21,414 research outputs found
On Content-centric Wireless Delivery Networks
The flux of social media and the convenience of mobile connectivity has
created a mobile data phenomenon that is expected to overwhelm the mobile
cellular networks in the foreseeable future. Despite the advent of 4G/LTE, the
growth rate of wireless data has far exceeded the capacity increase of the
mobile networks. A fundamentally new design paradigm is required to tackle the
ever-growing wireless data challenge.
In this article, we investigate the problem of massive content delivery over
wireless networks and present a systematic view on content-centric network
design and its underlying challenges. Towards this end, we first review some of
the recent advancements in Information Centric Networking (ICN) which provides
the basis on how media contents can be labeled, distributed, and placed across
the networks. We then formulate the content delivery task into a content rate
maximization problem over a share wireless channel, which, contrasting the
conventional wisdom that attempts to increase the bit-rate of a unicast system,
maximizes the content delivery capability with a fixed amount of wireless
resources. This conceptually simple change enables us to exploit the "content
diversity" and the "network diversity" by leveraging the abundant computation
sources (through application-layer encoding, pushing and caching, etc.) within
the existing wireless networks. A network architecture that enables wireless
network crowdsourcing for content delivery is then described, followed by an
exemplary campus wireless network that encompasses the above concepts.Comment: 20 pages, 7 figures,accepted by IEEE Wireless
Communications,Sept.201
QuLa: service selection and forwarding table population in service-centric networking using real-life topologies
The amount of services located in the network has drastically increased over the last decade which is why more and more datacenters are located at the network edge, closer to the users. In the current Internet it is up to the client to select a destination using a resolution service (Domain Name System, Content Delivery Networks ...). In the last few years, research on Information-Centric Networking (ICN) suggests to put this selection responsibility at the network components; routers find the closest copy of a content object using the content name as input.
We extend the principle of ICN to services; service routers forward requests to service instances located in datacenters spread across the network edge. To solve this problem, we first present a service selection algorithm based on both server and network metrics. Next, we describe a method to reduce the state required in service routers while minimizing the performance loss caused by this data reduction. Simulation results based on real-life networks show that we are able to find a near-optimal load distribution with only minimal state required in the service routers
On Constructing Persistent Identifiers with Persistent Resolution Targets
Persistent Identifiers (PID) are the foundation referencing digital assets in
scientific publications, books, and digital repositories. In its realization,
PIDs contain metadata and resolving targets in form of URLs that point to data
sets located on the network. In contrast to PIDs, the target URLs are typically
changing over time; thus, PIDs need continuous maintenance -- an effort that is
increasing tremendously with the advancement of e-Science and the advent of the
Internet-of-Things (IoT). Nowadays, billions of sensors and data sets are
subject of PID assignment. This paper presents a new approach of embedding
location independent targets into PIDs that allows the creation of
maintenance-free PIDs using content-centric network technology and overlay
networks. For proving the validity of the presented approach, the Handle PID
System is used in conjunction with Magnet Link access information encoding,
state-of-the-art decentralized data distribution with BitTorrent, and Named
Data Networking (NDN) as location-independent data access technology for
networks. Contrasting existing approaches, no green-field implementation of PID
or major modifications of the Handle System is required to enable
location-independent data dissemination with maintenance-free PIDs.Comment: Published IEEE paper of the FedCSIS 2016 (SoFAST-WS'16) conference,
11.-14. September 2016, Gdansk, Poland. Also available online:
http://ieeexplore.ieee.org/document/7733372
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A semantic web services-based infrastructure for context-adaptive process support
Current technologies aimed at supporting processes whether it is a business or learning process - primarily follow a metadata- and data-centric paradigm. Whereas process metadata is usually based on a specific standard specification - such as the Business Process Modeling Notation (BPMN) or the IMS Learning Design Standard - the allocation of resources is done manually at design-time, and the used data is often specific to one process context only. These facts limit the reusability of process models across different standards and contexts. To overcome these issues, we introduce an innovative Semantic Web Service-based framework aimed at changing the current paradigm to a context-adaptive service-oriented approach. Following the idea of layered semantic abstractions, our approach supports the development of abstract semantic process model - reusable across different contexts and standards - that enables a dynamic adaptation to specific actor needs and objectives. To illustrate the application of our framework and establish its feasibility, we describe a prototypical application in the E-Learning domain
Context-adaptive learning designs by using semantic web services
IMS Learning Design (IMS-LD) is a promising technology aimed at supporting learning processes. IMS-LD packages contain the learning process metadata as well as the learning resources. However, the allocation of resources - whether data or services - within the learning design is done manually at design-time on the basis of the subjective appraisals of a learning designer. Since the actual learning context is known at runtime only, IMS-LD applications cannot adapt to a specific context or learner. Therefore, the reusability is limited and high development costs have to be taken into account to support a variety of contexts. To overcome these issues, we propose a highly dynamic approach based on Semantic Web Services (SWS) technology. Our aim is moving from the current data- and metadata-based to a context-adaptive service-orientated paradigm We introduce semantic descriptions of a learning process in terms of user objectives (learning goals) to abstract from any specific metadata standards and used learning resources. At runtime, learning goals are accomplished by automatically selecting and invoking the services that fit the actual user needs and process contexts. As a result, we obtain a dynamic adaptation to different contexts at runtime. Semantic mappings from our standard-independent process models will enable the automatic development of versatile, reusable IMS-LD applications as well as the reusability across multiple metadata standards. To illustrate our approach, we describe a prototype application based on our principles
Designing a novel virtual collaborative environment to support collaboration in design review meetings
Project review meetings are part of the project management process and are organised to assess progress and resolve any design conflicts to avoid delays in construction. One of the key challenges during a project review meeting is to bring the stakeholders together and use this time effectively to address design issues as quickly as possible. At present, current technology solutions based on BIM or CAD are information-centric and do not allow project teams to collectively explore the design from a range of perspectives and brainstorm ideas when design conflicts are encountered. This paper presents a system architecture that can be used to support multi-functional team collaboration more effectively during such design review meetings. The proposed architecture illustrates how information-centric BIM or CAD systems can be made human- and team-centric to enhance team communication and problem solving. An implementation of the proposed system architecture has been tested for its utility, likability and usefulness during design review meetings. The evaluation results suggest that the collaboration platform has the potential to enhance collaboration among multi-functional teams
Scalable bloom-filter based content dissemination in community networks using information centric principles
Information-Centric Networking (ICN) is a new communication paradigm that shifts the focus from content location to content objects themselves. Users request the content by its name or some other form of identifier. Then, the network is responsible for locating the requested content and sending it to the users. Despite a large number of works on ICN in recent years, the problem of scalability of ICN systems has not been studied and addressed adequately. This is especially true when considering real-world deployments and the so-called alternative networks such as community networks. In this work, we explore the applicability of ICN principles in the challenging and unpredictable environments of community networks. In particular, we focus on stateless content dissemination based on Bloom filters (BFs). We highlight the scalability limitations of the classical single-stage BF based approach and argue that by enabling multiple BF stages would lead to performance enhancements. That is, a multi-stage BF based content dissemination mechanism could support large network topologies with heterogeneous traffic and diverse channel conditions. In addition to scalability improvements, this approach also is more secure with regard to Denial of Service attacks
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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