IEEE Network - March / April 2017 - page 7

IEEE Network • March/April 2017
Investigating the Performance of Pull-Based Dynamic Adaptive
Streaming in NDN
Benjamin Rainer, Daniel Posch, and Hermann Hellwagner,
IEEE Journal on Select-
ed Areas in Communications
, vol. 34, no. 8, pp. 2130–2140, Aug. 2016
The authors investigate the performance of pull-based DAS
in NDN using different forwarding strategies at the network
level and different client-side adaptation mechanisms at the
application level, especially under non-optimal conditions. To
achieve this, they study the performance gap between the the-
oretically possible and realized streaming performance by NDN
considering concurrently streaming consumers, and further
compare these performance evaluations to classical MPEG-
DASH streaming in IP-based networks. In order to derive upper
bounds for the multimedia streaming performance in NDN
with and without caching, the authors model the concurrent
streaming activities by a given number of clients in a network as
a Multi-Commodity Flow Problem. This work is a forward look-
ing one by putting the state-of-the-art DAS technology into the
context of novel networking paradigm of NDN.
Online social networks such as Facebook allow developers
to introduce third-party applications to enhance the user expe-
rience. Such enhancements include interesting or entertain-
ing ways of communicating among online friends and diverse
activities such as playing games or listening to music. However,
hackers have started taking advantage of the popularity of this
third-party apps platform and deploying malicious apps. These
malicious apps can reach large numbers of users, access users’
privacy information, and propagate by promoting each other. In
order to deal with this severe problem, Sazzadur Rahman
et al
propose FRAppE in:
Detecting Malicious Facebook Applications
Sazzadur Rahman, Ting-Kai Huang, Harsha V. Madhyastha, and Michalis Falout-
IEEE/ACM Transactions on Networking
, vol. 24, no. 2, pp. 773–787, Apr. 2016
In this work, the authors design FRAppE, which is a detector
using on-demand and aggregation-based features to determine
whether an app is malicious or not. FRAppE achieves a detec-
tion accuracy of 99.5%. The authors analyze 111K apps that
made 91 million posts over nine months, and find that 13% of
these apps are malicious. A systematic profiling shows that mali-
cious apps and benign apps have significant differences. The
authors also demonstrate that malicious apps collude with each
other at massive scale by providing links to each other. Some of
the malicious apps even impersonate benign apps for malignant
purposes. Based on these findings, the authors present some
enlightening opinions on how to ameliorate the online social
networks. This work tackles a very practical real-world issue,
and it is highly correlated to people’s daily lives.
Wireless networks have become an increasingly indispens-
able part of our daily life, and in some cases, they are even
employed to transmit highly sensitive information. However,
due to the broadcast nature of wireless channels, security has
been a critical issue in today’s wireless communication net-
works. A promising direction toward achieving secured com-
munications is to exploit the randomness of wireless channels
to ensure secrecy. Although theoretical studies have shown
its potential to enhance the secrecy of communications, great
challenges remain when transforming the theory into practice.
In the following paper, Jiajia Liu
et al
. investigate strategies of
allowing and managing cooperative jammers for security in
wireless networks.
Cooperative Jammer Placement for Physical Layer Security
Jiajia Liu, Zhihong Liu, Yong Zeng, and Jianfeng Ma,
IEEE Network
, vol. 30, no. 6,
pp. 56–61, Dec. 2016
Cooperation in communication networks can be used to
increase the secrecy of the communication by weakening
eavesdropping links. In cooperative jamming, cooperation is
implemented by injecting additional noise to the wireless chan-
nels. In this paper, the authors propose a cooperative jamming
protocol that can jam eavesdroppers anywhere in the network.
Specifically, the cooperative jamming strategy is composed of
three steps: jammer selection and power adjustment, block
construction, and block transmission. Jammer placement algo-
rithms targeted toward optimizing the total number of jammers
are also introduced. Simulations show an improvement of the
security performance with the cooperative jamming strategy.
This work demonstrates a strong practical value.
Among so many killer applications of mobile networks,
mobile video streaming is more and more popular, which is
also expected to grow substantially in the near future. How-
ever, video streaming is very resource hungry, but the user
equipment such as the smartphone is resource-constrained;
therefore, solutions that optimize the energy efficiency of such
services are important. Siekkinen
et al
. propose an algorithm
that optimizes this energy consumption by computing an opti-
mal strategy for the delivery of the video content to a mobile
Using Viewing Statistics to Control Energy and Traffic Overhead in
Mobile Video Streaming
Matti Siekkinen, Mohammad Ashraful Hoque, and Jukka K. Nurminen,
Trans. Net
., vol. 24, no. 3, June 2016, pp. 1489–503
The authors identify two types of sources of energy waste
in mobile video streaming. The first is the idle energy expendi-
ture caused by radio resource management, which keeps the
radio powered on for a while after all bytes of transfer have
been received. This is called tail energy. The second is the ener-
gy spent in downloading content that the user will not watch
because of abandoning the session, which is a very common
phenomenon in practice. If video content is delivered to the
smartphone in small chunks, the first type of energy waste dom-
inates, but the amount of unnecessarily downloaded content is
constrained. In contrast, delivering the video content in large
chunks makes the total tail energy small, whereas the risk of
spending energy and network resources to download content
that a user will not watch is high. With these observations, the
authors propose the eSchedule algorithm, which utilizes view-
ing statistics to predict viewer behavior and computes an ener-
gy optimal download strategy for a given mobile client.
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