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Artykuły w czasopismach na temat "IoT Internet des objets"
Milivojević, Sanja, i Elizabeth Radulski. "The 'Future Internet' and crime: Towards a criminology of the Internet of Things". Crimen 11, nr 3 (2020): 255–71. http://dx.doi.org/10.5937/crimen2003255m.
Pełny tekst źródłaC. Sailaja. "Industrial Internet of Things – An Overview". December 2022 4, nr 4 (30.12.2022): 257–71. http://dx.doi.org/10.36548/jismac.2022.4.003.
Pełny tekst źródłaSuryani, Vera, Selo Sulistyo i Widyawan Widyawan. "Trust-Based Privacy for Internet of Things". International Journal of Electrical and Computer Engineering (IJECE) 6, nr 5 (1.10.2016): 2396. http://dx.doi.org/10.11591/ijece.v6i5.9678.
Pełny tekst źródłaSuryani, Vera, Selo Sulistyo i Widyawan Widyawan. "Trust-Based Privacy for Internet of Things". International Journal of Electrical and Computer Engineering (IJECE) 6, nr 5 (1.10.2016): 2396. http://dx.doi.org/10.11591/ijece.v6i5.pp2396-2402.
Pełny tekst źródłaPark, Jong. "Advances in Future Internet and the Industrial Internet of Things". Symmetry 11, nr 2 (16.02.2019): 244. http://dx.doi.org/10.3390/sym11020244.
Pełny tekst źródłaRaimundo, Ricardo Jorge, i Albérico Travassos Rosário. "Cybersecurity in the Internet of Things in Industrial Management". Applied Sciences 12, nr 3 (2.02.2022): 1598. http://dx.doi.org/10.3390/app12031598.
Pełny tekst źródłaBirje, Mahantesh N., Arun A. Kumbi i Ashok V. Sutagundar. "Internet of Things". International Journal of Hyperconnectivity and the Internet of Things 1, nr 2 (lipiec 2017): 45–71. http://dx.doi.org/10.4018/ijhiot.2017070104.
Pełny tekst źródłaLong, Rong, Xiaohui Fan, Kai Wei, Junxuan Bai i Shanpeng Xiao. "Internet-of-Things object model". Digital Twin 2 (12.04.2022): 5. http://dx.doi.org/10.12688/digitaltwin.17562.1.
Pełny tekst źródłaE, Umamaheswari, i Ajay Dm. "SCOPE OF INTERNET OF THINGS: A SURVEY". Asian Journal of Pharmaceutical and Clinical Research 10, nr 13 (1.04.2017): 187. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19633.
Pełny tekst źródłaSusanto, Fredy, Ni Komang Prasiani i Putu Darmawan. "IMPLEMENTASI INTERNET OF THINGS DALAM KEHIDUPAN SEHARI-HARI". Jurnal Imagine 2, nr 1 (21.04.2022): 35–40. http://dx.doi.org/10.35886/imagine.v2i1.329.
Pełny tekst źródłaRozprawy doktorskie na temat "IoT Internet des objets"
Aïssaoui, François. "Autonomic Approach based on Semantics and Checkpointing for IoT System Management". Thesis, Toulouse 1, 2018. http://www.theses.fr/2018TOU10061/document.
Pełny tekst źródłaLaarouchi, Mohamed Emine. "A safety approach for CPS-IoT". Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAS010.
Pełny tekst źródłaFor 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
Hassan, Basma Mostafa. "Monitoring the Internet of Things (IoT) Networks". Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS100.
Pełny tekst źródłaBy 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
Ammar, Nesrine. "Autonomous IoT device type identification". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS073.
Pełny tekst źródłaWith 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
Brun-Laguna, Keoma. "Deterministic Networking for the Industrial IoT". Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS157.
Pełny tekst źródłaThe 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
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.
Pełny tekst źródłaThe 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
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.
Pełny tekst źródłaThe 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
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.
Pełny tekst źródłaAs 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
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.
Pełny tekst źródłaWireless 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
De, Moura Donassolo Bruno. "L'orchestration des applications IoT dans le Fog". Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM051.
Pełny tekst źródłaInternet 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
Książki na temat "IoT Internet des objets"
Iot. Taylor & Francis Group, 2022.
Znajdź pełny tekst źródłaVermesan, Ovidiu. Advancing IoT Platforms Interoperability. River Publishers, 2022.
Znajdź pełny tekst źródłaVermesan, Ovidiu. Advancing IoT Platforms Interoperability. River Publishers, 2018.
Znajdź pełny tekst źródłaVermesan, Ovidiu. Advancing IoT Platforms Interoperability. River Publishers, 2022.
Znajdź pełny tekst źródłaAl-Turjman, Fadi. Smart-Grid in Iot-enabled Spaces. Taylor & Francis Group, 2020.
Znajdź pełny tekst źródłaCognitive Sensors and IoT: Architecture, Deployment, and Data Delivery. Taylor & Francis Group, 2017.
Znajdź pełny tekst źródłaAl-Turjman, Fadi. Cognitive Sensors and IoT: Architecture, Deployment, and Data Delivery. Taylor & Francis Group, 2017.
Znajdź pełny tekst źródłaAl-Turjman, Fadi. Cognitive Sensors and IoT: Architecture, Deployment, and Data Delivery. Taylor & Francis Group, 2017.
Znajdź pełny tekst źródłaBuilding Enterprise Iot Applications. Taylor & Francis Group, 2019.
Znajdź pełny tekst źródłaVuppalapati, Chandrasekar. Building Enterprise IoT Applications. Taylor & Francis Group, 2019.
Znajdź pełny tekst źródłaCzęści książek na temat "IoT Internet des objets"
Bouhaï, Nasreddine. "The IoT: Intrusive or Indispensable Objects?" W Internet of Things, 1–19. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119427391.ch1.
Pełny tekst źródłaMcGibney, Alan, Alejandro Esquiva Rodriguez, Oliva Brickley i Susan Rea. "Managing Connected Smart Objects". W Internet of Things. IoT Infrastructures, 3–9. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47075-7_1.
Pełny tekst źródłaGupta, Vishal, i Monish Gupta. "IoT-Based Artificial Intelligence System in Object Detection". W Internet of Things, 33–49. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003140443-3.
Pełny tekst źródłaGranell, Carlos, Andreas Kamilaris, Alexander Kotsev, Frank O. Ostermann i Sergio Trilles. "Internet of Things". W Manual of Digital Earth, 387–423. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9915-3_11.
Pełny tekst źródłaNikolic, Predrag K. "Multimodal Interactions: Embedding New Meanings to Known Forms and Objects". W Internet of Things. IoT Infrastructures, 107–21. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47075-7_13.
Pełny tekst źródłaRizzardi, Alessandra, Daniele Miorandi, Sabrina Sicari, Cinzia Cappiello i Alberto Coen-Porisini. "Networked Smart Objects: Moving Data Processing Closer to the Source". W Internet of Things. IoT Infrastructures, 28–35. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47075-7_4.
Pełny tekst źródłaFortino, Giancarlo, Anna Rovella, Wilma Russo i Claudio Savaglio. "Towards Cyberphysical Digital Libraries: Integrating IoT Smart Objects into Digital Libraries". W Internet of Things, 135–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26869-9_7.
Pełny tekst źródłaMariani, Stefano, Andrea Bicego, Marco Lippi, Marco Mamei i Franco Zambonelli. "Argumentation-Based Coordination in IoT: A Speaking Objects Proof-of-Concept". W Internet and Distributed Computing Systems, 169–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34914-1_17.
Pełny tekst źródłaKarakostas, Bill. "Towards Autonomous IoT Logistics Objects". W Securing the Internet of Things, 213–25. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9866-4.ch012.
Pełny tekst źródłaMilovanović, Dragorad, Vladan Pantović i Gordana Gardašević. "Converging Technologies for the IoT". W Securing the Internet of Things, 1070–95. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9866-4.ch048.
Pełny tekst źródłaStreszczenia konferencji na temat "IoT Internet des objets"
Kawsar, Fahim, Gerd Kortuem i Bashar Altakrouri. "Supporting interaction with the Internet of Things across objects, time and space". W 2010 Internet of Things (IOT). IEEE, 2010. http://dx.doi.org/10.1109/iot.2010.5678441.
Pełny tekst źródłaAlothman, Hussam Ali, Mohammad T. Khasawneh i Nagen N. Nagarur. "Internet of Things in Manufacturing: An Overview". W ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88262.
Pełny tekst źródłaCamargo, Leandro, Ana Marilza Pernas i Adenauer Yamin. "Abordagem VISO: uma Contribuição à Socialização entre Objetos da Internet das Coisas". W Seminário Integrado de Software e Hardware. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/semish.2023.230258.
Pełny tekst źródłaMazzei, Daniele, Gualtiero Fantoni, Gabriele Montelisciani i Giacomo Baldi. "Internet of Things for designing smart objects". W 2014 IEEE World Forum on Internet of Things (WF-IoT). IEEE, 2014. http://dx.doi.org/10.1109/wf-iot.2014.6803175.
Pełny tekst źródłaGama, Kiev, Rafael Wanderley, Daniel Maranhão i Vinicius Garcia. "TagHunt: Uma plataforma Combinando a Internet das Coisas com Scavenger Hunt Games". W VII Simpósio Brasileiro de Computação Ubíqua e Pervasiva. Sociedade Brasileira de Computação - SBC, 2015. http://dx.doi.org/10.5753/sbcup.2015.10177.
Pełny tekst źródłaSilva, Romulo, Windson Viana i Paulo Filipe Dantas. "Using images to extend smart object discovery in an Internet of Things scenario". W XXIV Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/webmedia.2018.4564.
Pełny tekst źródłaGeorgescu, Mircea, i Roxana Hucanu. "AN APPROACH ABOUT TURNING CHALLENGES INTO OPPORTUNITIES USING INTERNET OF THINGS". W eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-013.
Pełny tekst źródłaLuis Oliveira, Fernando, i Julio Mattos. "State-of-the-Art Javascript Language for Internet of Things". W IX Simpósio Brasileiro de Engenharia de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbesc_estendido.2019.8651.
Pełny tekst źródłaSrinivasan, K., i V. R. Azhaguramyaa. "Internet of Things (IoT) based Object Recognition Technologies". W 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.
Pełny tekst źródłaTanbo, Masaya, Ryoma Nojiri, Yuusuke Kawakita i Haruhisa Ichikawa. "Active RFID attached object clustering method based on RSSI series for finding lost objects". W 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT). IEEE, 2015. http://dx.doi.org/10.1109/wf-iot.2015.7389081.
Pełny tekst źródłaRaporty organizacyjne na temat "IoT Internet des objets"
Hong, J., X. de, M. Kovatsch, E. Schooler i D. Kutscher. Internet of Things (IoT) Edge Challenges and Functions. RFC Editor, kwiecień 2024. http://dx.doi.org/10.17487/rfc9556.
Pełny tekst źródłaGomez, C., J. Crowcroft i M. Scharf. TCP Usage Guidance in the Internet of Things (IoT). RFC Editor, marzec 2021. http://dx.doi.org/10.17487/rfc9006.
Pełny tekst źródłaMegas, Katerina, Ben Piccarreta i Danna Gabel O'Rourke. Internet of things (IoT) cybersecurity colloquium: a NIST workshop proceedings. Gaithersburg, MD: National Institute of Standards and Technology, grudzień 2017. http://dx.doi.org/10.6028/nist.ir.8201.
Pełny tekst źródłaSimmon, Eric. Internet of Things (IoT) component capability model for research testbed. Gaithersburg, MD: National Institute of Standards and Technology, wrzesień 2020. http://dx.doi.org/10.6028/nist.ir.8316.
Pełny tekst źródłaBoeckl, Katie, Michael Fagan, William Fisher, Naomi Lefkovitz, Katerina N. Megas, Ellen Nadeau, Danna Gabel O'Rourke, Ben Piccarreta i Karen Scarfone. Considerations for managing Internet of Things (IoT) cybersecurity and privacy risks. Gaithersburg, MD: National Institute of Standards and Technology, czerwiec 2019. http://dx.doi.org/10.6028/nist.ir.8228.
Pełny tekst źródłaGarcia-Morchon, O., S. Kumar i M. Sethi. Internet of Things (IoT) Security: State of the Art and Challenges. RFC Editor, kwiecień 2019. http://dx.doi.org/10.17487/rfc8576.
Pełny tekst źródłaPhillips, Paul. The Application of Satellite-based Internet of Things for New Mobility. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, styczeń 2024. http://dx.doi.org/10.4271/epr2024001.
Pełny tekst źródłaJimenez, J., H. Tschofenig i D. Thaler. Report from the Internet of Things (IoT) Semantic Interoperability (IOTSI) Workshop 2016. RFC Editor, październik 2018. http://dx.doi.org/10.17487/rfc8477.
Pełny tekst źródłaSymington, Susan, William Polk i Murugiah Souppaya. Trusted Internet of Things (IoT) Device Network-Layer Onboarding and Lifecycle Management (Draft). Gaithersburg, MD: National Institute of Standards and Technology, wrzesień 2020. http://dx.doi.org/10.6028/nist.cswp.09082020-draft.
Pełny tekst źródłaPassos, João, Sérgio Ivan Lopes, Filipe Manuel Clemente, Pedro Miguel Moreira, Markel Rico-González, Pedro Bezerra i 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, czerwiec 2021. http://dx.doi.org/10.37766/inplasy2021.6.0041.
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