4,799 research outputs found
Social-aware Forwarding in Opportunistic Wireless Networks: Content Awareness or Obliviousness?
With the current host-based Internet architecture, networking faces
limitations in dynamic scenarios, due mostly to host mobility. The ICN paradigm
mitigates such problems by releasing the need to have an end-to-end transport
session established during the life time of the data transfer. Moreover, the
ICN concept solves the mismatch between the Internet architecture and the way
users would like to use it: currently a user needs to know the topological
location of the hosts involved in the communication when he/she just wants to
get the data, independently of its location. Most of the research efforts aim
to come up with a stable ICN architecture in fixed networks, with few examples
in ad-hoc and vehicular networks. However, the Internet is becoming more
pervasive with powerful personal mobile devices that allow users to form
dynamic networks in which content may be exchanged at all times and with low
cost. Such pervasive wireless networks suffer with different levels of
disruption given user mobility, physical obstacles, lack of cooperation,
intermittent connectivity, among others. This paper discusses the combination
of content knowledge (e.g., type and interested parties) and social awareness
within opportunistic networking as to drive the deployment of ICN solutions in
disruptive networking scenarios. With this goal in mind, we go over few
examples of social-aware content-based opportunistic networking proposals that
consider social awareness to allow content dissemination independently of the
level of network disruption. To show how much content knowledge can improve
social-based solutions, we illustrate by means of simulation some
content-oblivious/oriented proposals in scenarios based on synthetic mobility
patterns and real human traces.Comment: 7 pages, 6 figure
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
Magazin
The Quest for a Killer App for Opportunistic and Delay Tolerant Networks (Invited Paper)
Delay Tolerant Networking (DTN) has attracted a lot of attention from the research community in recent years. Much work have been done regarding network architectures and algorithms for routing and forwarding in such networks. At the same time as many show enthusiasm for this exciting new research area there are also many sceptics, who question the usefulness of research in this area. In the past, we have seen other research areas become over-hyped and later die out as there was no killer app for them that made them useful in real scenarios. Real deployments of DTN systems have so far mostly been limited to a few niche scenarios, where they have been done as proof-of-concept field tests in research projects. In this paper, we embark upon a quest to find out what characterizes a potential killer applications for DTNs.
Are there applications and situations where DTNs provide
services that could not be achieved otherwise, or have potential to do it in a better way than other techniques? Further, we highlight some of the main challenges that needs to be solved to realize these applications and make DTNs a part of the mainstream network landscape
Towards new methods for mobility data gathering: content, sources, incentives
Over the past decade, huge amounts of work has been done in mobile and opportunistic networking research. Unfortunately, much of this has had little impact as the results have not been applicable to reality, due to incorrect assumptions and models used in the design and evaluation of the systems.
In this paper, we outline some of the problems of the assumptions of early research in the field, and provide a survey of some initial work that has started to take place to alleviate this through more realistic modelling and measurements of real systems. We do note that there is still much work to be done in this area, and then go on to identify some important properties of the network that must be studied further. We identify the types of data that are important to measure, and also give some guidelines on finding existing and potentially new sources for such data and incentivizing the holders of the data to share it
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