Relevant bibliographies by topics / Network; Internet of Things

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

Academic literature on the topic 'Network; Internet of Things'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Network; Internet of Things"

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Jin, Wang *. Yi bin Hou. "INVESTIGATION ON THE INTERNET OF THINGS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 12 (2016): 861–79. https://doi.org/10.5281/zenodo.221118.

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Do IOT problem definition and research. Research on Internet of things, first research object, Re research alliance,Re study network. Objects are things in the Internet of things, Link is how objects connect to the network, Network is what this network is. Objective function is the key problem. Can start with simple and critical questions. Algorithm is the solution to the problem steps. What is the Internet of things, objects connected to the Internet is the Internet of things, cup networking, car networking. Things better than other networks, is composed of what objects, what composition, what nature, what innovation and superiority. Internet of things four key technologies are widely used, these four technologies are mainly RFID, WSN, M2M, as well as the integration of the two. RFID can be achieved using MATLAB, NS2, Android, WSN can use NS2, OMNET++ implementation, M2M can be developed using JAVA. Therefore, this paper focuses on the advantages of Internet of things than the internet.
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He, Shen, Jing Huang, and Penglin Yang. "Build with intrinsic security: Trusted autonomy security system." International Journal of Distributed Sensor Networks 16, no. 11 (2020): 155014772097453. http://dx.doi.org/10.1177/1550147720974535.

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Security of Internet of things network is difficult to manage because Internet of things devices are heterogeneous and always deployed in remote places. Based on these features, attacks against Internet of things network like distributed denial-of-service and system intrusion are easier than traditional network. In order to enhance Internet of things network’s security, in this article we propose the concept of intrinsic security to establish a trusted and autonomy Internet of things network. Intrinsic security emphasizes that network itself must be trusted and security autonomous to address the security problems which are caused by remote and heterogeneous. Based on this thought, we designed the architecture of Trusted Autonomy Security System. Trusted Autonomy Security System maintains a LAN characterized by trusted and security autonomy, in which all Internet of things network gateways formed a decentralized autonomy system to evaluate if other gateways or Internet of things devices are secure. Compared to traditional Internet of things networks, this architecture uses trusted computing technique to build the basic of security, and makes Internet of things network available to manage security automatically. In addition, the decentralized architecture could reduce security risks caused by compromise of single node in Internet of things network. With these advantages, it can be expected that this novel idea will have more development in Internet of things scenarios with high security requirements.
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Espada, Jordán Pascual, Ronald R. Yager, and Bin Guo. "Internet of things: Smart things network and communication." Journal of Network and Computer Applications 42 (June 2014): 118–19. http://dx.doi.org/10.1016/j.jnca.2014.03.003.

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Kumar, Sumit, and Zahid Raza. "Internet of Things." International Journal of Systems and Service-Oriented Engineering 7, no. 3 (2017): 32–52. http://dx.doi.org/10.4018/ijssoe.2017070103.

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Internet of Things (IoT) is a novel approach of connecting things/objects and thus transmitting information between various entities of the physical world or to the control centers where this information can be interpreted. IoT has been poised as the next evolution of internet promising to change our lives by involving a seamless access to people and devices in a ubiquitous way leading to a smart world. These devices, often referred to as smart items or intelligent things can be home appliances, healthcare devices, vehicles, buildings, factories and almost anything networked and fitted with sensors, actuators, and/or embedded computers. IoT promises to make the world smarter and proactive by enabling things to talk and others to understand. This work first presents an insight into the origin of IoT and its network as well as data centric architecture while listing the major possibilities. The seemingly important role and challenges of using Wireless Sensor Networks (WSN) which acts as the base in sensing and monitoring has been discussed. Since, the future lies in utility computing, best realized in the form of cloud computing, a cloud centric view of IoT is also presented.
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Nourildean, Shayma Wail, Mustafa Dhia Hassib, and Yousra Abd Mohammed. "Internet of things based wireless sensor network: a review." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (2022): 246. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp246-261.

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Recently, Internet of Things (IoT) technologies are developing technology with a variety of applications. The Internet of Things (IoTs) is defined as a network of ordinary objects such as Internet TVs, smartphones, actuators and sensors that are smartly connected together to enable new types of communication between people and things as well as between things themselves. Wireless sensor networks (WSNs) play an important part in Internet of Things (IoT) technology. A contribution to wireless sensor networks and IoT applications is wireless sensor nodes’ construction with high-speed CPUs and low-power radio links. The IoT-based wireless Sensor network (WSN) is a game-changing smart monitoring solution. ZigBee standard is an important wireless sensor network (WSN) and Internet of Things (IoT) communication protocol in order to facilitate low-power, low-cost IoT applications and to handle numerous network topologies. This paper presented a review on the energy efficient and routing topologies of ZigBee WSN, applications of IoT enabled Wireless Sensor Network as well IoT WSN security challenges.
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Ganapathy, 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, no. 3 (2019): 101–6. http://dx.doi.org/10.18034/ajtp.v6i3.543.

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Internet is the world's network of connected computer networks. Internet means an interconnected network. It is a network of connected web servers. Internet helps data and people across the globe. Internet of things refers to network-connected things with embedded computer chips. Things on the internet would include devices enabled for internet access. IoT association of images on content management websites with unique watermark signature to account for Royal to the owner of the picture will help against piracy, copyright infringement, and misuse of photos registered with unique identification keys. This will make content management easier. It will generate revenue for the person who takes the copyrighted picture. A watermark is an embedded signature in a thing. It could be embedded in a video, image, and other file types for distinction and marking for ownership. It could be visible or invisible. It also provides a means to trace a product to the owner. This work looks into how images with watermark can be connected to the IoT for tracking and fighting piracy.
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Fragkou, Evangelia, Dimitrios Papakostas, Theodoros Kasidakis, and Dimitrios Katsaros. "Multilayer Backbones for Internet of Battlefield Things." Future Internet 14, no. 6 (2022): 186. http://dx.doi.org/10.3390/fi14060186.

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The Internet of Battlefield Things is a newly born cyberphysical system and, even though it shares a lot with the Internet of Things and with ad hoc networking, substantial research is required to cope with its scale and peculiarities. This article examines a fundamental problem pertaining to the routing of information, i.e., the calculation of a backbone network. We model an IoBT network as a network with multiple layers and employ the concept of domination for multilayer networks. This is a significant departure from earlier works, and in spite of the huge literature on the topic during the past twenty years, the problem in IoBT networks is different since these networks are multilayer networks, thus making inappropriate all the past, related literature because it deals with single layer (flat) networks. We establish the computational complexity of our problem, and design a distributed algorithm for computing connected dominating sets with small cardinality. We analyze the performance of the proposed algorithm on generated topologies, and compare it against two—the only existing—competitors. The proposed algorithm establishes itself as the clear winner in all experiments concerning the dominating set from a size-wise and an energy-wise perspective achieving a performance gain of about 15%.
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Imran, Zeba Ghaffar, Abdullah Alshahrani, Muhammad Fayaz, Ahmed Mohammed Alghamdi, and Jeonghwan Gwak. "A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges." Electronics 10, no. 8 (2021): 880. http://dx.doi.org/10.3390/electronics10080880.

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In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.
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K., Thamizhmaran. "Network Privacy Reflection using Internet of Things." Recent Trends in Control and Converter 3, no. 3 (2021): 1–11. https://doi.org/10.5281/zenodo.4477853.

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<em>The current IP and other networks such as Power Smart Grids are fast developing, thus ensuing in diverse connectivity methodologies. This has led to the emergence of &quot;the Internet of Things&rdquo; (IoT) methodology whose goal is to transform the current IP and related networks to Device-to-Device (D-2-D) basis. It will seamlessly interconnect the globe via intelligent devices and sensors of varying types, this resulting in voluminous generation and exchange of data in excess of 20 billion Internet-connected objects and sensors (things) by 2022. The resultant structure will benefit mankind by helping us make tough decisions as well as be provisioned of beneficial services. In this paper, we overview both IoT enabled network architecture as well as security for associated objects and devices. We commence with a description of a generalized IoT enabled network&#39;s security architecture as well as how the various elements constituting them interact. We then describe an approach that allows the protection of both constrained and unconstrained IoT devices through robust encryption as well as authentication so that both can equally leverage from the same security framework, but still maintaining low computational loads, by avoiding excessive computational operations</em>
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Chen, Guobin, and Tangsen Huang. "Community privacy estimation method based on key node method in space social Internet of Things." International Journal of Distributed Sensor Networks 15, no. 10 (2019): 155014771988313. http://dx.doi.org/10.1177/1550147719883131.

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Based on the research of social network and the Internet of Things, a new research topic in the field of Internet of Things, Social Internet of Things is gradually formed. The SIoT applies the research results of SIoT from different aspects of the Internet of Things, and solves the specific problems in the research of Internet of Things, which brings new opportunities for the development of the Internet of Things. With the development of the Internet of Things technology, in the spatial social Internet of Things structure, user information includes sensitive attributes and non-sensitive attribute information. This information can be inferred from public user information to infer the information of the private user and even speculate on sensitive attributes. This article proposes an information speculation method based on the core users of spatial social networks, and estimates the non-core user information through the core user public information. First, the user’s spatial social network is divided into communities, and the core nodes of the community in the spatial social network are calculated by PageRank algorithm and the convergence of the algorithm is proved. Then, through the public information of the core nodes divided by the community in the space social network, the private information of relevant users to these core nodes can be speculated. Finally, by experimental analyzing the community structures of SIoT (Social Internet of Things) like Twitter, Sina Weibo, ER random networks, and NW small-world network, and making 5%, 10%, 15%, 20% information anonymous respectively in these four kinds of networks, we can analyze their clustering coefficient, Q-modularity and properties. Finally, the key node information of the four spatial social structures is speculated to analyze the effectiveness of the proposed method. Compared with the non-core speculation method, this method has advantages in speculative information integrity and time.
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Dissertations / Theses on the topic "Network; Internet of Things"

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Okumura, Brandon M. "IoTA: Internet of Things Assistant." DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1769.

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The Internet of Things is the networking of electronic devices, or “Things”, that enables them to collect and share data, as well as interact with their physical surround- ings. Analyzing this collected data allows us to make smarter economic decisions. These interconnected networks are usually driven by low-powered micro-controllers or cheap CPUs that are designed to function optimally with very little hardware. As scale and computational requirements increase, these micro-controllers are unable to grow without being physically replaced. This thesis proposes a system, IoTA, that assists the Internet of Things by pro- viding a shared computational resource for endpoint devices. This solution extends the functionality of endpoint devices without the need of physical replacement. The IoTA system is designed to be easily integrable to any existing IoT network. This system presents a model that allows for seamless processing of jobs submitted by endpoint devices while keeping scalability and flexibility in mind. Additionally, IoTA is built on top of existing IoT protocols. Evaluation shows there is a significant performance benefit in processing computationally heavy algorithms on the IoTA system as compared to processing them locally on the endpoint devices themselves.
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Ramos, Alex Lacerda. "Network security metrics for the Internet of things." Universidade de Fortaleza, 2018. http://dspace.unifor.br/handle/tede/108423.

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Made available in DSpace on 2019-03-30T00:02:10Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-11-26<br>Recent advances in networking technologies, such as the IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) standard, have allowed to interconnect wireless sensor networks (WSNs) to the Internet, thus forming the Internet of Things (IoT). Despite the availability of different message security mechanisms, sensor networks are still vulnerable to several types of attack. To identify such attacks, an Intrusion Detection System (IDS) can be deployed. However, IDSs can generate several false positives and false negatives. Moreover, the alerts raised by IDSs provide no information regarding the impact an attack has on the security of a sensor network. As a consequence, it becomes difficult for WSN administrators and users to take proper responsive actions when attacks occur. To address these issues, this thesis proposes three security metrics. The first metric, called Trust Probability, quantifies by how much an IDS output could be trusted (to be correct). Such metric can help administrators decide which alerts deserve careful attention or which alerts might be safely ignored. Since this type of metric provides a measure of IDS effectiveness, it can also be used to compare different IDSs as well as to fine-tune a given IDS. The second metric, named Damage Level, quantifies the severity of an attack. This metric, when combined with the Trust Probability metric, enables the administrator to correctly prioritize and respond to alerts by evaluating them in terms of accuracy and attack impact. Finally, the third metric, namely Data Security Level, quantifies the degree to which sensor data can be trusted when the sensor is under attack. The security situational awareness provided by this metric helps WSN users make better decisions about the use of the gathered sensor data. Experimental results show that the proposed metrics can accurately quantify security level with low performance overhead and power consumption. Keywords: Network Security Metrics, Quantitative Security Analysis, Security Situational Awareness, Internet of Things, Wireless Sensor Networks.<br>Recentes avanços nas tecnologias de rede, tais como o padrão IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN), permitiram a interconexão de redes de sensores sem fio (RSSF) à Internet, formando assim a Internet das Coisas (Internet of Things -- IoT). Apesar da disponibilidade de diferentes mecanismos de segurança de mensagens, as redes de sensores ainda são vulneráveis a vários tipos de ataques. Para identificar esses ataques, um Sistema de Detecção de Intrusão (Intrusion Detection System -- IDS) pode ser implantado. No entanto, os IDSs podem gerar vários falsos positivos e falsos negativos. Além disso, os alertas gerados pelos IDSs não fornecem nenhuma informação sobre o impacto de um ataque sobre a segurança de uma RSSF. Consequentemente, torna-se difícil para os administradores e usuários da rede tomarem as devidas ações responsivas quando ataques ocorrerem. Para tratar estas questões, esta tese propõe três métricas de segurança. A primeira delas, chamada Trust Probability, quantifica o quão confiável (correto) é um output de um IDS. Essa métrica pode ajudar os administradores a decidir quais alertas merecem mais atenção ou quais podem ser ignorados com segurança. Já que essa métrica fornece uma medida da efetividade de um IDS, ela também pode ser usada para comparar diferentes IDSs, bem como para otimizar um dado IDS. A segunda métrica, denominada Damage Level, quantifica a gravidade de um ataque. Esta métrica, quando combinada com a Trust Probability, permite ao administrador priorizar e responder corretamente a alertas, avaliando-os em termos de precisão e impacto de ataque. Por fim, a terceira métrica, chamada de Data Security Level, quantifica quão confiáveis os dados dos sensores são quando a rede está sob ataque. Conhecer a informação fornecida por esta métrica ajuda os usuários a tomar melhores decisões sobre o uso dos dados coletados pelos sensores. Os resultados experimentais mostram que as métricas propostas podem quantificar com precisão o nível de segurança da rede, com baixo consumo de energia e sobrecarga de desempenho. Palavras-chave:Métricas de Segurança de Rede, Análise Quantitativa de Segurança, Consciência Situacional de Segurança, Internet das Coisas, Redes de Sensores sem Fio.
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Cobârzan, Cosmin. "Internet of highly mobile things." Thesis, Strasbourg, 2015. http://www.theses.fr/2015STRAD037/document.

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La mobilité devienne un partie intégrante de l’Internet des Object d’aujourd’hui, comme beaucoup d’applications (monitorage des animaux sauvage, suivi des cible dans le champs de bataille) sont impossible de mettre en œuvre juste avec des nœuds statiques. L’objective de cette thèse est de définir une nouvelle architecture de communication articule autour de la mobilité dans les réseaux avec pertes et à bas puissance (Low Power and Lossy Networks - LLNs) (réseaux des capteurs sans fils). Tout d’abord, nous avons analysé théoriquement l’auto configuration des adresses IPv6, fait avec toutes les optimisations disponibles dans Neighbor Discovery Optimization for IPv6 over 6LoWPAN. Cette étape est cruciale pour des protocoles qui donnent de support pour la mobilité dans des réseaux IP, comme MIPv6. Les résultats obtenues – taille des paquets trop grande et consumations énergétique importante pour les routeurs qui tournent Neighbor Discovery – n’ont amener a utiliser le IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). RPL est développe d’el debout pour les LLN. Notre deuxième contribution sont améliorer les opérations du RPL pour mieux supporter les nœuds mobiles. Enfin, nous avons développe une mécanisme inter-couche – Mobility Triggered-RPL – qui profite des actions dans le protocole avec préambule X-Machiavel à la couche accès au medium dans le protocole RPL à la couche routage<br>Mobility is becoming an integrating part of todays Internet of Things, as many applications such as wildlife monitoring or target tracking in the battlefield cannot be done only with the help of static nodes. The goal of this thesis is to provide new communication architecture articulated around providing mobility support in Low Power and Lossy Networks (LLNs). First we analyzed from a theoretical point of view the IPv6 address auto-configuration with all optimizations made in Neighbor Discovery Optimization for IPv6 over 6LoWPAN. This step is of crucial importance for protocols that offer mobility support in IP networks, such as MIPv6. Our findings, increased message size that leads to fragmentation and high energy consumption for routers that are involved in Neighbor Discovery message exchange, have lead us to use the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) in order to provide mobility support. RPL is build from ground up with respect to LLN requirements. Our second contribution enhanced RPL operations to support mobility management. Finally, we proposed a cross-layer protocol – Mobility Triggered-RPL – that leverages actions from the X-Machiavel preamble sampling MAC protocol into RPL
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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.

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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 qualité<br>The 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
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Крамаренко, Є. С. "Iнтелектуальна мережа Internet of Things". Master's thesis, Сумський державний університет, 2019. http://essuir.sumdu.edu.ua/handle/123456789/76473.

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Зпроектовано інтелектуальну мережу «розумного будинку» у симуляторі Cisco Packet Tracer_7, яка базується на роботі дротової та бездротової мереж, де усі пристрої звязані зі шлюзом. Доступ контролю та моніторингу забезпечено з планшету та смартфону через зручний веб-інтерфейс. Для налаштування пожежної безпеки було розроблено програму для мікроконтролера на мові JavaScript. Візуальне програмування було використано для забезпечення системи комфорту. Розроблена схема при тестуванні показала високі результати, що говорить про доступність та зрозумілість взаємодії із можливостями симулятора. Вона дає змогу отримати наочне уявлення про функціонування розгалудженої системи «розумного будинку» та навчитися формулювати правила роботи окремих елементів на базі отриманих показників датчиків і сенсорів.
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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.

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This thesis summarizes my project in setting up a Thread network. The idea of this project was presented by the company ÅF in Karlstad, Sweden. ÅF wishes to upgrade their current demonstrator for IoT. The current demonstrator includes Azure Cloud component, Raspberry Pi, Bluetooth and Arduino components. The upgrade includes implementing Thread technology together with Thread verified hardware from Nordic semiconductor and the Raspberry Pi Foundation. Thread is an IoT mesh networking protocol that was released year 2014. Compared to Bluetooth it offers IP communication (including IPv6) combined with higher reliability, performance and security. The process of installing, compiling and configuring the Thread network is explained. The result is an operational thread network that has sensor devices sending data to an HTTP web server, where the data is stored and monitored. Though, there are many improvements and functions that can be implemented to make this demonstrator more appealing.
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Koutsoumpakis, Iakovos. "An Internet of Things network for proximity based distributed processing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-267865.

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The Internet of Things, the interconnection of all computing devices, is a concept that has become very popular nowadays and many companies try to achieve a leading role in shaping its future. Billions of devices are already connected to IoT cloud networks and this number is expected to rapidly increase in the near future. Devices in an IoT cloud network can be producers or consumers of data, while some can be processors. As data often needs processing in order to be transformed from lower to higher conceptual value, before being delivered to the consumers, this processing has to be done in an efficient manner. Ideally processing should take place in the proximity of data producers as opposed to having to transfer large volumes of data over the network in order to reach the processor. For this problem to be solved, scheduling algorithms require additional information that quantifies the "distance" between the different nodes in an IoT cloud network. Consequently, the main focus of this work is the development and the evaluation of an efficient mechanism that uses a heuristic technique to estimate this information, the latency between nodes, greatly reducing to linear the running time complexity that, if every device had to contact every other to calculate it, would be O(n^2).
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Carlquist, 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.

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The market of IOT devices continues to grow at a rapid speed as well as constrained wireless sensor networks. Today, the main network paradigm is host centric where a users have to specify which host they want to receive their data from. Information-centric networking is a new paradigm for the future internet, which is based on named data instead of named hosts. With ICN, a user needs to send a request for a perticular data in order to retrieve it. When sent, any participant in the network, router or server, containing the data will respond to the request. In order to achieve low latency between data creation and its consumption, as well as being able to follow data which is sequentially produced at a fixed rate, an algortihm was developed. This algortihm calculates and determines when to send the next interest message towards the sensor. It uses a ‘one time subscription’ approach to send its interest message in advance of the creation of the data, thereby enabling a low latency from data creation to consumption. The result of this algorithm shows that a consumer can retrieve the data with minimum latency from its creation by the sensor over an extended period of time, without using a publish/subscribe system such as MQTT or similar which pushes their data towards their consumers. The performance evaluation carried out which analysed the Content Centric Network application on the sensor shows that the application has little impact on the overall round trip time in the network. Based on the results, this thesis concluded that the ICN paradigm, together with a ’one-time subscription’ model, can be a suitable option for communication within the IoT domain where consumers ask for sequentially produced data.
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Corino, Gianni. "Internet of props : a performative ontology and design framework for the Internet of Things." Thesis, University of Plymouth, 2017. http://hdl.handle.net/10026.1/9511.

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Set in the relatively new and fast developing field of investigation known as Internet of Things (IoT), this research starts by looking at the lack of critical and conceptual reflection on the area. With a main research question that challenges the underlying concepts of the IoT, the study develops a performative design framework to critique the field of investigation. The main corpus consists of: 1. speculative inquiry into the ontological dualisms of ‘objects’ and ‘things’ and the emerging social dimension of humans and non-humans; 2. the identification of an ontological-performative model based on the idea of Props; 3. the entanglement of theory and practice to construct a performative design framework, called the Internet of Props, which includes: an enabling platform (Smarter Planet Lab) and a set of design strategies (Transactional Props) to demonstrate and evaluate this model and framework; 4. a combined-evaluation conversational analysis methodology that assesses the performativity of the setting and the Props, through linguistic and socio-behavioural studies. Inspired by the concepts of ontological theatre, the entanglement of humans and non-humans, and the Internet of People; the IoT is imagined and performed in a theory-driven, practice-based investigation of the Internet of Props, which aims to bring new theoretical and practical knowledge for the future of the IoT.
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Ljungblad, Robert. "Blockchain in an Internet-of-Things Network Based on User Participation." Thesis, Luleå tekniska universitet, Datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-75041.

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The internet-of-things is the relatively new and rapidly growing concept of connecting everyday devices to the internet. Every day more and more devices are added to the internet-of-things and it is not showing any signs of slowing down. In addition, advancements in new technologies such as blockchains, artificial intelligence, virtual reality and machine learning are made practically every day. However, there are still much to learn about these technologies. This thesis explores the possibilities of blockchain technology by applying it to an internet-of-things network based on user participation. More specifically, it is applied to a use case derived from Luleå Kommun’s wishes to easier keep track of how full the city’s trash cans are. The goal of the thesis is to learn more about how blockchains can help an internet-of-things network as well as what issues can arise. The method takes an exploratory approach to the problem by partaking in a workshop with Luleå Kommun and by performing a literature study. It also takes a qualitative approach by creating a proof-of-concept solution to experience the technology firsthand. The final proof-of-concept as well as issues that arose during the project are analysed with the help of information gathered and experience gained throughout the project. It is concluded that blockchain technology can help communication in an internet-of-things network based on user participation. However, there is still a lot more to learn and uncover in future research.<br>Internet-of-things är ett relativt nytt men snabbt växande koncept som handlar om att koppla upp vardagliga saker till internet. Varje dag kopplas mer och mer enheter upp och det visar inga tecken på att sakta ner. Det görs även framsteg inom andra nya teknologier som blockkedjor, artificiell intelligens, virtuell verklighet och maskininlärning i stort sett varje dag. Dock finns det fortfarande mycket att lära sig om dessa teknologier. Denna rapport utforskar blockkedjeteknologins möjligheter genom att applicera det på ett internet-of-things-nätverk baserat på användarmedverkan. Mer specifikt, det är applicerat på ett användningsfall grundat i Luleå Kommuns önskan att lättare hålla koll på hur fulla deras soptunnor i och runtom staden är. Målet med denna rapport är att lära sig mer om hur blockkedjor kan stödja ett internet-of-things-nätverk och utforska vilka problem som kan uppstå. Metoden tar en explorativ ansats till problemet genom att delta i en workshop tillsammans med Luleå Kommun och genomföra en litteraturstudie. Dessutom tas en kvalitativ ansats genom att skapa en prototyplösning för att få förstahandserfarenhet av teknologin. Den slutliga prototypen och problemen som uppstod under projektets gång är analyserade med hjälp av information och erfarenhet som samlats genom hela projektet. Sammanfattningsvis kan blockkedjeteknologi hjälpa till kommunikationen i ett internet-of-things-nätverk baserat på användarmedverkan. Dock finns det fortfarande mycket att lära sig om denna teknologi i framtida forskning.
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Books on the topic "Network; Internet of Things"

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Patel, Rajan, Nimisha Patel, Linda Smail, Pariza Kamboj, and Mukesh Soni. Intelligent Green Communication Network for Internet of Things. CRC Press, 2023. http://dx.doi.org/10.1201/9781003371526.

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Khurana Batra, Payal, Pawan Singh Mehra, and Sudeep Tanwar. Network Optimization in Intelligent Internet of Things Applications. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003405535.

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Udgata, Siba Kumar, and Nagender Kumar Suryadevara. Internet of Things and Sensor Network for COVID-19. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7654-6.

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Yao, Haipeng, and Mohsen Guizani. Intelligent Internet of Things Networks. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26987-5.

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Leebow, Ken. 300 incredible things for travelers on the Internet. 300Incredible.com, 1999.

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Leebow, Ken. 300 incredible things for travelers on the Internet. 300Incredible.com, 2000.

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Murtala Zungeru, Adamu, Joseph M. Chuma, Caspar K. Lebekwe, Pendukeni Phalaagae, and Jwaone Gaboitaolelwe. Green Internet of Things Sensor Networks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54983-1.

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Ouaissa, Mariyam, Mariya Ouaissa, Inam Ullah Khan, Zakaria Boulouard, and Junaid Rashid. Low-Power Wide Area Network for Large Scale Internet of Things. CRC Press, 2024. http://dx.doi.org/10.1201/9781003426974.

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Leebow, Ken. 300 more incredible things to do on the Internet. 300incredible.com, 2000.

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Spizman, Robyn Freedman. 300 incredible things for women on the Internet. 300Incredible.COM, LLC, 2000.

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Book chapters on the topic "Network; Internet of Things"

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Ibraheem, Amani. "Cross Network Slicing in Vehicular Networks." In Internet of Things. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76493-7_5.

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Minerva, Roberto, and Noel Crespi. "The New Network." In Internet of Things. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33995-5_4.

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Cao, Haotong. "Network Function Virtualization." In Internet of Things. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89328-6_8.

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Jha, Vikas Kumar, Bishwajeet Pandey, and Ciro Rodriguez Rodriguez. "Internet of Things." In Network Evolution and Applications. CRC Press, 2022. http://dx.doi.org/10.1201/9781003302902-13.

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Cavallaro, Lucia, Ovidiu Bagdasar, Pasquale De Meo, Giacomo Fiumara, and Antonio Liotta. "Graph and Network Theory for the Analysis of Criminal Networks." In Internet of Things. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67197-6_8.

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Kotenko, Igor, Igor Saenko, and Fadey Skorik. "IoT Network Administration by Intelligent Decision Support Based on Combined Neural Networks." In Internet of Things. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21940-5_1.

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Minerva, Roberto, and Noel Crespi. "Service Control Paradigms and Network Architectures." In Internet of Things. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33995-5_2.

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Sarwesh, P., N. Shekar V. Shet, and K. Chandrasekaran. "Energy-Efficient Network Architecture for IoT Applications." In Internet of Things. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50758-3_5.

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Salam, Abdul. "IoT in Hyperspectral Space: Terrestrial Integration Network." In Internet of Things. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-62162-8_12.

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Meng, Xuejun. "Network Interaction Analysis of 3G and WiFi." In Internet of Things. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32427-7_10.

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Conference papers on the topic "Network; Internet of Things"

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Nursyahid, Arif, Helmy, Thomas Agung Setyawan, Sari Wahyuningsih, Naufal Faterna Zakky, and Ari Sriyanto Nugroho. "Internet of Things Network Handover for Hydroponic Cultivation." In 2024 11th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). IEEE, 2024. https://doi.org/10.1109/icitacee62763.2024.10761949.

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Singh, Rama, Swati Gupta, and D. T. Arunkumar. "Automation of Network Security in Cloud Based Internet of Things Networks." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724515.

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Al-Habob, Ahmed A., Octavia A. Dobre, and Yindi Jing. "Energy-Efficient Network Slicing for UAV-enabled Networks." In 2024 IEEE 10th World Forum on Internet of Things (WF-IoT). IEEE, 2024. https://doi.org/10.1109/wf-iot62078.2024.10811335.

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Rajendran, Dharshan, Sangeetha M, Gowtham Balasubbiramani, and Kavin Sekar. "Intelli-Helmet for Motorcycles Using Internet Of Things." In 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2025. https://doi.org/10.1109/csnt64827.2025.10968588.

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Lu, Chenpeng, and Hao Wu. "Network Situation Assessment Method for Industrial Internet of Things." In 2024 IEEE 24th International Conference on Communication Technology (ICCT). IEEE, 2024. https://doi.org/10.1109/icct62411.2024.10946472.

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R Ahmed, Muhammad, Ahmed Al Shihimi, Thirein Myo, Badar Al Baroomi, and 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.

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The Internet of Things (IoT) has transformed not only the way we communicate and operate our devices, but it has also brought us significant security challenges. A typical IoT network architecture consists of four levels: a device, a network, an application, and a service, each with its own security considerations. There are three types of IoT networks: Personal Area Networks (PANs), Local Area Networks (LANs), and Wide Area Networks (WANs). Each type has its own security requirements, so it is important to understand their particular security requirements. Several communication protocols that are used in IoT networks, like Wi-Fi and Bluetooth, are also susceptible to vulnerabilities that require the implementation of additional security measures. In addition to physical security challenges, there are numerous security challenges in the form of authentication, encryption, software vulnerabilities, DoS attacks, data privacy, and supply chain security. In order to deal with these challenges, we need to take a multi-layered approach that is comprised of physical, technical, and organizational measures. In this paper, we present an overview of IoT network architecture, along with an analysis of security challenges.
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Amirjavid, Farzad, Petros Spachos, Liang Song, and Konstantinos N. Plataniotis. "Network coding in internet of things." In 2016 IEEE 21st International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD). IEEE, 2016. http://dx.doi.org/10.1109/camad.2016.7790345.

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Lianhong Ding, Peng Shi, and Bingwu Liu. "The clustering of Internet, Internet of Things and social network." In 2010 3rd International Symposium on Knowledge Acquisition and Modeling (KAM). IEEE, 2010. http://dx.doi.org/10.1109/kam.2010.5646274.

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Sari, Ir Riri Fitri. ""bioinspired algorithms for Internet of Things network"." In 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). IEEE, 2017. http://dx.doi.org/10.1109/icitacee.2017.8257662.

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Kokane, Dipa Nitin, Geeta Nijhawan, and Shruti Vashist. "Network Connectivity Technologies for Internet of Things." In 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS). IEEE, 2022. http://dx.doi.org/10.1109/icais53314.2022.9742813.

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Reports on the topic "Network; Internet of Things"

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Watrobski, Paul, Murugiah Souppaya, Joshua Klosterman, and William Barker. Methodology for Characterizing Network Behavior of Internet of Things Devices. National Institute of Standards and Technology, 2022. http://dx.doi.org/10.6028/nist.ir.8349-draft.

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Symington, Susan, William Polk, and Murugiah Souppaya. Trusted Internet of Things (IoT) Device Network-Layer Onboarding and Lifecycle Management (Draft). National Institute of Standards and Technology, 2020. http://dx.doi.org/10.6028/nist.cswp.09082020-draft.

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Phillips, Paul. The Application of Satellite-based Internet of Things for New Mobility. SAE International, 2024. http://dx.doi.org/10.4271/epr2024001.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;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.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;&lt;b&gt;The Application of Satellite-based Internet of Things for New Mobility&lt;/b&gt; 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.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;&lt;a href="https://www.sae.org/publications/edge-research-reports" target="_blank"&gt;Click here to access the full SAE EDGE&lt;/a&gt;&lt;sup&gt;TM&lt;/sup&gt;&lt;a href="https://www.sae.org/publications/edge-research-reports" target="_blank"&gt; Research Report portfolio.&lt;/a&gt;&lt;/div&gt;&lt;/div&gt;
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Dodson, Donna, Douglas Montgomery, Tim Polk, 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, 2021. http://dx.doi.org/10.6028/nist.sp.1800-15.

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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.
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Niles, Kendall, Jason Ray, Kenneth Niles, Andrew Maxwell, and Anton Netchaev. Monitoring for analytes through LoRa and LoRaWAN technology. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49457.

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The purpose of this research is to investigate the use of a communication network utilizing Long Range (LoRa) low-power wide-area network protocol for the telemetry of detected target chemicals. The sensor platform under development allows traditional electrochemical techniques to be executed with a low Size, Weight, and Power (low SWaP) system that is deployable using an autonomous or remote-controlled vehicle. Once in the field, the sensors communicate target chemical concentrations to a centralized gateway through LoRa to an Internet of Things (IoT) network. Researchers are currently deploying the prototype network for field testing of each node's sensing, meshing, and communication capabilities throughout all weather climates. Developing a robust and dynamic sensor network to monitor the environment and provide a live data feed to detect chemicals of interest in the environment eliminates the need for direct human interaction. The low power consumption of LoRa allows the network nodes to be deployed for an extended amount of time on a single battery supply.
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Winseck, D. Growth and Upheaval in the Network Media Economy in Canada, 1984-2019. Canadian Media Concentration Research Project (CMCRP), Carleton University, 2020. http://dx.doi.org/10.22215/cmcrp/2020.1.

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This report examines the development of the media economy over the past thirty-five years. Since beginning this project a decade ago, we have focused on analyzing a comprehensive as possible selection of the biggest telecoms, Internet and media industries (based on revenue) in Canada, including: mobile wireless and wireline telecoms; Internet access; cable, satellite &amp; IPTV; broadcast television, specialty and pay television services as well as Internet-based video subscription and download services; radio; newspapers; magazines; music; Internet advertising; social media; operating systems; browsers, etc.
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Balakrishna Rao, Vismayashree. Internet of Things. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-918.

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Galli, Mattia, Massimo Scaglioni, Anna Sfardini, and Paolo Carelli. Communications, media and internet concentration in Italy, 2019-2021. Global Media and Internet Concentration Project, Carleton University, 2024. http://dx.doi.org/10.22215/gmicp/2024.1.

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This report scrutinizes revenue and concentration trends in Italian media industries from 2019 to 2021, following the Global Media and Internet Concentration Project (GMICP) blueprint. Twenty-seven industries are categorized into three macro-sectors: Telecommunications and Internet Access, Online and Traditional Media, and Core Internet Applications. Key findings include the Italian network media economy's worth of 47.385 billion EUR in 2021, a 3.5% drop from 2019 due to declines in traditional media, countered by digital industry growth. Wireless remains the largest sector, but core GAFAM and Netflix's influence rises. The Italian media economy, while concentrated, is gradually internationalizing, reflecting global industry integration.
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García Zaballos, Antonio, Maribel Dalio, Jesús Garran, Enrique Iglesias Rodriguez, Pau Puig Gabarró, and Ricardo Martínez Garza Fernández. Estructuración de un centro de operación de redes (NOC). Banco Interamericano de Desarrollo, 2022. http://dx.doi.org/10.18235/0004520.

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El desarrollo de la tecnología 5G nos permitirá descubrir cosas que hasta ahora no podíamos hacer tecnológicamente. Y habilitará la fusión del mundo físico (operational technology, OT) con el mundo digital (information technology, IT), abriendo nuevos mundos como son internet de las cosas (Internet of Things, IoT), metaverso, digital twin, entre otros. El centro de operaciones de red (Network Operations Center, NOC) es la herramienta que nos permite garantizar la disponibilidad y el rendimiento de las redes. En particular, el NOC será responsable de monitorizar, identificar, investigar, priorizar, resolver o escalar incidentes en la red, que pueden afectar o que están afectando su disponibilidad o rendimiento. Esta publicación presenta y desarrolla la hoja de ruta para diseñar y poner en marcha un NOC que contribuya al éxito de la implementación de la estrategia nacional de conectividad y garantice el funcionamiento correcto de la infraestructura digital, facilitando la conectividad de comunidades e instituciones.
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