60,652 research outputs found
Leveraging Edge Computing through Collaborative Machine Learning
The Internet of Things (IoT) offers the ability
to analyze and predict our surroundings through sensor
networks at the network edge. To facilitate this predictive
functionality, Edge Computing (EC) applications are developed
by considering: power consumption, network lifetime and
quality of context inference. Humongous contextual data from
sensors provide data scientists better knowledge extraction,
albeit coming at the expense of holistic data transfer that
threatens the network feasibility and lifetime. To cope with this,
collaborative machine learning is applied to EC devices to (i)
extract the statistical relationships and (ii) construct regression
(predictive) models to maximize communication efficiency. In
this paper, we propose a learning methodology that improves
the prediction accuracy by quantizing the input space and
leveraging the local knowledge of the EC devices
Data-Efficient, Federated Learning for Raw Network Traffic Detection
Traditional machine learning (ML) models used for enterprise network intrusion detection systems (NIDS) typically rely on vast amounts of centralized data with expertly engineered features. Previous work, however, has shown the feasibility of using deep learning (DL) to detect malicious activity on raw network traffic payloads rather than engineered features at the edge, which is necessary for tactical military environments. In the future Internet of Battlefield Things (IoBT), the military will find itself in multiple environments with disconnected networks spread across the battlefield. These resource-constrained, data-limited networks require distributed and collaborative ML/DL models for inference that are continually trained both locally, using data from each separate tactical edge network, and then globally in order to learn and detect malicious activity represented across the multiple networks in a collaborative fashion. Federated Learning (FL), a collaborative paradigm which updates and distributes a global model through local model weight aggregation, provides a solution to train ML/DL models in NIDS utilizing learning from multiple edge devices from the disparate networks without the sharing of raw data. We develop and experiment with a data-efficient, FL framework for IoBT settings for intrusion detection using only raw network traffic in restricted, resource-limited environments. Our results indicate that regardless of the DL model architecture used on edge devices, the Federated Averaging FL algorithm achieved over 93% accuracy in model performance in detecting malicious payloads after only five episodes of FL training
Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G
By caching content at network edges close to the users, the content-centric
networking (CCN) has been considered to enforce efficient content retrieval and
distribution in the fifth generation (5G) networks. Due to the volume,
velocity, and variety of data generated by various 5G users, an urgent and
strategic issue is how to elevate the cognitive ability of the CCN to realize
context-awareness, timely response, and traffic offloading for 5G applications.
In this article, we envision that the fundamental work of designing a cognitive
CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to
associatively learn and control the states of edge devices (such as phones,
vehicles, and base stations) and in-network resources (computing, networking,
and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework
for C-CCN in 5G, which can aggregate the idle computing resources of the
neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive
learning tasks. By leveraging artificial intelligence (AI) to jointly
processing sensed environmental data, dealing with the massive content
statistics, and enforcing the mobility control at network edges, the FEL makes
it possible for mobile users to cognitively share their data over the C-CCN in
5G. To validate the feasibility of proposed framework, we design two
FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network
acceleration, 2) enhanced mobility management. Simultaneously, we present the
simulations to show the FEL's efficiency on serving for the mobile users'
delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201
The long and winding road ...
The long and winding road is a metaphor for a journey, often used to describe life
journeys and the challenges encountered. The metaphor was used for the title of
my keynote to refer both to the journey towards the current position of virtual
exchange in education policy \u2013 but also the long road ahead. This paper aims to explore
the emergence of virtual exchange in educational policy and how it has been adopted by
non-profit organisations, educational institutions, and policy makers to address geo- and
socio-political tensions. Though still a relatively new field, in recent years there have been
some important developments in terms of policy statements and public investments in
virtual exchange. The paper starts by looking at the current state-of-the-art in terms of
virtual exchange in education policy and initiatives in Europe. Then, using an approach
based on \u2018episode studies\u2019 from the policy literature, the paper explores the main virtual
exchange schemes and initiatives that have drawn the attention of European policy
makers. The paper closes by looking at some of the lessons we have learnt from research
on the practice of virtual exchange, and how this can inform us as we face the long road
ahead of us. The focus of this paper is on the European context not because I assume it to
be the most important or influential, but rather because it is the one I know best, since it is
the context in which I have been workin
The network structure of visited locations according to geotagged social media photos
Businesses, tourism attractions, public transportation hubs and other points
of interest are not isolated but part of a collaborative system. Making such
collaborative network surface is not always an easy task. The existence of
data-rich environments can assist in the reconstruction of collaborative
networks. They shed light into how their members operate and reveal a potential
for value creation via collaborative approaches. Social media data are an
example of a means to accomplish this task. In this paper, we reconstruct a
network of tourist locations using fine-grained data from Flickr, an online
community for photo sharing. We have used a publicly available set of Flickr
data provided by Yahoo! Labs. To analyse the complex structure of tourism
systems, we have reconstructed a network of visited locations in Europe,
resulting in around 180,000 vertices and over 32 million edges. An analysis of
the resulting network properties reveals its complex structure.Comment: 8 pages, 3 figure
A parallel grid-based implementation for real time processing of event log data in collaborative applications
Collaborative applications usually register user interaction in the form of semi-structured plain text event log data. Extracting and structuring of data is a prerequisite for later key processes such as the analysis of interactions, assessment of group activity, or the provision of awareness and feedback. Yet, in real situations of online collaborative activity, the processing of log data is usually done offline since structuring event log data is, in general, a computationally costly process and the amount of log data tends to be very large. Techniques to speed and scale up the structuring and processing of log data with minimal impact on the performance of the collaborative application are thus desirable to be able to process log data in real time. In this paper, we present a parallel grid-based implementation for processing in real time the event log data generated in collaborative applications. Our results show the feasibility of using grid middleware to speed and scale up the process of structuring and processing semi-structured event log data. The Grid prototype follows the Master-Worker (MW) paradigm. It is implemented using the Globus Toolkit (GT) and is tested on the Planetlab platform
The usage of computer integrated classroom (cic) technology tools in the study of interactions of knowledge construction among esl pre-service teacher
This paper takes a glimpse at the possible tools for collecting data on interactions of knowledge construction among ESL pre-service teacher. The main tool identified to compile the data collection of the study is a customized of computer integrated classroom (CiC) system. For that purpose, a pilot study on computer support face to face peer response using CiC was trialed with a group of students enrolled in a Microteaching course at the Faculty of Education, University Technology Malaysia. CiC was explored to see whether the system could facilitate both modes of synchronous interactions: text-based reporting and verbal interaction. With the assistance of software and hardware integrated in CIC, many computer supported collaborative learning activities could be carried out by ESL pre-service teachers such as recording, storing, retrieving, and monitoring of user profiles’ activities, learning materials and interactions
A framework for smart production-logistics systems based on CPS and industrial IoT
Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems
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