Academic literature on the topic 'Large-scale IoT network'
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Journal articles on the topic "Large-scale IoT network"
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Tiruvayipati, Sujanavan, and Ramadevi Yellasiri. "Leveraging Nano Block-Lattice for Cost-Effective Large-Scale Wi-Fi-Based IoT Network Expansion." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 08 (2025): 224–38. https://doi.org/10.3991/ijim.v19i08.53319.
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The rapid expansion of Internet of Things (IoT) networks necessitates cost-effective and scalable solutions for secure data transmission and storage. This study explores the integration of Nano (XNO) cryptocurrency with Wi-Fi repeater networks to address the challenges of IoT scalability and data security. By leveraging XNO’s block-lattice structure, this study demonstrates how the cryptocurrency’s minimal transaction fees, high transaction throughput, and low latency make it an optimal solution for real-time IoT data handling. In combination with Wi-Fi repeater networks, which extend coverage and reduce signal loss, the proposed system enables secure, decentralized storage of IoT data. Performance metrics, including latency, transaction throughput, and cost, show that the XNO-integrated IoT network outperforms traditional blockchain technologies such as Ethereum and IOTA, offering a promising solution for large-scale IoT deployments.
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Shao, Yulin, Soung Chang Liew, He Chen, and Yuyang Du. "Flow Sampling: Network Monitoring in Large-Scale Software-Defined IoT Networks." IEEE Transactions on Communications 69, no. 9 (2021): 6120–33. http://dx.doi.org/10.1109/tcomm.2021.3093320.
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Moe, Z. Win, Meyer Florian, Liu Zhenyu, Dai Wenhan, Bartoletti Stefania, and Conti Andrea. "Efficient Multisensor Localization for the Internet of Things: Exploring a New Class of Scalable Localization Algorithms." IEEE Signal Processing Magazine 35, no. 5 (2018): 153–67. https://doi.org/10.1109/MSP.2018.2845907.
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In the era of the Internet of Things (IoT), efficient localization is essential for emerging mass-market services and applications. IoT devices are heterogeneous in signaling, sensing, and mobility, and their resources for computation and communication are typically limited. Therefore, to enable location awareness in large-scale IoT networks, there is a need for efficient, scalable, and distributed multisensor fusion algorithms. This article presents a framework for designing network localization and navigation (NLN) for the IoT. Multisensor localization and operation algorithms developed within NLN can exploit spatiotemporal cooperation, are suitable for arbitrary, large network sizes, and only rely on an information exchange among neighboring devices. The advantages of NLN are evaluated in a large-scale IoT network with 500 agents. In particular, because of multisensor fusion and cooperation, the presented network localization and operation algorithms can provide attractive localization performance and reduce communication overhead and energy consumption.
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Salva-Garcia, Pablo, Jose M. Alcaraz-Calero, Qi Wang, Jorge Bernal Bernabe, and Antonio Skarmeta. "5G NB-IoT: Efficient Network Traffic Filtering for Multitenant IoT Cellular Networks." Security and Communication Networks 2018 (December 10, 2018): 1–21. http://dx.doi.org/10.1155/2018/9291506.
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Internet of Things (IoT) is a key business driver for the upcoming fifth-generation (5G) mobile networks, which in turn will enable numerous innovative IoT applications such as smart city, mobile health, and other massive IoT use cases being defined in 5G standards. To truly unlock the hidden value of such mission-critical IoT applications in a large scale in the 5G era, advanced self-protection capabilities are entailed in 5G-based Narrowband IoT (NB-IoT) networks to efficiently fight off cyber-attacks such as widespread Distributed Denial of Service (DDoS) attacks. However, insufficient research has been conducted in this crucial area, in particular, few if any solutions are capable of dealing with the multiple encapsulated 5G traffic for IoT security management. This paper proposes and prototypes a new security framework to achieve the highly desirable self-organizing networking capabilities to secure virtualized, multitenant 5G-based IoT traffic through an autonomic control loop featured with efficient 5G-aware traffic filtering. Empirical results have validated the design and implementation and demonstrated the efficiency of the proposed system, which is capable of processing thousands of 5G-aware traffic filtering rules and thus enables timely protection against large-scale attacks.
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Wu, Xing, Jing Duan, Mingyu Zhong, Peng Li, and Jianjia Wang. "VNF Chain Placement for Large Scale IoT of Intelligent Transportation." Sensors 20, no. 14 (2020): 3819. http://dx.doi.org/10.3390/s20143819.
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With the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full potential. The major challenge in intelligent transportation is that vehicles and pedestrians, as the main types of edge nodes in IoT infrastructure, are on the constant move. Hence, the topology of the large scale network is changing rapidly over time and the service chain may need reestablishment frequently. Existing Virtual Network Function (VNF) chain placement methods are mostly good at static network topology and any evolvement of the network requires global computation, which leads to the inefficiency in computing and the waste of resources. Mapping the network topology to a graph, we propose a novel VNF placement method called BVCP (Border VNF Chain Placement) to address this problem by elaborately dividing the graph into multiple subgraphs and fully exploiting border hypervisors. Experimental results show that BVCP outperforms the state-of-the-art method in VNF chain placement, which is highly efficient in large scale IoT of intelligent transportation.
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Shreyas, J., N. V. Priya, P. K. Udayprasad, N. N. Srinidhi, Chouhan Dharmendra, and Kumar S. M. Dilip. "Opportunistic Routing for Large Scale IoT Network to Reduce Transmission Overhead." Journal of Advancement in Parallel Computing 5, no. 1 (2022): 1–8. https://doi.org/10.5281/zenodo.6379125.
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<em>Increase in popularity of sensor electronics have gained much attention for wireless sensor technologies and demands many IoT (Internet of things) applications for real time and industrial applications. In IoT the proliferation of devices which are able to directly connected to internet and can be monitored. Sensed data from the device has to be forwarded to base station or end user (EU) which is achieved by efficient routing protocols to improve data transmission for large scale IoT. During routing process redundant data may be forwarded by nodes causing more overhead which may lead to congestion. There exist many challenges like low power links, multiple disjoint path, and energy while designing efficient communication protocol. In this paper we propose an enhanced opportunistic routing (e-OR) protocol for self-disciplined and self-healing large scale IoT devices. Enhanced opportunistic routing protocol uses best fit traversing algorithm to find optimal and reliable routes. The e- OR estimates link quality of nodes to avoid frequent disconnections. During route discovery process e-OR adapts greedy behaviour for finding optimal and shortest routes. Further we integrate congestion avoidance using clear channel assignment (CCA) for better channel availability to avoid packet loss and achieve QoS.</em>
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Pan, Pengyu, Xiaobo Ma, Yingjie Fu, and Feitong Chen. "Automating Group Management of Large-Scale IoT Botnets for Antitracking." Security and Communication Networks 2022 (April 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/4196945.
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With the popularity of Internet of Things (IoT) devices, IoT botnets like Mirai have been infecting as many devices as possible such as IP cameras and home routers. Because of the sheer volume and continual operation of many vulnerabilities (many users do not pay much attention to IoT update alerts and leave the configurations by default) of IoT devices, the population of an IoT botnet becomes increasingly tremendous. The growing population, though making a botnet powerful, results in an increased risk of exposure. Specifically, once a bot is captured, the command and control (C&C) channel may be cracked and then tracked, potentially rendering more bots being discovered. To solve this problem, this paper proposes an automated approach to group management of large-scale IoT bots. The basic idea of the proposed approach is to establish a reliable and unsuspicious social network-based C&C channel capable of automatically grouping bots, wherein a group of bots have a unique ID that is against cross-group tracking. The Diffie–Hellman key exchange method is leveraged for efficiently generating the unique group ID, thereby scaling up automatic bot grouping. We refer to the botnet proposed in this paper as a multichannel automatic grouping botnet (MCG botnet) and conduct verification experiments using social networks and more than 2,000 docker nodes. The experimental results show that the MCG botnet has the ability of automatic grouping and antitracking.
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Yin, Lihua, Weizhe Chen, Xi Luo, and Hongyu Yang. "Efficient Large-Scale IoT Botnet Detection through GraphSAINT-Based Subgraph Sampling and Graph Isomorphism Network." Mathematics 12, no. 9 (2024): 1315. http://dx.doi.org/10.3390/math12091315.
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In recent years, with the rapid development of the Internet of Things, large-scale botnet attacks have occurred frequently and have become an important challenge to network security. As artificial intelligence technology continues to evolve, intelligent detection solutions for botnets are constantly emerging. Although graph neural networks are widely used for botnet detection, directly handling large-scale botnet data becomes inefficient and challenging as the number of infected hosts increases and the network scale expands. Especially in the process of node level learning and inference, a large number of nodes and edges need to be processed, leading to a significant increase in computational complexity and posing new challenges to network security. This paper presents a novel approach that can accurately identify diverse intricate botnet architectures in extensive IoT networks based on the aforementioned circumstance. By utilizing GraphSAINT to process large-scale IoT botnet graph data, efficient and unbiased subgraph sampling has been achieved. In addition, a solution with enhanced information representation capability has been developed based on the Graph Isomorphism Network (GIN) for botnet detection. Compared with the five currently popular graph neural network (GNN) models, our approach has been tested on C2, P2P, and Chord datasets, and higher accuracy has been achieved.
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Qian, Kexiang, Hongyu Yang, Ruyu Li, Weizhe Chen, Xi Luo, and Lihua Yin. "Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning." Applied Sciences 14, no. 4 (2024): 1615. http://dx.doi.org/10.3390/app14041615.
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With the rapid growth of IoT devices, the threat of botnets is becoming increasingly worrying. There are more and more intelligent detection solutions for botnets that have been proposed with the development of artificial intelligence. However, due to the current lack of computing power in IoT devices, these intelligent methods often cannot be well-applied to IoT devices. Based on the above situation, this paper proposes a distributed botnet detection method based on graph partitioning, efficiently detecting botnets using graph convolutional networks. In order to alleviate the wide range of IoT environments and the limited computing power of IoT devices, the algorithm named METIS is used to divide the network traffic structure graph into small graphs. To ensure robust information flow between nodes while preventing gradient explosion, diagonal enhancement is applied to refine the embedding representations at each layer, facilitating accurate botnet attack detection. Through comparative analysis with GATv2, GraphSAGE, and GCN across the C2, P2P, and Chord datasets, our method demonstrates superior performance in both accuracy and F1 score metrics. Moreover, an exploration into the effects of varying cluster numbers and depths revealed that six cluster levels yielded optimal results on the C2 dataset. This research significantly contributes to mitigating the IoT botnet threat, offering a scalable and effective solution for diverse IoT ecosystems.
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Y. Khan, Jamil, Dong Chen, and Oliver Hulin. "Enabling Technologies for Effective Deployment of Internet of Things (IoT) Systems." Journal of Telecommunications and the Digital Economy 2, no. 4 (2020): 21. http://dx.doi.org/10.18080/jtde.v2n4.276.
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The demand for IoT (Internet of Things) systems that encompass cloud computing, the multitude of low power sensing and data collection electronic devices and distributed communications architecture is increasing at an exponential pace. With increasing interests from different industrial, business and social groups, in the near future it will be necessary to support massive deployment of diverse IoT systems in different geographical areas. Large scale deployment of IoT systems will introduce challenging problems for the communication designers, as the networking is one of the key enabling technologies for the IoT systems. Major challenges include cost effective network architecture, support of large area of coverage and diverse QoS (Quality of Service) requirements, reliability, spectrum requirements, energy requirements, and many other related issues. The paper initially reviews different classes of IoT applications and their communication requirements. Following the review, different communications and networking technologies that can potentially support large scale deployment of IoT systems for different industrial, business and social applications are discussed. The paper then concentrates on wireless networking technologies for IoT systems with specific focus on deployment issues. The deployment discussion concentrates on different IoT systems QoS and networking requirements, cost, coverage area and energy supply requirements. We introduce a sustainable low cost heterogeneous network design using short range radio standards such as IEEE 802.15.4/Zigbee, IEEE 802.11/WLAN that can be used to develop a wide area networks to support large number of IoT devices for various applications. Finally the paper makes some general recommendations towards sustainable network design techniques for future IoT systems that can reduce the OPEX and CAPEX requirements.
Dissertations / Theses on the topic "Large-scale IoT network"
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Yang, Shang-Wen, and 楊尚文. "Two-Stage Wireless Mesh Networks for Fixed Large-Scale IoT Sensor Network Applications." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/rhm927.
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碩士<br>國立臺灣海洋大學<br>資訊工程學系<br>106<br>In a large-scale IoT environment, a network environment that efficiently transmits the collected data is needed. Wireless mesh networks can provide services as wireless access points. Moreover they can be extended to increase coverage and improve availability. However, if there are too many mesh nodes, it will cause a lot of interference between nodes. Even worse, the too long transmission path will result in higher packet loss. In this thesis study, we propose a hierarchical mesh network architecture for the problem. In the proposed scheme, the commnuication network is divided into two layers: general wireless network inside each group of nodes and mesh network between all the group head nodes. We compared our proposed approach with conventional wireless mesh network through intensive performance investigation based on OMNeT++ network simulator. The simulation results justify the benefits of our proposed approach. Meanwhile, a small but full function system prototype was built to show that the proposed system architecture is pratical and feasible.
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SCIANCALEPORE, Savio. "From Security to Localization: Advanced Services for Large-Scale IoT Networks." Doctoral thesis, 2017. http://hdl.handle.net/11589/98114.
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This PhD disseration is focused on the topic of Internet of Things (IoT) technology, with specific focus on security issues and localization functionalities in IoT deployments composed by many constrained devices.
In fact, the last decade has been the witness of the explosion of the IoT, and today billions of smart devices are hitting the market. They are equipped with sensing and actuating capabilities and they have the capabilities to interact between them over the Internet. This phenomenon paves the way to a variety of innovative applications in different contexts, such as environmental monitoring, smart building, home automation, health care, logistics and energy management, to name a few. Moreover, the innovations provided by such technological evolution especially in industrial applications is currently enabling a wide set of advanced services, indicated as the forth industrial revolution, Industry 4.0, that will leverage on the new specific paradigm of the, Industrial Internet of Things (IIoT). Also, the major leading companies of the sector expect up to 50 billion of smart devices connected by 2020.
In this ebullient context, the need for developing accurate and tailored services, resized on the requirements and features of the constrained devices at the basis of IoT technology, is still an incumbent hurdle.
From one side, security aspects stand in a prominent position. In fact, the unique features of the involved devices require the design of tailored routines and protocols, able to minimally affect the regular communications, operations and the execution of the standard protocol stack.
From the other side, the deployment of mobile sensor nodes requires the presence of smart and lightweight static and dynamic localization techniques, able to provide instant-by-instant the position of the sensing entities, without affecting the execution of their fundamental tasks and the lifetime of battery-powered devices.
Starting from these premises, the present PhD thesis wraps around the main topic of providing advanced services for IoT networks, with the aim of affecting the normal network operations only in a minimal part.
With the focus on security aspects, the procedures and services recommended by one of the most important standard for IoT protocol stack, IEEE 802.15.4e, have been implemented. The overhead of link-layer security in IIoT networks has been evaluated, in terms of minimal timeslot length and memory footprint. The provided implementation of link-layer security covers a range of hardware platforms, exploring different hardware/software implementation strategies. Through an extensive measurement campaign, the advantage of hardware accelerators for link-layer security have been precisely quantified. Furthermore, the impact that the resulting timeslot duration has on both high-level application design and energy consumption have been investigated.
Moreover, starting from a careful inquiry about the gaps left open by the MAC layer standard IEEE 802.15.4, a lightweight and standard compatible framework, namely “LICITUS”, has been developed, with the aim of providing a wide variety of security configurations in homogeneous and heterogeneous scenarios, integration support for adaptation to dynamic networks, lean and scalable initialization functionalities, a lightweight Key Management Protocol (KMP) and resilience to several attacks.
Starting from the KMP developed in the LICITUS framework, an advanced version has been designed. It integrates implicit Elliptic Curve Qu-Vanstone (ECQV) certificates with a standard Elliptic Curve Diffie-Hellman (ECDH) exchange. From a security perspective, it performs authentication and key derivation between any two peers an IIoT network. As confirmed by a proof-of-concept implementation and relevant experimental results, this innovative KMP guarantees the minimal airtime consumption with respect to conventional approaches, while providing also robust key negotiation, fast re-keying, and efficient protection against replay attacks.
The research activity about security has also covered the authorization problem, which is quickly emerging as one of the most important barrier to protect against external threats coming from the public Internet. Within the context of the symbIoTe H2020 European project, an authorization architecture based on the widely known OAuth 2.0 authorization framework has been proposed. Thanks to security services provided by the OAuth 2.0 Authorization Server deployed at the edge of the IoT network, the proposed solution assures the precise identification of all accesses to resources exposed by the smart devices, while not affecting in any way their computational burden.
As regarding localization solutions, a 4-months period of research abroad at the Faculty of Computer and Information Science in Ljubljana (Slovenia) have been conducted to study and provide a lightweight off-line algorithm that addresses the problem of estimating the position and velocity of a radio transmitter moving with a constant, unknown speed. The provided technique is based on arrival timestamps gathered by fixed sensor nodes. The technique is completely asynchronous in its logic, because no assumption is made about any time synchronization between the involved devices. The performances of this solution have been investigated not only through a wide set of Monte-Carlo simulations, but also by using real data provided by off-the-shelf Wi-Fi devices, demonstrating its feasibility for adoption in specific IoT services.
To conclude, a brief description of the structure of the thesis is provided below:
1. Chapter 1: The Internet of Things technology. It introduces the main features of the upcoming IoT technology, with a focus on the standardized protocol stack and the variety of hardware devices specifically designed for such applications.
2. Chapter 2: Security issues in IoT networks. It provides an extensive summary of the state of the art in the IoT security area, discussing the most adopted solutions and algorithms.
3. Chapter 3: MAC-layer security in IoT networks. In this chapter the implementation of security procedures, services and algorithm recommended by the IEEE 802.15.4 standard is detailed, as well as a lightweight security framework that integrates and smartly uses such services.
4. Chapter 4: Security solutions at the application layer for the IoT. It discusses the solutions specifically designed to address security issues at the application layer, starting from a lightweight KMP guaranteeing the minimal airtime consumption, up to advanced solutions addressing the authorization problem.
5. Chapter 5: Time-based wireless localization techniques. It focuses on an algorithm developed to estimate the position and velocity of a node moving at a constant, unknown velocity. The performance evaluation of the algorithm is described, both in a simulated environment and through real Wi-Fi data, along with the advantages derived by its adoption in the IoT context.
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Lin, Jia-Jhun, and 林家諄. "Design of Large-Scale IoT Honeynet form the Perspective of Virtualized Netwworks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/jkz53e.
Full textBooks on the topic "Large-scale IoT network"
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Fedorovich, Tat'yana, Natal'ya Kubrak, S. K. Tevs, and A. V. Dmitrenko. Organization and management of a private fleet of freight cars: an economic approach. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1860935.
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The monograph discusses theoretical and practical issues related to the completion of the implementation of the introduction of new forms of ownership in the formation of the railway holding JSC "Russian Railways". The organizational and economic results of the structural reform of railway transport in Russia are analyzed in comparison with the experience of modern reform of foreign railway transport. The variants of transformation of the modern cargo transportation management system in the new economic conditions are proposed.
 The main results of the study of modern economic and organizational conditions of cargo transportation, taking into account the specifics of working with a private car fleet, are presented. Based on a large-scale analysis of the loading dynamics of the main directions of the Russian Railways network and the resulting unevenness of operational work, new approaches are proposed for the formation of the market of freight operators of rolling stock.
 An economic assessment of the implementation of scenario options for the introduction of technological outsourcing is given on the example of JSC "Federal Freight Company" with the justification of a methodological approach for the formation of a consolidated fleet of wagons based
 on the algorithm of the cost method for calculating the throughput during the consolidation of a fleet of freight wagons.
 For practitioners and researchers involved in cargo transportation management, forecasting and formation of private fleets of freight cars. It will be useful for graduate students, undergraduates and students of economic faculties of railway universities when studying courses in the economics of railway transport and the cost of freight transportation.
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Garofalo, Giuseppe, ed. Capitalismo distrettuale, localismi d'impresa, globalizzazione. Firenze University Press, 2008. http://dx.doi.org/10.36253/978-88-8453-605-1.
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From the late Sixties on, industrial development in Italy evolved through the spread of small and medium sized firms, aggregated in district networks, with an elevated propensity to enterprise and the marked presence of owner-families. Installed within the local systems, the industrial districts tended to simulate large-scale industry exploiting lower costs generated by factors that were not only economic. The districts are characterised in terms of territorial location (above all the thriving areas of the North-east and Centre) and sector, since they are concentrated in the "4 As" (clothing-fashion, home-decor, agri-foodstuffs, automation-mechanics), with some overlapping with "Made in Italy". How can this model be assessed? This is the crucial question in the debate on the condition and prospects of the Italian productive system between the supporters of its capacity to adapt and the critics of economic dwarfism. A dispassionate judgement suggests that the prospects of "small is beautiful" have been superseded, but that the "declinist" view, that sees only the dangers of globalisation and the IT revolution for our SMEs is risky. The concept of irreversible crisis that prevails at present is limiting, both because it is not easy either to "invent", or to copy, a model of industrialisation, and because there is space for a strategic repositioning of the district enterprises. The book develops considerations in this direction, showing how an evolution of the district model is possible, focusing on: gains in productivity, scope economies (through diversification and expansion of the range of products), flexibility of organisation, capacity to meld tradition and innovation aiming at product quality, dimensional growth of the enterprises, new forms of financing, active presence on the international markets and valorisation of the resources of the territory. It is hence necessary to reactivate the behavioural functions of the entrepreneurs.
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Nobre, Anna C. (Kia), and M.-Marsel Mesulam. Large-scale Networks for Attentional Biases. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.035.
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Selective attention is essential for all aspects of cognition. Using the paradigmatic case of visual spatial attention, we present a theoretical account proposing the flexible control of attention through coordinated activity across a large-scale network of brain areas. It reviews evidence supporting top-down control of visual spatial attention by a distributed network, and describes principles emerging from a network approach. Stepping beyond the paradigm of visual spatial attention, we consider attentional control mechanisms more broadly. The chapter suggests that top-down biasing mechanisms originate from multiple sources and can be of several types, carrying information about receptive-field properties such as spatial locations or features of items; but also carrying information about properties that are not easily mapped onto receptive fields, such as the meanings or timings of items. The chapter considers how selective biases can operate on multiple slates of information processing, not restricted to the immediate sensory-motor stream, but also operating within internalized, short-term and long-term memory representations. Selective attention appears to be a general property of information processing systems rather than an independent domain within our cognitive make-up.
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Thalamocortical Assemblies: How Ion Channels, Single Neurons and Large-Scale Networks Organize Sleep Oscillations. Oxford University Press, USA, 2001.
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Schadt, Eric E. Network Methods for Elucidating the Complexity of Common Human Diseases. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0002.
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The life sciences are now a significant contributor to the ever expanding digital universe of data, and stand poised to lead in both the generation of big data and the realization of dramatic benefit from it. We can now score variations in DNA across whole genomes; RNA levels and alternative isoforms, metabolite levels, protein levels, and protein state information across the transcriptome, metabolome and proteome; methylation status across the methylome; and construct extensive protein–protein and protein–DNA interaction maps, all in a comprehensive fashion and at the scale of populations of individuals. This chapter describes a number of analytical approaches aimed at inferring causal relationships among variables in very large-scale datasets by leveraging DNA variation as a systematic perturbation source. The causal inference procedures are also demonstrated to enhance the ability to reconstruct truly predictive, probabilistic causal gene networks that reflect the biological processes underlying complex phenotypes like disease.
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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVI: Special Issue on Data and Security Engineering. Springer, 2017.
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Duruflé, Gilles, Thomas Hellmann, and Karen Wilson. From Start-Up to Scale-Up. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198815815.003.0011.
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This chapter examines the challenge for entrepreneurial companies of going beyond the start-up phase and growing into large successful companies. We examine the long-term financing of these so-called scale-up companies, focusing on the United States, Europe, and Canada. The chapter first provides a conceptual framework for understanding the challenges of financing scale-ups. It emphasizes the need for investors with deep pockets, for smart money, for investor networks, and for patient money. It then shows some data about the various aspects of financing scale-ups in the United States, Europe, and Canada, showing how Europe and Canada are lagging behind the US relatively more at the scale-up than the start-up stage. Finally, the chapter raises the question of long-term public policies for supporting the creation of a better scale-up environment.
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Stromer-Galley, Jennifer. Presidential Campaigning in the Internet Age. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190694043.001.0001.
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Presidential candidates and their campaigns in the United States are fully invested in the use of social media. Yet, since 1996 presidential campaigns have been experimenting with ways to use digital communication technologies on the Internet to their advantage. This book tells the stories of the practices of campaigning online between 1996 and 2016, looking at winners and also-rans. The stories provide rich details of the factors that contribute to the success or failure of candidates, including the influence of digital media. The stories also show how political campaigns over six election cycles transitioned from the paradigm of mass media campaigning, to networked campaigning, and finally to mass-targeted campaigning. Campaigns shifted from efforts at mass persuasion to networked persuasion by identifying and communicating with super-supporters to give them the right digital tools and messages to take to their social network. Campaigns learned over time how to use the Internet’s interactive affordances to communicate with the public in ways that structures what supporters do for the campaign that maximizes strategic benefit—what I call “controlled interactivity.” By the 2016 campaign, technology companies made it easier and more effective to engage in mass-targeted campaigning—using large-scale data analytics by campaigns and tech companies to identify target audiences for campaigns to advertise to online.
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Tanasoca, Ana. Deliberation Naturalized. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198851479.001.0001.
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Democratic theory’s deliberative turn has hit a dead end. It is unable to find a good way to scale up its small-scale, formally organized deliberative mini-publics to include the entire community. Some turn to deliberative systems for a way out, but none have found a credible way to deliberatively involve the citizenry at large. Deliberation Naturalized offers an alternative way out—one we have been using all along. The key sites of democratic deliberation are everyday political conversations among people networked across the community. Informal networked deliberation is how all citizens deliberate together, directly or indirectly. That is how public opinion emerges in civil society. Networked deliberation satisfies the classic deliberative desiderata of inclusion, equality, and reciprocity reasonably well, albeit differently than standard mini-publics. Reconceptualizing democratic deliberation in this way highlights some real threats to the networked mode of deliberative democracy, such as polarization, message repetition, and pluralistic ignorance. Deliberation Naturalized assesses the extent of each of those threats and proposes ways of protecting real existing deliberative democracy against them. By focusing on the mechanisms underpinning every democratic deliberation among citizens, Deliberation Naturalized offers a truly novel approach to deliberative democracy.
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Todder, Doron, Keren Avirame, and Hagit Cohen. Neuromodulation Methods in PTSD. Edited by Charles B. Nemeroff and Charles R. Marmar. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190259440.003.0039.
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This chapter discusses the rationale and methodology for applying techniques of active and passive neuromodulation for treatment-refractory post-traumatic stress disorder (PTSD). Neuromodulation derives from the concept of neuroplasticity, which signifies long-term changes in the effectiveness of connections between distinct parts of the central nervous system. These changes are reflected across multiple levels of the nervous system, going from the cellular level to circuits and large-scale brain networks. It has been long suggested that altered neuroplasticity is a biomarker of neuropsychiatric diseases. With recent advances in neuroscience, research is emerging on evaluating the potential of modulating neural circuits by using innovative technologies, including noninvasive and invasive brain stimulation, EEG-neurofeedback, and fMRI neurofeedback.
Book chapters on the topic "Large-scale IoT network"
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Khalil, Maysa, Qasem Abu Al-Haija, and Samir Ahmad. "Healthcare IoT networks using LPWAN." In Low-Power Wide Area Network for Large Scale Internet of Things. CRC Press, 2024. http://dx.doi.org/10.1201/9781003426974-11.
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Ouaissa, Mariyam, Mariya Ouaissa, Zakaria Boulouard, Sarah El Himer, and Inam Ullah Khan. "Low-power wide area network for large-scale IoT." In Low-Power Wide Area Network for Large Scale Internet of Things. CRC Press, 2024. http://dx.doi.org/10.1201/9781003426974-1.
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Pullagura, Lokaiah, Nalli Vinaya Kumari, and Siva Kumar Gowda Katta. "LPWAN communication-based ML for IoT networks." In Low-Power Wide Area Network for Large Scale Internet of Things. CRC Press, 2024. http://dx.doi.org/10.1201/9781003426974-2.
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Siddiqui, Muhammad Farhan, Waqas Ahmed Siddique, Awais Khan Jumani, and Muhammad Arabi Tayyab. "SDN-based cloud computing secure communication in IoT systems." In Low-Power Wide Area Network for Large Scale Internet of Things. CRC Press, 2024. http://dx.doi.org/10.1201/9781003426974-9.
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Kanwal, Samina, Junaid Rashid, Samira Kanwal, Nasreen Anjum, and Sapna Juneja. "A study on IoT networks and machine learning using low-power wide area networks technology." In Low-Power Wide Area Network for Large Scale Internet of Things. CRC Press, 2024. http://dx.doi.org/10.1201/9781003426974-4.
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Pravinth Raja, S., Shaleen Bhatnagar, Ruchi Vyas, Thomas M. Chen, and Mithileysh Sathiyanarayanan. "Malicious Bot Detection in Large Scale IoT Network Using Unsupervised Machine Learning Technique." In Evolution in Computational Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6702-5_49.
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Wetzker, Ulf, Anna Richter, Vineeta Jain, and Jakob Wicht. "AI-assisted Condition Monitoring and Failure Analysis for Industrial Wireless Systems." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_17.
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AbstractWith the increasing proliferation of wireless devices and Internet-of- Things (IoT) applications in various fields, such as patient monitoring, vehicle-to everything (V2X) communication and industrial automation, there is a growing significance in developing robust methods and tools for evaluating and predicting link quality, monitoring information flow, as well as conducting failure analysis. This is particularly important in safety-critical industrial IoT (IIoT) environments such as smart factories, where challenging signal propagation conditions and interference from coexisting wireless technologies can severely impact network performance and application reliability. This contribution provides a comprehensive analysis of coexistence issues in industrial IIoT networks and highlights the complexities and challenges associated with performing failure analysis on a large scale. The necessity of using data-driven methods in the development of efficient and user-friendly failure analysis systems is discussed and the challenges regarding required datasets are highlighted.
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Rajab, Husam, and Tibor Cinkler. "Enhanced Energy Efficiency and Scalability in Cellular Networks for Massive IoT." In 5G and Beyond. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3668-7_13.
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AbstractThe significant expansion of cellular networks has increased their potential to support a wide range of use cases beyond their original purpose of providing broadband access. One such development is using cellular networks to support the Internet of Things (IoT), called Cellular IoT (CIoT). The growth of CIoT is an important trend in the evolution of cellular networks, it leads to broader and more comprehensive ecosystem circumstances. The extensive IoT business evolution is transforming a diverse sector, including health, smart cities, security, and agriculture. Nevertheless, a large scale with very different characteristics and use cases struggle with connectivity challenges due to the unique traffic features of massive IoT and the tremendous density of IoT devices. This study aims to identify the critical obstacles that hinder the widespread deployment of IoT over cellular networks and suggest an innovative algorithm to mitigate them effectively. We discovered that the primary challenges revolve around three specific areas: connection setup, network resource management, and energy consumption. In this regard, we investigate the integration of massive Machine-Type Communication (mMTC) into cellular networks, focusing on the performance of Narrowband IoT (NB-IoT) in supporting mMTC.
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Trabelsi, Rahma, Ghofrane Fersi, and Mohamed Jmaiel. "Virtual Private Network Blockchain-based Dynamic Access Control Solution for Inter-organisational Large Scale IoT Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31108-6_16.
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Ahmad, Awais, Muhammad Adeel Pasha, Shahid Masud, and Axel Sikora. "Design and Simulation of an Energy-efficient Sensor Network Routing Protocol for Large-scale Distributed Environmental Monitoring Systems." In Dependable IoT for Human and Industry. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337843-6.
Full textConference papers on the topic "Large-scale IoT network"
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Xiao, Jianhua, Shuyin Duan, Xiaobo Lin, et al. "Research on Collaborative System of Anti-Island Safety Protection Equipment for Large-Scale Distributed Photovoltaic Connected to Distribution Network." In 2024 2nd International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII). IEEE, 2024. http://dx.doi.org/10.1109/icmiii62623.2024.00155.
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Sodeinde, Tolu. "Digitalization of the Energy Sector through Transformative and Emerging Technologies." In MPWT 2019. NACE International, 2019. https://doi.org/10.5006/mpwt19-15486.
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Abstract Organizations are undergoing radical changes to improve their business performance to stay competitive in today’s digital environment. From the C-suite to shop floor, decision makers are going through radical rethinking (and exploring new ways) of how their organizations utilize and manage the intersection of technology, people, and processes to fundamentally change the way they do business, remain competitive, and maximize business value for their customers. The “fourth industrial revolution” (Industry 4.0) brought about digitization of data and information. With “internet of things” (IoT) sensors, companies are able to capture information that was inaccessible digitally prior and make it available for optimizing production, enhancing performance, and improving reliability of operating assets. Industrial IoT (IIoT) technologies are now commonplace in the energy sector; employed across upstream, downstream, and midstream assets, as well as in utilities. However, with increased deployments and adoption of IIoT also comes the need to deploy latest technologies in pursuit of new business models. The energy sector and particularly the oil and gas industry has traditionally been slow in adopting new and emerging technologies. Other sectors of the economy are undergoing disruption at a much faster pace than the energy sector, with new Startups using technology to radically change interaction of people and processes, develop new business models, and create value. However, in recent years, technology is being employed not only to replace manual processes with automated electronic systems, but to reinvent ways oil and gas companies do business, execute work functions, and interact with customers. Disruptive technologies are being harnessed and deployed at scale by connecting machines and intelligent devices to create a smart network of interconnected systems across the value chain. However, the depth of what can be achieved is yet to be fully harnessed. For instance, analyzing industry data collected on a large scale both at the edge and on cloud to identify operational patterns and make optimal business predictions across the value chain. The “fifth industrial revolution,” Industry 5.0 is the new emerging industrial technology that enables collaboration between humans and smart systems. Industry 4.0 lays the foundation for this next emerging technology that incorporates human behavior and cognitive capabilities into industry automation
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Andrade, Isha, Shalaka S. Mahadik, Pranav M. Pawar, and Raja Muthalagu. "Intelligent Intrusion Detection Using ML for Large-Scale IoT Networks." In 2024 Advances in Science and Engineering Technology International Conferences (ASET). IEEE, 2024. http://dx.doi.org/10.1109/aset60340.2024.10708701.
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Naik, G. Jagan, Kanchan Yadav, Perumalla Varaprasada Rao, N. Kalyana Sundaram, Myasar Mundher Adnan, and V. ViswaPriya. "AI-Based Energy-Efficient Routing Protocol for Large-Scale Underwater Wireless Sensor Networks." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923260.
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Fan, Xiao, Peiran Wu, Teng Sun, Junhui Zhao, and Minghua Xia. "UAV-Based Coverage Hole Detection and Recovery For Large-Scale IoT Networks." In 2024 IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE, 2024. http://dx.doi.org/10.1109/icccworkshops62562.2024.10693829.
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Xie, Haihui, Shuwu Chen, Teng Sun, Junhui Zhao, and Minghua Xia. "Collaborative Optimization for Resource-constrained Federated Learning in Large-scale IoT Networks." In 2024 IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE, 2024. http://dx.doi.org/10.1109/icccworkshops62562.2024.10693830.
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Lu, Xiaoqiang, Qian Zhang, and Haiqing Yao. "Time-constraint Multiple Mobile Chargers Scheduling Method for Large-scale Wireless Rechargeable Sensor Networks." In 2024 International Symposium on Sensing and Instrumentation in 5G and IoT Era (ISSI). IEEE, 2024. http://dx.doi.org/10.1109/issi63632.2024.10720476.
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Zeng, Qi, Kun Yang, and Zilong Liu. "Factor Graph Hopping Based FH-SCMA for Band-Limited and Large-Scale IoT Networks." In 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall). IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-fall63153.2024.10757957.
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Fujikawa, Kenji, and Hiroaki Harai. "Autoconfiguration of L3 network for large-scale IoT emulation testbed." In ICC 2017 - 2017 IEEE International Conference on Communications. IEEE, 2017. http://dx.doi.org/10.1109/icc.2017.7996642.
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Oyekanlu, Emmanuel, and Kevin Scoles. "Real-Time Distributed Computing at Network Edges for Large Scale Industrial IoT Networks." In 2018 IEEE World Congress on Services (SERVICES). IEEE, 2018. http://dx.doi.org/10.1109/services.2018.00045.
Full textReports on the topic "Large-scale IoT network"
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Phillips, Paul. The Application of Satellite-based Internet of Things for New Mobility. SAE International, 2024. http://dx.doi.org/10.4271/epr2024001.
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<div class="section abstract"><div class="htmlview paragraph">With the increased use of devices requiring the Internet of Things (IoT) to enable “New Mobility,” the demand for satellite-enabled IoT is growing steadily, owing to the extensive coverage provided by satellites (over existing ground-based infrastructure). Satellite-based IoT provides precise and real-time vehicle location and tracking services, large-scale geographical vehicle and/or infrastructure monitoring, and increased coverage for remote locations where it may not be possible to install ground-based solutions.</div><div class="htmlview paragraph"><b>The Application of Satellite-based Internet of Things for New Mobility</b> discusses satellite-based IoT topics that still need addressing, which can be broadly classifieds into two areas: (1) affordable technology and (2) network connectivity and data management. While recent innovations are driving down the cost of satellite-based IoT, it remains relatively expensive, and widespread adoption is still not as high as terrestrial, ground-based systems. Security concerns over data and privacy also create significant barriers to entry and need to be addressed along with issues such as intermittent connectivity, latency and bandwidth limitations, and data storage and processing restrictions.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>
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Pasupuleti, Murali Krishna. Scalable Quantum Networks: Entanglement-Driven Secure Communication. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi525.
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Abstract: Scalable quantum networks, powered by entanglement-driven secure communication, are poised to revolutionize global information exchange, cybersecurity, and quantum computing infrastructures. Unlike classical communication systems, quantum networks leverage quantum entanglement and superposition to enable ultra-secure data transmission, quantum key distribution (QKD), and instantaneous information sharing across large-scale networks. This research explores the fundamental principles of entanglement-based communication, the role of quantum repeaters, quantum memory, and multi-nodal entanglement distribution in overcoming photon loss, decoherence, and distance limitations in quantum networks. Additionally, it examines the hybrid integration of quantum-classical networking architectures, real-world experimental implementations such as satellite-based quantum communication and metropolitan-scale quantum cryptography, and the scalability challenges related to hardware, error correction, and network synchronization. The study also addresses post-quantum cryptography, quantum-resistant algorithms, and cybersecurity vulnerabilities in quantum communication, offering a comprehensive roadmap for the development of secure, scalable, and globally interconnected quantum networks. Keywords: Scalable quantum networks, quantum entanglement, entanglement distribution, quantum key distribution (QKD), secure communication, quantum repeaters, quantum memory, photon loss mitigation, quantum cryptography, post-quantum security, hybrid quantum-classical networks, metropolitan-scale quantum networks, satellite-based quantum communication, quantum internet, quantum coherence, quantum error correction, quantum teleportation, multi-nodal quantum entanglement, cybersecurity in quantum networks, quantum-resistant algorithms.
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Riley, Brad. Scaling up: Renewable energy on Aboriginal lands in north west Australia. Nulungu Research Institute, 2021. http://dx.doi.org/10.32613/nrp/2021.6.
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This paper examines renewable energy developments on Aboriginal lands in North-West Western Australia at three scales. It first examines the literature developing in relation to large scale renewable energy projects and the Native Title Act (1993)Cwlth. It then looks to the history of small community scale standalone systems. Finally, it examines locally adapted approaches to benefit sharing in remote utility owned networks. In doing so this paper foregrounds the importance of Aboriginal agency. It identifies Aboriginal decision making and economic inclusion as being key to policy and project development in the 'scaling up' of a transition to renewable energy resources in the North-West.
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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 computation. The study examines synaptic transistors, memristors, and AI-driven optimization techniques that enhance the performance of neuromorphic chips for autonomous AI, smart IoT systems, and real-time decision-making at the edge. Additionally, it discusses manufacturing challenges, economic feasibility, and policy implications related to large-scale adoption of 2D materials in nanoelectronics and semiconductor industries. Through case studies and emerging trends, this report provides a roadmap for integrating neuromorphic nanotech into mainstream AI-powered edge computing, ensuring scalability, sustainability, and high-performance intelligence for next-generation computing applications. Keywords: Neuromorphic computing, 2D materials, energy-efficient AI, edge computing, graphene, transition metal dichalcogenides, black phosphorus, MXenes, synaptic transistors, memristors, nanotechnology, low-power AI, spiking neural networks, AI-driven material discovery, quantum simulations, AI hardware optimization, semiconductor nanotech, real-time AI inference, autonomous AI, AI-powered IoT, sustainable computing.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Fourrier, Marine. Integration of in situ and satellite multi-platform data (estimation of carbon flux for trop. Atlantic). EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d7.6.
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This report presents the results of task 7.3 on “Quantification of improvements in carbon flux data for the tropical Atlantic based on the multi-platform and neural network approach”. To better constrain changes in the ocean’s capture and sequestration of CO2 emitted by human activities, in situ measurements are needed. Tropical regions are considered to be mostly sources of CO2 to the atmosphere due to specific circulation features, with large interannual variability mainly controlled by physical drivers (Padin et al., 2010). The tropical Atlantic is the second largest source, after the tropical Pacific, of CO2 to the atmosphere (Landschützer et al., 2014). However, it is not a homogeneous zone, as it is affected by many physical and biogeochemical processes that vary on many time scales and affect surrounding areas (Foltz et al., 2019). The Tropical Atlantic Observing System (TAOS) has progressed substantially over the past two decades. Still, many challenges and uncertainties remain to require further studies into the area’s role in terms of carbon fluxes (Foltz et al., 2019). Monitoring and sustained observations of surface oceanic CO2 are critical for understanding the fate of CO2 as it penetrates the ocean and during its sequestration at depth. This deliverable relies on different observing platforms deployed specifically as part of the EuroSea project (a Saildrone, and 5 pH-equipped BGC-Argo floats) as well as on the platforms as part of the TAOS (CO2-equipped moorings, cruises, models, and data products). It also builds on the work done in D7.1 and D7.2 on the deployment and quality control of pH-equipped BGC-Argo floats and Saildrone data. Indeed, high-quality homogeneously calibrated carbonate variable measurements are mandatory to be able to compute air-sea CO2 fluxes at a basin scale from multiple observing platforms. (EuroSea Deliverable, D7.6)
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Pasupuleti, Murali Krishna. Smart Nanomaterials and AI-Integrated Grids for Sustainable Renewable Energy. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1025.
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Abstract: The transition to sustainable and intelligent renewable energy systems is being driven by advancements in smart nanomaterials and AI-integrated smart grids. Nanotechnology has enabled the development of high-performance energy materials, such as graphene, perovskites, quantum dots, and MXenes, which enhance the efficiency, durability, and scalability of renewable energy solutions. Simultaneously, AI-driven smart grids leverage machine learning, deep learning, and digital twins to optimize energy distribution, predictive maintenance, and real-time load balancing in renewable energy networks. This research explores the synergistic integration of AI and nanomaterials to develop self-regulating, adaptive, and fault-tolerant energy infrastructures. The study examines AI-powered energy storage, decentralized smart microgrids, quantum AI for grid cybersecurity, and blockchain-integrated energy trading. Furthermore, the report assesses global industry adoption, policy frameworks, and economic growth trends, providing a strategic roadmap for the large-scale implementation of AI-enhanced nanomaterial-based energy systems. Through case studies and real-world applications, this research highlights how AI and nanotechnology will drive the next-generation sustainable energy revolution. Keywords Smart nanomaterials, AI-integrated grids, sustainable renewable energy, graphene-based solar cells, perovskite photovoltaics, quantum dots in energy, MXenes for energy storage, AI-driven energy optimization, machine learning for smart grids, deep learning energy forecasting, predictive maintenance in energy grids, digital twins for grid management, AI-powered decentralized microgrids, blockchain energy trading, hydrogen storage nanomaterials, AI-enhanced lithium-ion batteries, reinforcement learning in energy distribution, AI for demand-side energy management, quantum AI for grid cybersecurity, scalable nanomaterial-based energy solutions, AI-driven self-healing energy materials.
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Rémy, Elisabeth, Romain Escudier, and Alexandre Mignot. Access impact of observations. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d4.8.
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The accuracy of the Copernicus Marine Environment and Monitoring Service (CMEMS) ocean analysis and forecasts highly depend on the availability and quality of observations to be assimilated. In situ observations are complementary to satellite observations that are restricted to the ocean surface. Higher resolution model forecasts are required by users of the CMEMS global and regional ocean analysis and forecasts. To support this with an efficient observational constrain of the model forecast via data assimilation, an increase observation coverage is needed, associated with an improved usage of the available ocean observations. This work exploits the capabilities of operational systems to provide comprehensive information for the evolution of the GOOS. In this report, we analyse the use and the efficiency of the in-situ observations to constrain regional and global Mercator Ocean systems. Physical and biogeochemical variables are considered. The in-situ observations are used either to estimate physical ocean state at global and regional scale via data assimilation or to estimate BGC model parameters. The impact of the physical in situ observations assimilated in open ocean and coastal areas is assessed with numerical data assimilation experiments. The experiments are conducted with the regional 1/36° resolution and global 1/12° resolution systems operated by Mercator Ocean for the Copernicus Marine Service. For the global physical ocean, the focus is on the tropical ocean to better understand how the tropical mooring observations constrain the intraseasonal to daily variability and the complementarity with satellite observations and the deep ocean. The tropical moorings provide unique high frequency observations at different depth, but they are far away from each other, so part of the signal in the observation are decorrelated from one mooring to the others. It is only via an integrated approach, as data assimilation into a dynamical model and complementarity with other observing networks that those observations can efficiently constrain the different scales of variability of the tropical ocean circulation. As the satellite observations brings higher spatial resolution between the tropical moorings but for the ocean surface, we show that the tropical mooring and Argo profile data assimilation constrain the larger scale ocean thermohaline vertical structure (EuroSea D2.2; Gasparin et al., 2023). The representation of the high frequency signals observed at mooring location is also significantly improved in the model analysis compared to a non-assimilative simulation. The ocean below 2000 m depth is still largely under constrained as very few observations exist. Some deep ocean basins, as the Antarctic deep ocean, shows significant trend over the past decade but they are still not accurately monitored. Based on the spread of four deep ocean reanalysis estimates, large uncertainties were estimated in representing local heat and freshwater content in the deep ocean. Additionally, temperature and salinity field comparison with deep Argo observations demonstrates that reanalysis errors in the deep ocean are of the same size as or even stronger than the observed deep ocean signal. OSSE already suggested that the deployment of a global deep Argo array will significantly constrain the deep ocean in reanalysis to be closer to the observations (Gasparin et al., 2020). At regional and coastal scales, the physical ocean circulation is dominated by higher frequency, smaller scale processes than the open ocean which requires different observation strategy to be well monitor. The impact of assimilating high frequency and high-resolution observations provided by gliders on European shelves is analysed with the regional Iberic Biscay and Irish (IBI) system. It was found that repetitive glider sections can efficiently help to constrain the transport of water masses flowing across those sections. BGC ocean models are less mature than physical ocean models and some variable dependencies are still based on empirical functions. In this task, Argo BGC profile observations were used to optimize the parameters of the global CMEMS biogeochemical model, PISCES. A particle filter algorithm was chosen to optimize a 1D configuration of PISCES in the North Atlantic. The optimization of the PISCES 1D model significantly improves the model's ability to reproduce the North Atlantic bloom Recommendations on the in-situ network extensions for real time ocean monitoring are given based on those results, and the one also obtained in the WP2, Task 2.2 where data assimilation experiments but with simulated observations where conducted. Argo extension and the complementarity with satellite altimetry was also extensively studied. (EuroSea Deliverable ; D4.8)
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Cooper, Christopher, Jacob McDonald, Eric Starkey, and Wendy Wright. Wadeable stream habitat monitoring at Ocmulgee Mounds National Historical Park: 2017 baseline report. National Park Service, 2019. https://doi.org/10.36967/2268263.
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The Southeast Coast Network (SECN) stream habitat monitoring protocol collects data to give park resource managers insight into the status of and trends in stream and near-channel habitat conditions (McDonald et al. 2018a). Wadeable stream assessments are currently implemented at the five SECN inland parks with wadeable streams. These parks include Horseshoe Bend National Military Park (HOBE), Kennesaw Mountain National Battlefield (KEMO), Ocmulgee Mounds National Historical Park (OCMU), Chattahoochee River National Recreation Area (CHAT), and Congaree National Park (CONG). Streams chosen for assessment were specifically targeted for management interest (e.g., trail erosion issues and upstream urban development) or to provide a context for similar-sized stream(s) within the park or network (McDonald and Starkey 2019a). The objectives of the SECN stream habitat monitoring protocol are to: • Determine status of upstream watershed characteristics (basin morphology) and trends in land cover that may affect stream habitat; • Determine the status of and trends in benthic and near-channel habitat in selected wadeable stream reaches (e.g., bed sediment, geomorphic channel units [GCUs], and large woody debris [LWD]); and • Determine the status of and trends in cross-sectional morphology, longitudinal gradient, and sinuosity of selected wadeable stream reaches. On September 27, 2017, data were collected at Ocmulgee Mounds National Historical Park (OCMU) to characterize the in- and near-channel habitat on a permanently monumented reach on Walnut Creek (OCMU001). These data, along with in-office analysis of remotely-sensed geographic information system (GIS) data, are presented in this report to describe and compare the watershed-, reach-, and transect-scale characteristics of OCMU001 to selected third-order streams at Chattahoochee River National Recreation Area (CHAT001 and CHAT013) and Kennesaw Mountain National Battlefield Park (KEMO001 and KEMO002). Reach OCMU001’s watershed (Walnut Creek) has low slope/relief and is covered mostly by forest and agricultural pasture. The major defining characteristics of the stream were the presence of floodplains, natural levees, lack of riffle habitat, and homogeneous channel slope throughout the reach. Though the majority of the watershed lies in the Appalachian Piedmont physiographic province, the OCMU001 reach of Walnut Creek lies in the Upper Coastal Plain and is a low energy, depositional environment which is subject to regular flooding and slow post-flood draining. The channel surveyed at OCMU001 is smaller than expected, given the large size of its watershed. The small channel size is interpreted as evidence that the majority of flood waters that come through Ocmulgee Mounds NHP by way of Walnut Creek do not follow this flow path. Alternatively, flood waters are diverted away from the hillslope, into the wetlands to the west of OCMU001 and into a power line right-of-way east of OCMU001, through a series of flood chutes and side channels. It is suggested that discharge monitoring stations be installed at the railroad bridge that crosses Walnut Creek (upstream of OCMU001), just upstream of OCMU001, and immediately downstream of OCMU001 to determine the proportion of flow that bypasses the channel at OCMU001 and to determine how much floodwater enters OCMU001 through flood chutes and side channels. Though the reach had a high abundance of large woody debris (LWD), based on the results of the pebble count and the type/distribution of the geomorphic channel unit (GCU) within the reach, in-stream habitat within the surveyed stream was classified as “poor to fair.” These classifications are generalized interpretations of in-stream habitat and additional work is needed to determine how flow (e.g., flashiness of flow and baseflow conditions), water quality, and in-stream physical characteristics affect biodiversity and stream health at Ocmulgee Mounds NHP. Given the complex hydrologic setting of OCMU
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Lucas, Brian. Approaches to Implementing National Action Plans on Women, Peace and Security. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/k4d.2022.049.
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This report aims to identify a selection of programmes and projects undertaken by countries under their respective National Action Plans. It focuses on discrete, large-scale initiatives that specifically target aspects of the WPS agenda and aim to influence change outside the implementing agencies, rather than changing agencies’ own policies and practices. Common themes that appear frequently across these programmes and projects include: supporting global pools of technical capacity on WPS and on peacebuilding generally; training military, police, and other personnel from partner countries, including building women’s professional capacities as well as training personnel in WPS-related good practices; supporting WPS networks and forums to share experience and expertise; extensive use of multilateral mechanisms for channelling funding and for sharing technical capacity; extensive support to and collaboration with civil society organisations; initiatives focusing on combating violent extremism and counter-terrorism; initiatives focusing on preventing sexual exploitation and abuse in peacekeeping and humanitarian contexts; a wide range of commitments to stopping gender-based violence; and support for sexual and reproductive health initiatives. All of the countries discussed in this report also undertake considerable efforts to change policies and practices within their own agencies. In addition, all of the countries discussed in this report undertake a range of initiatives focused on individual countries; smaller donors, in particular, often focus many of their own programmes on single countries while using multilateral mechanisms to engage at the regional and global scales. However, in accordance with the terms of reference for this report, these types of activities are not discussed below. In the time available for this report, it was possible to review six countries’ activities. These countries were selected for inclusion because they had sufficient documentation readily accessible in the form of action plans, implementation plans, and progress reports; they are donor countries with significant international activities that may be considered peers to the UK; and/or they have been cited in the literature as being leaders in promoting the WPS agenda.