Relevant bibliographies by topics / Edge IoT

Contents

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

Academic literature on the topic 'Edge IoT'

Author: Grafiati
Published: 16 November 2024

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Journal articles on the topic "Edge IoT"

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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 (December 20, 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 application of artificial intelligence (AI) technology in the EC-IoT reference architecture is analyzed. Platforms for different EC-IoT reference architecture edge locations are classified by comparing IoT edge computing platforms. On the basis of EC-IoT reference architecture, an industrial Internet of Things (IIoT) edge computing solution, an Internet of Vehicles (IoV) edge computing architecture and a reference architecture of the IoT edge gateway-based smart home are proposed. Finally, the trends and challenges of EC-IoT are examined, and the EC-IoT architecture will have very promising applications.
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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 system, three different parameters are selected: IoT Device Security (IDSC), IoT Device Storage (IDST), and IoT Device Communication Speed (IDCS). We also find out the most significant parameters from the given set of parameters. It is evident that our proposed system is effective for the selection of edge computing devices in the IoT network.
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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 (June 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 limitations of fixed hardware functions, relative to cloud computing. Recently, as application fields and collected data types diversify, and, in particular, applications requiring complex computation such as artificial intelligence (AI) and signal processing are applied to edges, flexible processing and computation capabilities based on hardware acceleration are required. In this paper, to meet these needs, we propose a new IoT platform, called a metamorphic IoT (mIoT) platform, which can various hardware acceleration with limited hardware platform resources, through on-demand transmission and reconfiguration of required hardware at edges instead of via transference of sensing data to a server. The proposed platform reconfigures the edge’s hardware with minimal overhead, based on a probabilistic value, known as callability. The mIoT consists of reconfigurable edge devices based on RISC-V architecture and a server that manages the reconfiguration of edge devices based on callability. Through various experimental results, we confirmed that the callability-based mIoT platform can provide the hardware required by the edge device in real time. In addition, by performing various functions with small hardware, power consumption, which is a major constraint of IoT, can be reduced.
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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 (August 28, 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 be installed in edge devices, but performance cannot be improved except for specific applications due to a fixed function of an application-specific hardware. This paper introduces an edge-centric metamorphic IoT (mIoT) platform that can use various hardware modules through on-demand partial reconfiguration, despite the limited hardware resources of edge devices. In addition, this paper introduces an RISC-V based metamorphic IoT processor (mIoTP) with reconfigurable peripheral modules. We experimented to prove that the proposed structure can reduce the server access of edges and can be applied to a large-scale IoT system. Experiments were conducted in a single-edge environment and a large-scale environment combining one physical edge and 99 virtual edges. According to the experimental results, the edge-centric mIoT platform that executes the reconfiguration prediction algorithm at the edge was able to reduce the number of server accesses by up to 82.2% compared to our previous study in which the prediction process was executed at the server. Furthermore, we confirmed that there is no additional reconfiguration time overhead even for the large IoT systems.
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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 (August 13, 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 architecture provides reliable access and an unlimited repository for scalable and secure transactions. The architecture eliminates traditional centralized servers using an edge computing framework that represents cloud computing for computer and security issues. Also, blockchain-based edge computing-compatible IoT design is supported to achieve the level of security and scalability required for data integrity. Furthermore, we present the blockchain agent to provide internetworking between blockchain networks and edge computing. For experimenting with the proposed architecture in the IoT environment, we implement and perform a concrete IoT environment based on the EdgeX framework and Hyperledger Fabric. The evaluation results are collected by measuring the performance of the edge computing and blockchain platform based on service execution time to verify the proposed architecture in the IoT environment.
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Bansal, Malti, and Harshit. "IoT based Edge Computing." December 2020 2, no. 4 (January 5, 2021): 204–10. http://dx.doi.org/10.36548/jtcsst.2020.4.005.

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Edge computing is a new way of calculating where most computer and storage devices are located on the internet, near mobile devices, sensors, end users, and internet of things devices. This physical approach improves delays, bandwidth, trust and survival.
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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 (March 1, 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 intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.
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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 (June 18, 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 cloud computing and machine learning. However, the machine learning-based autonomous control devices lack flexibility, and cloud computing is challenging with latency and security. In this paper, we propose a rule-based optimization mechanism on an embedded edge platform to provide dynamic home appliance control and advanced intelligence in a smart home. To provide actional control ability, we design and developed a rule-based objective function in the EdgeX edge computing platform to control the temperature states of the smart home. Compared to cloud computing, edge computing can provide faster response and higher quality of services. The edge computing paradigm provides better analysis, processing, and storage abilities to the data generated from the IoT sensors to enhance the capability of IoT devices concerning computing, storage, and network resources. In order to satisfy the paradigm of distributed edge computing, all the services are implemented as microservices. The microservices are connected to each other through REST APIs based on the constrained IoT devices to provide all the functionalities that accomplish a trade-off between energy consumption and occupant-desired environment setting for the smart home appliances. We simulated our proposed system to control the temperature of a smart home; through experimental findings, we investigated the application against the delay time and overall memory consumption by the embedded edge system of EdgeX. The result of this research work suggests that the implemented services operated efficiently in the raspberry pi 3 hardware of IoT devices.
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Borra, Praveen, Mahidhar Mullapudi, Harshavardhan Nerella, and Lalith Kumar Prakashchand. "Analyzing AWS Edge Computing Solutions to Enhance IoT Deployments." International Journal of Engineering and Advanced Technology 13, no. 6 (August 30, 2024): 8–12. http://dx.doi.org/10.35940/ijeat.f4519.13060824.

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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 exchanging data, while edge computing processes data near its source, decentralizing data processing and minimizing data transmission to centralized servers. AWS facilitates scalable and secure infrastructures for IoT and edge computing. AWS IoT Core manages IoT device connectivity and data ingestion, AWS Greengrass extends AWS capabilities to edge devices, and AWS Lambda enables serverless computing, empowering efficient deployment and scaling of IoT applications. Centralized cloud architectures often struggle with vast IoT data. Edge computing decentralizes data processing, reducing latency, enhancing real-time capabilities, and minimizing bandwidth. AWS ensures secure device connectivity through AWS IoT Core, supporting various protocols for seamless integration with IoT devices. AWS Greengrass allows local data processing and machine learning at the edge, vital for environments with limited connectivity or stringent latency requirements. AWS Lambda supports serverless computing, enabling scalable, event-driven architectures without server management, crucial for fluctuating IoT workloads. In conclusion, AWS advances IoT capabilities at the edge, with practical implementations across industries. As IoT evolves, AWS remains pivotal, innovating to meet dynamic IoT deployment demands.
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Anarbayevich, Abdurakhmanov Ravshan. "HARNESSING EDGE COMPUTING FOR ENHANCED SECURITY AND EFFICIENCY IN IOT NETWORKS." American Journal of Applied Science and Technology 4, no. 3 (March 1, 2024): 18–23. http://dx.doi.org/10.37547/ajast/volume04issue03-04.

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The Internet of Things (IoT) has revolutionized numerous sectors by enabling seamless connectivity and data exchange among devices. However, with the proliferation of IoT devices, concerns regarding security vulnerabilities and network efficiency have escalated. This article explores the integration of edge computing within IoT networks as a solution to address these challenges. Edge computing, by bringing computation closer to the data source, offers enhanced security measures and alleviates bandwidth constraints, thereby optimizing network performance. Through a comprehensive review of existing literature and case studies, this article examines the implementation of edge computing techniques such as data filtering, encryption, and decentralized processing to fortify IoT systems against cyber threats. Furthermore, it delves into the benefits of edge computing in improving real-time data analytics, reducing latency, and facilitating autonomous decision-makingin IoT environments. By elucidating the synergistic relationship between IoT and edge computing technologies, this article aims to provide valuable insights for researchers, practitioners, and policymakers in advancing the security and efficiency of IoT deployments.
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Dissertations / Theses on the topic "Edge IoT"

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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), LTE-M, BLE, ZigBee, Sigfox, LoRa, to give some examples, which all need to be implemented if they are supposed to be supported. The current approach requires to deploy parallel networks with dedicated base stations for different standards in one place. This further increases network costs. Cloud Radio Access Network (RAN) (c-RAN) has been proposed to centralize and cloudify baseband processing in a cloud infrastructure based on GPPs, which can potentially increase network flexibility and reduce the network Total Cost of Ownership (TCO) significantly. It can also be beneficiary for network performance by increased coordination possibilities. Nowadays, c-RAN still is on a concept level, because it is deemed difficult to implement due to complexity and reliability issues, e.g. for 4G/5G which requires sophisticated processing capabilities. The terminology of C-RAN today refers more to Centralized-RAN based on Digital Signal Processing (DSP) microcontrollers and ASICs, instead of c-RAN. However, the MMTC technologies are usually narrowband and designed with low complexity (considering cost of User Equipment (UE), power consumption, battery life time, etc.). Therefore, they are rather suitable for cloud implementation. Latency may be another issue for c-RAN. However, most of the MMTC applications are based on best-effort strategies and delay-tolerance. Therefore, c-RAN offers a promising solution to deliver the required efficiency and scalability for MMTC services. This master thesis is part of an effort to explore the possibilities, to increase the understanding and to gain hands-on experiences of IoT c-RAN implementation at the edge. It focuses on the NB-IoT downlink (DL) Physical (PHY) implementation as one example. However, IEEE 802.15.4 (PHY layer of e.g. Zigbee) has been integrated into the system within a collaboration between Ericsson and RISE SICS. This also shows, that c-RAN technology is able to unite multiple radio interfaces in one system leveraging Software (SW). In this study, we built a Software Defined Radio (SDR) testbed based on GNU Radio. The USRP B210 is the Hardware (HW) tool to test the implementation. Key components of the NB-IoT DL have been implemented. Orthogonal Frequency-Division Multiplex (OFDM) transmitter and receiver follow the NB-IoT numerology and implement algorithms for signal generation, time and frequency synchronization, as well as equalization and demodulation. The convolutional code of the Voyager missions with a coding rate R = 12 is used for performance evaluation. Different baseband modules have been tested and verified. Investigations have been carried out on the topic of latency. The measurements reveal a latency, which is higher than expected. Most likely, this is due to the large buffers underlying the GNU Radio scheduler in combination with the low speed of NB-IoT. The end-to-end system has been evaluated by field measurements (Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), Packet Error Rate (PER)) conducted in an Ericsson office environment. With no Line-Of-Sight (LOS), the implemented system has a reach of >= 65 m (from the office lab on floor 4 to the other end of the corridor where GFTB ER NAP NIT Fronhaul Technologies is located) with only 0.5 % PER and a SNR of 15.9 dB. In this work, system and SW design of the testbed and implementation are presented, as well as the hands-on experiences. The testbed is ready for human interaction with a fascinating Telegram bot live demo.
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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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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.

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 brings the challenge of congfiuring a large number of duplicated applications with low latency in a scalable manner. Finally, intelligent IoT applications, such as occupancy prediction and activity recognition, depend on analyzing user and event patterns from historical data. In order to achieve real-time interaction between humans and things, reliable yet real-time analytic support should be included to leverage the interplay and complementary roles of edge and cloud computing.

In this dissertation, I address the above issues from the service oriented point of view. Service oriented architecture (SOA) provides the integration and management flexibility using the abstraction of services deployed on devices. We have designed the WuKong IoT middleware to facilitate connectivity, deployment, and run-time management of IoT applications.

For energy efficient mapping, this thesis presents an energy saving methodology for co- locating several services on the same physical device in order to reduce the computing and communication energy. In a multi-hop network, the service co-location problem is formulated as a quadratic programming problem. I propose a reduction method that reduces it to the integer programming problem. In a single hop network, the service co-location problem can be modeled as the Maximum Weighted Independent Set (MWIS) problem. I design algorithm to transform a service flow to a co-location graph. Then, known heuristic algorithms to find the maximum independent set, which is the basis for making service co-location decisions, are applied to the co-location graph.

For low latency scalable deployment, I propose a region-based hierarchical management structure. A congestion zone that covers multiple regions is identified. The problem of deploying a large number of copies of a flow-based program (FBP) in a congestion zone is modeled as a network traffic congestion problem. Then, the problem of mapping in a congestion zone is modeled as an Integer Quadratic Constrained Programming (IQCP) problem, which is proved to be a NP-hard problem. Given that, an approximation algorithm based on LP relaxation and an efficient service relocating heuristic algorithm are designed for reducing the computation complexity. For each congestion zone, the algorithm will perform global optimized mapping for multiple regions, and then request multiple deployment delegators for reprogramming individual devices.

Finally, with the growing adoption of IoT applications, dedicated and single-purpose devices are giving way to smart, adaptive devices with rich capabilities using a platform or API, collecting and analyzing data, and making their own decisions. To facilitate building intelligent applications in IoT, I have implemented the edge framework for supporting reliable streaming analytics on edge devices. In addition, a progression framework is built to achieve the self-management capability of applications in IoT. A progressive architecture and a programming paradigm for bridging the service oriented application with the power of big data on the cloud are designed in the framework. In this thesis, I present the detailed design of the progression framework, which incorporates the above features for building scalable management of IoT systems through a flexible middleware.

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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 provides the rate-distortion analysis that is extended to consider how information content is processed for detection purposes. Considering an actual Structural Health Monitoring application, two corner cases are analysed: detection in high distortion based on a feature extraction method and detection with low distortion based on Principal Component Analysis. Secondly, we focus on streaming methods for Subspace Analysis. In this context, we revise and study state-of-the-art methods to target devices with limited computational resources. We also consider a real case of deployment of an algorithm for streaming Principal Component Analysis for signal compression in a Structural Health Monitoring application, discussing the trade-off between the possible implementation strategies. Finally, we focus on an alternative compression framework suited for low-end devices that is Compressed Sensing. We propose a different decoding approach that splits the recovery problem into two stages and effectively adopts a deep neural network and basic linear algebra to reconstruct biomedical signals. This novel approach outperforms the state-of-the-art in terms of quality of reconstruction and requires lower computational resources.
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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 for a few years, cloud-centric approaches have started showing some of their main limitations when dealing with the connectivity of many devices with remote endpoints, like high latency, bandwidth usage, big data volumes, reliability, privacy, and so on. At the same time, a few new distributed computing paradigms emerged and gained attention. Among all, Edge Computing allows to shift the execution of applications at the edge of the network (a partition of the network physically close to data-sources) and provides improvement over the Cloud Computing paradigm. Its success has been fostered by new powerful embedded computing devices able to satisfy the everyday-increasing computing requirements of many IoT applications. Given this context, how can next-generation IoT applications take advantage of the opportunity offered by Edge Computing to shift the processing from the cloud toward the data sources and exploit everyday-more-powerful devices? This thesis provides the ingredients and the guidelines for practitioners to foster the migration from cloud-centric to novel distributed design approaches for IoT applications at the edge of the network, addressing the issues of the original approach. This requires the design of the processing pipeline of applications by considering the system requirements and constraints imposed by embedded devices. To make this process smoother, the transition is split into different steps starting with the off-loading of the processing (including the Artificial Intelligence algorithms) at the edge of the network, then the distribution of computation across multiple edge devices and even closer to data-sources based on system constraints, and, finally, the optimization of the processing pipeline and AI models to efficiently run on target IoT edge devices. Each step has been validated by delivering a real-world IoT application that fully exploits the novel approach. This paradigm shift leads the way toward the design of Edge Intelligence IoT applications that efficiently and reliably execute Artificial Intelligence models at the edge of the network.
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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 for a few years, cloud-centric approaches have started showing some of their main limitations when dealing with the connectivity of many devices with remote endpoints, like high latency, bandwidth usage, big data volumes, reliability, privacy, and so on. At the same time, a few new distributed computing paradigms emerged and gained attention. Among all, Edge Computing allows to shift the execution of applications at the edge of the network (a partition of the network physically close to data-sources) and provides improvement over the Cloud Computing paradigm. Its success has been fostered by new powerful embedded computing devices able to satisfy the everyday-increasing computing requirements of many IoT applications. Given this context, how can next-generation IoT applications take advantage of the opportunity offered by Edge Computing to shift the processing from the cloud toward the data sources and exploit everyday-more-powerful devices? This thesis provides the ingredients and the guidelines for practitioners to foster the migration from cloud-centric to novel distributed design approaches for IoT applications at the edge of the network, addressing the issues of the original approach. This requires the design of the processing pipeline of applications by considering the system requirements and constraints imposed by embedded devices. To make this process smoother, the transition is split into different steps starting with the off-loading of the processing (including the Artificial Intelligence algorithms) at the edge of the network, then the distribution of computation across multiple edge devices and even closer to data-sources based on system constraints, and, finally, the optimization of the processing pipeline and AI models to efficiently run on target IoT edge devices. Each step has been validated by delivering a real-world IoT application that fully exploits the novel approach. This paradigm shift leads the way toward the design of Edge Intelligence IoT applications that efficiently and reliably execute Artificial Intelligence models at the edge of the network.
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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 ridurre il carico sulle spalle degli sviluppatori.
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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 antennas are suitable for direct print on the system circuit board of a device. Related theory and research work are discussed and referenced, providing a strong configuration for future use. Recommendations and suggestions on future work are also discussed. The proposed antenna system is more than promising and with further adjustments and refinement can lead to a fully working solution.
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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 application components is not a straightforward task, and IoT system designer should be supported for the decision. To support the designers, in this thesis we focused on the system qualities, and aimed for three main contributions. First, by reviewing the literature, we identified the relevant and most used qualities and metrics. Moreover, to analyse how computer simulation can be used as a supporting tool, we investigated the edge computing simulators, and in particular the metrics they provide for modeling and analyzing IoT systems in edge computing. Finally, we introduced a method to represent how multiple qualities can be considered in the decision. In particular, we considered distributing Deep Neural Network layers as a use case and raked the deployment options by measuring the relevant metrics via simulation.
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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 work, we introduce an edge-based architecture which create end-to-end secure communication between IoT sensors in a remote WSN and central cloud via UAV, which assist the data collection, processing and managing procedures of the remote WSN. Since power is a limited resource, we propose Bluetooth Low Energy (BLE) as the communication media between UAV and sensors in the WSN, where BLE is considered as an ultra-low power radio access technology. To examine the performance of the system model, we have presented a simulation analysis considering three sensor nodes array types that can realize in the practical environment. The impact of BLE data rate, impact of speed of the UAV, impact of distance between adjacent sensors and impact of data generation rate of the sensor node have been analysed to examine the performance of system. Moreover, to observe the practical functionality of the proposed architecture, prototype implementation is presented using commercially available off-the-shelf devices. The prototype of the system is implemented assuming ideal environment.
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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 devices were on a ubiquitous network – the Internet – they would have far more value. Trending data-centric technologies in the IoT involve security and data governance, infrastructure (edge & cloud analytics), data processing, advanced analytics, and data integrating and messaging. These technologies are supported by cloud computing service models that include three major layers – Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Of the three, IaaS is the foundation while SaaS is the top layer functioning off both PaaS and IaaS. Interestingly enough, although SaaS is normally represented in graphics as the smallest layer of Cloud infrastructure, it is anything but. The IaaS layer of Cloud Computing is comprised of all the hardware needed to make Cloud Computing possible. The PaaS layer of the Cloud is a framework for developers that they can build upon and use to create customized applications. Built on top of both IaaS and PaaS, Software as a Service provides applications, programs, software, and web tools to the public for free or for a price. By the year 2020, trillions of gigabytes of data will be generated through the Internet of Things. This is no doubt difficult to comprehend easily. However, with the growing number of connected devices it is not surprising that by 2020, more than ten billion sensors and devices will be connected to the internet. Furthermore, all of these devices will gather, analyze, share, and transmit data in real-time. Hence, without the data, IoT devices would not hold the functionalities and capabilities which have made them achieve so much worldwide attention. If organizations are not in a position to somehow ingest, process and analyze these data, then it becomes worthless, and the IoT project will be considered a failure. Unlike a traditional IT system, IoT systems are cyber-physical systems involving both humans and machines as end-users. Their interaction forms a complex web of M2M (Machine to Machine) and H2M (Human to Machine) transactions. Right from device firmware, to network interfaces, extending all the way to business logic defined in cloud application and user app, software remains the most critical driver in IoT. Similarly, Edge computing presents great opportunities to achieve ubiquitous computation in the Internet ecosystem. It is proposed to overcome the intrinsic challenges of computing on the cloud side. Edge computing offers to gather more sensory data, reducing the response time, freeing up network bandwidth, and ultimately reducing the workload on the cloud. In the effort to elevate support for technologies that are directed toward IoT in smart cities concept, support for developers and service providers is critical especially regarding fast and feasible deployment of IoT solutions and assets. To that end, I focused during my research on ways and methods to exploit generic IoT solutions; Application Programming Interfaces (APIs) and edge engines. In this book, I present Atmosphere, a novel edge-to-cloud solution for supporting development and deployment by IoT developers and service providers. Atmosphere cloud is a SaaS deployment-ready model, while Atmosphere edge is a lightweight edge engine for IoT device management. Needless to say, testing the various software components is essential to ensure a safe and reliable IoT system. The solutions I contributed to were tested in multiple projects of varying volumes and challenges. In some projects, using the generic concept was straight forward, while in others, where the structure of the IoT data was complicated and restrictions were established by the partners, the integration was challenging.
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Books on the topic "Edge IoT"

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Pal, Souvik, Claudio Savaglio, Roberto Minerva, and Flávia C. Delicato, eds. IoT Edge Intelligence. Cham: 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. Berkeley, CA: 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. Cham: 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. Berkeley, CA: 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. Cham: 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. New York: 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. Cham: 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. Boca Raton: 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. Cham: 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, Moamar Sayed-Mouchawe, Holger Fröning, Franz Pernkopf, Gregor Schiele, and Michaela Blott, eds. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66770-2.

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Book chapters on the topic "Edge IoT"

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Venu, Sibeesh. "IoT Edge." In Asp.Net Core and Azure with Raspberry Pi 4, 129–52. Berkeley, CA: 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, 1–15. Cham: 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, 271–96. Berkeley, CA: 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, 219–76. First edition. | Boca Raton : CRC Press/Taylor & Francis Group, [2020]: 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, 111–44. Cham: 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, 103–10. Cham: 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, 83–117. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4536-1_4.

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Rehan, Syed. "Industrial IoT with AWS IoT." In AWS IoT With Edge ML and Cybersecurity, 177–210. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0011-5_7.

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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, 49–69. Cham: 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, 205–25. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4536-1_8.

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Conference papers on the topic "Edge IoT"

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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), 67–73. IEEE, 2024. http://dx.doi.org/10.1109/edge62653.2024.00018.

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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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AlZahrani, Yazeed, Jun Shen, and Jun Yan. "Spatial Goal Refinement Patterns for IoT Applications." In 2022 IEEE International Conference on Edge Computing and Communications (EDGE). IEEE, 2022. http://dx.doi.org/10.1109/edge55608.2022.00019.

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Cao, Charles Qing, and Yunhe Feng. "Probabilistic Error Reasoning on IoT Edge Devices." In 2023 IEEE International Conference on Edge Computing and Communications (EDGE). IEEE, 2023. http://dx.doi.org/10.1109/edge60047.2023.00031.

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"Empowering Industrial IoT with Narrowband: The Role of NB-IoT in Industry 4.0." In International Conference on Cutting-Edge Developments in Engineering Technology and Science. ICCDETS, 2024. http://dx.doi.org/10.62919/mtlk8978.

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This paper investigates the role of Narrowband Internet of Things (NB-IoT) in empowering Industrial IoT (IIoT) within the context of Industry 4.0. NB-IoT, a low-power wide-area network technology, is pivotal for facilitating reliable, efficient, and cost-effective connectivity for a multitude of industrial devices and sensors. We analyze the technical features of NB-IoT, including its extended coverage, low power consumption, and high connection density, which make it ideal for industrial applications. The paper examines various use cases, such as asset tracking, predictive maintenance, and remote monitoring, demonstrating how NB-IoT enhances operational efficiency and productivity. Challenges related to scalability, integration with existing systems, and security are discussed. Additionally, future prospects for NB-IoT in advancing Industry 4.0 initiatives are explored. This research aims to provide a comprehensive overview of how NB-IoT is shaping the future of industrial connectivity and automation.
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Jain, Rakesh, and Samir Tata. "Cloud to Edge: Distributed Deployment of Process-Aware IoT Applications." In 2017 IEEE International Conference on Edge Computing (EDGE). IEEE, 2017. http://dx.doi.org/10.1109/ieee.edge.2017.32.

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Nikolopoulos, Basil, Maria Voreakou, Mara Nikolaidou, and Dimosthenis Anagnostopoulos. "Enhancing Context-Awareness in Autonomous Fog Nodes for IoT Systems." In 2019 IEEE International Conference on Edge Computing (EDGE). IEEE, 2019. http://dx.doi.org/10.1109/edge.2019.00034.

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Reports on the topic "Edge IoT"

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

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Leathers, Emily, Clayton Thurmer, and Kendall Niles. Encryption for edge computing applications. Engineer Research and Development Center (U.S.), May 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 issues in edge computing, particularly in the realm of encryption. A prominent limitation of edge devices is the hardware’s ability to support the computational complexity of traditional encryption methodologies (Alwarafy et al. 2020). Furthermore, encryption on the edge poses challenges in key management, the process by which cryptographic keys are transferred and stored among devices (Zeyu et al. 2020). Though edge computing provides reduced latency in data processing, encryption mechanism utilization reintroduces delay and can hinder achieving real-time results (Yu et al. 2018). The IoT is composed of a wide range of devices with a diverse set of computational capabilities, rendering a homogeneous solution for encryption impractical (Dar et al. 2019). Edge devices are often deployed in operational locations that are vulnerable to physical tampering and attacks. Sensitive data may be compromised if not sufficiently encrypted or if keys are not managed properly. Furthermore, the distributed nature and quantity of edge devices create a vast attack surface that can be compromised in other ways (Xiao et al. 2019). Understanding established mechanisms and exploring emerging methodologies for encryption reveals potential solutions for developing a robust solution for edge computing applications. The purpose of this document is to detail the current research for encryption methods in the edge computing space and highlight the major challenges associated with executing successful encryption on the edge.
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Phillips, Paul. The Application of Satellite-based Internet of Things for New Mobility. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, January 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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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.), September 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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Lehrman, I. S. ICRF (Ion Cyclotron Range of Frequencies) edge modeling. Office of Scientific and Technical Information (OSTI), January 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, February 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, the problem is the dereliction of key US-based business corporations to take the lead in making the investments in organizational learning required to generate cutting edge communication-infrastructure products. No company in the United States exemplifies this deficiency more than Cisco Systems, the business corporation founded in Silicon Valley in 1984 that had explosive growth in the 1990s to become the foremost global enterprise-networking equipment producer in the Internet revolution. This paper provides in-depth analysis of Cisco’s organizational failure, attributing it ultimately to the company’s turn from innovation in the last decades of 20th century to financialization in the early decades of the 21st century. Since 2001, Cisco’s top management has chosen to allocate corporate cash to open-market share repurchases— aka stock buybacks—for the purpose of giving manipulative boosts to the company stock price rather than make the investments in organizational learning required to become a world leader in communication-infrastructure equipment for the era of 5G and IoT. From October 2001 through October 2022, Cisco spent $152.3 billion—95 percent of its net income over the period—on stock buybacks for the purpose of propping up its stock price. These funds wasted in pursuit of “maximizing shareholder value” were on top of the $55.5 billion that Cisco paid out to shareholders in dividends, representing an additional 35 percent of net income. In this paper, we trace how Cisco grew from a Silicon Valley startup in 1984 to become, through its innovative products, the world leader in enterprise-networking equipment over the next decade and a half. As the company entered the 21st century, building on its dominance of enterprise-networking, Cisco was positioned to upgrade its technological capabilities to become a major infrastructureequipment vendor to service providers. We analyze how and why, when the Internet boom turned to bust in 2001, the organizational structure that enabled Cisco to dominate enterprise networking posed constraints related to manufacturing and marketing on the company’s growth in the more sophisticated infrastructure-equipment segment. We then document how from 2002 Cisco turned from innovation to financialization, as it used its ample profits to do stock buybacks to prop up its stock price. Finally, we ponder the larger policy implications of Cisco’s turn from innovation to financialization for the competitive position of the US information-and-communication technology (ICT) industry in the global economy.
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Cully, John, and Susie Wright. Edge computing. Parliamentary Office of Science and Technology, September 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), December 2004. http://dx.doi.org/10.2172/836477.

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T.M. Biewer, R.E. Bell, J.R. Wilson, and P.M. Ryan. Observations of Anisotropic Ion Temperature in the NSTX Edge during RF Heating. Office of Scientific and Technical Information (OSTI), October 2004. http://dx.doi.org/10.2172/835926.

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T.M. Biewer, R.E. Bell, P.M. Ryan, and J.R. Wilson. Observations of Anisotropic Ion Temperature in the NSTX Edge during RF Heating. Office of Scientific and Technical Information (OSTI), June 2004. http://dx.doi.org/10.2172/828257.

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