Thematische Bibliographien / Edge IoT

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Auswahl der wissenschaftlichen Literatur zum Thema „Edge IoT“

Autor: Grafiati

Veröffentlicht am 16. November 2024

Zuletzt aktualisiert am 25. Juli 2025

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Zeitschriftenartikel zum Thema "Edge IoT"

Zhang, Yongqiang, Hongchang Yu, Wanzhen Zhou, and Menghua Man. "Application and Research of IoT Architecture for End-Net-Cloud Edge Computing." Electronics 12, no. 1 (2022): 1. http://dx.doi.org/10.3390/electronics12010001.

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At the edge of the network close to the source of the data, edge computing deploys computing, storage and other capabilities to provide intelligent services in close proximity and offers low bandwidth consumption, low latency and high security. It satisfies the requirements of transmission bandwidth, real-time and security for Internet of Things (IoT) application scenarios. Based on the IoT architecture, an IoT edge computing (EC-IoT) reference architecture is proposed, which contained three layers: The end edge, the network edge and the cloud edge. Furthermore, the key technologies of the app

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Shafiq, Muhammad, Zhihong Tian, Ali Kashif Bashir, Korhan Cengiz, and Adnan Tahir. "SoftSystem: Smart Edge Computing Device Selection Method for IoT Based on Soft Set Technique." Wireless Communications and Mobile Computing 2020 (October 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/8864301.

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The Internet of Things (IoT) is growing day by day, and new IoT devices are introduced and interconnected. Due to this rapid growth, IoT faces several issues related to communication in the edge computing network. The critical issue in these networks is the effective edge computing IoT device selection whenever there are several edge nodes to carry information. To overcome this problem, in this paper, we proposed a new framework model named SoftSystem based on the soft set technique that recommends useful IIoT devices. Then, we proposed an algorithm named Softsystemalgo. For the proposed syste

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Lee, Dongkyu, Hyeongyun Moon, Sejong Oh, and Daejin Park. "mIoT: Metamorphic IoT Platform for On-Demand Hardware Replacement in Large-Scaled IoT Applications." Sensors 20, no. 12 (2020): 3337. http://dx.doi.org/10.3390/s20123337.

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As the Internet of Things (IoT) is becoming more pervasive in our daily lives, the number of devices that connect to IoT edges and data generated at the edges are rapidly increasing. On account of the bottlenecks in servers, due to the increase in data, as well as security and privacy issues, the IoT paradigm has shifted from cloud computing to edge computing. Pursuant to this trend, embedded devices require complex computation capabilities. However, due to various constraints, edge devices cannot equip enough hardware to process data, so the flexibility of operation is reduced, because of the

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Moon, Hyeongyun, and Daejin Park. "An Efficient On-Demand Hardware Replacement Platform for Metamorphic Functional Processing in Edge-Centric IoT Applications." Electronics 10, no. 17 (2021): 2088. http://dx.doi.org/10.3390/electronics10172088.

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The paradigm of Internet-of-things (IoT) systems is changing from a cloud-based system to an edge-based system. These changes were able to solve the delay caused by the rapid concentration of data in the communication network, the delay caused by the lack of server computing capacity, and the security issues that occur in the data communication process. However, edge-based IoT systems performance was insufficient to process large numbers of data due to limited power supply, fixed hardware functions, and limited hardware resources. To improve their performance, application-specific hardware can

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Riane, Driss, Widad Ettazi, and Mahmoud Nassar. "An Allele Based-Approach for Internet of Transactional Things Service Placement in Intelligent Edge Environments." IoT 5, no. 4 (2024): 785–800. http://dx.doi.org/10.3390/iot5040035.

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The rapid expansion of the Internet of Things (IoT) has steered in a new generation of connectivity and data-driven decision-making across diverse industrial sectors. As IoT deployments continue to expand, the need for robust and reliable data management systems at the network’s edge becomes increasingly critical, especially for time-sensitive IoT applications requiring real-time responses. This study delves into the emerging research area known as the Internet of Transactional Things (Io2T) at the edge architecture, where the integration of transactional ACID properties into IoT devices and o

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Xu, Rongxu, Lei Hang, Wenquan Jin, and Dohyeun Kim. "Distributed Secure Edge Computing Architecture Based on Blockchain for Real-Time Data Integrity in IoT Environments." Actuators 10, no. 8 (2021): 197. http://dx.doi.org/10.3390/act10080197.

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The traditional cloud-based Internet of Things (IoT) architecture places extremely high demands on computers and storage on cloud servers. At the same time, the strong dependence on centralized servers causes major trust problems. Blockchain provides immutability, transparency, and data encryption based on safety to solve these problems of the IoT. In this paper, we present a distributed secure edge computing architecture using multiple data storages and blockchain agents for the real-time context data integrity in the IoT environment. The proposed distributed secure edge computing architectur

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Sai, Sandeep Ogety. "Enhancing Cloud Security Governance with AI and Data Analytics." European Journal of Advances in Engineering and Technology 8, no. 7 (2024): 132–42. https://doi.org/10.5281/zenodo.14274546.

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The group of real-world physical devices like sensors, machines, vehicles and various “things” connected to Internet is called as Internet of things (IoT). The major challenge in IoT is that  it is fully dependent on the cloud for all kinds of computation, which leads to high latency in the IoT devices. To overcome this latency issue, the Serverless edge computing and AI approaches were introduced newline. Serverless edge computing allows moving the data goverence and managing closer to the Serverless edge of the device. ICT’s three pillars namely computing, network and

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Praveen, Borra. "Analyzing AWS Edge Computing Solutions to Enhance IoT Deployments." International Journal of Engineering and Advanced Technology (IJEAT) 13, no. 6 (2024): 8–12. https://doi.org/10.35940/ijeat.F4519.13060824.

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<strong>Abstract:</strong> This paper explores integrating Internet of Things (IoT) deployments with edge computing, focusing on Amazon Web Services (AWS) as a key facilitator. It provides an analysis of AWS IoT services and their integration with edge computing technologies, addressing challenges, and practical applications across industries, and outlining future research directions. IoT and edge computing revolutionize data processing by enabling real-time analytics, reduced latency, and enhanced operational efficiency. IoT involves interconnected devices autonomously gathering and exchangin

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Firouzi, Ramin, Rahim Rahmani, and Theo Kanter. "Context-based Reasoning through Fuzzy Logic for Edge Intelligence." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 01 (2021): 17–25. http://dx.doi.org/10.5383/juspn.15.01.003.

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With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that are physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level i

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Xu, Rongxu, Wenquan Jin, Yonggeun Hong, and Do-Hyeun Kim. "Intelligent Optimization Mechanism Based on an Objective Function for Efficient Home Appliances Control in an Embedded Edge Platform." Electronics 10, no. 12 (2021): 1460. http://dx.doi.org/10.3390/electronics10121460.

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In recent years the ever-expanding internet of things (IoT) is becoming more empowered to revolutionize our world with the advent of cutting-edge features and intelligence in an IoT ecosystem. Thanks to the development of the IoT, researchers have devoted themselves to technologies that convert a conventional home into an intelligent occupants-aware place to manage electric resources with autonomous devices to deal with excess energy consumption and providing a comfortable living environment. There are studies to supplement the innate shortcomings of the IoT and improve intelligence by using c

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Dissertationen zum Thema "Edge IoT"

Stiefel, Maximilian. "IOT CONNECTIVITY WITH EDGE COMPUTING." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-372094.

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Billions of Internet of Things (IoT) devices will be connected in the next decades. Most devices are for Massive Machine Type Communication (MMTC) applications. This requires the IoT infrastructure to be extremely efficient and scalable (like today’s Internet) to support more and more devices connected to the network over time. The cost per connection needs to be very low (like today’s Web services). The current network design with dedicated HW-based base stations (or IoT gateways) may be too costly. Furthermore, there is a vast amount of IoT radio standards, such as Narrowband-IoT (NB-IoT), L

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Huang, Zhenqiu. "Progression and Edge Intelligence Framework for IoT Systems." Thesis, University of California, Irvine, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10168486.

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<p> This thesis studies the issues of building and managing future Internet of Things (IoT) systems. IoT systems consist of distributed components with services for sensing, processing, and controlling through devices deployed in our living environment as part of the global cyber-physical ecosystem. </p><p> Systems with perpetually running IoT devices may use a lot of energy. One challenge is implementing good management policies for energy saving. In addition, a large scale of devices may be deployed in wide geographical areas through low bandwidth wireless communication networks. This brin

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Marchioni, Alex <1989&gt. "Algorithms and Systems for IoT and Edge Computing." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10084/1/marchioni_alex_tesi.pdf.

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The idea of distributing the signal processing along the path that starts with the acquisition and ends with the final application has given light to the Internet of Things and Edge Computing, which have demonstrated several advantages in terms of scalability, costs, and reliability. In this dissertation, we focus on designing and implementing algorithms and systems that allow performing a complex task on devices with limited resources. Firstly, we assess the trade-off between compression and anomaly detection from both a theoretical and a practical point of view. Information theory provide

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Antonini, Mattia. "From Edge Computing to Edge Intelligence: exploring novel design approaches to intelligent IoT applications." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/308630.

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The Internet of Things (IoT) has deeply changed how we interact with our world. Today, smart homes, self-driving cars, connected industries, and wearables are just a few mainstream applications where IoT plays the role of enabling technology. When IoT became popular, Cloud Computing was already a mature technology able to deliver the computing resources necessary to execute heavy tasks (e.g., data analytic, storage, AI tasks, etc.) on data coming from IoT devices, thus practitioners started to design and implement their applications exploiting this approach. However, after a hype that lasted f

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Piscaglia, Daniele. "Supporto e Infrastrutture DevOps per Microservizi IoT su Edge Gateway." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Progetto svolto durante il tirocinio presso l'azienda Bonfiglioli Riduttori che descrive il processo di modifica di una soluzione di predictive maintenance esistente. La soluzione che coinvolge sensori IoT per la raccolta dati e Edge gateway per analisi e processamento, è stata rivisitata in funzione di importanti meccaniche di amministrazione e manutenzione. Il focus principale della tesi è su come questi sistemi distribuiti richiedano un approccio differente alla distribuzione dei microservizi e alla gestione dei dispositivi e come l'utilizzo delle varie tipologie di piattaforme IoT possano

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Broumas, Ioannis. "Design of Cellular and GNSS Antenna for IoT Edge Device." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-39239.

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Antennas are one of the most sensitive elements in any wireless communication equipment. Designing small-profile, multiband and wideband internal antennas with a simple structure has become a necessary challenge. In this thesis, two planar antennas are designed, simulated and implemented on an effort to cover the LTE-M1 and NB-IoT radio frequencies. The cellular antenna is designed to receive and transmit data over the eight-band LTE700/GSM/UMTS, and the GNSS antenna is designed to receive signal from the global positioning system and global navigation systems, GPS (USA) and GLONASS. The anten

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Ashouri, Majid. "Towards Supporting IoT System Designers in Edge Computing Deployment Decisions." Licentiate thesis, Malmö universitet, Malmö högskola, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-37068.

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The rapidly evolving Internet of Things (IoT) systems demands addressing new requirements. This particularly needs efficient deployment of IoT systems to meet the quality requirements such as latency, energy consumption, privacy, and bandwidth utilization. The increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage, known as edge computing. Edge computing may help and complement cloud computing to facilitate deployment of IoT systems and improve their quality. However, deciding where to deploy the various a

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Rajakaruna, A. (Archana). "Lightweight edge-based networking architecture for low-power IoT devices." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201906072483.

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Abstract. The involvement of low power Internet of Things (IoT) devices in the Wireless Sensor Networks (WSN) allow enhanced autonomous monitoring capability in many application areas. Recently, the principles of edge computing paradigm have been used to cater onsite processing and managing actions in WSNs. However, WSNs deployed in remote sites require human involvement in data collection process since internet accessibility is still limited to population dense areas. Nowadays, researchers propose UAVs for monitoring applications where human involvement is required frequently. In this thesis

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KOBEISSI, AHMAD. "VERSO IL CONCETTO DI SMART CITY: SOLUZIONI IOT EDGE-CLOUD." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/996248.

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Since the term was coined by Kevin Ashton in 1999, the Internet of Things (IoT) did not gain considerable popularity until 2010 where it became a strategic priority for governments, companies, and research centers. Despite this large-scale interest, IoT only reached mass markets in 2014 in the form of wearable devices and fitness trackers, home automation, industrial asset monitoring, and smart energy meters. The ‘things’ refer to sensors and other smart devices with the ability to monitor an object’s state, or even control it using actuators. Ashton envisaged that when such sensors and smart

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Bücher zum Thema "Edge IoT"

Pal, Souvik, Claudio Savaglio, Roberto Minerva, and Flávia C. Delicato, eds. IoT Edge Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58388-9.

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Jensen, David. Beginning Azure IoT Edge Computing. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4536-1.

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Cicirelli, Franco, Antonio Guerrieri, Andrea Vinci, and Giandomenico Spezzano, eds. IoT Edge Solutions for Cognitive Buildings. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15160-6.

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Rehan, Syed. AWS IoT With Edge ML and Cybersecurity. Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0011-5.

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Jiang, Hongbo, Hongyi Wu, and Fanzi Zeng, eds. Edge Computing and IoT: Systems, Management and Security. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73429-9.

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Sofia, Rute C., and John Soldatos. Shaping the Future of IoT with Edge Intelligence. River Publishers, 2023. http://dx.doi.org/10.1201/9781032632407.

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Xiao, Zhu, Ping Zhao, Xingxia Dai, and Jinmei Shu, eds. Edge Computing and IoT: Systems, Management and Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28990-3.

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Kulkarni, Shrikaant, Jaiprakash Dwived, Dinda Pramanta, and Yuichiro Tanaka. Edge Computational Intelligence for AI-Enabled IoT Systems. CRC Press, 2024. http://dx.doi.org/10.1201/9781032650722.

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Muzaffar, Shahzad, and Ibrahim M. Elfadel. Secure, Low-Power IoT Communication Using Edge-Coded Signaling. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95914-2.

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Gama, Joao, Sepideh Pashami, Albert Bifet, et al., eds. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66770-2.

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Buchteile zum Thema "Edge IoT"

Venu, Sibeesh. "IoT Edge." In Asp.Net Core and Azure with Raspberry Pi 4. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6443-0_8.

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Tadakamalla, Uma, and Daniel A. Menascé. "Characterization of IoT Workloads." In Edge Computing – EDGE 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23374-7_1.

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Desbiens, Frédéric. "Edge Computing." In Building Enterprise IoT Solutions with Eclipse IoT Technologies. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8882-5_11.

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Vuppalapati, Chandrasekar. "Edge Analytics." In Building Enterprise IoT Applications. CRC Press, 2019. http://dx.doi.org/10.1201/9780429056437-8.

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Pozveh, AmirHossein Jafari, and Hadi Shahriar Shahhoseini. "IoT Integration with MEC." In Mobile Edge Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69893-5_6.

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Alamri, Bandar, Ibrahim Tariq Javed, and Tiziana Margaria. "Preserving Patients’ Privacy in Medical IoT Using Blockchain." In Edge Computing – EDGE 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59824-2_9.

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Jensen, David. "Hello Edge." In Beginning Azure IoT Edge Computing. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4536-1_4.

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Jayashree, L. S., and G. Selvakumar. "Edge Computing in IoT." In Getting Started with Enterprise Internet of Things: Design Approaches and Software Architecture Models. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30945-9_3.

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Jensen, David. "Azure IoT Edge Security." In Beginning Azure IoT Edge Computing. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4536-1_8.

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Hasan, Balqees Talal, and Ali Kadhum Idrees. "Edge Computing for IoT." In Learning Techniques for the Internet of Things. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-50514-0_1.

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Konferenzberichte zum Thema "Edge IoT"

Laughner, Theo, J. ames Anderson, and Bob Marshall. "Power Quality Grid Measurements from IoT Sensor Network." In 2025 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge). IEEE, 2025. https://doi.org/10.1109/gridedge61154.2025.10887445.

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Čilić, Ivan, Valentin Jukanović, Ivana Podnar Žarko, Pantelis Frangoudis, and Schahram Dustdar. "QEdgeProxy: QoS-Aware Load Balancing for IoT Services in the Computing Continuum." In 2024 IEEE International Conference on Edge Computing and Communications (EDGE). IEEE, 2024. http://dx.doi.org/10.1109/edge62653.2024.00018.

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Khawam, Kinda, Hussein Taleb, Hassan Fawaz, Samer Lahoud, Dominique Quadri, and Steven Martin. "Edge Selection Non-Cooperative Game in IoT Edge Computing." In 2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2024. https://doi.org/10.1109/pimrc59610.2024.10817426.

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Singh, Pragya, Preeti Gautam, and Namita Arya. "Role of Edge Computing in IoT." In 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2025. https://doi.org/10.1109/csnt64827.2025.10968010.

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Tiwari, Aanya, Ahsaan Ahmad Ahanger, Anjali Meena, and Pawan Singh Mehra. "Quantum Edge Cloud Computing: Revolutionizing IoT." In 2025 3rd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT). IEEE, 2025. https://doi.org/10.1109/dicct64131.2025.10986380.

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Saxena, Mohit, Swapnil Srivastava, Vijay Kumar Dwivedi, Roshan Chitranshi, and Pradeep Kumar Mishra. "Minimizing End-to-End Latency in Edge Computing-Enabled IoT Networks Through Edge-to-Edge Resource Allocation." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923436.

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Jalali, Fatemeh, Safieh Khodadustan, Chrispin Gray, Kerry Hinton, and Frank Suits. "Greening IoT with Fog: A Survey." In 2017 IEEE International Conference on Edge Computing (EDGE). IEEE, 2017. http://dx.doi.org/10.1109/ieee.edge.2017.13.

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Gedawy, Hend, Karim Habak, Khaled Harras, and Mounir Hamdi. "An Energy-Aware IoT Femtocloud System." In 2018 IEEE International Conference on Edge Computing (EDGE). IEEE, 2018. http://dx.doi.org/10.1109/edge.2018.00015.

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Anagnostopoulos, Christos, Fani Deligiani, Kostas Kolomvatsos, and Jordi Mateo Fornés. "Workshop: Converge of Edge Intelligence in IoT (EdgeA-IoT 2022)." In 2022 IEEE 8th World Forum on Internet of Things (WF-IoT). IEEE, 2022. http://dx.doi.org/10.1109/wf-iot54382.2022.10152257.

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Anisetti, Marco, Claudio A. Ardagna, Nicola Bena, and Ruslan Bondaruc. "Towards an Assurance Framework for Edge and IoT Systems." In 2021 IEEE International Conference on Edge Computing (EDGE). IEEE, 2021. http://dx.doi.org/10.1109/edge53862.2021.00015.

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Berichte der Organisationen zum Thema "Edge IoT"

Hong, J., X. de, M. Kovatsch, E. Schooler, and D. Kutscher. Internet of Things (IoT) Edge Challenges and Functions. RFC Editor, 2024. http://dx.doi.org/10.17487/rfc9556.

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Latorre, Lucia, Valeria Lovaisa, Lorenzo De Leo, Alexander Riobó, Eduardo Riego, and Mariana Gutierrez. Tech Report: Edge Computing. Inter-American Development Bank, 2024. https://doi.org/10.18235/0013021.

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The adoption of edge computing in Latin America and the Caribbean (LAC) underscores its ability to address the regions unique challenges, including limited connectivity and the need to process data in remote locations. Innovative projects in smart public lighting, urban security, and sustainable resource management highlight the transformative impact of edge computing in improving quality of life and operational efficiency. Looking ahead, edge computing stands at the forefront of technological innovation. Deeper integration with artificial intelligence and the rise of 5G will unlock fresh oppo

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Leathers, Emily, Clayton Thurmer, and Kendall Niles. Encryption for edge computing applications. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48596.

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As smart sensors and the Internet of Things (IoT) exponentially expand, there is an increased need for effective processing solutions for sensor node data located in the operational arena where it can be leveraged for immediate decision support. Current developments reveal that edge computing, where processing and storage are performed close to data generation locations, can meet this need (Ahmed and Ahmed 2016). Edge computing imparts greater flexibility than that experienced in cloud computing architectures (Khan et al. 2019). Despite these benefits, the literature highlights open security i

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Pasupuleti, Murali Krishna. Neuromorphic Nanotech: 2D Materials for Energy-Efficient Edge Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr325.

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Abstract The demand for energy-efficient, real-time computing is driving the evolution of neuromorphic computing and edge AI systems. Traditional silicon-based processors struggle with power inefficiencies, memory bottlenecks, and scalability limitations, making them unsuitable for next-generation low-power AI applications. This research report explores how 2D materials, such as graphene, transition metal dichalcogenides (TMDs), black phosphorus, and MXenes, are enabling the development of neuromorphic architectures that mimic biological neural networks for high-speed, ultra-low-power computat

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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.<

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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. Acceler

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Lehrman, I. S. ICRF (Ion Cyclotron Range of Frequencies) edge modeling. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/5007603.

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Carpenter, Marie, and William Lazonick. The Pursuit of Shareholder Value: Cisco’s Transformation from Innovation to Financialization. Institute for New Economic Thinking Working Paper Series, 2023. http://dx.doi.org/10.36687/inetwp202.

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Once the global leader in telecommunication systems and the Internet, over the past two decades the United States has fallen behind global competitors, and in particular China, in mobile communication infrastructure—specifically 5G and Internet of Things (IoT). This national failure, with the socioeconomic and geopolitical tensions that it creates, is not due to a lack of US government investment in the knowledge required for the mobility revolution. Nor is it because of a dearth of domestic demand for the equipment, devices, and applications that can make use of this infrastructure. Rather, t

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Cully, John, and Susie Wright. Edge computing. Parliamentary Office of Science and Technology, 2020. http://dx.doi.org/10.58248/pn631.

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This POSTnote describes edge computing, the use of computing resources in close proximity to the place where data are processed within a network, and some of the opportunities and challenges associated with its use. It supplements POSTnote 629.

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T.M. Biewer, R.E. Bell, S.J. Diem, C.K. Phillips, J.R. Wilson, and P.M. Ryan. Edge Ion Heating by Launched High Harmonic Fast Waves in NSTX. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/836477.

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