Presentacio sobre l'Oficina de Proteccio de Dades Personals de la UAB i la politica Open Science. Va formar part de la conferencia "Les politiques d'Open Data / Open Acces: Implicacions a la recerca" orientada a investigadors i gestors de projectes europeus que va tenir lloc el 20 de setembre de 2018 a la Universitat Autonoma de Barcelona

/pdf/general-data-protection-regulation-2z11cloyp7.pdf

General data protection regulation

Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people’s lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of “providing artificial intelligence for every person and every organization at everywhere”. Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence , aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge , this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge , i.e., Edge DL.

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people's lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of "providing artificial intelligence for every person and every organization at everywhere". Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence, aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge, this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.

This literature review presents the History, Technology, and Applications of Natural Dialog Systems or simply chatbots. It aims to organize critical information that is a necessary background for further research activity in the field of chatbots. More specifically, while giving the historical evolution, from the generative idea to the present day, we point out possible weaknesses of each stage. After we present a complete categorization system, we analyze the two essential implementation technologies, namely, the pattern matching approach and machine learning. Moreover, we compose a general architectural design that gathers critical details, and we highlight crucial issues to take into account before system design. Furthermore, we present chatbots applications and industrial use cases while we point out the risks of using chatbots and suggest ways to mitigate them. Finally, we conclude by stating our view regarding the direction of technology so that chatbots will become really smart.

Chatbots: History, technology, and applications

Method and system for supporting serverless VoIP service are provided. Network information of a first device and a second device is exchanged through a telecommunication network. A VoIP connection between the first and second devices can be established through an internet based network according to the exchanged network information. The network information may comprise an IP address and a port number, and can be delivered by short message service.

Method and system for serverless voip service in personal communication network

Low-latency demands for cellular networks have attracted much attention. Mobile edge computing (MEC), which deploys a cloud computing platform at the edge closer to mobile users, has been introduced as an enabler of low-latency performance in 4G and 5G networks. In this paper, we propose an MEC platform deployment solution in 4G LTE networks using a middlebox approach. It is standard-compliant and transparent to existing cellular network components, so they need not be modified. The MEC middlebox sits on the S1 interface, which connects an LTE base station to its core network, and does traffic filtering, manipulation and forwarding. It enables the MEC service for mobile users by hosting application servers. Such middlebox approach can save deployment cost and be easy to install. It is different from other studies that require modifications on base stations or/and core networks. We have confirmed its viability through a prototype based on the OpenAirInterface cellular platform.

/pdf/mobile-edge-computing-platform-deployment-in-4g-lte-networks-59f9ijgku2.pdf

Mobile Edge Computing Platform Deployment in 4G {LTE} Networks: A Middlebox Approach

Both voice and data are indispensable services in current cellular networks. In this work, we study the inter-play of voice and data in operational LTE networks. We assess how the popular CSFB-based voice service affects the IP-based data sessions in 4G LTE networks, and visa versa. Our findings reveal that the interference between them is mutual. On one hand, voice calls may incur throughput drop, lost 4G connectivity, and application aborts for data sessions. One the other hand, users may miss incoming voice calls when turning on data access. The fundamental problem is that, signaling and control for circuit-switched voice and packet-switched data have dependency and coupling effect via the LTE phone client. We further propose fixes to the identified issues.

/pdf/how-voice-calls-affect-data-in-operational-lte-networks-gl71gx9414.pdf

How voice calls affect data in operational LTE networks

With the demand for economical and high-speed wireless services, Optical Wireless Communication (OWC) has attracted increasing attention in both research and the market. In the past decades, numerous optical-related techniques (e.g., LEDs, displays, cameras) and systems (e.g., VLC, LiFi, LiDAR) have been invented. OWC techniques, which are considered as a competitive mechanics in next-generation networks as an alternative to RF approaches, offer 10,000 times more bandwidth than conventional radio frequency (RF)-based wireless techniques (e.g., WiFi, LoRa, Bluetooth, LTE), as well as tremendous spatial reuse potential with even less interference. Because optical communications have a limited wavelength and travel in the line of sight (LoS) manner, the OWC is commonly thought as a secure wireless approach to confine light transmissions within physical bounds. However, in the real world, it is completely untrue. The privacy leakages and security risks broadly exist in the optical-related wireless applications including OWC networks. These threats and weaknesses have recently been the subject of several initial studies. However, they lack systematic analysis and are isolated. This survey first presents a general workflow of OWC systems which consists of three stages: before signal emission (BSE), during signal propagation (DSP), and after signal receiving (ASV). For each stage, related risks are reviewed. Then, we summarize existing attacks in optical-related wireless applications and corresponding counter-attack solutions. Finally, we outline the future trends for improving OWC security.

https://dl.acm.org/doi/pdf/10.1145/3594718

The Security in Optical Wireless Communication: A Survey

In this paper, we study how mobility affects mobile data accounting, which records the usage volume for each roaming user. We find out that, current 2G/3G/4G systems have well-tested mobility support solutions and generally work well. However, under certain biased, less common yet possible scenarios, accounting gap between the operator's log and the user's observation indeed exists. The gap can be as large as 69.6% in our road tests. We further discover that the root causes are diversified. In addition to the no-signal case reported in the prior work [23], they also include handoffs, as well as insufficient coverage of hybrid 2G/3G/4G systems. Inter-system handoffs (that migrate user devices between radio access technologies of 2G, 3G, and 4G) may incur non-negligible accounting discrepancy.

/pdf/accounting-for-roaming-users-on-mobile-data-access-issues-1xj9fcurv7.pdf

Accounting for roaming users on mobile data access: issues and root causes

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Guan-Hua Tu

Author Tools

Chat about Author