2,376 research outputs found
Adaptive Network Coding for Scheduling Real-time Traffic with Hard Deadlines
We study adaptive network coding (NC) for scheduling real-time traffic over a
single-hop wireless network. To meet the hard deadlines of real-time traffic,
it is critical to strike a balance between maximizing the throughput and
minimizing the risk that the entire block of coded packets may not be decodable
by the deadline. Thus motivated, we explore adaptive NC, where the block size
is adapted based on the remaining time to the deadline, by casting this
sequential block size adaptation problem as a finite-horizon Markov decision
process. One interesting finding is that the optimal block size and its
corresponding action space monotonically decrease as the deadline approaches,
and the optimal block size is bounded by the "greedy" block size. These unique
structures make it possible to narrow down the search space of dynamic
programming, building on which we develop a monotonicity-based backward
induction algorithm (MBIA) that can solve for the optimal block size in
polynomial time. Since channel erasure probabilities would be time-varying in a
mobile network, we further develop a joint real-time scheduling and channel
learning scheme with adaptive NC that can adapt to channel dynamics. We also
generalize the analysis to multiple flows with hard deadlines and long-term
delivery ratio constraints, devise a low-complexity online scheduling algorithm
integrated with the MBIA, and then establish its asymptotical
throughput-optimality. In addition to analysis and simulation results, we
perform high fidelity wireless emulation tests with real radio transmissions to
demonstrate the feasibility of the MBIA in finding the optimal block size in
real time.Comment: 11 pages, 13 figure
Optimized Live 4K Video Multicast
4K videos are becoming increasingly popular. However, despite advances in
wireless technology, streaming 4K videos over mmWave to multiple users is
facing significant challenges arising from directional communication,
unpredictable channel fluctuation and high bandwidth requirements. This paper
develops a novel 4K layered video multicast system. We (i) develop a video
quality model for layered video coding, (ii) optimize resource allocation,
scheduling, and beamforming based on the channel conditions of different users,
and (iii) put forward a streaming strategy that uses fountain code to avoid
redundancy across multicast groups and a Leaky-Bucket-based congestion control.
We realize an end-to-end system on commodity-off-the-shelf (COTS) WiGig
devices. We demonstrate the effectiveness of our system with extensive testbed
experiments and emulation
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
Emulation platform design for multimedia applications over vehicular networks
Safety applications seems that will be decisive for a successful introduction to the automotive market for the vehicular networks.
However, another kind of applications could be very helpful in order to reach the maximum number of equipped vehicles after
market introduction, because can attract a greater number of users and facilitate a vehicular infrastructure investment because
vehicular communication must provide business opportunities for Internet service providers to generate revenue. One of these kind
of applications is live video streaming over vehicular networks. Video streaming is an attractive feature to many applications, such
as emergency live video transmission, video on demand services, road-side video advertisement broadcasting and inter-vehicle
video conversation. Test and evaluate implementations in a real testbed environment could be very costly and di cult in this kind
of networks. Simulations are still commonly used as a first step in any development for vehicular networks research. Therefore, to
test this kind of applications an emulation platform for multimedia applications over vehicular networks is presented in this article.
We’ve studied the performance of video streaming services in a infrastructure environment over a highways taking special account
in the losses that produces handovers during the communication caused by the network mobility
EVEREST IST - 2002 - 00185 : D23 : final report
Deliverable pĂşblic del projecte europeu EVERESTThis deliverable constitutes the final report of the project IST-2002-001858 EVEREST. After its successful completion, the project presents this document that firstly summarizes the context, goal and the approach objective of the project. Then it presents a concise summary of the major goals and results, as well as highlights the most valuable lessons derived form the project work. A list of deliverables and publications is included in the annex.Postprint (published version
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