Thematische Bibliographien / Internet of Things Network (IoTN)

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Auswahl der wissenschaftlichen Literatur zum Thema „Internet of Things Network (IoTN)“

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Veröffentlicht am 25. Mai 2024

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Zeitschriftenartikel zum Thema "Internet of Things Network (IoTN)"

Itagi, Manjunath, Dankan Gowda V, KDV Prasad, Pullela SVVSR Kumar, Shekhar R, and B. Ashreetha. "Performance Analysis of Energy Efficiency and Security Solutions of Internet of Things Protocols." International Journal of Electrical and Electronics Research 11, no. 2 (2023): 442–50. http://dx.doi.org/10.37391/ijeer.110226.

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The scientific and business communities are showing considerable interest in wireless sensor networks (WSN). The availability of low-cost, small-scale components like CPUs, radios, and sensors, which are often combined into a single chip, is crucial. Parallel to the evolution of WSNs, the concepts of the IoT have been evolving in recent years. Wireless communication technologies may play a significant role in the implementation of IoT, despite the fact that IoT does not need or require any particular technology for communication. WSN assisted IoT networks can drive several applications in many industries. The proposed research explores the possibility of enhancing energy efficiency in WSN-assisted IoTN by balancing various challenging sensor network performance metrics. The base station's current placement inside the sensing field is predetermined by the preexisting routing algorithms. Our study examines the impact of base station placement outside and within the prescribed sensing domains on energy consumption and network longevity. In addition, methods for transferring data from the distributed source sensor to the base station while minimizing energy consumption are investigated. In this preliminary study, we focus on developing an algorithm for WSN-Assisted IoTN that can balance network factors such as hop count, communication distance, and residual energy. To further optimize the routing route between local cluster heads and the base station, a novel network architecture is built based on the Ant-optimization model, which uses centroid routing to balance energy consumption among local clusters. An open-source Network Simulator (NS-3) is used to model the behaviour of the proposed routing protocols and compare them to comparable existing network protocols. All of the suggested protocols have the same fundamentals for creating networks, however they vary in terms of routing, optimization, and performance depending on the development effort under consideration.

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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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Nikhat Akhtar and Yusuf Perwej. "The internet of nano things (IoNT) existing state and future Prospects." GSC Advanced Research and Reviews 5, no. 2 (2020): 131–50. http://dx.doi.org/10.30574/gscarr.2020.5.2.0110.

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The increase of intelligent environments suggests the interconnectivity of applications and the use of the Internet. For this reason, arise what is known as the Internet of Things (IoT). The expansion of the IoT concept gives access to the Internet of Nano Things (IoNT). A new communication networks paradigm based on nano technology and IoT, in other words, a paradigm with the capacity to interconnect nano-scale devices through existing networks. From the interconnection of these nano machines with the Internet emerged the concept of Internet of Nano Things (IoNT). The Internet of Nano-Things (IoNT) is a system of nano connected devices, objects, or organisms that have unique identifiers to transfer data over a computer or cellular network wirelessly to the Cloud. The data delivery, caching, and energy consumption are among the most significant topics in the IoNT nowadays. The nano-networks paradigm can empower the consumers to make a difference to their well-being by connecting data to personalized analysis within timely insights. The real-time data can be used in a diversification of nano-applications in the Internet of Nano-Things (IoNT), from preventive treatment to diagnostics and rehabilitation. In this paper intelligibly explains the Internet of Nano Things (IoNT), its architecture, challenges, explains the role of IoNT in global market, IoNT applications in various domains. Internet of things has provided countless new opportunity to create a powerful industrialized structure and many more. The key applications for IoNT communication including healthcare, transportation and logistics, defense and aerospace, media and entertainment, manufacturing, oil and gas, high speed data transfer & cellular, multimedia, immune system support and others services. In the end, since security is considered to be one of the main issues of the IoNT system, we provide an in-depth discussion on security, communication network and Internet of Nano Things (IoNT) market trends.

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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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Sankar, S., Ramasubbareddy Somula, R. Lakshmana Kumar, P. Srinivasan, and M. Amala Jayanthi. "Trust-Aware Routing Framework for Internet of Things." International Journal of Knowledge and Systems Science 12, no. 1 (2021): 48–59. http://dx.doi.org/10.4018/ijkss.2021010104.

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Establishing security in internet of things (IoT) is a critical challenge, as it is connected to the network's extremely resource-constrained devices. The RPL is a standard routing protocol for IoT. It is well-suited for low power and lossy networks (LLN). The RPL provides little security in the IoT network against various attacks. However, one needs to strengthen the security concern in RPL. So, this paper proposes a trust-aware, energy-based reliable routing (TAER-RPL) for IoT to enhance security among network nodes. The TAER-RPL is taken into account the routing metrics, namely trust, ETX, RER to pick the optimal parent for data transmission. The simulation is conducted in COOJA simulator. TAER-RPL's efficiency is compared with SecTrust-RPL and RPL. The TAER-RPL increases the lifespan of the network by 15%.

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Fadziso, Takudzwa. "Internet of Things in Agriculture for Smart Farming." Malaysian Journal of Medical and Biological Research 5, no. 2 (2018): 147–56. http://dx.doi.org/10.18034/mjmbr.v5i2.565.

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Internet of Things in Agricultural Farming’ deals with the use IoTs in providing farmers the means to do multiple parallel things with wifi connected and increase their productivity in turn increasing their yearly revenue and profits. This will not only help the farmer but the raw materials which come out will be more than what would have yielded if the farmer had done all by themselves. The IoT network comprises systems and a network of web-connected intelligent devices that employ encoded networks like sensors, processors, and interactive hardware to receive, send and store data. The use of IoT in Agricultural Farming is no doubt going to greatly enhance farming and improve yields.

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Eltayeb, Mohamed A. "Internet of Things." International Journal of Hyperconnectivity and the Internet of Things 1, no. 1 (2017): 1–18. http://dx.doi.org/10.4018/ijhiot.2017010101.

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In the contemporary world, we are surrounded by a complex network of interconnected sensors. These allows us to share, collate, transmit, and store vast amounts of data. These systems are known as the Internet of Things (IoT), and this technological offering has attracted a large amount of interest from academics, researchers, and developers in recent years due to its highly scalable and agile nature. However, while the IoT delivers significant benefits, it also poses some risks. The data that is stored and exchanged via the IoT is extremely valuable to individuals who have malevolent intent. In more recent years, the increasing popularity of the IoT as a means of sharing information has been associated with privacy and security risks that have undermined users' trust in the IoT. This paper examines what these risks are and some of the actions that can be taken to mitigate them.

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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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Maseleno, Andino, Wahidah Hashim, Y. C. Tang Alicia, A. Mahmoud Moamin, and Marini Othman. "A Review on Smart Grid Internet of Things." Journal of Computational and Theoretical Nanoscience 17, no. 6 (2020): 2770–75. http://dx.doi.org/10.1166/jctn.2020.8941.

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Smart electrical network or better known as Smart Grid is one form of transformation and technological reform in the electricity industry. Smart Grid is a modern electrical energy network that intelligently integrates power grids with communications devices that support electricity generation and distribution networks to be more attractive, communicative and qualified. Smart Grid is also able to prevent and to isolate the interference quickly and present information on electrical data in real time. While the Internet of Thing (IoT) is a method that aims to maximize the benefits of internet connectivity to transfer and to process data or information through an internet network wirelessly, virtual and autonomous. IoT technically can be encouraged in developing smart grid network by integrating main power system infrastructure from generating side to end consumer through wireless sensor network automatically. With the utilization of IoT is expected to improve the reliability of information systems from the power grid as well as improve the efficiency of existing electrical infrastructure. This paper will present the concept of smart grid technology, IoT and discuss the IoT design and application model in smart grid network.

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Bynagari, Naresh Babu. "Industrial Application of Internet of Things." Asia Pacific Journal of Energy and Environment 3, no. 2 (2016): 75–82. http://dx.doi.org/10.18034/apjee.v3i2.576.

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‘Industrial application of Internet of Things deals with the application of Internet of things to produce industrial services. It analyzed how industries can carry out multiple services with function remotely using IoT-connected devices. The several benefits and drawbacks to the application of IoT services were also investigated. The IoT is a network of connected systems and smart devices that use encoded networks like sensors, processors, and interactive hardware to receive, send and store data. The utilization of IoT for industrial functions will significantly improve industrial output, and in the future, more industries will come to apply IoT devices and systems for greater efficiency.

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Dissertationen zum Thema "Internet of Things Network (IoTN)"

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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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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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.

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Les réseaux de capteurs visuels sans fil basés sur les réseaux maillés IEEE 802.11 sont des solutions efficaces et adaptées aux systèmes de vidéosurveillance pour surveiller les intrusions dans des zones sélectionnées. Les réseaux de capteurs visuels basés sur IEEE 802.11 offrent des transmissions vidéo à haut débit mais souffrent de problèmes d'inefficacité énergétique. De plus, la transmission vidéo dans les réseaux de capteurs visuels nécessite une qualité de service (QoS) stricte en termes de bande passante et de délai. En outre, il est difficile de réduire la consommation énergétique globale du réseau tout en garantissant une qualité de service garantie en termes de bande passante et de délai dans les réseaux de capteurs visuels sans fil à énergie limitée. La principale contribution de cette thèse est de fournir un réseau de vidéosurveillance économe en énergie sans compromettre l'exigence de qualité de service de la transmission vidéo. Premièrement, nous proposons une nouvelle architecture de réseau hybride IoT pour un système de vidéosurveillance qui détecte et suit un intrus dans la zone de surveillance. Le réseau IoT hybride intègre les réseaux de capteurs visuels multi-sauts basés sur IEEE 802.11 et le réseau LoRa pour fournir un système de vidéosurveillance autonome, économe en énergie et à haut débit. Tirant parti des caractéristiques du réseau LoRa, le réseau LoRa est utilisé comme un réseau toujours actif pour la détection et le suivi préliminaires des mouvements. De plus, le réseau LoRa décide également quels nœuds de capteurs visuels réveiller en fonction des informations de suivi. Le filtre de Kalman est étudié pour suivre la trajectoire de l'intrus à partir des mesures de bruit des capteurs de mouvement à faible puissance afin d'activer uniquement les nœuds de capteurs visuels le long de la trajectoire de l'intrus pour fournir une surveillance vidéo efficace. Nous avons montré par simulation que le filtre de Kalman estime et prédit la trajectoire de l'intrus avec une précision raisonnable. De plus, l'approche de réseau hybride IoT proposée réduit considérablement la consommation d'énergie par rapport à un réseau de capteurs visuels à un seul niveau de surveillance continue traditionnelle et toujours active. Ensuite, la contribution de cette thèse se concentre sur un mécanisme de routage sensible à l'énergie et QoS pour le réseau de capteurs visuels multi-sauts basé sur IEEE 802.11 du réseau hybride IoT. Nous proposons un algorithme de routage qui route un ensemble de flux vidéo vers la passerelle avec une QoS garantie en termes de bande passante et de délai tout en minimisant le nombre de nœuds capteurs visuels impliqués dans le routage. Cela maximise le nombre de nœuds pouvant être complètement désactivés pour optimiser la consommation énergétique globale du réseau sans compromettre les performances QoS. Le problème de routage proposé est formulé comme un programme linéaire entier (ILP) et résolu à l'aide d'un algorithme branch-and-bound. Grâce à la simulation informatique, les performances de l'approche proposée sont comparées aux algorithmes de routage de pointe existants dans la littérature. Les résultats montrent clairement que le mécanisme proposé permet d'économiser une quantité significative de la consommation d'énergie globale tout en garantissant la QoS en termes de bande passante et de délai<br>Wireless 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

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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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Xu, Ran. "Federated Sensor Network architectural design for the Internet of Things (IoT)." Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/13453.

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An information technology that can combine the physical world and virtual world is desired. The Internet of Things (IoT) is a concept system that uses Radio Frequency Identification (RFID), WSN and barcode scanners to sense and to detect physical objects and events. This information is shared with people on the Internet. With the announcement of the Smarter Planet concept by IBM, the problem of how to share this data was raised. However, the original design of WSN aims to provide environment monitoring and control within a small scale local network. It cannot meet the demands of the IoT because there is a lack of multi-connection functionality with other WSNs and upper level applications. As various standards of WSNs provide information for different purposes, a hybrid system that gives a complete answer by combining all of them could be promising for future IoT applications. This thesis is on the subject of `Federated Sensor Network' design and architectural development for the Internet of Things. A Federated Sensor Network (FSN) is a system that integrates WSNs and the Internet. Currently, methods of integrating WSNs and the Internet can follow one of three main directions: a Front-End Proxy solution, a Gateway solution or a TCP/IP Overlay solution. Architectures based on the ideas from all three directions are presented in this thesis; this forms a comprehensive body of research on possible Federated Sensor Network architecture designs. In addition, a fully compatible technology for the sensor network application, namely the Sensor Model Language (SensorML), has been reviewed and embedded into our FSN systems. The IoT as a new concept is also comprehensively described and the major technical issues discussed. Finally, a case study of the IoT in logistic management for emergency response is given. Proposed FSN architectures based on the Gateway solution are demonstrated through hardware implementation and lab tests. A demonstration of the 6LoWPAN enabled federated sensor network based on the TCP/IP Overlay solution presents a good result for the iNET localization and tracking project. All the tests of the designs have verified feasibility and achieve the target of the IoT concept.

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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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Hsu, Alexander Sirui. "Automatic Internet of Things Device Category Identification using Traffic Rates." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/88421.

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Due to the ever increasing supply of new Internet of Things (IoT) devices being added onto a network, it is vital secure the devices from incoming cyber threats. The manufacturing process of creating and developing a new IoT device allows many new companies to come out with their own device. These devices also increase the network risk because many IoT devices are created without proper security implementation. Utilizing traffic patterns as a method of device type detection will allow behavior identification using only Internet Protocol (IP) header information. The network traffic captured from 20 IoT devices belonging to 4 distinct types (IP camera, on/off switch, motion sensor, and temperature sensor) are generalized and used to identify new devices previously unseen on the network. Our results indicate some categories have patterns that are easier to generalize, while other categories are harder but we are still able recognize some unique characteristics. We also are able to deploy this in a test production network and adapted previous methods to handle streaming traffic and an additional noise categorization capable of identify non-IoT devices. The performance of our model is varied between classes, signifying that much future work has to be done to increase the classification score and overall usefulness.<br>Master of Science<br>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.

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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.

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An increasing number of devices, from coffee makers to electric kettles, are becoming connected to the Internet. These are all a part of the Internet of Things, or IoT. Each device generates unique network traffic and power consumption patterns. Until now, there has not been a comprehensive set of data that captures these traffic and power patterns. This thesis documents how we collected 10 to 15 weeks of network traffic and power consumption data from 15 different IoT devices and provides an analysis of a subset of 6 devices. Devices including an Amazon Echo Dot, Google Home Mini, and Google Chromecast were used on a regular basis and all of their network traffic and power consumption was logged to a MySQL database. The database currently contains 64 million packets and 71 gigabytes of data and is still growing in size as more data is collected 24/7 from each device. We show that it is possible to see when users are asking their smart speaker a question or whether the lights in their home are on or off based on power consumption and network traffic from the devices. These trends can be seen even if the data being sent is encrypted.

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Hobring, Linus, and 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.

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Internet of Things is rapidly evolving. This thesis includes a study of single-board computers suitable for machine-to-machine communication together with the developing process of a sensor network integrated with a bidirectional communication platform. Raspberry Pi was selected as the single-board computer used in the proposed system. The Message Queuing Telemetry Transport protocol was selected as main communication protocol to handle all exchange of information between the network and the bidirectional communication platform. It was selected because of its reliability, low bandwidth and publish/subscribe architecture. Decision-making procedures were implemented to work with both local sensor data and data from different Message Queuing Telemetry Transport streams, such as GPS data, used to calculate the distance between the user’s smart phone and the office to prepare the workstation, temperature sensors and ambient light sensors controlling Philips HUE light bulbs. The finished sensor network was design to work within office environments to prepare workstations and monitor the work climate. The number of sensors connected to the single-board computer has a major impact in the CPU usage. Measurements and calculations show that 17 connected physical sensors will cause a CPU usage of 96%.

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NITTI, 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.

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Society is moving towards an “always connected” paradigm, where the Internet user is shifting from persons to things, leading to the so called Internet of Things (IoT) scenario. The IoT vision integrates a large number of technologies and foresees to embody a variety of smart objects around us (such as sensors, actuators, smartphones, RFID, etc.) that, through unique addressing schemes and standard communication protocols, are able to interact with each Others and cooperate with their neighbors to reach common goals [2, 3]. IoT is a hot research topic, as demonstrated by the increasing attention and the large worldwide investments devoted to it. It is believed that the IoT will be composed of trillions of elements interacting in an extremely heterogeneous way in terms of requirements, behavior and capabilities; according to [4], by 2015 the RIFD devices alone will reach hundreds of billions. Unquestionably, the IoT will pervade every aspect of our world and will have a huge impact in our everyday life: indeed, as stated by the US National Intelligence Council (NIC) [5], “by 2025 Internet nodes may reside in everyday things − food packages, furniture, paper documents, and more”. Then, communications will not only involve persons but also things thus bringing about the IoT environment in which objects will have virtual counterparts on the Internet. Such virtual entities will produce and consume services, collaborate toward common goals and should be integrated with all the other services. One of the biggest challenges that the research community is facing right now is to be able to organize such an ocean of devices so that the discovery of objects and services is performed efficiently and in a scalable way. Recently, several attempts have been made to apply concepts of social networking to the IoT. There are scientific evidences that a large number of individuals tied in a social network can provide far more accurate answers to complex problems than a single individual (or a small group of – even knowledgeable – individuals) [1]. The exploitation of such a principle, applied to smart objects, has been widely investigated in Internet-related researches. Indeed, several schemes have been proposed that use social networks to search Internet resources, to route traffic, or to select effective policies for content distribution. The idea that the convergence of the “Internet of Things” and the “Social Networks” worlds, which up to now were mostly kept separate by both scientific and industrial communities, is possible or even advisable is gaining momentum very quickly. This is due to the growing awareness that a “Social Internet of Things” (SIoT) paradigm carries with it many desirable implications in a future world populated by objects permeating the everyday life of human beings. Therefore, the goal of this thesis is to define a possible architecture for the SIoT, which includes the functionalities required to integrate things into a social network, and the needed strategies to help things to create their relationships in such a way that the resulting social network is navigable. Moreover, it focuses on the trustworthiness management, so that interaction among objects that are friends can be done in a more reliable way and proposes a possible implementation of a SIoT network. Since this thesis covers several aspects of the Social internet of Things, I will present the state of the art related to the specific research activities at the beginning of every Chapter. The rest of the thesis is structured as follows. In Chapter 1, I identify appropriate policies for the establishment and the management of social relationships between objects, describe a possible architecture for the IoT that includes the functionalities required to integrate things into a social network and analyze the characteristics of the SIoT network structure by means of simulations. Chapter 2 addresses the problem of the objects to manage a large number of friends, by analyzing possible strategies to drive the objects to select the appropriate links for the benefit of overall network navigability and to speed up the search of the services. In Chapter 3, I focus on the problem of understanding how the information provided by members of the social IoT has to be processed so as to build a reliable system on the basis of the behavior of the objects and define two models for trustworthiness management starting from the solutions proposed for P2P and social networks. Chapter 4 presents an implementation of a SIoT platform and its major functionalities: how to register a new social object to the platform, how the system manages the creation of new relationships, and how the devices create groups of members with similar characteristics. Finally, in Chapter 5, conclusions will be drawn regarding the effectiveness of the proposed Introduction 3 algorithms, and some possible future works will be sketched

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Bücher zum Thema "Internet of Things Network (IoTN)"

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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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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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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300 incredible things for beanie lovers on the Internet. VIP Pub., 1999.

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Ken, Leebow, ed. 300 incredible things for seniors on the Internet. 300incredible.com, 2000.

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Japan) IOT (Conference) (2010 Tokyo. 2010 Internet of things: (IOT 2010) , Tokyo, Japan, 29 November- 1 December 2010. IEEE, 2010.

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

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Ken, Leebow, ed. 300 incredible things to learn on the Internet. 300Incredible.Com, LLC, 2000.

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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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Buchteile zum Thema "Internet of Things Network (IoTN)"

Wu, Chuan-Kun. "IoT Network Layer Security." In Internet of Things Security. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1372-2_7.

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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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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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Kant, Umang, and Vinod Kumar. "IoT Network Used in Fog and Cloud Computing." In Internet of Things. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1585-7_8.

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Wu, Fan, Christoph Rüdiger, Jean-Michel Redouté, and Mehmet Rasit Yuce. "A Wearable Multi-sensor IoT Network System for Environmental Monitoring." In Internet of Things. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02819-0_3.

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Qian, Zhihong, Yijun Wang, Xue Wang, and Shuang Zhu. "M/I Adaptation Layer Network Protocol for IoT Based on 6LoWPAN." In Internet of Things. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32427-7_28.

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Khelifi, Fekher. "Monitoring System Based in Wireless Sensor Network for Precision Agriculture." In Internet of Things (IoT). Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37468-6_24.

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Tomaš, Boris, and Neven Vrček. "Smart City Vehicular Mobile Sensor Network." In Internet of Things. IoT Infrastructures. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19743-2_11.

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Hauschild, Sebastian, and Horst Hellbrück. "Latency and Energy Consumption of Convolutional Neural Network Models from IoT Edge Perspective." In Internet of Things. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20936-9_31.

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Dalal, Upena, Shweta Shah, and Jigisha Patel. "MAC and Network Layer Issues and Challenges for IoT." In The Internet of Things. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315156026-3.

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Konferenzberichte zum Thema "Internet of Things Network (IoTN)"

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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Nitti, Michele, Luigi Atzori, and 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.

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Athreya, Arjun P., and 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.

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Singh, Meena, M. A. Rajan, V. L. Shivraj, and 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.

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Borkar, Suresh, and 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.

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Sahlmann, Kristina, Thomas Scheffler, and 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.

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Vreca, Jure, Iva Ivanov, Gregor Papa, and 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.

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Ramachandra, 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.

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Singh, Rashmi, Rajesh Mehra, and 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.

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He, Weiwei, Shuang-Hua Yang, Lili Yang, and 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.

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Berichte der Organisationen zum Thema "Internet of Things Network (IoTN)"

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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<div class="section abstract"><div class="htmlview paragraph">With the increased use of devices requiring the Internet of Things (IoT) to enable “New Mobility,” the demand for satellite-enabled IoT is growing steadily, owing to the extensive coverage provided by satellites (over existing ground-based infrastructure). Satellite-based IoT provides precise and real-time vehicle location and tracking services, large-scale geographical vehicle and/or infrastructure monitoring, and increased coverage for remote locations where it may not be possible to install ground-based solutions.</div><div class="htmlview paragraph"><b>The Application of Satellite-based Internet of Things for New Mobility</b> discusses satellite-based IoT topics that still need addressing, which can be broadly classifieds into two areas: (1) affordable technology and (2) network connectivity and data management. While recent innovations are driving down the cost of satellite-based IoT, it remains relatively expensive, and widespread adoption is still not as high as terrestrial, ground-based systems. Security concerns over data and privacy also create significant barriers to entry and need to be addressed along with issues such as intermittent connectivity, latency and bandwidth limitations, and data storage and processing restrictions.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>

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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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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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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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Ruvinsky, Alicia, Timothy Garton, Daniel Chausse, Rajeev Agrawal, Harland Yu, and 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.), 2021. http://dx.doi.org/10.21079/11681/42169.

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Managing the ever-growing volume and velocity of data across the battlefield is a critical problem for warfighters. Solving this problem will require a fundamental change in how battlefield analyses are performed. A new approach to making decisions on the battlefield will eliminate data transport delays by moving the analytical capabilities closer to data sources. Decision cycles depend on the speed at which data can be captured and converted to actionable information for decision making. Real-time situational awareness is achieved by locating computational assets at the tactical edge. Accelerating the tactical decision process leverages capabilities in three technology areas: (1) High-Performance Computing (HPC), (2) Machine Learning (ML), and (3) Internet of Things (IoT). Exploiting these areas can reduce network traffic and shorten the time required to transform data into actionable information. Faster decision cycles may revolutionize battlefield operations. Presented is an overview of an artificial intelligence (AI) system design for near-real-time analytics in a tactical operational environment executing on co-located, mobile HPC hardware. The report contains the following sections, (1) an introduction describing motivation, background, and state of technology, (2) descriptions of tactical decision process leveraging HPC problem definition and use case, and (3) HPC tactical data analytics framework design enabling data to decisions.

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Videa, Aldo, and Yiyi Wang. Inference of Transit Passenger Counts and Waiting Time Using Wi-Fi Signals. Western Transportation Institute, 2021. http://dx.doi.org/10.15788/1715288737.

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Passenger data such as real-time origin-destination (OD) flows and waiting times are central to planning public transportation services and improving visitor experience. This project explored the use of Internet of Things (IoT) Technology to infer transit ridership and waiting time at bus stops. Specifically, this study explored the use of Raspberry Pi computers, which are small and inexpensive sets of hardware, to scan the Wi-Fi networks of passengers’ smartphones. The process was used to infer passenger counts and obtain information on passenger trajectories based on Global Positioning System (GPS) data. The research was conducted as a case study of the Streamline Bus System in Bozeman, Montana. To evaluate the reliability of the data collected with the Raspberry Pi computers, the study conducted technology-based estimation of ridership, OD flows, wait time, and travel time for a comparison with ground truth data (passenger surveys, manual data counts, and bus travel times). This study introduced the use of a wireless Wi-Fi scanning device for transit data collection, called a Smart Station. It combines an innovative set of hardware and software to create a non-intrusive and passive data collection mechanism. Through the field testing and comparison evaluation with ground truth data, the Smart Station produced accurate estimates of ridership, origin-destination characteristics, wait times, and travel times. Ridership data has traditionally been collected through a combination of manual surveys and Automatic Passenger Counter (APC) systems, which can be time-consuming and expensive, with limited capabilities to produce real-time data. The Smart Station shows promise as an accurate and cost-effective alternative. The advantages of using Smart Station over traditional data collection methods include the following: (1) Wireless, automated data collection and retrieval, (2) Real-time observation of passenger behavior, (3) Negligible maintenance after programming and installing the hardware, (4) Low costs of hardware, software, and installation, and (5) Simple and short programming and installation time. If further validated through additional research and development, the device could help transit systems facilitate data collection for route optimization, trip planning tools, and traveler information systems.

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