44,469 research outputs found
Towards a Mobile Learning Environment using Reference Architectures
Mobile learning environments have emerged as a way to support the m-learning initiatives, providing benefits to learners, teachers and tutors. However, despite their relevance, the development of mobile learning environments present problems and challenges that must be investigated, especially with respect to the definition and adoption of architectural patterns. Motivated by this scenario, in this paper we discuss the development of a mobile learning environment, called ICMC MLE, following the precepts of a specific reference architecture for mobile learning. ICMC MLE was also evaluated through an experiment; the results showed a high level of satisfaction and convenience in relation to the use of ICMC MLE in real learning scenarios
Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices
Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth
Generation (5G) mobile networks. MEC facilitates distributed cloud computing
capabilities and information technology service environment for applications
and services at the edges of mobile networks. This architectural modification
serves to reduce congestion, latency, and improve the performance of such edge
colocated applications and devices. In this paper, we demonstrate how reactive
service migration can be orchestrated for low-power MEC-enabled Internet of
Things (IoT) devices. Here, we use open-source Kubernetes as container
orchestration system. Our demo is based on traditional client-server system
from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As
the use case scenario, we post-process live video received over web real-time
communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1
handovers, demonstrating MEC-based software defined network (SDN). Now, edge
applications may reactively follow the UE within the radio access network
(RAN), expediting low-latency. The collected data is used to analyze the
benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end
(E2E) latency and power requirements of the UE are improved. We further discuss
the challenges of implementing such schemes and future research directions
therein
Recommended from our members
AmbieSense: a system and reference architecture for personalised and context-sensitive information services for mobile users
The purpose of AmbieSense is to provide personalised, context-sensitive information to the mobile user. It is about augmenting digital information to physical objects, rooms, and areas. The aim is to provide relevant information to the right user and situation. Digital content is distributed from the surroundings and onto your mobile phone. An ambient information environment is provided by a combination of context tag technology, a software platform to manage and deliver the information, and personal computing devices to which the information is served. This paper describes how the AmbieSense reference architecture has been defined and used in order to deliver information to the mobile citizen at the right time, place and situation. Information is provided via specialist content providers. The application area addresses the information needs of travellers and tourists
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
View additional affiliations
View references (107)
Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work
Deep networks thrive when trained on large scale data collections. This has
given ImageNet a central role in the development of deep architectures for
visual object classification. However, ImageNet was created during a specific
period in time, and as such it is prone to aging, as well as dataset bias
issues. Moving beyond fixed training datasets will lead to more robust visual
systems, especially when deployed on robots in new environments which must
train on the objects they encounter there. To make this possible, it is
important to break free from the need for manual annotators. Recent work has
begun to investigate how to use the massive amount of images available on the
Web in place of manual image annotations. We contribute to this research thread
with two findings: (1) a study correlating a given level of noisily labels to
the expected drop in accuracy, for two deep architectures, on two different
types of noise, that clearly identifies GoogLeNet as a suitable architecture
for learning from Web data; (2) a recipe for the creation of Web datasets with
minimal noise and maximum visual variability, based on a visual and natural
language processing concept expansion strategy. By combining these two results,
we obtain a method for learning powerful deep object models automatically from
the Web. We confirm the effectiveness of our approach through object
categorization experiments using our Web-derived version of ImageNet on a
popular robot vision benchmark database, and on a lifelong object discovery
task on a mobile robot.Comment: 8 pages, 7 figures, 3 table
- …