Relevant bibliographies by topics / Network embedded cloud

Contents

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

Academic literature on the topic 'Network embedded cloud'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Network embedded cloud"

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Wang, Jinglu, Bo Sun, and Yan Lu. "MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8949–56. http://dx.doi.org/10.1609/aaai.v33i01.33018949.

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In this paper, we address the problem of reconstructing an object’s surface from a single image using generative networks. First, we represent a 3D surface with an aggregation of dense point clouds from multiple views. Each point cloud is embedded in a regular 2D grid aligned on an image plane of a viewpoint, making the point cloud convolution-favored and ordered so as to fit into deep network architectures. The point clouds can be easily triangulated by exploiting connectivities of the 2D grids to form mesh-based surfaces. Second, we propose an encoder-decoder network that generates such kind of multiple view-dependent point clouds from a single image by regressing their 3D coordinates and visibilities. We also introduce a novel geometric loss that is able to interpret discrepancy over 3D surfaces as opposed to 2D projective planes, resorting to the surface discretization on the constructed meshes. We demonstrate that the multi-view point regression network outperforms state-of-the-art methods with a significant improvement on challenging datasets.
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Yan, Yan, Yuxing Mao, and Bo Li. "SECOND: Sparsely Embedded Convolutional Detection." Sensors 18, no. 10 (2018): 3337. http://dx.doi.org/10.3390/s18103337.

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LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when processing point cloud LiDAR data. However, problems remain, including a slow inference speed and low orientation estimation performance. We therefore investigate an improved sparse convolution method for such networks, which significantly increases the speed of both training and inference. We also introduce a new form of angle loss regression to improve the orientation estimation performance and a new data augmentation approach that can enhance the convergence speed and performance. The proposed network produces state-of-the-art results on the KITTI 3D object detection benchmarks while maintaining a fast inference speed.
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Ghassemi, Sina, and Enrico Magli. "Convolutional Neural Networks for On-Board Cloud Screening." Remote Sensing 11, no. 12 (2019): 1417. http://dx.doi.org/10.3390/rs11121417.

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A cloud screening unit on a satellite platform for Earth observation can play an important role in optimizing communication resources by selecting images with interesting content while skipping those that are highly contaminated by clouds. In this study, we address the cloud screening problem by investigating an encoder–decoder convolutional neural network (CNN). CNNs usually employ millions of parameters to provide high accuracy; on the other hand, the satellite platform imposes hardware constraints on the processing unit. Hence, to allow an onboard implementation, we investigate experimentally several solutions to reduce the resource consumption by CNN while preserving its classification accuracy. We experimentally explore approaches such as halving the computation precision, using fewer spectral bands, reducing the input size, decreasing the number of network filters and also making use of shallower networks, with the constraint that the resulting CNN must have sufficiently small memory footprint to fit the memory of a low-power accelerator for embedded systems. The trade-off between the network performance and resource consumption has been studied over the publicly available SPARCS dataset. Finally, we show that the proposed network can be implemented on the satellite board while performing with reasonably high accuracy compared with the state-of-the-art.
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Zhang, Chunjiao, Shenghua Xu, Tao Jiang, et al. "Integrating Normal Vector Features into an Atrous Convolution Residual Network for LiDAR Point Cloud Classification." Remote Sensing 13, no. 17 (2021): 3427. http://dx.doi.org/10.3390/rs13173427.

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LiDAR point clouds are rich in spatial information and can effectively express the size, shape, position, and direction of objects; thus, they have the advantage of high spatial utilization. The point cloud focuses on describing the shape of the external surface of the object itself and will not store useless redundant information to describe the occupation. Therefore, point clouds have become the research focus of 3D data models and are widely used in large-scale scene reconstruction, virtual reality, digital elevation model production, and other fields. Since point clouds have various characteristics, such as disorder, density inconsistency, unstructuredness, and incomplete information, point cloud classification is still complex and challenging. To realize the semantic classification of LiDAR point clouds in complex scenarios, this paper proposes the integration of normal vector features into an atrous convolution residual network. Based on the RandLA-Net network structure, the proposed network integrates the atrous convolution into the residual module to extract global and local features of the point clouds. The atrous convolution can learn more valuable point cloud feature information by expanding the receptive field. Then, the point cloud normal vector is embedded in the local feature aggregation module of the RandLA-Net network to extract local semantic aggregation features. The improved local feature aggregation module can merge the deep features of the point cloud and mine the fine-grained information of the point cloud to improve the model’s segmentation ability in complex scenes. Finally, to resolve the imbalance of the distribution of the various categories of point clouds, the original loss function is optimized by adopting a reweighted method to prevent overfitting so that the network can focus on small target categories in the training process to effectively improve the classification performance. Through the experimental analysis of a Vaihingen (Germany) urban 3D semantic dataset from the ISPRS website, it is verified that the proposed algorithm has a strong generalization ability. The overall accuracy (OA) of the proposed algorithm on the Vaihingen urban 3D semantic dataset reached 97.9%, and the average reached 96.1%. Experiments show that the proposed algorithm fully exploits the semantic features of point clouds and effectively improves the accuracy of point cloud classification.
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Dybedal, Joacim, Atle Aalerud, and Geir Hovland. "Embedded Processing and Compression of 3D Sensor Data for Large Scale Industrial Environments." Sensors 19, no. 3 (2019): 636. http://dx.doi.org/10.3390/s19030636.

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This paper presents a scalable embedded solution for processing and transferring 3D point cloud data. Sensors based on the time-of-flight principle generate data which are processed on a local embedded computer and compressed using an octree-based scheme. The compressed data is transferred to a central node where the individual point clouds from several nodes are decompressed and filtered based on a novel method for generating intensity values for sensors which do not natively produce such a value. The paper presents experimental results from a relatively large industrial robot cell with an approximate size of 10 m × 10 m × 4 m. The main advantage of processing point cloud data locally on the nodes is scalability. The proposed solution could, with a dedicated Gigabit Ethernet local network, be scaled up to approximately 440 sensor nodes, only limited by the processing power of the central node that is receiving the compressed data from the local nodes. A compression ratio of 40.5 was obtained when compressing a point cloud stream from a single Microsoft Kinect V2 sensor using an octree resolution of 4 cm.
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Hu, Jin-Xin, Chin-Ling Chen, Chun-Long Fan, and Kun-hao Wang. "An Intelligent and Secure Health Monitoring Scheme Using IoT Sensor Based on Cloud Computing." Journal of Sensors 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3734764.

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Internet of Things (IoT) is the network of physical objects where information and communication technology connect multiple embedded devices to the Internet for collecting and exchanging data. An important advancement is the ability to connect such devices to large resource pools such as cloud. The integration of embedded devices and cloud servers offers wide applicability of IoT to many areas of our life. With the aging population increasing every day, embedded devices with cloud server can provide the elderly with more flexible service without the need to visit hospitals. Despite the advantages of the sensor-cloud model, it still has various security threats. Therefore, the design and integration of security issues, like authentication and data confidentiality for ensuring the elderly’s privacy, need to be taken into consideration. In this paper, an intelligent and secure health monitoring scheme using IoT sensor based on cloud computing and cryptography is proposed. The proposed scheme achieves authentication and provides essential security requirements.
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Lee, Hyunsoo. "Effective Dynamic Control Strategy of a Key Supplier with Multiple Downstream Manufacturers Using Industrial Internet of Things and Cloud System." Processes 7, no. 3 (2019): 172. http://dx.doi.org/10.3390/pr7030172.

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Intelligent data analytics-based cloud computing is a leading trend for managing a large-scale network in contemporary manufacturing environments. The data and information are shared using the cloud environments and valuable knowledge is driven using the embedded intelligence analytics. This research applied this trend to the control of a key supplier’s real-time production planning for solving joint production goals with downstream producers. As a key supplier has several downstream producers in general, several uncertainties are embedded on the supply chain network such as the quality issue in the supplier and the occurrence of unexpected orders from the downstream industries. While the control of a supply plan is difficult considering these dynamics in traditional frameworks, the proposed framework detects the dynamic changes accurately using the constructed cloud system. Moreover, the real-time control considering uncertain scenarios as well as the extracted knowledge is achieved using the provided Industrial Internet of Things (IIoT) and simulation-based control model using stochastic network. To show the effective of the suggested framework, real manufacturing cases and their numerical analyses are provided.
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Issac, Namitha, Anandmayee Tej, Tie Liu, and Yuefang Wu. "G133.50+9.01: a likely cloud–cloud collision complex triggering the formation of filaments, cores, and a stellar cluster." Monthly Notices of the Royal Astronomical Society 499, no. 3 (2020): 3620–29. http://dx.doi.org/10.1093/mnras/staa3061.

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ABSTRACT We present compelling observational evidence of G133.50+9.01 being a bona fide cloud–cloud collision candidate with signatures of induced filament, core, and cluster formation. The CO molecular line observations reveal that the G133.50+9.01 complex is made of two colliding molecular clouds with systemic velocities, $\rm -16.9$ and $\rm -14.1\, km\, s^{-1}$. The intersection of the clouds is characterized by broad bridging features characteristic of collision. The morphology of the shocked layer at the interaction front resembles an arc-like structure with enhanced excitation temperature and H2 column density. A complex network of filaments is detected in the Submillimeter Common-User Bolometer Array 2 850 $\rm \mu m$ image with 14 embedded dense cores, all well correlated spatially with the shocked layer. A stellar cluster revealed through an overdensity of identified Classes I and II young stellar objects is found located along the arc in the intersection region corroborating with a likely collision induced origin.
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Espinosa-Aranda, Jose, Noelia Vallez, Jose Rico-Saavedra, et al. "Smart Doll: Emotion Recognition Using Embedded Deep Learning." Symmetry 10, no. 9 (2018): 387. http://dx.doi.org/10.3390/sym10090387.

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Computer vision and deep learning are clearly demonstrating a capability to create engaging cognitive applications and services. However, these applications have been mostly confined to powerful Graphic Processing Units (GPUs) or the cloud due to their demanding computational requirements. Cloud processing has obvious bandwidth, energy consumption and privacy issues. The Eyes of Things (EoT) is a powerful and versatile embedded computer vision platform which allows the user to develop artificial vision and deep learning applications that analyse images locally. In this article, we use the deep learning capabilities of an EoT device for a real-life facial informatics application: a doll capable of recognizing emotions, using deep learning techniques, and acting accordingly. The main impact and significance of the presented application is in showing that a toy can now do advanced processing locally, without the need of further computation in the cloud, thus reducing latency and removing most of the ethical issues involved. Finally, the performance of the convolutional neural network developed for that purpose is studied and a pilot was conducted on a panel of 12 children aged between four and ten years old to test the doll.
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Srirampavan, J. "Smart Secured Real Time Agriculture Monitoring System." International Journal of Engineering & Technology 7, no. 3.6 (2018): 281. http://dx.doi.org/10.14419/ijet.v7i3.6.15043.

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Embedded systems in Agriculture play a vital role in unifying the work involved and improve conservations. Designing a smart as well as a cost efficient and more user-friendly system will be idealistic challenge. The following system that has been proposed is designed with those ideal constraints in mind. It consists of a Raspberry pi3 as a gateway that links the sensor networks with the cloud. To improve security an MQTT protocol is used for cloud connectivity. The communication between the sensor networks is managed by NRF24L01. The Sensor network is a separate entity that can used like a plug and play device and is built by a micro controller with a LCD display and an interfaced GPS. Multicasting is also possible between sensor networks and the gateway. The processed data from the sensor networks is sent through NRF24L01 to the gateway. The gateway further processes and encapsulates the data and through MQTT the data gets stored on the cloud. This cloud data can be accessed through computer or mobile device
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Dissertations / Theses on the topic "Network embedded cloud"

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Roozbeh, Amir. "Resource monitoring in a Network Embedded Cloud : An extension to OSPF-TE." Thesis, KTH, Radio Systems Laboratory (RS Lab), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-124367.

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The notions of "network embedded cloud", also known as a "network enabled cloud" or a "carrier cloud", is an emerging technology trend aiming to integrate network services while exploiting the on-demand nature of the cloud paradigm. A network embedded cloud is a distributed cloud environment where data centers are distributed at the edge of the operator's network. Distributing data centers or computing resources across the network introduces topological and geographical locality dependency. In the case of a network enabled cloud, in addition to the information regarding available processing, memory, and storage capacity, resource management requires information regarding the network's topology and available bandwidth on the links connecting the different nodes of the distributed cloud. This thesis project designed, implemented, and evaluated the use of open shortest path first with traffic engineering (OSPF-TE) for propagating the resource status in a network enabled cloud. The information carried over OSPF-TE are used for network-aware scheduling of virtual machines. In particular, OSPF-TE was extended to convey virtualization and processing related information to all the nodes in the network enabled cloud. Modeling, emulation, and analysis shows the proposed solution can provide the required data to a cloud management system by sending a data center's resources information in the form of new opaque link-state advertisement with a minimum interval of 5 seconds. In this case, each embedded data centers injects a maximum 38.4 bytes per second of additional traffic in to the network.&lt;p&gt;<br>Ett "network embedded cloud", även känt som ett "network enabled cloud" eller ett "carrier cloud", är en ny teknik trend som syftar till att tillhandahålla nätverkstjänster medan on-demand egenskapen av moln-paradigmet utnyttjas.  Traditionella telekommunikationsapplikationer bygger ofta på en distributed service model och kan använda ett "network enabled cloud" som dess exekverande plattform. Dock kommer sådana inbäddade servrar av naturliga skäl vara geografiskt utspridda, varför de är beroende av topologisk och geografisk lokalisering. Detta ändrar på resurshanteringsproblemet jämfört med resurshantering i datacentrum. I de fall med ett network enabled cloud, utöver informationen om tillgängliga CPU, RAM och lagring, behöver resursfördelningsfunktionen information om nätverkets topologi och tillgänglig bandbredd på länkarna som förbinder de olika noderna i det distribuerade molnet. Detta examensarbete har utformat, tillämpat och utvärderat ett experiment-orienterad undersökning av användningen av open shortest path first med traffich engineering (OSPF-TE) för resurshantering i det network enabled cloud. I synnerhet utvidgades OSPF-TE till att förmedla virtualisering och behandla relaterad information till alla noder i nätverket. Detta examensarbete utvärderar genomförbarheten och lämpligheten av denna metod, dess flexibilitet och prestanda. Analysen visade att den föreslagna lösningen kan förse nödvändiga uppgifter till cloud management system genom att skicka ett datacenters resursinformation i form av ny opaque LSA (kallat Cloud LSA) med ett minimumintervall av 5 sekunder och maximal nätverksbelastning av 38,4 byte per sekund per inbäddade data center.
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Karlbäck, Rasmus, and Anton Orö. "Holistic View on Alternative Programming languages for Radio Access Network Applications in Cloud and Embedded Deployments." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176307.

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With the emergence of cloud based solutions, companies such as Ericsson AB have started investigating different means of modernizing current implementations of software systems. With many new programming languages emerging such as Rust and Go, investigating the suitability of these languages compared to C++ can be seen as a part of this modernization process. There are many important aspects to consider when investigating the suitability of new programming languages, and this thesis makes an attempt at considering most of them. Therefore both performance which is a common metric as well as development efficiency which is a less common metric, were combined to provide a holistic view. Performance was defined as CPU usage, maximum memory usage, processing time per sequence and latency at runtime, which was measured on both x86 and ARM based hardware. Development efficiency was defined as the combination of the productivity metric, the maintainability index metric and the cognitive complexity metric. Combining these two metrics resulted in two general guidelines: if the application is constantly under change and performance is not critical, Go should be the language of choice. If instead performance is critical C++ should be the language of choice. Overall, when choosing a suitable programming language, one needs to weigh development efficiency against performance to make a decision.
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Huang, Tzu-Wei, and 黃子維. "Design and Implementation of Workload Migration Mechanism between Networked Embedded System and Cloud Server." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/68924344885967577603.

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碩士<br>國立臺灣大學<br>資訊網路與多媒體研究所<br>99<br>With the improvement of embedded system, mobile devices have become secondary computing devices of most people. There are more and more applications on mobile devices to solve multifarious problems in daily life. Unfortunately, hardware is limited in terms processor frequency, memory size, power consumption, bandwidth of wireless network, thus limiting the potentiality of applications. Thus, cloud computing technology is used to augment the capability of mobile devices. However, the traditional cloud-based mobile applications cause some issues such as network condition and service availability, privacy of personal data and information security. This research aims at the design and the implementation of a workload migration system. We suppose that there are virtual/physical machines with the same instruction set and operation system as those of the mobile device on the cloud. Our system provides an interface for users to migrate the workloads of applications from the mobile device to the machine on cloud. In addition, the users can execute the workloads locally on the device to keep the availability when the network condition is pretty bad. Furthermore, we design and implement a streaming execution mechanism. With this mechanism, we can reduce the migration overhead by overlapping the transmission and the omputation.
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Book chapters on the topic "Network embedded cloud"

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Prathyusha, Damai Jessica, and K. Govinda. "Analysis of Network Flow for Mitigation of DDoS Attacks in a Cloud Environment." In Embedded Systems and Artificial Intelligence. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0947-6_79.

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Qiao, Yanhua, Lin Zhao, and Jianna Li. "Multi-path Channel Modeling and Analysis of Embedded LTE Wireless Communication Network Under Cloud Computing." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36402-1_8.

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Cassimon, Thomas, Simon Vanneste, Stig Bosmans, Siegfried Mercelis, and Peter Hellinckx. "Using Neural Architecture Search to Optimize Neural Networks for Embedded Devices." In Advances on P2P, Parallel, Grid, Cloud and Internet Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33509-0_64.

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Raj, Pethuru. "The Network Infrastructures for Big Data Analytics." In Cloud Technology. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6539-2.ch045.

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The most delectable factor here is that the stability and maturity of networking and communication technologies enable the seamless and spontaneous interconnectivity of diverse and distributed consumer electronics, electrical, mechanical, and manufacturing devices at ground level and a bevy of services (Web, enterprise, cloud, embedded, analytical, etc.) at cyber level. Any tangible artefact and article gets connected with another to get the right and relevant empowerment, which in turn facilitates more data generation and transmission. Regulated interactions amongst digitalized entities have put a stimulating foundation for hitherto unforeseen and creative new capabilities and competencies. In short, data has grandly acquired the status of an asset not only in business organizations but also in personal lives, and hence, the data gathering, storage, and leverage tasks are fast-growing. With the data explosion happening feverishly, the discipline of big data computing and analytics has become a much-discoursed and deliberated domain of study and research. In this chapter, the authors discuss the emerging and evolving network infrastructures and architectures for big data analytics.
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Arslan, Ayse Kok. "A Design Model of Embedded Engineering Learning on Social Cloud." In Analyzing Human Behavior in Cyberspace. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7128-5.ch006.

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Based on the results of the evaluation of an embedded engineering learning on social cloud model, the author suggests whether an “Imagineering” approach to learning is and complies with design principles leading to creative products. It can also provide an evidence for whether the SC supports co-learning environments which contributes to the efficiency of the process. Not only training institutions, but also knowledge enterprises should have a ready infrastructure for network systems to access the cloud technology. This chapter discusses the options of a design model on social cloud.
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Garg, Pradeep Kumar. "The Internet of Things-Based Technologies." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4685-7.ch003.

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The internet of things (IoT) is the network of physical objects—devices, vehicles, buildings, and other objects—embedded with software, electronic devices, sensors, and network connectivity that enable these objects to collect and share information or data. Its applications include smart homes, healthcare, industries, transportation systems, logistics, and energy. Building an IoT real-time-based application involves the proper selection of combination of sensors, technology, networks, and communication modules, supported with the concepts of data processing, remote sensing, cloud computing, etc. This chapter highlights advantages and disadvantages IoT and various techniques, such as computer vision, remote sensing, artificial intelligence, cloud computing, big data, ubiquitous computing, which are widely used in various applications. Many new IoT-based applications will evolve, as new devices, sensors, chips, and computational techniques are developed.
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Li, Lei, Min Feng, Lianwen Jin, Shenjin Chen, Lihong Ma, and Jiakai Gao. "Domain Knowledge Embedding Regularization Neural Networks for Workload Prediction and Analysis in Cloud Computing." In Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5339-8.ch055.

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Online services are now commonly deployed via cloud computing based on Infrastructure as a Service (IaaS) to Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). However, workload is not constant over time, so guaranteeing the quality of service (QoS) and resource cost-effectiveness, which is determined by on-demand workload resource requirements, is a challenging issue. In this article, the authors propose a neural network-based-method termed domain knowledge embedding regularization neural networks (DKRNN) for large-scale workload prediction. Based on analyzing the statistical properties of a real large-scale workload, domain knowledge, which provides extended information about workload changes, is embedded into artificial neural networks (ANN) for linear regression to improve prediction accuracy. Furthermore, the regularization with noisy is combined to improve the generalization ability of artificial neural networks. The experiments demonstrate that the model can achieve more accuracy of workload prediction, provide more adaptive resource for higher resource cost effectiveness and have less impact on the QoS.
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Raj, Pethuru. "The Network Infrastructures for Big Data Analytics." In Advances in Data Mining and Database Management. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5864-6.ch007.

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The most delectable factor here is that the stability and maturity of networking and communication technologies enable the seamless and spontaneous interconnectivity of diverse and distributed consumer electronics, electrical, mechanical, and manufacturing devices at ground level and a bevy of services (Web, enterprise, cloud, embedded, analytical, etc.) at cyber level. Any tangible artefact and article gets connected with another to get the right and relevant empowerment, which in turn facilitates more data generation and transmission. Regulated interactions amongst digitalized entities have put a stimulating foundation for hitherto unforeseen and creative new capabilities and competencies. In short, data has grandly acquired the status of an asset not only in business organizations but also in personal lives, and hence, the data gathering, storage, and leverage tasks are fast-growing. With the data explosion happening feverishly, the discipline of big data computing and analytics has become a much-discoursed and deliberated domain of study and research. In this chapter, the authors discuss the emerging and evolving network infrastructures and architectures for big data analytics.
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Bhargavi, Peyakunta, and Singaraju Jyothi. "Object Detection in Fog Computing Using Machine Learning Algorithms." In Advances in Computer and Electrical Engineering. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0194-8.ch006.

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The moment we live in today demands the convergence of the cloud computing, fog computing, machine learning, and IoT to explore new technological solutions. Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the end users. Machine learning is a subfield of computer science and is a type of artificial intelligence (AI) that provides machines with the ability to learn without explicit programming. IoT has the ability to make decisions and take actions autonomously based on algorithmic sensing to acquire sensor data. These embedded capabilities will range across the entire spectrum of algorithmic approaches that is associated with machine learning. Here the authors explore how machine learning methods have been used to deploy the object detection, text detection in an image, and incorporated for better fulfillment of requirements in fog computing.
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Babu, R., K. Jayashree, and R. Abirami. "Fog Computing Qos Review and Open Challenges." In Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5339-8.ch054.

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Internet of Things (IoT) enables inters connectivity among devices and platforms. IoT devices such as sensors, or embedded systems offer computational, storage, and networking resources and the existence of these resources permits to move the execution of IoT applications to the edge of the network and it is known as fog computing. It is able to handle billions of Internet-connected devices and is well situated for real-time big data analytics and provides advantages in advertising and personal computing. The main issues in fog computing includes fog networking, QoS, interfacing and programming model, computation offloading, accounting, billing and monitoring, provisioning and resource management, security and privacy. A particular research challenge is the Quality of Service metric for fog services. Thus, this paper gives a survey of cloud computing, discusses the QoS metrics, and the future research directions in fog computing.
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Conference papers on the topic "Network embedded cloud"

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Chou, Yu-Cheng. "Sensor Agent Cloud: A Cloud-Based Autonomic System for Physical Sensor Nodes Management." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48732.

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An embedded sensor network is a network of sensor nodes deployed in the physical world that interacts with the environment. Each sensor node is a physically small and relatively inexpensive computer that has one or more sensors. These sensor nodes are often networked, allowing them to communicate and cooperate with each other to monitor the environment. Typically, an embedded sensor network is controlled by its own applications that can access the sensor nodes within the network. On the other hand, the sensor nodes cannot be easily accessed by applications outside of the network. Moreover, even within the same network, different applications might encounter a race condition when they are trying to access a sensor node simultaneously. The issue is related to system management. However, not much research has been done with a focus on the management of sensor nodes. In the past few years, Cloud computing has emerged as a new computing paradigm to provide reliable resources, software, and data on demand. As for resources, essentially, Cloud computing services provide users with virtual servers. Users can utilize virtual servers without concerning about their locations and specifications. With such an inspiration, this paper proposes a system, Sensor Agent Cloud, where users can access the sensor nodes without worrying about their locations and detailed specifications. Sensor Agent Cloud virtualizes a physical sensor node as a virtual “sensor agent”. Users can use and control sensor agents with standard functions. Each sensor agent operates on behalf of its user. The mandatory coordination of these sensor agents is related to the system management. Therefore, Sensor Agent Cloud must be an autonomic system that manages itself with minimum human interference. In addition, Sensor Agent Cloud supports international standard technologies regarding programming and agent communication (C and IEEE FIPA standard). Thus, it is expected that the proposed Sensor Agent Cloud can enhance the applicability and usability of embedded sensor networks in many application areas.
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Riliskis, Laurynas, and Philip Levis. "Ravel a framework for embedded-gateway-cloud applications." In SenSys '14: The 12th ACM Conference on Embedded Network Sensor Systems. ACM, 2014. http://dx.doi.org/10.1145/2668332.2668356.

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Chen, JiaYou, Hong Guo, and Wei Hu. "Research on Improving Network Security of Embedded System." In 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). IEEE, 2019. http://dx.doi.org/10.1109/cscloud/edgecom.2019.000-6.

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Ezdiani, Syarifah, Indrajit S. Acharyya, Sivaramakrishnan Sivakumar, and Adnan Al-Anbuky. "An Architectural Concept for Sensor Cloud QoSaaS Testbed." In SenSys '15: The 13th ACM Conference on Embedded Network Sensor Systems. ACM, 2015. http://dx.doi.org/10.1145/2820990.2820996.

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Miao, Chenglin, Wenjun Jiang, Lu Su, et al. "Cloud-Enabled Privacy-Preserving Truth Discovery in Crowd Sensing Systems." In SenSys '15: The 13th ACM Conference on Embedded Network Sensor Systems. ACM, 2015. http://dx.doi.org/10.1145/2809695.2809719.

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Perepelkin, Dmitry, and Maria Ivanchikova. "Problem of Network Traffic Classification in Multiprovider Cloud Infrastructures Based on Machine Learning Methods." In 2021 10th Mediterranean Conference on Embedded Computing (MECO). IEEE, 2021. http://dx.doi.org/10.1109/meco52532.2021.9460171.

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Roozbeh, Amir, Azimeh Sefidcon, and Gerald Q. Maguire. "Resource Monitoring in a Network Embedded Cloud: An Extension to OSPF-TE." In 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing (UCC). IEEE, 2013. http://dx.doi.org/10.1109/ucc.2013.36.

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Sathya, G., and K. Vasanthraj. "Network activity classification schema in IDS and log audit for cloud computing." In 2013 International Conference on Information Communication and Embedded Systems (ICICES 2013). IEEE, 2013. http://dx.doi.org/10.1109/icices.2013.6508322.

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Cico, Orges, and Zamir Dika. "Performance and load testing of cloud vs. classic server platforms (Case study: Social network application)." In 2014 3rd Mediterranean Conference on Embedded Computing (MECO). IEEE, 2014. http://dx.doi.org/10.1109/meco.2014.6862723.

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Li, Dingding, Yong Tang, Bing Liu, Zhendong Yang, Gansen Zhao, and Jianguo Li. "A Network-Friendly Disk I/O Optimization Framework in a Virtualized Cloud System." In 2014 IEEE 8th International Symposium on Embedded Multicore/Manycore SoCs (MCSoC). IEEE, 2014. http://dx.doi.org/10.1109/mcsoc.2014.11.

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Reports on the topic "Network embedded cloud"

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Taiber, Joachim. Unsettled Topics Concerning the Impact of Quantum Technologies on Automotive Cybersecurity. SAE International, 2020. http://dx.doi.org/10.4271/epr2020026.

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Abstract:
Quantum computing is considered the “next big thing” when it comes to solving computational problems impossible to tackle using conventional computers. However, a major concern is that quantum computers could be used to crack current cryptographic schemes designed to withstand traditional cyberattacks. This threat also impacts future automated vehicles as they become embedded in a vehicle-to-everything (V2X) ecosystem. In this scenario, encrypted data is transmitted between a complex network of cloud-based data servers, vehicle-based data servers, and vehicle sensors and controllers. While the vehicle hardware ages, the software enabling V2X interactions will be updated multiple times. It is essential to make the V2X ecosystem quantum-safe through use of “post-quantum cryptography” as well other applicable quantum technologies. This SAE EDGE™ Research Report considers the following three areas to be unsettled questions in the V2X ecosystem: How soon will quantum computing pose a threat to connected and automated vehicle technologies? What steps and measures are needed to make a V2X ecosystem “quantum-safe?” What standardization is needed to ensure that quantum technologies do not pose an unacceptable risk from an automotive cybersecurity perspective?
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