internet.
Kata kunci : IoT, RFID, Wireless Sensor Network, Smart Object
Tesi sul tema "IoT Internet des objets":
1
Aïssaoui, François. "Autonomic Approach based on Semantics and Checkpointing for IoT System Management". Thesis, Toulouse 1, 2018. http://www.theses.fr/2018TOU10061/document.
2
Laarouchi, Mohamed Emine. "A safety approach for CPS-IoT". Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAS010.
Abstract (sommario):
Depuis plusieurs années, nous assistons à une convergence entre les systèmes cyber-physiques (CPS) et l’Internet des Objets (IoT). Les CPS intègrent les systèmes embarqués avec leur environnement physique et humain en assurant une communication entre différents capteurs et actionneurs. L’IoT vise le réseau et les protocoles de communication entre les objets connectés. Cette convergence offre des perspectives d’applications diverses allant des véhicules connectés aux réseaux électriques intelligents ainsi qu’aux usines du futur. Le but de cette thèse est d’assurer et garantir la sûreté de fonctionnement des systèmes CPS-IoT. Pour ceci, nous avons considéré un cas d’étude spécifique tout au long de la thèse qui est les drones. Dans un premier temps, on s’est focalisé sur les différentes méthodes d’analyse de sûreté de fonctionnement qui sont déjà existantes. Ces méthodes ont fait leurs preuves pour la conception et la réalisation des systèmes embarqués. Tout au long de ce process, on a essayé de répondre à la question suivante: est-ce que ces méthodes existantes sont adéquates pour réaliser les analyses de sûreté de fonctionnement nécessaires pour les CPS-IoT? On a conclu la nécessité de nouvelles approches pour analyser la sûreté de fonctionnement des systèmes CPS-IoT du fait de la complexité significative de ces systèmes. Dans un second temps, on a proposé une méthodologie pour l’analyse prédictive de la résilience des CPS-IoT. La résilience est définie comme étant la capacité d’un système à tolérer les pannes, à continuer à fournir le service demandé tout en considérant les différentes contraintes internes et externes au système. On a différencié deux types différents de résilience qui sont la résilience endogène et exogène. La résilience endogène est la capacité inhérente du système à détecter et à traiter les défauts internes et les attaques malveillantes. La résilience exogène est la capacité permanente du système à maintenir un fonctionnement sûr dans son environnement ambiant. La dernière partie de notre travail a consisté à investiguer l’impact de l’intelligence artificielle sur la sûreté de fonctionnement des CPS-IoT. Plus spécifiquement, on s’est intéressé à comment serait-il possible d’utiliser l’intelligence artificielle pour accroître la sûreté des drones lors de la phase de planification de chemin. Les résultats obtenus ont été comparés avec les algorithmes de planification existantsFor several years, we have been witnessing a convergence between cyber-physical systems (CPS) and the Internet of Things (IoT). CPS integrate embedded systems with their physical and human environment by ensuring communication between different sensors and actuators. The IoT targets the network and communication protocols between connected objects. This convergence offers prospects for various applications ranging from connected vehicles to smart grids and the factories of the future. The aim of this thesis is to ensure and guarantee the operational safety of CPS-IoT systems. For this, we have considered a specific case study throughout the thesis which is UAVs. Initially, we focused on the different methods of analysis of operational safety that already exist. These methods have proved their worth for the design and implementation of on-board systems. Throughout this process, we tried to answer the following question: are these existing methods adequate to perform the necessary safety analyses for CPS-IoT? It was concluded that new approaches to analyse the safety of operation of CPS-IoT systems are needed due to the significant complexity of these systems. As a second step, a methodology for predictive analysis of the resilience of CPS-IoTs was proposed. Resilience is defined as being the ability of a system to tolerate failures, to continue to provide the requested service while considering the various internal and external constraints of the system. Two different types of resilience have been differentiated: endogenous and exogenous resilience. Endogenous resilience is the inherent ability of the system to detect and deal with internal faults and malicious attacks. Exogenous resilience is the ongoing ability of the system to maintain safe operation in its surrounding environment. The last part of our work was to investigate the impact of artificial intelligence on the safe operation of CPS-IoTs. More specifically, we looked at how artificial intelligence could be used to enhance UAV safety in the path planning phase. The results obtained were compared with existing planning algorithms
3
Hassan, Basma Mostafa. "Monitoring the Internet of Things (IoT) Networks". Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS100.
Abstract (sommario):
Les réseaux « Internet des Objets » se composent de plusieurs millions d’objets qui possèdent une adresse IP et qui peuvent connecter sur Internet. En général, ces objets sont supposé d’être autonomes et peuvent résoudre des tâches; mesurer, traiter et fournir des informations pour les systèmes connectés et pour les utilisateurs. Aussi, ces réseaux sont vulnérables (c.-à-d. : les éléments peuvent être mobiles et la topologie du réseau peut changer dynamiquement), les changements peuvent influencer le (bon) fonctionnement du réseau. De plus, ils peuvent être alimentés par des batteries de durée de vie limitée, ce que nécessite la réduction de leur consommation.Ce travail de thèse aborde un sujet important dans le domaine de l’Internet des Objets, qui consiste à savoir comment assurer la robustesse et le fonctionnement tolérant aux pannes du réseau pour répondre aux exigences des missions critiques. Avec le large déploiement des services IdO, ce problème est deventé ou de détection de pannes et de sécurité industriel où l’état des objets communicants doit être constamment vérifié pour le rétablissement rapide en cas de problème particulièrement crucial pour les applications telles que le monitorage intelligent de sames de communication inattendus. On cherche alors de minimiser le coût du monitorage et l’utilisation de l’énergie, et aussi les charges additionnelles sur les réseaux.Nous avons proposé un algorithme qui vise à réaliser un placement distribué des moniteurs avec une complexité minimale pour le calcul. L’algorithme proposé fonctionne avec RPL. L’objectif principal est d’augmenter la robustesse dans les réseaux IdO ciblant les applications critiques en temps réel via le monitorage des liaisons dans les DODAGs construits par RPL. Dans notre première contribution, le problème est modélisé comme un problème de couverture minimale des sommets (VCP) sur le DODAG. Nous avons développé un algorithme à temps polynomial qui transforme le DODAG en une décomposition arborescente (Nice-Tree Decomposition) avec une largeur arborescente (treewidth) d’unité. Cette stratégie profite de la spécificité des DODAG et a abouti à une réduction significative de la complexité de la résolution du VCP sur les DODAG. Elle peut être résolue en temps polynomial.La deuxième proposition est un modèle approché pour l’optimisation de l’ordonnancement du rôle de monitorage des nœuds dans les réseaux IdO, afin de maximiser la durée de vie des dispositifs embarqués à ressources limitées, tout en minimisant le coût global du monitorage de réseau. Le monitorage de réseau est très coûteux, en particulier pour les réseaux à ressources limitées tels que l’IdO. Par conséquent, le monitorage doit être économe en énergie et avec des frais généraux minimaux sur la performance normale du réseau. Notre travail correspondant contient une proposition d’un modèle mathématique en trois phases pour assurer l’exigence d’une couverture des moniteurs tout en minimisant la consommation d’énergie de monitorage et les frais de communication.Notre modèle proposé décompose le problème abordé en trois problèmes d’optimisation bien connus, il s’agit du problème de couverture de sommets, problème d’affectation généralisé multi-objectives et problème de voyageur de commerce.Dans cette troisième partie, une approche exacte est proposée pour résoudre le problème décrit dans (Contribution 2). Comme nous avons vu, la décomposition en trois phases ne donne pas la solution exacte. Nous avons donc proposé une formulation exacte du problème qui consiste en un problème de l'affectation minimum des tâches de surveillance avec un fonctionnement de surveillance cyclique. Pour cela, nous avons formulé un programme en nombres entiers binaires. L'ordonnancement optimal garantit la couverture du graphe pour la surveillance avec une consommation d'énergie minimaleBy connecting billions of things to the Internet, IoT created a plethora of applications that touch every aspect of human life. Time-sensitive, mission-critical services, require robust connectivity and strict reliability constraints. On the other hand, the IoT relies mainly on Low-power Lossy Networks, which are unreliable by nature due to their limited resources, hard duty cycles, dynamic topologies, and uncertain radio connectivity. Faults in LLNs are common rather than rare events, therefore, maintaining continuous availability of devices and reliability of communication, are critical factors to guarantee a constant, reliable flow of application data.After a comprehensive literature review, and up to our knowledge, it is clear that there is a call for a new approach to monitoring the unreliable nodes and links in an optimized, energy-efficient, proactive manner, and complete interoperability with IoT protocols. To target this research gap, our contributions address the correct assignment (placement) of the monitoring nodes. This problem is known as the minimum assignment problem, which is NP-hard. We target scalable monitoring by mapping the assignment problem into the well-studied MVC problem, also NP-hard. We proposed an algorithm to convert the DODAG into a nice-tree decomposition with its parameter (treewidth) restricted to the value one. As a result of these propositions, the monitor placement becomes only Fixed-Parameter Tractable, and can also be polynomial-time solvable.To prolong network longevity, the monitoring role should be distributed and balanced between the entire set of nodes. To that end, assuming periodical functioning, we propose in a second contribution to schedule between several subsets of nodes; each is covering the entire network. A three-phase centralized computation of the scheduling was proposed. The proposition decomposes the monitoring problem and maps it into three well-known sub-problems, for which approximation algorithms already exist in the literature. Thus, the computational complexity can be reduced.However, the one major limitation of the proposed three-phase decomposition is that it is not an exact solution. We provide the exact solution to the minimum monitor assignment problem with a duty-cycled monitoring approach, by formulating a Binary Integer Program (BIP). Experimentation is designed using network instances of different topologies and sizes. Results demonstrate the effectiveness of the proposed model in realizing full monitoring coverage with minimum energy consumption and communication overhead while balancing the monitoring role between nodes.The final contribution targeted the dynamic distributed monitoring placement and scheduling. The dynamic feature of the model ensures real-time adaptation of the monitoring schedule to the frequent instabilities of networks, and the distributed feature aims at reducing the communication overhead
4
Ammar, Nesrine. "Autonomous IoT device type identification". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS073.
Abstract (sommario):
Avec la prolifération des objets connectés, de plus en plus de personnes achètent des objets et des électroménagers connectés pour bénéficier de nouveaux services, leur permettant de contrôler leur maison n'importe où, à tout moment et de rester informés. Le nombre de services activés par les objets IoT augmente rapidement, tout comme la diversité des types de ces objets: caméras, capteurs, téléphones intelligents, tablettes, haut-parleurs provenant de plusieurs fournisseurs et avec différents modèles. Les objets et les systèmes de gestion des services IoT d'un réseau domestique doivent savoir quels objets IoT sont connectés au réseau. Un système de gestion d’objets pour tous les types dans un réseau domestique est nécessaire. Dans cette thèse, nous proposons une méthodologie basée sur l'analyse des messages de protocole réseau pour extraire des informations pertinentes sur les dispositifs afin d'identifier leur type. Ensuite, nous avons proposé une autre méthodologie d'identification basée sur des algorithmes de Machine Learning. Notre approche de classification est basée sur la combinaison de fonctionnalités textuelles extraites de la charge utile des paquets et des caractéristiques de communication du réseau statistique. Nous évaluons notre solution et montrons qu'elle surpasse les solutions mentionnées dans l'état de l'art de la thèse avec une précision égale à 0,98With the proliferation of smart devices, more and more people buy IP devices and home appliances to benefit from new services, allowing them to control their home anywhere, anytime and to remain informed about. The number of services enabled by the IoT devices is quickly increasing, and so is the diversity of types of such devices: cameras, sensors, smart phones, tablets, speakers coming from several vendors and with different models. Devices and IoT service management systems in a home network needs to find out which IoT devices are connected to the network. A device management system for all kinds of devices being connected to the home network is necessary. In this thesis, we propose a methodology based on the analysis of network protocol messages to extract relevant information about the devices in order to identify their type. Then, we proposed another identification methodology based on Machine Learning algorithms. Our classification approach is based on the combination of textual features extracted from packets payload and statistical network communication features. We evaluate our proposal and show that it outperforms the state of the art in this field with an accuracy equal to 0.98
5
Brun-Laguna, Keoma. "Deterministic Networking for the Industrial IoT". Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS157.
Abstract (sommario):
L’Internet des Objets (IoT) a évolué d’un toaster connecté en 1990 vers des réseaux de centaines de petit appareils utilisés dans des applications industrielle. Ces « Objects » sont habituellement de petit appareils électroniques capable de mesurer une valeur physique (température, humidité, etc.) et/ou d’agir sur le monde physique (pump, valve, etc.). De part leur faible coût et leur facilité de déploiement, ces réseaux sans fil alimentés par batteries ont étés rapidement adoptés. La promesse des communications sans fil est d’offrir une connectivité similaire au réseau filaires. De nombreuses amélioration ont étés fait dans ce sens, mais plein de défis restent à surmonter car les applications industrielles ont de fortes exigences opérationnelles. Cette section de l’IoT s’appelle l’Internet Industriel des Objets. La principale exigence est la fiabilité. Chaque bout d’information transmit dans le réseau ne doit pas être perdu. Des solutions commerciales sont aujourd’hui accessibles et propose des fiabilités de l’ordre de 99.999 %. C’est à dire, pour chaque centaine de paquet d’information généré, moins d’un est perdu. Vient ensuite la latence et l’efficience énergétique. Comme ces appareils sont alimentés par des batteries, ils doivent consommer le moins possible et être capable d’opérer pendant des années. La prochaine étape pour l’IoT est d’être appliqué au applications nécessitant des garanties de latence. Les technologies de l’IIoT sont maintenant adoptés par de nombreuses entreprises autour du monde et sont maintenant des technologies éprouvées. Néanmoins des défis restent à accomplir et certaines limites de ces technologies ne sont pas encore connues. Dans ce travail, nous nous adressons au réseaux sans fils fondés sur TSCH dont nous testons les limites de latence et de durée de vie dans des conditions réelles. Nous avons collecté plus de 3M statistiques réseaux et 32M données de capteurs dans 11 déploiements sur un total de 170,037 heures machines dans des environnements réels ainsi que dans des bancs d’essais. Nous avons réuni ce que nous pensons être le plus grand jeu de données de réseau TSCH disponible à la communauté réseau. En s’appuyant sur ces données et sur notre expérience des réseaux sans fils en milieu réel, nous avons étudié les limites des réseaux TSCH et avons fourni des méthodes et outils qui permettent d’estimer des performances de ces réseaux dans diverses conditions. Nous pensons avoir assemblé les bons outils pour que les architectes de protocoles réseaux construise des réseaux déterministes pour l’IIoTThe Internet of Things (IoT) evolved from a connected toaster in 1990 to networks of hundreds of tiny devices used in industrial applications. Those “Things” usually are tiny electronic devices able to measure a physical value (temperature, humidity, etc.) and/or to actuate on the physical world (pump, valve, etc). Due to their cost and ease of deployment, battery-powered wireless IoT networks are rapidly being adopted. The promise of wireless communication is to offer wire-like connectivity. Major improvements have been made in that sense, but many challenges remain as industrial application have strong operational requirements. This section of the IoT application is called Industrial IoT (IIoT). The main IIoT requirement is reliability. Every bit of information that is transmitted in the network must not be lost. Current off-the-shelf solutions offer over 99.999% reliability. That is, for every 100k packets of information generated, less than one is lost. Then come latency and energy-efficiency requirements. As devices are battery-powered, they need to consume as little as possible to be able to operate during years. The next step for the IoT is to target time-critical applications. Industrial IoT technologies are now adopted by companies over the world, and are now a proven solution. Yet, challenges remain and some of the limits of the technologies are still not fully understood. In this work we address TSCH-based Wireless Sensor Networks and study their latency and lifetime limits under real-world conditions. We gathered 3M network statistics 32M sensor measurements on 11 datasets with a total of 170,037 mote hours in real-world and testbeds deployments. We assembled what we believed to be the largest dataset available to the networking community. Based on those datasets and on insights we learned from deploying networks in real-world conditions, we study the limits and trade-offs of TSCH-based Wireless Sensor Networks. We provide methods and tools to estimate the network performances of such networks in various scenarios. We believe we assembled the right tools for protocol designer to built deterministic networking to the Industrial IoT
6
Borges, caldas da silva Pedro Victor. "Middleware support for energy awareness in the Internet of Things (IoT)". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS016.
Abstract (sommario):
L'Internet des objets (IoT) se caractérise par une myriade de dispositifs et de composants logiciels géographiquement dispersés ainsi que par une grande hétérogénéité en termes de matériel, de format de données et de protocoles. Au cours des dernières années, les plateformes IoT ont été proposées pour fournir une variété de services aux applications, tels que la découverte de dispositifs, la gestion du contexte et l'analyse des données. Cependant, le manque de standardisation fait que chaque plateforme IoT propose ses propres abstractions, API et patrons d'interactions. Par conséquent, la programmation des interactions entre une application IoT consommatrice de données et une plateforme IoT est complexe, sujette à des erreurs et demande un niveau de connaissance de la plateforme IoT approfondi de la part des développeurs. Les intergiciels IoT peuvent atténuer cette hétérogénéité, ils doivent fournir des services pertinents et ainsi faciliter le développement des applications.L'efficacité énergétique de la technologie numérique devenant une priorité, l'augmentation du nombre de systèmes IoT pose des problèmes énergétiques. Dans ce contexte, il est essentiel de concevoir soigneusement les interactions entre les applications IoT grand public et les plateformes IoT en tenant compte de l'efficacité énergétique. Les intergiciels IoT ne doivent pas uniquement considérer l'efficacité énergétique comme une exigence non fonctionnelle laissée à l'application, Au contraire, parce qu'ils sont utilisés par de nombreuses applications, l'efficacité énergétique doit être au cœur de leur conception.Cette thèse présente trois contributions concernant l'efficacité énergétique et la sensibilisation à l'énergie dans les intergiciels IoT pour les applications IoT consommatrices de données. La première contribution est la proposition d'un intergiciel IoT appelé IoTvar qui abstrait les capteurs virtuels IoT dans des variables IoT qui sont automatiquement mises à jour par l'intergiciel. La deuxième contribution est l'évaluation de la consommation d'énergie des interactions entre les applications IoT grand public et les plateformes IoT via les protocoles HTTP et MQTT. Cette évaluation a conduit à la proposition de lignes directrices pour améliorer l'efficacité énergétique des interactions. La troisième contribution est la proposition de stratégies d'efficacité énergétique pour des middleware IoT. Ces stratégies ont été intégrées dans l'intergiciel IoTvar pour assurer l'efficacité énergétique, mais aussi la sensibilisation à l'énergie par le biais d'un modèle énergétique et la gestion d'un budget énergétique fonction des exigences des utilisateurs. Les implémentations de l'architecture middleware IoT, avec et sans stratégie d'efficacité énergétique, ont été évaluées, et les résultats montrent que nous avons une diminution allant jusqu'à 60% de l'énergie consommée par les applications IoT en appliquant des stratégies pour réduire la consommation d'énergie au niveau du middlewareThe Internet of Things (IoT) is characterized by a myriad of geographically dispersed devices and software components as well as high heterogeneity in terms of hardware, data, and protocols. Over the last few years, IoT platforms have been used to provide a variety of services to applications such as device discovery, context management, and data analysis. However, the lack of standardization makes each IoT platform come with its abstractions, APIs, and interactions. As a consequence, programming the interactions between a consuming IoT application and an IoT platform is often time-consuming, error-prone, and depends on the developers' level of knowledge about the IoT platform. IoT middleware are proposed to alleviate such heterogeneity, provide relevant services, and ease application development.As the energy efficiency of digital technology becomes a priority, the increase in IoT systems brings energy concerns. In this context, carefully designing interactions between IoT consumer applications and IoT systems with an energy-efficiency concern becomes essential. IoT middleware should not solely consider energy efficiency as a non-functional requirement. Instead, it needs to be at the solution's core as the middleware is expected to be shared by many applications and offer facilities to ease application development.This work presents three contributions regarding energy-efficiency/awareness in IoT middleware for IoT consumer applications.The first contribution is the proposal of an IoT middleware for IoT consumer applications called IoTVar that abstracts IoT virtual sensors in IoT variables that are automatically updated by the middleware.The second contribution is the evaluation of the energy consumption of the interactions between IoT consumer applications and IoT platforms through the HTTP and MQTT protocols. This evaluation has led to the proposal of guidelines to improve energy efficiency when developing applications.The third contribution is the proposal of strategies for energy efficiency to be integrated into IoT middleware. Those strategies have been integrated into the IoTVar middleware to provide energy efficiency, but also energy awareness through an energy model and the management of an energy budget driven by user requirements. The implementations of the IoT middleware architecture, with and without energy-efficiency strategies, have been evaluated, and the results show that we have a difference of up to 60% the energy used by IoT applications by applying strategies to reduce energy consumption at the middleware level
7
Shahid, Mustafizur Rahman. "Deep learning for Internet of Things (IoT) network security". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAS003.
Abstract (sommario):
L’internet des objets (IoT) introduit de nouveaux défis pour la sécurité des réseaux. La plupart des objets IoT sont vulnérables en raison d'un manque de sensibilisation à la sécurité des fabricants d'appareils et des utilisateurs. En conséquence, ces objets sont devenus des cibles privilégiées pour les développeurs de malware qui veulent les transformer en bots. Contrairement à un ordinateur de bureau, un objet IoT est conçu pour accomplir des tâches spécifiques. Son comportement réseau est donc très stable et prévisible, ce qui le rend bien adapté aux techniques d'analyse de données. Ainsi, la première partie de cette thèse tire profit des algorithmes de deep learning pour développer des outils de surveillance des réseaux IoT. Deux types d'outils sont explorés: les systèmes de reconnaissance de type d’objets IoT et les systèmes de détection d'intrusion réseau IoT. Pour la reconnaissance des types d’objets IoT, des algorithmes d'apprentissage supervisé sont entrainés pour classifier le trafic réseau et déterminer à quel objet IoT le trafic appartient. Le système de détection d'intrusion consiste en un ensemble d'autoencoders, chacun étant entrainé pour un type d’objet IoT différent. Les autoencoders apprennent le profil du comportement réseau légitime et détectent tout écart par rapport à celui-ci. Les résultats expérimentaux en utilisant des données réseau produites par une maison connectée montrent que les modèles proposés atteignent des performances élevées. Malgré des résultats préliminaires prometteurs, l’entraînement et l'évaluation des modèles basés sur le machine learning nécessitent une quantité importante de données réseau IoT. Or, très peu de jeux de données de trafic réseau IoT sont accessibles au public. Le déploiement physique de milliers d’objets IoT réels peut être très coûteux et peut poser problème quant au respect de la vie privée. Ainsi, dans la deuxième partie de cette thèse, nous proposons d'exploiter des GAN (Generative Adversarial Networks) pour générer des flux bidirectionnels qui ressemblent à ceux produits par un véritable objet IoT. Un flux bidirectionnel est représenté par la séquence des tailles de paquets ainsi que de la durée du flux. Par conséquent, en plus de générer des caractéristiques au niveau des paquets, tel que la taille de chaque paquet, notre générateur apprend implicitement à se conformer aux caractéristiques au niveau du flux, comme le nombre total de paquets et d'octets dans un flux ou sa durée totale. Des résultats expérimentaux utilisant des données produites par un haut-parleur intelligent montrent que notre méthode permet de générer des flux bidirectionnels synthétiques réalistes et de haute qualitéThe growing Internet of Things (IoT) introduces new security challenges for network activity monitoring. Most IoT devices are vulnerable because of a lack of security awareness from device manufacturers and end users. As a consequence, they have become prime targets for malware developers who want to turn them into bots. Contrary to general-purpose devices, an IoT device is designed to perform very specific tasks. Hence, its networking behavior is very stable and predictable making it well suited for data analysis techniques. Therefore, the first part of this thesis focuses on leveraging recent advances in the field of deep learning to develop network monitoring tools for the IoT. Two types of network monitoring tools are explored: IoT device type recognition systems and IoT network Intrusion Detection Systems (NIDS). For IoT device type recognition, supervised machine learning algorithms are trained to perform network traffic classification and determine what IoT device the traffic belongs to. The IoT NIDS consists of a set of autoencoders, each trained for a different IoT device type. The autoencoders learn the legitimate networking behavior profile and detect any deviation from it. Experiments using network traffic data produced by a smart home show that the proposed models achieve high performance.Despite yielding promising results, training and testing machine learning based network monitoring systems requires tremendous amount of IoT network traffic data. But, very few IoT network traffic datasets are publicly available. Physically operating thousands of real IoT devices can be very costly and can rise privacy concerns. In the second part of this thesis, we propose to leverage Generative Adversarial Networks (GAN) to generate bidirectional flows that look like they were produced by a real IoT device. A bidirectional flow consists of the sequence of the sizes of individual packets along with a duration. Hence, in addition to generating packet-level features which are the sizes of individual packets, our developed generator implicitly learns to comply with flow-level characteristics, such as the total number of packets and bytes in a bidirectional flow or the total duration of the flow. Experimental results using data produced by a smart speaker show that our method allows us to generate high quality and realistic looking synthetic bidirectional flows
8
Xia, Ye. "Combining Heuristics for Optimizing and Scaling the Placement of IoT Applications in the Fog". Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM084/document.
Abstract (sommario):
Alors que l’informatique en brouillard amène les ressources de traitement et de stockage à la périphérie du réseau, il existe un besoin croissant de placement automatisé (c.-à-d. La sélection de l'hôte) pour déployer des applications distribuées. Un tel placement doit être conforme aux besoins en ressources des applications dans une infrastructure de brouillard hétérogène et dynamique, et traiter la complexité apportée par les applications Internet des objets (IoT) liées aux capteurs / actionneurs. Cette thèse présente un modèle, une fonction objective et des heuristiques pour résoudre le problème de la mise en place d'applications IoT distribuées dans le brouillard. En combinant les heuristiques proposées, notre approche est capable de gérer les problèmes à grande échelle et de prendre efficacement des décisions de placement adaptées à l'objectif - en optimisant les performances des applications placées. L'approche proposée est validée par une analyse de complexité et une simulation comparative avec des tailles et des applications de tailles variablesAs fog computing brings processing and storage resources to the edge of the network, there is an increasing need of automated placement (i.e., host selection) to deploy distributed applications. Such a placement must conform to applications' resource requirements in a heterogeneous fog infrastructure, and deal with the complexity brought by Internet of Things (IoT) applications tied to sensors and actuators. This paper presents four heuristics to address the problem of placing distributed IoT applications in the fog. By combining proposed heuristics, our approach is able to deal with large scale problems, and to efficiently make placement decisions fitting the objective: minimizing placed applications' average response time. The proposed approach is validated through comparative simulation of different heuristic combinations with varying sizes of infrastructures and applications
9
Rosseel, Joachim. "DÉCODAGE DE CODES CORRECTEURS D'ERREURS ASSISTÉ PAR APPRENTISSAGE POUR L'IOT". Electronic Thesis or Diss., CY Cergy Paris Université, 2023. http://www.theses.fr/2023CYUN1260.
Abstract (sommario):
Les communications sans fil, déjà très présentes dans notre société, soulèvent de nouveaux défis dans le cadre du déploiement de l'Internet des Objets (IoT) tels que le développement de nouvelles méthodes de décodage au niveau de la couche physique permettant d'assurer de bonnes performances pour la transmission de messages courts. En particulier, les codes LDPC (Low Density Parity Check) sont une famille de codes correcteurs d'erreurs très connus pour leurs excellentes performances asymptotiques lorsqu'ils sont décodés par l'algorithme de propagation de croyance (BP, pour Belief Propagation, en anglais). Cependant, la capacité de correction de l'algorithme BP se retrouve fortement dégradée pour les codes LDPC courts. Ainsi, cette thèse porte sur l'amélioration du décodage des codes LDPC courts, grâce notamment à des outils d'apprentissage automatique, tels que les réseaux de neurones.Après avoir introduit les notions et caractéristiques des codes LDPC et du décodage BP,ainsi que la modélisation du BP par un réseau de neurones récurrent (BP-Recurrent NeuralNetwork ou BP-RNN), nous développons de nouvelles méthodes d'entraînement afin de spécialiser le décodeur BP-RNN sur des motifs d'erreurs partageant des propriétés structurelles similaires. Ces approches de spécialisation sont associées à des architectures de décodage composées de plusieurs BP-RNNs spécialisés, où chaque BP-RNN est entraîné à corriger un type différent de motif d'erreurs (diversité de décodage). Nous nous intéressons ensuite au post-traitement du BP (ou du BP-RNN) avec un décodage par statistiques ordonnées (Ordered Statistics Decoding ou OSD) afin de se rapprocher de la performance du décodage par maximum de vraisemblance. Pour améliorer les performances du post-traitement, nous optimisons son entrée grâce à un neurone simple, puis nous introduisons une stratégie de décodage pour un post-traitement par OSD multiples. Il est alors montré que cette stratégie tire efficacement partie de la diversité de ses entrées, fournissant ainsi un moyen efficace de combler l'écart avec le décodage par maximum de vraisemblanceWireless communications, already very present in our society, still raise new challengesas part of the deployment of the Internet of Things (IoT) such as the development of newdecoding methods at the physical layer ensuring good performance for the transmission ofshort messages. In particular, Low Density Parity Check (LDPC) codes are a family of errorcorrecting codes well-known for their excellent asymptotic error correction performanceunder iterative Belief Propagation (BP) decoding. However, the error correcting capacity ofthe BP algorithm is severely deteriorated for short LDPC codes. Thus, this thesis focuses on improving the decoding of short LDPC codes, thanks in particular to machine learning tools such as neural networks.After introducing the notions and characteristics of LDPC codes and BP decoding, aswell as the modeling of the BP algorithm by a Recurrent Neural Network (BP-RecurrentNeural Network or BP-RNN), we develop new training methods specializing the BP-RNN ondecoding error events sharing similar structural properties. These specialization approaches are subsequently associated decoding architectures composed of several specialized BP-RNNs, where each BP-RNN is trained to decode a specific kind of error events (decoding diversity). Secondly, we are interested in the post-processing of the BP (or the BP-RNN) with an Ordered Statistics Decoding (OSD) in order to close the gap the maximum likelihood (ML) decoding performance. To improve the post-processing performance, we optimize its input thanks to a single neuron and we introduce a multiple OSD post-processing decoding strategy. We then show that this strategy effectively takes advantage of the diversity of its inputs, thus providing an effective way to close the gap with ML decoding
10
De, Moura Donassolo Bruno. "L'orchestration des applications IoT dans le Fog". Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM051.
Abstract (sommario):
L’Internet des objets (IdO) poursuit son évolution, provoquant une croissance drastique du trafic et des demandes de calcul. Par conséquent, les acteurs de la 5G sont invités à repenser leurs infrastructures. Dans ce contexte, l’informatique en brouillard (Fog computing) comble le fossé entre le Cloud et les terminaux en bordure de réseau, en fournissant des équipements à proximité avec des capacités d’analyse et de stockage de données, ce qui augmente considérablement la capacité de l’infrastructure.Cependant, le brouillard soulève plusieurs défis qui ralentissent son adoption. Parmi eux, l’orchestration est cruciale puisqu’elle gère le cycle de vie des applications IdO. Dans cette thèse, nous sommes principalement intéressés par : i) le problème du provisionnement, c.-à-d. le placement d’applications IdO multi-composants sur l’infrastructure hétérogène du brouillard et ii) le problème de la reconfiguration, c.-à-d. comment adapter dynamiquement le placement des applications, en fonction de leurs besoins et de l’évolution de l’utilisation des ressources.Pour réaliser les études d’orchestration, nous proposons d’abord FITOR, un système d’orchestration pour les applications IdO dans l’environnement du brouillard. Cette solution répond au manque des environnements pratiques pour le brouillard, en créant une plate-forme réaliste sur lequel nous pouvons évaluer les propositions d’orchestration.Nous étudions la question du provisionnement des services de brouillard dans cet environnement pratique. À cet égard, nous proposons deux nouvelles stratégies, O-FSP et GO-FSP, qui optimisent le placement des composants d’application de l’IdO tout en répondant à leurs strictes exigences de performance. Pour y parvenir, nous proposons d’abord une formulation de programmation linéaire entière pour le problème du provisionnement des applications IdO. Sur la base d’expériences approfondies, les résultats obtenus montrent que les stratégies proposées sont capables de réduire le coût de provisionnement tout en répondant aux exigences des applications.Enfin, nous abordons le problème de la reconfiguration, en proposant et en évaluant une série d’algorithmes de reconfiguration, basés à la fois sur des approches d’ordonnancement et d’apprentissage en ligne. Grâce à un vaste ensemble d’expériences, nous démontrons que les performances dépendent fortement de la qualité et de la disponibilité des informations provenant de l’infrastructure de brouillard et des applications IdO. En outre, nous montrons qu’une stratégie réactive et gourmande peut dépasser les performances des algorithmes d’apprentissage en ligne de pointe, à condition que la stratégie ait accès à un peu plus d’informations supplémentairesInternet of Things (IoT) continues its evolution, causing a drastically growth of traffic and processing demands. Consequently, 5G players are urged to rethink their infrastructures. In this context, Fog computing bridges the gap between Cloud and edge devices, providing nearby devices with analytics and data storage capabilities, increasing considerably the capacity of the infrastructure.However, the Fog raises several challenges which decelerate its adoption. Among them, the orchestration is crucial, handling the life-cycle management of IoT applications. In this thesis, we are mainly interested in: i) the provisioning problem, i.e., placing multi-component IoT applications on the heterogeneous Foginfrastructure; and ii) the reconfiguration problem, i.e., how to dynamically adapt the placement of applications, depending on application needs and evolution of resource usage.To perform the orchestration studies, we first propose FITOR, an orchestration system for IoT applications in the Fog environment. This solution addresses the lack of practical Fog solutions, creating a realistic environment on which we can evaluate the orchestration proposals.We study the Fog service provisioning issue in this practical environment. In this regard, we propose two novel strategies, OFSP and GOFSP, which optimize the placement of IoT application components while coping with their strict performance requirements. To do so, we first propose an Integer Linear Programming formulation for the IoT application provisioning problem. Based on extensive experiments, the results obtained show that the proposed strategies are able to decrease the provisioning cost while meeting the applicationrequirements.Finally, we tackle the reconfiguration problem, proposing and evaluating a series of reconfiguration algorithms, based on both online scheduling and online learning approaches. Through an extensive set of experiments, we demonstrate that the performance strongly depends on the quality and availability of information from Fog infrastructure and IoT applications. In addition, we show that a reactive and greedy strategy can overcome the performance of state-of-the-art online learning algorithms, as long as the strategy has access to a little extra information
Libri sul tema "IoT Internet des objets":
1
Le, Dac-Nhuong, Souvik Pal e Vicente García Díaz. Iot. Taylor & Francis Group, 2022.
2
Vermesan, Ovidiu. Advancing IoT Platforms Interoperability. River Publishers, 2022.
3
Vermesan, Ovidiu. Advancing IoT Platforms Interoperability. River Publishers, 2018.
4
Vermesan, Ovidiu. Advancing IoT Platforms Interoperability. River Publishers, 2022.
5
Al-Turjman, Fadi. Smart-Grid in Iot-enabled Spaces. Taylor & Francis Group, 2020.
6
Al-Turjman, Fadi. Cognitive Sensors and IoT: Architecture, Deployment, and Data Delivery. Taylor & Francis Group, 2017.
7
Al-Turjman, Fadi. Cognitive Sensors and IoT: Architecture, Deployment, and Data Delivery. Taylor & Francis Group, 2017.
8
Al-Turjman, Fadi. Cognitive Sensors and IoT: Architecture, Deployment, and Data Delivery. Taylor & Francis Group, 2017.
9
Vuppalapati, Chandrasekar. Building Enterprise Iot Applications. Taylor & Francis Group, 2019.
10
Vuppalapati, Chandrasekar. Building Enterprise IoT Applications. Taylor & Francis Group, 2019.
Capitoli di libri sul tema "IoT Internet des objets":
1
Bouhaï, Nasreddine. "The IoT: Intrusive or Indispensable Objects?" In Internet of Things, 1–19. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119427391.ch1.
2
McGibney, Alan, Alejandro Esquiva Rodriguez, Oliva Brickley e Susan Rea. "Managing Connected Smart Objects". In Internet of Things. IoT Infrastructures, 3–9. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47075-7_1.
3
Gupta, Vishal, e Monish Gupta. "IoT-Based Artificial Intelligence System in Object Detection". In Internet of Things, 33–49. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003140443-3.
4
Granell, Carlos, Andreas Kamilaris, Alexander Kotsev, Frank O. Ostermann e Sergio Trilles. "Internet of Things". In Manual of Digital Earth, 387–423. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9915-3_11.
Abstract (sommario):
Abstract Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things (IoT) emerged as a holistic proposal to enable an ecosystem of varied, heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth.
5
Nikolic, Predrag K. "Multimodal Interactions: Embedding New Meanings to Known Forms and Objects". In Internet of Things. IoT Infrastructures, 107–21. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47075-7_13.
6
Rizzardi, Alessandra, Daniele Miorandi, Sabrina Sicari, Cinzia Cappiello e Alberto Coen-Porisini. "Networked Smart Objects: Moving Data Processing Closer to the Source". In Internet of Things. IoT Infrastructures, 28–35. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47075-7_4.
7
Fortino, Giancarlo, Anna Rovella, Wilma Russo e Claudio Savaglio. "Towards Cyberphysical Digital Libraries: Integrating IoT Smart Objects into Digital Libraries". In Internet of Things, 135–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26869-9_7.
8
Mariani, Stefano, Andrea Bicego, Marco Lippi, Marco Mamei e Franco Zambonelli. "Argumentation-Based Coordination in IoT: A Speaking Objects Proof-of-Concept". In Internet and Distributed Computing Systems, 169–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34914-1_17.
9
Karakostas, Bill. "Towards Autonomous IoT Logistics Objects". In Securing the Internet of Things, 213–25. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9866-4.ch012.
Abstract (sommario):
To improve the overall impact of the Internet of Things (IoT), intelligent capabilities must be developed at the edge of the IoT ‘Cloud.' ‘Smart' IoT objects must not only communicate with their environment, but also use embedded knowledge to interpret signals, and by making inferences augment their knowledge of their own state and that of their environment. Thus, intelligent IoT objects must improve their capabilities to make autonomous decisions without reliance to external computing infrastructure. In this chapter, we illustrate the concept of smart autonomous logistic objects with a proof of concept prototype built using an embedded version of the Prolog language, running on a Raspberry Pi credit-card-sized single-board computer to which an RFID reader is attached. The intelligent object is combining the RFID readings from its environment with embedded knowledge to infer new knowledge about its status. We test the system performance in a simulated environment consisting of logistics objects.
10
Milovanović, Dragorad, Vladan Pantović e Gordana Gardašević. "Converging Technologies for the IoT". In Securing the Internet of Things, 1070–95. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9866-4.ch048.
Abstract (sommario):
The Internet of Things (IoT) is the concept of linking various objects to the Internet that sense/acquire and transmit data in the environment to create a new application. From a standardization perspective, the IoT can be viewed as a global infrastructure, enabling advanced services by interconnecting (physical and virtual) objects based on evolving interoperable information and communication technologies (ICT). The success of the IoT will depend strongly on the existence and effective operation of global standards. The standardization initiative, research projects, national initiatives and industrial activities are outlined in this chapter. There are already many standardization activities related to the IoT, covering broad research areas: wireless and cellular technologies, networking protocols, emerging applications, media-centric IoT. What is needed, therefore, are a harmonization of standards and effective frameworks for large-scale deployment.
Atti di convegni sul tema "IoT Internet des objets":
1
Kawsar, Fahim, Gerd Kortuem e Bashar Altakrouri. "Supporting interaction with the Internet of Things across objects, time and space". In 2010 Internet of Things (IOT). IEEE, 2010. http://dx.doi.org/10.1109/iot.2010.5678441.
2
Alothman, Hussam Ali, Mohammad T. Khasawneh e Nagen N. Nagarur. "Internet of Things in Manufacturing: An Overview". In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88262.
Abstract (sommario):
Internet of things or IoT represents an emerging concept where the objects and humans are identifiable, connected and can communicate over the internet or the wireless world. With IoT, everything can communicate anytime and at anyplace. IoT has many applications and one of the most important applications is the manufacturing. The IoT in industry sector, sometimes referred to as IIoT, is considered to be a very important factor in the introduction of the fourth industrial revolution. Major manufacturing powers around the world are already trying to adopt IoT in their production systems and lead the way in this new era of advanced manufacturing. A huge number of IoT devices are already being used and connected and it is expected that the number of these applications and devices will increase dramatically in the next few years. In this work, an overview of IoT in manufacturing will be presented. This includes a discussion of some of the advantages and benefits of adopting IoT in manufacturing in addition to the issues and challenges that accompany this IoT application. The discussion will also include the concept of smart manufacturing, how production processes and other related activities can be connected in real time and how this can be achieved by adopting IoT in manufacturing. Furthermore, the enabling technologies needed to realize IoT (whether it is to be applied in a new plant or in already existing machines that don’t have IoT capabilities) are shown along with the different layers or phases needed for this IoT adoption. Finally, some real life examples of factories that adopted IoT are shown.
3
Camargo, Leandro, Ana Marilza Pernas e Adenauer Yamin. "Abordagem VISO: uma Contribuição à Socialização entre Objetos da Internet das Coisas". In Seminário Integrado de Software e Hardware. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/semish.2023.230258.
Abstract (sommario):
A crescente conectividade dos objetos na IoT permite que eles interajam, compartilhando informações e colaborando de diversas maneiras para realizar tarefas coletivas. No entanto, isso envolve vários desafios de pesquisa ainda abertos. Neste contexto, este artigo apresenta a concepção da abordagem Virtual Interactions between Social Objects (VISO). Esta abordagem trata de aspectos funcionais que uma arquitetura voltada à Social IoT deve prover, especialmente a recomendação autônoma de amizades. A VISO explora o conceito de virtualização de objetos para uma atuação autônoma no ambiente. Resultados preliminares indicam que o agrupamento de objetos em comunidades potencializa as recomendações de serviços ou amizades, de maneira socialmente consciente e personalizada.
4
Mazzei, Daniele, Gualtiero Fantoni, Gabriele Montelisciani e Giacomo Baldi. "Internet of Things for designing smart objects". In 2014 IEEE World Forum on Internet of Things (WF-IoT). IEEE, 2014. http://dx.doi.org/10.1109/wf-iot.2014.6803175.
5
Gama, Kiev, Rafael Wanderley, Daniel Maranhão e Vinicius Garcia. "TagHunt: Uma plataforma Combinando a Internet das Coisas com Scavenger Hunt Games". In VII Simpósio Brasileiro de Computação Ubíqua e Pervasiva. Sociedade Brasileira de Computação - SBC, 2015. http://dx.doi.org/10.5753/sbcup.2015.10177.
Abstract (sommario):
The “Internet of Things” (IoT) brings the notion of heterogeneous objects using ubiquitous technologies to interact among them and with the physical environment through technologies such as Bluetooth, ZigBee, GPRS, NFC, QR code, among others. Based on the possibility of linking ordinary objects from the physical world to the Internet, this paper proposes and details a platform called TagHunt, for creating and playing scavenger hunt games. This platform leverages on smartphones’ capability to interact with ordinary objects using IoT-based technologies such as NFC and QR Code, stimulating the player to interact with physical environments looking for “clues” in the game.
6
Silva, Romulo, Windson Viana e Paulo Filipe Dantas. "Using images to extend smart object discovery in an Internet of Things scenario". In XXIV Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/webmedia.2018.4564.
Abstract (sommario):
The Internet of Things (IoT) represents a new paradigm in the Internet history and in the way people interact with everyday objects. Academics and several industry segments have been working over the past years to make this vision possible. It is estimated that there will be dozens or hundreds of devices simultaneously connected to the user’s network (e.g., in their home) in the next years, which can make the discovery and interaction with smart object more complicated to IoT users increasingly. This work proposes a smart objects discovery approach using image recognition, which aims to make this task quicker and more selective from an user perspective. An initial assessment has shown that the proposed mechanism can reduce the discovery time in a scenario with several devices, and additionally ensure a good level of user satisfaction.
7
Georgescu, Mircea, e Roxana Hucanu. "AN APPROACH ABOUT TURNING CHALLENGES INTO OPPORTUNITIES USING INTERNET OF THINGS". In eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-013.
Abstract (sommario):
The Internet of Things is almost a new paradigm that is revolutionizing not only our society but also our entire life. Like any growing industry the Internet of Things it is in a continuous growth being a part of the Internet of the future. The concept of Internet of Things means a structure of physical objects or "things" that have Internet connections, sensors, software and electronic components, which collect and distributes data. The number of objects it is also growing year by year so, each object have one or more embedded sensors that can capture huge amounts of data, data to be stored, processed and communicated with a reduced human intervention. The building blocks of IoT vision are the smart things - objects around us, not necessarily visible, being connected in order to provide seamless communication and contextual services, able to respond to their internal states and/or to external factors, to sense, compute and integrate with the surrounding environment. IoT is a fabric of numerous connections between things and between humans and things, potentially more complex and dynamic than the Internet of People. In the same time, the development of Internet of Things (IoT) give rise to a number of security-related and ethical issues which gain a new dimension in the light of the increased complexity of these technologies. This paper discusses emerging Sensing as a Service as a business model of Internet of Things, large scale sensor network application, sensor data and related context capturing techniques and processing of sensor data.
8
Luis Oliveira, Fernando, e Julio Mattos. "State-of-the-Art Javascript Language for Internet of Things". In IX Simpósio Brasileiro de Engenharia de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbesc_estendido.2019.8651.
Abstract (sommario):
Currently, two technologies stand out in the field of research, the Internet of Things (IoT) and Javascript (JS). On one hand, IoT enables everyday objects to connect to the network, analyze, capture, and interact with the environment. On the other hand, we have JS, a programming language that was initially inside browsers but now it is being used in several backgrounds. This paper correlates IoT and JS technologies, showing how Javascript can be applied in the context of the Internet of Things. The survey has considered the work of the last ten years and presents state of the art from Javascript applied to the IoT-side.
9
Srinivasan, K., e V. R. Azhaguramyaa. "Internet of Things (IoT) based Object Recognition Technologies". In 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2019. http://dx.doi.org/10.1109/i-smac47947.2019.9032689.
10
Tanbo, Masaya, Ryoma Nojiri, Yuusuke Kawakita e Haruhisa Ichikawa. "Active RFID attached object clustering method based on RSSI series for finding lost objects". In 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT). IEEE, 2015. http://dx.doi.org/10.1109/wf-iot.2015.7389081.
Rapporti di organizzazioni sul tema "IoT Internet des objets":
1
Hong, J., X. de, M. Kovatsch, E. Schooler e D. Kutscher. Internet of Things (IoT) Edge Challenges and Functions. RFC Editor, aprile 2024. http://dx.doi.org/10.17487/rfc9556.
2
Gomez, C., J. Crowcroft e M. Scharf. TCP Usage Guidance in the Internet of Things (IoT). RFC Editor, marzo 2021. http://dx.doi.org/10.17487/rfc9006.
3
Megas, Katerina, Ben Piccarreta e Danna Gabel O'Rourke. Internet of things (IoT) cybersecurity colloquium: a NIST workshop proceedings. Gaithersburg, MD: National Institute of Standards and Technology, dicembre 2017. http://dx.doi.org/10.6028/nist.ir.8201.
4
Simmon, Eric. Internet of Things (IoT) component capability model for research testbed. Gaithersburg, MD: National Institute of Standards and Technology, settembre 2020. http://dx.doi.org/10.6028/nist.ir.8316.
5
Boeckl, Katie, Michael Fagan, William Fisher, Naomi Lefkovitz, Katerina N. Megas, Ellen Nadeau, Danna Gabel O'Rourke, Ben Piccarreta e Karen Scarfone. Considerations for managing Internet of Things (IoT) cybersecurity and privacy risks. Gaithersburg, MD: National Institute of Standards and Technology, giugno 2019. http://dx.doi.org/10.6028/nist.ir.8228.
6
Garcia-Morchon, O., S. Kumar e M. Sethi. Internet of Things (IoT) Security: State of the Art and Challenges. RFC Editor, aprile 2019. http://dx.doi.org/10.17487/rfc8576.
7
Phillips, Paul. The Application of Satellite-based Internet of Things for New Mobility. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, gennaio 2024. http://dx.doi.org/10.4271/epr2024001.
Abstract (sommario):
<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>
8
Jimenez, J., H. Tschofenig e D. Thaler. Report from the Internet of Things (IoT) Semantic Interoperability (IOTSI) Workshop 2016. RFC Editor, ottobre 2018. http://dx.doi.org/10.17487/rfc8477.
9
Symington, Susan, William Polk e Murugiah Souppaya. Trusted Internet of Things (IoT) Device Network-Layer Onboarding and Lifecycle Management (Draft). Gaithersburg, MD: National Institute of Standards and Technology, settembre 2020. http://dx.doi.org/10.6028/nist.cswp.09082020-draft.
10
Passos, João, Sérgio Ivan Lopes, Filipe Manuel Clemente, Pedro Miguel Moreira, Markel Rico-González, Pedro Bezerra e Luis Paulo Rodrigues. Wearables and Internet of Things (IoT) Technologies for Fitness Assessment: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, giugno 2021. http://dx.doi.org/10.37766/inplasy2021.6.0041.