Literatura científica selecionada sobre o tema "Internet of Things Network (IoTN)"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Índice
Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "Internet of Things Network (IoTN)".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Artigos de revistas sobre o assunto "Internet of Things Network (IoTN)"
Itagi, Manjunath, Dankan Gowda V, KDV Prasad, Pullela SVVSR Kumar, Shekhar R e B. Ashreetha. "Performance Analysis of Energy Efficiency and Security Solutions of Internet of Things Protocols". International Journal of Electrical and Electronics Research 11, n.º 2 (26 de junho de 2023): 442–50. http://dx.doi.org/10.37391/ijeer.110226.
Texto completo da fonteNourildean, Shayma Wail, Mustafa Dhia Hassib e Yousra Abd Mohammed. "Internet of things based wireless sensor network: a review". Indonesian Journal of Electrical Engineering and Computer Science 27, n.º 1 (1 de julho de 2022): 246. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp246-261.
Texto completo da fonteNikhat Akhtar e Yusuf Perwej. "The internet of nano things (IoNT) existing state and future Prospects". GSC Advanced Research and Reviews 5, n.º 2 (30 de novembro de 2020): 131–50. http://dx.doi.org/10.30574/gscarr.2020.5.2.0110.
Texto completo da fonteKumar, Sumit, e Zahid Raza. "Internet of Things". International Journal of Systems and Service-Oriented Engineering 7, n.º 3 (julho de 2017): 32–52. http://dx.doi.org/10.4018/ijssoe.2017070103.
Texto completo da fonteSankar, S., Ramasubbareddy Somula, R. Lakshmana Kumar, P. Srinivasan e M. Amala Jayanthi. "Trust-Aware Routing Framework for Internet of Things". International Journal of Knowledge and Systems Science 12, n.º 1 (janeiro de 2021): 48–59. http://dx.doi.org/10.4018/ijkss.2021010104.
Texto completo da fonteFadziso, Takudzwa. "Internet of Things in Agriculture for Smart Farming". Malaysian Journal of Medical and Biological Research 5, n.º 2 (31 de dezembro de 2018): 147–56. http://dx.doi.org/10.18034/mjmbr.v5i2.565.
Texto completo da fonteEltayeb, Mohamed A. "Internet of Things". International Journal of Hyperconnectivity and the Internet of Things 1, n.º 1 (janeiro de 2017): 1–18. http://dx.doi.org/10.4018/ijhiot.2017010101.
Texto completo da fonteGanapathy, Apoorva. "Image Association to URLs across CMS Websites with Unique Watermark Signatures to Identify Who Owns the Camera". American Journal of Trade and Policy 6, n.º 3 (2019): 101–6. http://dx.doi.org/10.18034/ajtp.v6i3.543.
Texto completo da fonteMaseleno, Andino, Wahidah Hashim, Y. C. Tang Alicia, A. Mahmoud Moamin e Marini Othman. "A Review on Smart Grid Internet of Things". Journal of Computational and Theoretical Nanoscience 17, n.º 6 (1 de junho de 2020): 2770–75. http://dx.doi.org/10.1166/jctn.2020.8941.
Texto completo da fonteBynagari, Naresh Babu. "Industrial Application of Internet of Things". Asia Pacific Journal of Energy and Environment 3, n.º 2 (31 de dezembro de 2016): 75–82. http://dx.doi.org/10.18034/apjee.v3i2.576.
Texto completo da fonteTeses / dissertações sobre o assunto "Internet of Things Network (IoTN)"
Okumura, Brandon M. "IoTA: Internet of Things Assistant". DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1769.
Texto completo da fonteShahid, 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.
Texto completo da fonteThe 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
Diratie, Eyassu Dilla. "Hybrid internet of things network for energy-efficient video surveillance system". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG066.
Texto completo da fonteWireless visual sensor networks based on IEEE 802.11 mesh networks are effective and suitable solutions for video surveillance systems in monitoring intrusions in selected areas. The IEEE 802.11-based visual sensor networks offer high bit rate video transmissions but suffer from energy inefficiency issues. Moreover, the video transmission in the visual sensor networks requires strict quality of service (QoS) in terms of bandwidth and delay. Also, it is challenging to decrease the overall energy consumption of the network while assuring guaranteed QoS in terms of bandwidth and delay in energy-constrained wireless visual sensor networks. The main contribution of this dissertation is to provide an energy-efficient video surveillance network without compromising the QoS requirement of video transmission. First, we propose a new hybrid IoT network architecture for a video surveillance system that detects and tracks an intruder in the monitoring area. The hybrid IoT network integrates the IEEE 802.11-based multi-hop visual Sensor Networks and LoRa network to provide an autonomous, energy-efficient, high-bitrate video surveillance system. Leveraging the LoRa network characteristics, the LoRa network is utilized as an always-active network for preliminary motion detection and tracking. Moreover, the LoRa network also decides which visual sensor nodes to wake up depending on the tracking information. The Kalman filter is investigated to track the intruder's trajectory from noise measurements of low-power motion sensors to activate only the visual sensor nodes along the intruder's trajectory to provide effective video vigilance. We showed through simulation that Kalman filter estimates and predicts intruder trajectory with reasonable accuracy. Moreover, the proposed hybrid IoT network approach reduces energy consumption significantly compared with a traditional, always active continuous monitoring single-tier visual sensor network. Next, the contribution of this dissertation focuses on an energy-aware and QoS routing mechanism for the IEEE 802.11-based multi-hop visual sensor network of the hybrid IoT network. We propose a routing algorithm that routes a set of video streams to the gateway with guaranteed QoS in terms of bandwidth and delay while minimizing the number of visual sensor nodes that are involved in routing. This maximizes the number of nodes that can be turned off completely to optimize the overall energy consumption of the network without compromising QoS performance. The proposed routing problem is formulated as an Integer Linear Program (ILP) and solved using the branch-and-bound algorithm. Through computer simulation, the performance of the proposed approach is compared with the existing state-of-the-art routing algorithms from the literature. The results clearly show that the proposed mechanism saves a significant amount of the overall energy consumption while guaranteeing QoS in terms of bandwidth and delay
Alm, Anton. "Internet of Things mesh network : Using the Thread networking protocol". Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-70809.
Texto completo da fonteXu, Ran. "Federated Sensor Network architectural design for the Internet of Things (IoT)". Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/13453.
Texto completo da fonteCarlquist, Johan. "Evaluating the use of ICN for Internet of things". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-343368.
Texto completo da fonteHsu, Alexander Sirui. "Automatic Internet of Things Device Category Identification using Traffic Rates". Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/88421.
Texto completo da fonteMaster of Science
IoT (Internet of Things) devices are an exploding field, with many devices being created, manufactured, and utilized per year. With the rise of so many internet capable devices, there is a risk that the devices may have vulnerabilities and exploits able to allow unauthorized users to access. While a problem for a consumer network, this is an increased problem in an enterprise network, since much of the information on the network is sensitive and should be kept confidential and private. While a ban of IoT devices on a network is able to solve this problem, with the rise of machine learning able to characterize and recognize patterns, a smarter approach can be created to distinguish when and which types of IoT devices enter the network. Previous attempts to identify IoT devices used signature schemes specific to a single device, but this paper aims to generalize traffic behaviors and identifying a device category rather than a specific IoT device to ensure future new devices can also be recognized. With device category identification in place on an internet network, smarter approaches can be implemented to ensure the devices remain secure while still able to be used.
Frawley, Ryan Joseph. "Logging and Analysis of Internet of Things (IoT) Device Network Traffic and Power Consumption". DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1911.
Texto completo da fonteHobring, Linus, e Philip Söderberg. "A sensor network for the Internet of Things Integrated with a bidirectional backend". Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2702.
Texto completo da fonteNITTI, MICHELE. "Managing the Internet of Things based on its Social Structure". Doctoral thesis, Università degli Studi di Cagliari, 2014. http://hdl.handle.net/11584/266422.
Texto completo da fonteLivros sobre o assunto "Internet of Things Network (IoTN)"
Patel, Rajan, Nimisha Patel, Linda Smail, Pariza Kamboj e Mukesh Soni. Intelligent Green Communication Network for Internet of Things. New York: CRC Press, 2023. http://dx.doi.org/10.1201/9781003371526.
Texto completo da fonteUdgata, Siba Kumar, e Nagender Kumar Suryadevara. Internet of Things and Sensor Network for COVID-19. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7654-6.
Texto completo da fonteLeebow, Ken. 300 incredible things for travelers on the Internet. Marietta, Ga: 300Incredible.com, 1999.
Encontre o texto completo da fonteLeebow, Ken. 300 incredible things for travelers on the Internet. Marietta, Ga: 300Incredible.com, 2000.
Encontre o texto completo da fonte300 incredible things for beanie lovers on the Internet. Marietta, Ga: VIP Pub., 1999.
Encontre o texto completo da fonteKen, Leebow, ed. 300 incredible things for seniors on the Internet. Marietta, Ga: 300incredible.com, 2000.
Encontre o texto completo da fonteJapan) IOT (Conference) (2010 Tokyo. 2010 Internet of things: (IOT 2010) , Tokyo, Japan, 29 November- 1 December 2010. Piscataway, NJ: IEEE, 2010.
Encontre o texto completo da fonteLeebow, Ken. 300 more incredible things to do on the Internet. Marietta, Ga: 300incredible.com, 2000.
Encontre o texto completo da fonteKen, Leebow, ed. 300 incredible things to learn on the Internet. Marietta, Ga: 300Incredible.Com, LLC, 2000.
Encontre o texto completo da fonteOuaissa, Mariyam, Mariya Ouaissa, Inam Ullah Khan, Zakaria Boulouard e Junaid Rashid. Low-Power Wide Area Network for Large Scale Internet of Things. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003426974.
Texto completo da fonteCapítulos de livros sobre o assunto "Internet of Things Network (IoTN)"
Wu, Chuan-Kun. "IoT Network Layer Security". In Internet of Things Security, 107–23. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1372-2_7.
Texto completo da fonteSarwesh, P., N. Shekar V. Shet e K. Chandrasekaran. "Energy-Efficient Network Architecture for IoT Applications". In Internet of Things, 119–44. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50758-3_5.
Texto completo da fonteKotenko, Igor, Igor Saenko e Fadey Skorik. "IoT Network Administration by Intelligent Decision Support Based on Combined Neural Networks". In Internet of Things, 1–24. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21940-5_1.
Texto completo da fonteKant, Umang, e Vinod Kumar. "IoT Network Used in Fog and Cloud Computing". In Internet of Things, 165–87. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1585-7_8.
Texto completo da fonteWu, Fan, Christoph Rüdiger, Jean-Michel Redouté e Mehmet Rasit Yuce. "A Wearable Multi-sensor IoT Network System for Environmental Monitoring". In Internet of Things, 29–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02819-0_3.
Texto completo da fonteQian, Zhihong, Yijun Wang, Xue Wang e Shuang Zhu. "M/I Adaptation Layer Network Protocol for IoT Based on 6LoWPAN". In Internet of Things, 208–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32427-7_28.
Texto completo da fonteKhelifi, Fekher. "Monitoring System Based in Wireless Sensor Network for Precision Agriculture". In Internet of Things (IoT), 461–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37468-6_24.
Texto completo da fonteTomaš, Boris, e Neven Vrček. "Smart City Vehicular Mobile Sensor Network". In Internet of Things. IoT Infrastructures, 70–77. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19743-2_11.
Texto completo da fonteHauschild, Sebastian, e Horst Hellbrück. "Latency and Energy Consumption of Convolutional Neural Network Models from IoT Edge Perspective". In Internet of Things, 385–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20936-9_31.
Texto completo da fonteDalal, Upena, Shweta Shah e Jigisha Patel. "MAC and Network Layer Issues and Challenges for IoT". In The Internet of Things, 49–78. Boca Raton : CRC Press, 2018.: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315156026-3.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Internet of Things Network (IoTN)"
R Ahmed, Muhammad, Ahmed Al Shihimi, Thirein Myo, Badar Al Baroomi e M. Shamim Kaiser. "Internet of Things Network Architecture and Security Challenges". In 12th International Conference on Digital Image Processing and Vision. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131313.
Texto completo da fonteNitti, Michele, Luigi Atzori e Irena Pletikosa Cvijikj. "Network navigability in the social Internet of Things". In 2014 IEEE World Forum on Internet of Things (WF-IoT). IEEE, 2014. http://dx.doi.org/10.1109/wf-iot.2014.6803200.
Texto completo da fonteAthreya, Arjun P., e Patrick Tague. "Network self-organization in the Internet of Things". In 2013 IEEE International Workshop of Internet-of-Things Networking and Control (IoT-NC). IEEE, 2013. http://dx.doi.org/10.1109/iot-nc.2013.6694050.
Texto completo da fonteSingh, Meena, M. A. Rajan, V. L. Shivraj e P. Balamuralidhar. "Secure MQTT for Internet of Things (IoT)". In 2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2015. http://dx.doi.org/10.1109/csnt.2015.16.
Texto completo da fonteBorkar, Suresh, e Himangi Pande. "Application of 5G next generation network to Internet of Things". In 2016 International Conference on Internet of Things and Applications (IOTA). IEEE, 2016. http://dx.doi.org/10.1109/iota.2016.7562769.
Texto completo da fonteSahlmann, Kristina, Thomas Scheffler e Bettina Schnor. "Ontology-driven Device Descriptions for IoT Network Management". In 2018 Global Internet of Things Summit (GIoTS). IEEE, 2018. http://dx.doi.org/10.1109/giots.2018.8534569.
Texto completo da fonteVreca, Jure, Iva Ivanov, Gregor Papa e Anton Biasizzo. "Detecting Network Intrusion Using Binarized Neural Networks". In 2021 IEEE 7th World Forum on Internet of Things (WF-IoT). IEEE, 2021. http://dx.doi.org/10.1109/wf-iot51360.2021.9595961.
Texto completo da fonteRamachandra, Manjunath. "IoT solution for scheduling in transport network". In 2016 International Conference on Internet of Things and Applications (IOTA). IEEE, 2016. http://dx.doi.org/10.1109/iota.2016.7562746.
Texto completo da fonteSingh, Rashmi, Rajesh Mehra e Lalita Sharma. "Design of Kalman filter for wireless sensor network". In 2016 International Conference on Internet of Things and Applications (IOTA). IEEE, 2016. http://dx.doi.org/10.1109/iota.2016.7562696.
Texto completo da fonteHe, Weiwei, Shuang-Hua Yang, Lili Yang e Ping Li. "In-network data processing architecture for energy efficient wireless sensor networks". In 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT). IEEE, 2015. http://dx.doi.org/10.1109/wf-iot.2015.7389070.
Texto completo da fonteRelatórios de organizações sobre o assunto "Internet of Things Network (IoTN)"
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, setembro de 2020. http://dx.doi.org/10.6028/nist.cswp.09082020-draft.
Texto completo da fontePhillips, Paul. The Application of Satellite-based Internet of Things for New Mobility. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, janeiro de 2024. http://dx.doi.org/10.4271/epr2024001.
Texto completo da fonteDodson, Donna, Douglas Montgomery, Tim Polk, Mudumbai Ranganathan, Murugiah Souppaya, Steve Johnson, Ashwini Kadam et al. Securing Small-Business and Home Internet of Things (IoT) Devices: Mitigating Network-Based Attacks Using Manufacturer Usage Description (MUD). National Institute of Standards and Technology, maio de 2021. http://dx.doi.org/10.6028/nist.sp.1800-15.
Texto completo da fonteGarcía Zaballos, Antonio, Maribel Dalio, Jesús Garran, Enrique Iglesias Rodriguez, Pau Puig Gabarró e Ricardo Martínez Garza Fernández. Estructuración de un centro de operación de redes (NOC). Banco Interamericano de Desarrollo, outubro de 2022. http://dx.doi.org/10.18235/0004520.
Texto completo da fonteWatrobski, Paul, Murugiah Souppaya, Joshua Klosterman e William Barker. Methodology for Characterizing Network Behavior of Internet of Things Devices. National Institute of Standards and Technology, janeiro de 2022. http://dx.doi.org/10.6028/nist.ir.8349-draft.
Texto completo da fonteRuvinsky, Alicia, Timothy Garton, Daniel Chausse, Rajeev Agrawal, Harland Yu e Ernest Miller. Accelerating the tactical decision process with High-Performance Computing (HPC) on the edge : motivation, framework, and use cases. Engineer Research and Development Center (U.S.), setembro de 2021. http://dx.doi.org/10.21079/11681/42169.
Texto completo da fonteVidea, Aldo, e Yiyi Wang. Inference of Transit Passenger Counts and Waiting Time Using Wi-Fi Signals. Western Transportation Institute, agosto de 2021. http://dx.doi.org/10.15788/1715288737.
Texto completo da fonte