Готові списки джерел за темами / Mobile fog computing

Добірка наукової літератури з теми "Mobile fog computing"

Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Mobile fog computing"

1

Soo, Sander, Chii Chang, Seng W. Loke, and Satish Narayana Srirama. "Proactive Mobile Fog Computing using Work Stealing." International Journal of Mobile Computing and Multimedia Communications 8, no. 4 (October 2017): 1–19. http://dx.doi.org/10.4018/ijmcmc.2017100101.

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Анотація:
A common design of the Internet of Things (IoT) system relies on distant Cloud for management and processing, which faces the challenge of latency, especially when the application requires rapid response in the edge network. Therefore, researchers have proposed the Fog computing architecture, which distributes the computational data processing tasks to the edge network nodes located in the vicinity of data sources and end-users to reduce the latency. Although the Fog computing architecture is promising, it still faces a challenge in mobility when the tasks come from ubiquitous mobile applications in which the data sources are moving objects. In order to address the challenge, this article proposes a proactive Fog service provisioning framework, which hastens the task distribution process in Mobile Fog use cases. Further, the proposed framework provides an optimization scheme in task allocation based on runtime context information. A proof-of-concept prototype has been implemented and tested on real devices.
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2

Artem, Volkov, Kovalenko Vadim, Ibrahim A. Elgendy, Ammar Muthanna, and Andrey Koucheryavy. "DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework." Future Internet 14, no. 1 (December 27, 2021): 13. http://dx.doi.org/10.3390/fi14010013.

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Анотація:
Nowadays, 5G networks are emerged and designed to integrate all the achievements of mobile and fixed communication networks, in which it can provide ultra-high data speeds and enable a broad range of new services with new cloud computing structures such as fog and edge. In spite of this, the complex nature of the system, especially with the varying network conditions, variety of possible mechanisms, hardware, and protocols, makes communication between these technologies challenging. To this end, in this paper, we proposed a new distributed and fog (DD-fog) framework for software development, in which fog and mobile edge computing (MEC) technologies and microservices approach are jointly considered. More specifically, based on the computational and network capabilities, this framework provides a microservices migration between fog structures and elements, in which user query statistics in each of the fog structures are considered. In addition, a new modern solution was proposed for IoT-based application development and deployment, which provides new time constraint services like a tactile internet, autonomous vehicles, etc. Moreover, to maintain quality service delivery services, two different algorithms have been developed to pick load points in the search mechanism for congestion of users and find the fog migration node. Finally, simulation results proved that the proposed framework could reduce the execution time of the microservice function by up to 70% by deploying the rational allocation of resources reasonably.
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3

Daraseliya, Anastasia V., and Eduard S. Sopin. "Optimization of mobile device energy consumption in a fog-based mobile computing offloading mechanism." Discrete and Continuous Models and Applied Computational Science 29, no. 1 (December 15, 2021): 53–62. http://dx.doi.org/10.22363/2658-4670-2021-29-1-53-62.

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Анотація:
The offloading of computing tasks to the fog computing system is a promising approach to reduce the response time of resource-greedy real-time mobile applications. Besides the decreasing of the response time, the offloading mechanisms may reduce the energy consumption of mobile devices. In the paper, we focused on the analysis of the energy consumption of mobile devices that use fog computing infrastructure to increase the overall system performance and to improve the battery life. We consider a three-layer computing architecture, which consists of the mobile device itself, a fog node, and a remote cloud. The tasks are processed locally or offloaded according to the threshold-based offloading criterion. We have formulated an optimization problem that minimizes the energy consumption under the constraints on the average response time and the probability that the response time is lower than a certain threshold. We also provide the numerical solution to the optimization problem and discuss the numerical results.
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4

Xu, Qiaozhi, Junxing Zhang, and Bulganmaa Togookhuu. "Support Mobile Fog Computing Test in piFogBedII." Sensors 20, no. 7 (March 29, 2020): 1900. http://dx.doi.org/10.3390/s20071900.

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Анотація:
IoT and 5G technologies are making smart devices, medical devices, cameras and various types of sensors become parts of the Internet, which provides feasibility to the realization of infrastructure and services such as smart homes, smart cities, smart medical technology and smart transportation. Fog computing (edge computing) is a new research field and can accelerate the analysis speed and decision-making for these delay-sensitive applications. It is very important to test functions and performances of various applications and services before they are deployed to the production environment, and current evaluations are more based on various simulation tools; however, the fidelity of the experimental results is a problem for most of network simulation tools. PiFogBed is a fog computing testbed built with real devices, but it does not support the testing of mobile end devices and mobile fog applications. The paper proposes the piFogBedII to support the testing of mobile fog applications by modifying some components in the piFogBed, such as extending the range of end devices, adding the mobile and migration management strategy and inserting a container agent to implement the transparent transmission between end devices and containers. The evaluation results show that it is effective and the delay resulting from the migration strategy and container agent is acceptable.
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Parlakkılıç, Alaattin. "Responsive Mobile Learning (M-Learning) Application Design And Architecture In Fog Computing." International Journal of Modern Education Studies 3, no. 2 (December 19, 2019): 82. http://dx.doi.org/10.51383/ijonmes.2019.40.

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Анотація:
Today, digital transformation is changing the educational and social life rapidly. In contrast, organizations and educational institutions, developers, and end users do not benefit from cloud-based mobile technologies to the desired level. Mobile systems are used extensively for educational purposes in the Internet of Things (IoT) environment, and the number of online students is increasing. The real problem is how user-friendly, aesthetic mobile learning courses can be effectively delivered on different mobile devices in the desired performance and manner. The responsive design developed with fog computing should be able to provide the ability to design and use mobile learning lessons with sufficient performance, automatically adapted to any browser or device. This should ensure that every person of the target audience can benefit from the lessons without worrying about screen size, resolution, speed and even security. In this study, the fog informatics teaching strategies of mobile learning sensitive teaching design are discussed. The fog computing architecture that can be used with responsive mobile learning, utilizing mobile computing to provide seamless and low latency mobile devices, is described. Finally, a fog-based, responsive designed mobile learning education architecture has been compiled with a better understanding of the lessons and a suitable structure for the use of mobile devices in education.
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6

Francis, T. "A Comparison of Cloud Execution Mechanisms Fog, Edge, and Clone Cloud Computing." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4646. http://dx.doi.org/10.11591/ijece.v8i6.pp4646-4653.

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Анотація:
Cloud computing is a technology that was developed a decade ago to provide uninterrupted, scalable services to users and organizations. Cloud computing has also become an attractive feature for mobile users due to the limited features of mobile devices. The combination of cloud technologies with mobile technologies resulted in a new area of computing called mobile cloud computing. This combined technology is used to augment the resources existing in Smart devices. In recent times, Fog computing, Edge computing, and Clone Cloud computing techniques have become the latest trends after mobile cloud computing, which have all been developed to address the limitations in cloud computing. This paper reviews these recent technologies in detail and provides a comparative study of them. It also addresses the differences in these technologies and how each of them is effective for organizations and developers.
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7

Lin, Fuhong, Lei Yang, Ke Xiong, and Xiaowen Gong. "Recent Advances in Cloud-Aware Mobile Fog Computing." Wireless Communications and Mobile Computing 2019 (January 23, 2019): 1–2. http://dx.doi.org/10.1155/2019/8204394.

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Ahmed, Ejaz, Periklis Chatzimisios, Brij B. Gupta, Yaser Jararweh, and Houbing Song. "Recent advances in fog and mobile edge computing." Transactions on Emerging Telecommunications Technologies 29, no. 4 (April 2018): e3307. http://dx.doi.org/10.1002/ett.3307.

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Shuminoski, Tomislav, Stojan Kitanov, and Toni Janevski. "Advanced QoS Provisioning and Mobile Fog Computing for 5G." Wireless Communications and Mobile Computing 2018 (June 7, 2018): 1–13. http://dx.doi.org/10.1155/2018/5109394.

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Анотація:
This paper presents a novel QoS and mobile cloud and fog computing framework for future fifth generation (5G) of mobile and fixed nodes with radio network aggregation capability. The proposed 5G framework is leading to high QoS provisioning for any given multimedia service, higher bandwidth utilization, traffic load sharing, mobile cloud plus fog computing features, and multi-radio interface capabilities. The framework is user-centric, targeted at always-on connectivity with using radio network aggregation for available mobile broadband connections, and empowered with mobile cloud and fog computing advantages. Moreover, our proposed framework is using Lyapunov drift-plus-penalty theorem that provides a methodology for designing algorithm to maximize the average throughput and stabilize the queuing. Also, we are showing the upper bound of the consumed power and the lower bound of the battery lifetime for the proposed 5G terminal. The advanced performance of our 5G QoS plus MCC framework is evaluated using simulations and analysis with multimedia traffic in heterogeneous mobile and wireless environment. The simulation results are showing that the maximal network utilization, maximal throughput, minimal end-to-end delay, efficient energy consumption, and other performance improvements are achieved.
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10

Liu, Zhou-zhou, and Shi-ning Li. "Sensor-cloud data acquisition based on fog computation and adaptive block compressed sensing." International Journal of Distributed Sensor Networks 14, no. 9 (September 2018): 155014771880225. http://dx.doi.org/10.1177/1550147718802259.

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Анотація:
The emergence of sensor-cloud system has completely changed the one-to-one service mode of traditional wireless sensor networks, and it greatly expands the application field of wireless sensor networks. As the high delay of large-scale data processing tasks in sensor-cloud, a sensor-cloud data acquisition scheme based on fog computing and adaptive block compressive sensing is proposed. First, the sensor-cloud framework based on fog computing is constructed, and the fog computing layer includes many wireless mobile nodes, which helps to realize the implementation of information transfer management between lower wireless sensor networks layer and upper cloud computing layer. Second, in order to further reduce network traffic and improve data processing efficiency, an adaptive block compressed sensing data acquisition strategy is proposed in the lower wireless sensor networks layer. By dynamically adjusting the size of the network block and building block measurement matrix, the implementation of sensor compressed sensing data acquisition is achieved; in order to further balance the lower wireless sensor networks’ node energy consumption, reduce the time delay of data processing task in fog computing layer, the mobile node data acquisition path planning strategy and multi-mobile nodes collaborative computing system are proposed. Through the introduction of the fitness value constraint transformation processing technique and parallel discrete elastic collision optimization algorithm, the efficient processing of the fog computing layer data is realized. Finally, the simulation results show that the sensor-cloud data acquisition scheme can effectively achieve large-scale sensor data efficient processing. Moreover, compared with cloud computing, the network traffic is reduced by 20% and network task delay is reduced by 12.8%–20.1%.
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Більше джерел

Дисертації з теми "Mobile fog computing"

1

Mebrek, Adila. "Fog Computing pour l’Internet des objets." Thesis, Troyes, 2020. http://www.theses.fr/2020TROY0028.

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Анотація:
Le Fog Computing constitue une approche prometteuse dans le contexte de l’Internet des Objets (IoT) car il fournit des fonctionnalités et des ressources à l’extrémité du réseau, plus près des utilisateurs finaux. Cette thèse étudie les performances du Fog Computing dans le cadre des applications IoT sensibles à la latence. La première problématique traitée concerne la modélisation mathématique d’un système IoT-fog-cloud, ainsi que les métriques de performances du système en termes d’énergie consommée et de latence. Cette modélisation nous permettra par la suite de proposer diverses stratégies efficaces de distribution de contenu et d’allocation des ressources dans le fog et le cloud. La deuxième problématique abordée dans cette thèse concerne la distribution de contenu et de données des objets dans des systèmes fog/cloud. Afin d’optimiser simultanément les décisions d’offloading et d’allocation des ressources du système, nous distinguons entre deux types d’applications IoT : (1) applications IoT à contenu statique ou avec des mises à jour peu fréquentes ; et (2) applications IoT à contenu dynamique. Pour chaque type d’application, nous étudions le problème d’offloading de requêtes IoT dans le fog. Nous nous concentrons sur les problèmes d'équilibrage de charge afin de minimiser la latence et l’énergie totale consommée par le système
Fog computing is a promising approach in the context of the Internet of Things (IoT) as it provides functionality and resources at the edge of the network, closer to end users. This thesis studies the performance of fog computing in the context of latency sensitive IoT applications. The first issue addressed is the mathematical modeling of an IoT-fogcloud system, and the performance metrics of the system in terms of energy consumed and latency. This modeling will then allow us to propose various effective strategies for content distribution and resource allocation in the fog and the cloud. The second issue addressed in this thesis concerns the distribution of content and object data in fog / cloud systems. In order to simultaneously optimize offloading and system resource allocation decisions, we distinguish between two types of IoT applications: (1) IoT applications with static content or with infrequent updates; and (2) IoT applications with dynamic content. For each type of application, we study the problem of offloading IoT requests in the fog. We focus on load balancing issues to minimize latency and the total power consumed by the system
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2

Peraccini, Simone. "Named Data Networking for Computing in the Mobile Edge." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17059/.

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Анотація:
Oggi connettersi a Internet è una pratica di uso comune, destinata a crescere notevolmente con l'avvento dell'Internet of Things. Nonostante il grande successo, Internet presenta diversi limiti architetturali che lo rendono un veicolo povero in confronto alla grande mole di contenuti trasmessi. La seconda importante limitazione riguarda la sicurezza che è applicata solamente a livello di host ma non di contenuto. Per promuovere la prototipizzazione di nuovi paradigmi architetturali, nel 2010 la National Science Fondation crea il programma Future Internet Architecture. Tra i progetti nati, il più emergente è Named Data Networking(NDN). Il suo intento è quello di distogliere l'attenzione da "dove" trovare una risorsa per concentrarsi su "cosa" applicazioni e utenti cerchino. Per questo NDN pensa che l'identificazione non debba più riguardare gli host ma i contenuti. Quest'ultimi sono distinti da un nome univoco, godono di immutabilità e racchiudono la strategia di sicurezza. L'obiettivo di questo progetto è quello di dare un piccolo contributo a Named Data Networking, ideando e sviluppando un protocollo di computazione cooperativa, che operi per mezzo del protocollo NDN. L'intento del protocollo è quello di permettere a generici dispositivi wifi di assegnare l'esecuzione di alcuni dei loro task ad un nodo nelle vicinanze. Inoltre, la tesi accenna come il protocollo si inserisca nel tema dell'Edge computing, che assieme al Fog computing pone le basi per l'evoluzione dell'architettura Cloud. Il lavoro ha portato alla realizzazione di un prototipo del protocollo che è possibile installare sui nodi del simulatore di reti ndnSIM. Esso soddisfa i comportamenti base richiesti ed offre buone prestazioni negli scenari statici. L'elaborato si conclude con alcuni test che ne confermano il corretto funzionamento ma allo stesso tempo denotano alcuni aspetti da migliorare negli sviluppi futuri.
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3

Segura, Danilo Costa Marim. "Integrando grades móveis em uma arquitetura orientada a serviços." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-13122016-095843/.

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Анотація:
O aumento no número de dispositivos móveis, como smartphones, tablets e laptops, e o avanço em seu potencial computacional permitiu considerá-los como recursos computacionais. O uso de recursos computacionais com maior proximidade vem crescendo ano após ano, sendo chamado de Fog computing, em que os elementos na borda da Internet são explorados, uma vez que os serviços computacionais convencionais podem estar indisponíveis ou sobrecarregados. Dessa forma, este projeto de Mestrado tem como foco possibilitar o uso de dispositivos móveis no provimento de serviços computacionais entre si de forma colaborativa através da heurística Maximum Regret adaptada, que busca alocar tarefas computacionais em dispositivos locais de forma a minimizar o consumo de energia e evitar dispositivos não confiáveis. Também há uma meta-heurística em um nível global, que interconecta os diferentes aglomerados de dispositivos móveis na borda da Internet, e possui informações globais de Quality of Service (QoS). Foram realizados experimentos que mostraram que evitar dispositivos móveis como recursos com um baixo grau de confiabilidade possibilitou diminuir o impacto no consumo de energia, além de ser possível diminuir os tempos de resposta e de comunicação ao ajustar a política de seleção de aglomerados externos.
The increasing number of mobile devices, such as smartphones, tablets and laptops, as well as advances in their computing power have enabled us to consider them as resources, exploring the proximity. The use of near computing resources is growing year by year, being called as Fog computing, where the elements on the edge of the Internet are exploited, once the computer services providers could be unavailable or overloaded. Thus, this Masters project focuses on using mobile devices to provide computing services among them through a heuristic called Adapted Maximum Regret, which tries to minimize energy consumption and avoid untrustable devices. There is also top-level metaheuristic which interconnects different clusters of devices on the edge of the Internet with global information to guarantee Quality of Services (QoS). We conducted a set of experiments that showed us to avoid devices with a high degree of failures to save more energy when allocating tasks among them, as well as decreasing the applications response time and communication through adjusts in the selection algorithm of external agglomerates.
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4

Abdul, Rehman Bin Omer. "Mobile Edge Computing Clustering Algorithms for Pedestrian Mobility Scenarios." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Анотація:
The purpose of this study is to provide a general framework for the latest trends of mobile network architectures. Edge computing brings the computing capability of the cloud to the edge of the network for minimizing the latency. In the D2D mode, Fog Nodes interact with each other. With the help of clustering, Fog nodes are categorized into two types: Fog Cluster Head (FCH) and Fog Cluster Member (FCM). In each cluster, FCMs offload the task towards their respective FCHs for computation. The characterization of the performance of system model taking into account the average energy consumption, average task delay, fairness, and packet loss. We provide results based on the numerical simulation performed in Matlab in order to show the difference in the performance of the network using different policies and clustering and cluster update frequencies. In this thesis, a theoretic framework is presented that aims to characterize the performance of Fog network with pedestrian mobility without priority approach and also pedestrian mobility with priority approach using clustering approach and compare the results. The simulation results show how the priority approach has the profound impact on the energy consumption, task delay, and packet loss and solve the problem of coverage constraint.
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5

Guardo, Ermanno Lorenzo. "Edge Computing: challenges, solutions and architectures arising from the integration of Cloud Computing with Internet of Things." Doctoral thesis, Università di Catania, 2018. http://hdl.handle.net/10761/3908.

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Анотація:
The rapid spread of the Internet of Things (IoT) is causing the exponential growth of objects connected to the network, in fact, according to estimates, in 2020 there will be about 3/4 devices per person totaling of over 20 billion connected devices. Therefore, the use of content that requires intensive bandwidth consumption is growing. In order to meet these growing needs, the computing power and storage space are transferred to the network edge to reduce the network latency and increase the bandwidth availability. Edge computing allows to approach high-bandwidth content and sensitive apps to the user or data source and is preferred to use it for many IoT applications respect to cloud computing. Its distributed approach addresses the needs of IoT and industrial IoT, as well as the immense amount of data generated by smart sensors and IoT devices, which would be costly and time-consuming to send to the cloud for processing and analysis. Edge computing reduces both the bandwidth needed and the communication among sensors and cloud, which can negatively affect the IoT performance. The goal of edge computing is to improve efficiency and reduce the amount of data transported to the cloud for processing, analysis and storage. The research activity carried out during the three years of the Ph.D. program focused on the study, design and development of architectures and prototypes based on the Edge Computing in various contexts such as smart cities and agriculture. Therefore, the well-known paradigms of Fog Computing and Mobile Edge Computing have been faced. In this thesis, will be discussed the work carried out through the exploitation of the Fog Computing and Mobile Edge Computing paradigms, considered suitable solutions to address the challenges of the fourth industrial revolution.
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6

Singh, Navjot. "Planning of Mobile Edge Computing Resources in 5G Based on Uplink Energy Efficiency." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38444.

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Анотація:
Increasing number of devices demand for low latency and high-speed data transmission require that the computation resources to be closer to users. The emerging Mobile Edge Computing (MEC) technology aims to bring the advantages of cloud computing which are computation, storage and networking capabilities in close proximity of user. MEC servers are also integrated with cloud servers which give them flexibility of reaching vast computational power whenever needed. In this thesis, leveraging the idea of Mobile Edge Computing, we propose algorithms for cost-efficient and energy-efficient the placement of Mobile Edge nodes. We focus on uplink energy-efficiency which is essential for certain applications including augmented reality and connected vehicles, as well as extending battery life of user equipment that is favorable for all applications. The experimental results show that our proposed schemes significantly reduce the uplink energy of devices and minimizes the number of edge nodes required in the network.
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7

Solimando, Michele. "Infrastrutture basate su Edge Computing per Supporto a Servizi Mobili in Ambienti Ostili." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11331/.

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Анотація:
Questa tesi è incentrata sulla revisione del classico modello di infrastruttura Cloud. Le motivazioni sono da ricercare nelle condizioni operative reali della maggior parte dei dispositivi connessi alla rete attualmente. Si parla di ambiente ostile riferendosi a network popolate da molti dispositivi dalle limitate caratteristiche tecniche e spesso collegati con canali radio, molto più instabili delle connessioni cablate. Allo scenario va ad aggiungersi la necessità crescente di mobilità che limita ulteriormente i vantaggi derivanti dall'utilizzo dell’infrastruttura Cloud originale. La trattazione propone il modello Edge come estensione del Cloud. Esso ne amplia il ventaglio di utilizzo, favorendo aree di applicazione che stanno acquisendo maggiore influenza negli ultimi periodi e che richiedono una revisione delle vecchie infrastrutture Cloud, dettata dalle caratteristiche stringenti che necessitano per un'operatività soddisfacente.
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8

Badokhon, Alaa. "An Adaptable, Fog-Computing Machine-to-Machine Internet of Things Communication Framework." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1492450137643915.

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9

CONCONE, Federico. "EFFICIENT AND SECURE ALGORITHMS FOR MOBILE CROWDSENSING THROUGH PERSONAL SMART DEVICES." Doctoral thesis, Università degli Studi di Palermo, 2021. http://hdl.handle.net/10447/481969.

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Анотація:
The success of the modern pervasive sensing strategies, such as the Social Sensing, strongly depends on the diffusion of smart mobile devices. Smartwatches, smart- phones, and tablets are devices capable of capturing and analyzing data about the user’s context, and can be exploited to infer high-level knowledge about the user himself, and/or the surrounding environment. In this sense, one of the most relevant applications of the Social Sensing paradigm concerns distributed Human Activity Recognition (HAR) in scenarios ranging from health care to urban mobility management, ambient intelligence, and assisted living. Even though some simple HAR techniques can be directly implemented on mo- bile devices, in some cases, such as when complex activities need to be analyzed timely, users’ smart devices should be able to operate as part of a more complex architecture, paving the way to the definition of new distributed computing paradigms. The general idea behind these approaches is to move early analysis to- wards the edge of the network, while relying on other intermediate (fog) or remote (cloud) devices for computations of increasing complexity. This logic represents the main core of the fog computing paradigm, and this thesis investigates its adoption in distributed sensing frameworks. Specifically, the conducted analysis focused on the design of a novel distributed HAR framework in which the heavy computation from the sensing layer is moved to intermediate devices and then to the cloud. Smart personal devices are used as processing units in order to guarantee real-time recognition, whereas the cloud is responsible for maintaining an overall, consistent view of the whole activity set. As compared to traditional cloud-based solutions, this choice allows to overcome processing and storage limitations of wearable devices while also reducing the overall bandwidth consumption. Then, the fog-based architecture allowed the design and definition of a novel HAR technique that combines three machine learning algorithms, namely k-means clustering, Support Vector Machines (SVMs), and Hidden Markov Models (HMMs), to recognize complex activities modeled as sequences of simple micro- activities. The capability to distribute the computation over the different entities in the network, allowing the use of complex HAR algorithms, is definitely one of the most significant advantages provided by the fog architecture. However, because both of its intrinsic nature and high degree of modularity, the fog-based system is particularly prone to cyber security attacks that can be performed against every element of the infrastructure. This aspect plays a main role with respect to social sensing since the users’ private data must be preserved from malicious purposes. Security issues are generally addressed by introducing cryptographic mechanisms that improve the system defenses against cyber attackers while, at the same time, causing an increase of the computational overhead for devices with limited resources. With the goal to find a trade-off between security and computation cost, the de- sign and definition of a secure lightweight protocol for social-based applications are discussed and then integrated into the distributed framework. The protocol covers all tasks commonly required by a general fog-based crowdsensing application, making it applicable not only in a distributed HAR scenario, discussed as a case study, but also in other application contexts. Experimental analysis aims to assess the performance of the solutions described so far. After highlighting the benefits the distributed HAR framework might bring in smart environments, an evaluation in terms of both recognition accuracy and complexity of data exchanged between network devices is conducted. Then, the effectiveness of the secure protocol is demonstrated by showing the low impact it causes on the total computational overhead. Moreover, a comparison with other state-of-art protocols is made to prove its effectiveness in terms of the provided security mechanisms.
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10

Houacine, Fatiha. "Service-Oriented Architecture for the Mobile Cloud Computing." Thesis, Paris, CNAM, 2016. http://www.theses.fr/2016CNAM1110/document.

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La croissance des appareils connectés, principalement due au grand nombre de déploiements de l'internet des objets et à l'émergence des services de cloud mobile, introduit de nouveaux défis pour la conception d'architectures de services dans le Cloud Computing Mobile (CCM) du cloud computing mobile. Nous montrons dans cette thèse comment l'architecture orientée services SOA peut être une solution clé pour fournir des services cloud mobiles distribués et comment la plate-forme OSGi peut être un cadre adaptatif et efficace pour fournir une telle implémentation. Nous adaptons le cadre CCM proposé à différents contextes d'architecture. Le premier est un modèle centré traditionnel, où les appareils mobiles sont réduits à consommer des services. Le second est un modèle distribué où la puissance de l'interaction de mobile à mobile offre des opportunités illimitées de services de valeur, et enfin, l'architecture à trois niveaux est considérée avec l'introduction de la notion de cloudlet. Pour chaque contexte, nous explorons la performance de notre cadre axé sur le service et le comparons à d'autres solutions existantes
The growth of connected devices, mostly due to the large number of internet of things IoT deployments and the emergence of mobile cloud services, introduces new challenges for the design of service architectures in mobile cloud computing MCC. An MCC framework should provide elasticity and scalability in a distributed and dynamic way while dealing with limited environment resources and variable mobile contexts web applications, real-time, enterprise services, mobile to mobile, hostile environment, etc. that may include additional constraints impacting the design foundation of cloud services. We show in this thesis how service-oriented architecture SOA can be a key solution to provide distributed mobile cloud services and how OSGi platform can be an adaptive and efficient framework to provide such implementation. We adapt the proposed MCC framework to different architecture contexts. The first one is a traditional centric model, where mobile devices are reduced to consuming services. The second one is a distributed model where the power of mobile-to-mobile interaction offers unlimited value-services opportunities, and finally, three-tier architecture is considered with the introduction of the cloudlet notion. For each context, we explore the performance of our service-oriented framework, and contrast it with alternative existing solutions
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Книги з теми "Mobile fog computing"

1

Context-aware mobile computing: Affordances of space, social awareness, and social influence. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2009.

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Burton, Jane, and Nancy Proctor. Mobile apps for museums: The AAM guide to planning and strategy. Washington, DC: AAM Press, 2011.

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3

Türkiye'de SMS haberciliği. Cağaloğlu, İstanbul: Anahtar Kitaplar, 2010.

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Mulvenna, Maurice D., and C. D. Nugent. Supporting people with dementia using pervasive health technologies. London: Springer, 2010.

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Mavromoustakis, Constandinos X., George Mastorakis, Evangelos Pallis, and Evangelos Markakis. Cloud and Fog Computing in 5G Mobile Networks: Emerging Advances and Applications. Institution of Engineering & Technology, 2017.

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Markakis, Evangelos Markakis, George Mastorakis Mastorakis, Constandinos X. Mavromoustakis Mavromoustakis, and Evangelos Pallis Pallis, eds. Cloud and Fog Computing in 5G Mobile Networks: Emerging advances and applications. Institution of Engineering and Technology, 2017. http://dx.doi.org/10.1049/pbte070e.

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Mavromoustakis, Constandinos X., George Mastorakis, Evangelos Pallis, and Evangelos Markakis. Cloud and Fog Computing in 5G Mobile Networks: Emerging Advances and Applications. Institution of Engineering & Technology, 2017.

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8

S, Kevin Andrews, and Josephine M. S. Mobile Computing. Jupiter Publications Consortium, 2021. http://dx.doi.org/10.47715/jpc.b.59.2021.9788194706922.

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Rapid advancements in wireless digital networking technologies have enabled the emergence of capabilities that software systems are just now starting to utilize. Wireless computing is becoming more affordable for both enterprise customers and private customers as the expense of contact and handheld computing devices (laptop computers, hand-held phones, etc.) continues to decline. Mobile computing is not a condensed and well-studied subset of the proven and wellstudied area of distributed computing. Because of the nature of wireless communication media and the versatility of devices, radically new challenges in networking, operating systems, and information systems have been created. Additionally, many of the mobile computing applications envisaged impose novel demands on software systems. While mobile computing is still in its infancy, several fundamental principles and some seminal experimental systems have been established. Mobile Computing includes chapters that explain certain principles and frameworks, as well as software that are being implemented and evaluated at the moment. Mobile Computing is an excellent reference book and can also serve as a text for a mobile computing course. This book has been developed as per the Course syllabus of Dr. M. G. R Educational And Research Institute, Chennai, Tamil Nadu, India. This syallbus is on par with the other Universities and Deemed to be Univsersites in India. Keywords: Mobile computing, Wireless Transmission, Radio Transmission, Media Access Control, Wireless LAN, Mobile Network Layer and Transport Layer
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9

S, Kevin Andrews, and Josephine M. S. Mobile Computing. Jupiter Publications Consortium, 2021. http://dx.doi.org/10.47715/jpc.b.59.2021.9788194706922.

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Rapid advancements in wireless digital networking technologies have enabled the emergence of capabilities that software systems are just now starting to utilize. Wireless computing is becoming more affordable for both enterprise customers and private customers as the expense of contact and handheld computing devices (laptop computers, hand-held phones, etc.) continues to decline. Mobile computing is not a condensed and well-studied subset of the proven and wellstudied area of distributed computing. Because of the nature of wireless communication media and the versatility of devices, radically new challenges in networking, operating systems, and information systems have been created. Additionally, many of the mobile computing applications envisaged impose novel demands on software systems. While mobile computing is still in its infancy, several fundamental principles and some seminal experimental systems have been established. Mobile Computing includes chapters that explain certain principles and frameworks, as well as software that are being implemented and evaluated at the moment. Mobile Computing is an excellent reference book and can also serve as a text for a mobile computing course. This book has been developed as per the Course syllabus of Dr. M. G. R Educational And Research Institute, Chennai, Tamil Nadu, India. This syallbus is on par with the other Universities and Deemed to be Univsersites in India. Keywords: Mobile computing, Wireless Transmission, Radio Transmission, Media Access Control, Wireless LAN, Mobile Network Layer and Transport Layer
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10

UMTS and Mobile Computing. Artech House Publishers, 2002.

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Частини книг з теми "Mobile fog computing"

1

Chen, Nanxi. "Microservices Deployment in Edge/Fog Computing Environments." In Mobile Microservices, 51–84. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003272960-3.

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Awaisi, Kamran Sattar, Assad Abbas, Samee U. Khan, Redowan Mahmud, and Rajkumar Buyya. "Simulating Fog Computing Applications Using iFogSim Toolkit." In Mobile Edge Computing, 565–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69893-5_22.

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Gao, Longxiang, Tom H. Luan, Bo Liu, Wanlei Zhou, and Shui Yu. "Fog Computing and Its Applications in 5G." In 5G Mobile Communications, 571–93. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34208-5_21.

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Ghosh, Shreya, and Soumya K. Ghosh. "Mobility Driven Cloud-Fog-Edge Framework for Location-Aware Services: A Comprehensive Review." In Mobile Edge Computing, 229–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69893-5_10.

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Das, Jaydeep, Soumya K. Ghosh, and Rajkumar Buyya. "Geospatial Edge-Fog Computing: A Systematic Review, Taxonomy, and Future Directions." In Mobile Edge Computing, 47–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69893-5_3.

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Lehman, Sarah M., and Chiu C. Tan. "Leveraging Edge Computing for Mobile Augmented Reality." In Fog/Edge Computing For Security, Privacy, and Applications, 327–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57328-7_13.

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Harnal, Shilpi, Gaurav Sharma, and Ravi Dutt Mishra. "QoS-Based Load Balancing in Fog Computing." In Mobile Radio Communications and 5G Networks, 331–44. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7018-3_25.

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Battula, Sudheer Kumar, Ranesh Kumar Naha, Ujjwal KC, Khizar Hameed, Saurabh Garg, and Muhammad Bilal Amin. "Mobility-Based Resource Allocation and Provisioning in Fog and Edge Computing Paradigms: Review, Challenges, and Future Directions." In Mobile Edge Computing, 251–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69893-5_11.

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Bindu Madavi, K. P., and P. Vijayakarthick. "Decoy Technique for Preserving the Privacy in Fog Computing." In Evolutionary Computing and Mobile Sustainable Networks, 89–94. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5258-8_10.

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Shih, Yuan-Yao, Hung-Yu Wei, and Ai-Chun Pang. "Fog Computing for Intelligent Mobile and IoT Networks." In Encyclopedia of Wireless Networks, 489–95. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_75.

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Тези доповідей конференцій з теми "Mobile fog computing"

1

Shi, Heng, Nan Chen, and Ralph Deters. "Combining Mobile and Fog Computing: Using CoAP to Link Mobile Device Clouds with Fog Computing." In 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS). IEEE, 2015. http://dx.doi.org/10.1109/dsdis.2015.115.

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Pinjari, Hameed, Anand Paul, Gwanggil Jeon, and Seungmin Rho. "Context-Driven Mobile Learning Using Fog Computing." In 2018 International Conference on Platform Technology and Service (PlatCon). IEEE, 2018. http://dx.doi.org/10.1109/platcon.2018.8472763.

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Hassan, Mohammed A., Mengbai Xiao, Qi Wei, and Songqing Chen. "Help your mobile applications with fog computing." In 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking - Workshops (SECON Workshops). IEEE, 2015. http://dx.doi.org/10.1109/seconw.2015.7328146.

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Li, Yuanjie, Nguyen Tung Anh, Azhar Saeed Nooh, Kuwon Ra, and Minho Jo. "Dynamic mobile cloudlet clustering for fog computing." In 2018 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2018. http://dx.doi.org/10.23919/elinfocom.2018.8330676.

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Alonso-Monsalve, Saul, Felix Garcia-Carballeira, and Alejandro Calderon. "Fog computing through public-resource computing and storage." In 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC). IEEE, 2017. http://dx.doi.org/10.1109/fmec.2017.7946412.

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Yi, Shanhe, Cheng Li, and Qun Li. "A Survey of Fog Computing." In MobiHoc'15: The Sixteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2757384.2757397.

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Brogi, Antonio, Stefano Forti, Ahmad Ibrahim, and Luca Rinaldi. "Bonsai in the Fog: An active learning lab with Fog computing." In 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC). IEEE, 2018. http://dx.doi.org/10.1109/fmec.2018.8364048.

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8

Tuvakov, Jemshit, and KeeHyun Park. "On the Fog Node Model for Multi-purpose Fog Computing Systems." In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2018. http://dx.doi.org/10.1109/iemcon.2018.8614845.

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Sayed, Muhammad Magdy, Mona S. Kashkoush, and Mohamed Azab. "Towards Resilient Adaptive Vehicular Fog Computing." In 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2020. http://dx.doi.org/10.1109/iemcon51383.2020.9284836.

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Ennya, Zainab, Moulay Youssef Hadi, and Amine Abouaomar. "Computing Tasks Distribution in Fog Computing: Coalition Game Model." In 2018 6th International Conference on Wireless Networks and Mobile Communications (WINCOM). IEEE, 2018. http://dx.doi.org/10.1109/wincom.2018.8629587.

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Звіти організацій з теми "Mobile fog computing"

1

Satyanarayanan, M., Brian Noble, Puneet Kumar, and Morgan Price. Application-Aware Adaptation for Mobile Computing. Fort Belvoir, VA: Defense Technical Information Center, July 1994. http://dx.doi.org/10.21236/ada288366.

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2

Pasquale, Joseph. System Software Support for Mobile-Agent Computing. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada409193.

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Roy, Sumit. Telecommunication Networks for Mobile & Distributed Communications/Computing. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada418978.

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Gupta, Rajiv, Santosh Pande, and Soner Onder. Power-Adaptive Microarchitecture and Compiler Design for Mobile Computing. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada416016.

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5

Horak, Karl Emanuel, Sharon Marie DeLand, and Dianna Sue Blair. The feasibility of mobile computing for on-site inspection. Office of Scientific and Technical Information (OSTI), September 2014. http://dx.doi.org/10.2172/1162192.

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6

Chen, Guanling, and David Kotz. Solar: A Pervasive-Computing Infrastructure for Context-Aware Mobile Applications. Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada440303.

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Fletcher, James H., Philip Cox, William J. Harrington, and Joseph L. Campbell. Recovery Act: Advanced Direct Methanol Fuel Cell for Mobile Computing. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1091798.

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Noble, Brian D., Morgan Price, and M. Satyanarayanan. A Programming Interface for Application-Aware Adaptation in Mobile Computing. Fort Belvoir, VA: Defense Technical Information Center, February 1995. http://dx.doi.org/10.21236/ada293107.

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9

Brian Wells. Direct Methanol Fuel Cell Power Supply For All-Day True Wireless Mobile Computing. Office of Scientific and Technical Information (OSTI), November 2008. http://dx.doi.org/10.2172/970458.

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Kwiat, Paul, Eric Chitambar, Andrew Conrad, and Samantha Isaac. Autonomous Vehicle-Based Quantum Communication Network. Illinois Center for Transportation, September 2022. http://dx.doi.org/10.36501/0197-9191/22-020.

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Quantum communication was demonstrated using autonomous vehicle-to-vehicle (V2V), as well as autonomous vehicle-to-infrastructure (V2I). Supporting critical subsystems including compact size, weight, and power (SWaP) quantum sources; optical systems; and pointing, acquisition, and tracking (PAT) subsystems were designed, developed, and tested. Novel quantum algorithms were created and analyzed, including quantum position verification (QPV) for mobile autonomous vehicles. The results of this research effort can be leveraged in support of future cross-platform, mobile quantum communication networks that provide improved security, more accurate autonomous sensors, and connected quantum computing nodes for next-generation, smart-infrastructure systems.
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