837 research outputs found
Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications
From its introduction to its quindecennial, network coding has built a strong reputation for enhancing packet recovery and achieving maximum information flow in both wired and wireless networks. Traditional studies focused on optimizing the throughput of the system by proposing elaborate schemes able to reach the network capacity. With the shift toward distributed computing on mobile devices, performance and complexity become both critical factors that affect the efficiency of a coding strategy. Instantly decodable network coding presents itself as a new paradigm in network coding that trades off these two aspects. This paper review instantly decodable network coding schemes by identifying, categorizing, and evaluating various algorithms proposed in the literature. The first part of the manuscript investigates the conventional centralized systems, in which all decisions are carried out by a central unit, e.g., a base-station. In particular, two successful approaches known as the strict and generalized instantly decodable network are compared in terms of reliability, performance, complexity, and packet selection methodology. The second part considers the use of instantly decodable codes in a device-to-device communication network, in which devices speed up the recovery of the missing packets by exchanging network coded packets. Although the performance improvements are directly proportional to the computational complexity increases, numerous successful schemes from both the performance and complexity viewpoints are identified
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency
The growing number of AI-driven applications in mobile devices has led to
solutions that integrate deep learning models with the available edge-cloud
resources. Due to multiple benefits such as reduction in on-device energy
consumption, improved latency, improved network usage, and certain privacy
improvements, split learning, where deep learning models are split away from
the mobile device and computed in a distributed manner, has become an
extensively explored topic. Incorporating compression-aware methods (where
learning adapts to compression level of the communicated data) has made split
learning even more advantageous. This method could even offer a viable
alternative to traditional methods, such as federated learning techniques. In
this work, we develop an adaptive compression-aware split learning method
('deprune') to improve and train deep learning models so that they are much
more network-efficient, which would make them ideal to deploy in weaker devices
with the help of edge-cloud resources. This method is also extended ('prune')
to very quickly train deep learning models through a transfer learning
approach, which trades off little accuracy for much more network-efficient
inference abilities. We show that the 'deprune' method can reduce network usage
by 4x when compared with a split-learning approach (that does not use our
method) without loss of accuracy, while also improving accuracy over
compression-aware split-learning by 4 percent. Lastly, we show that the 'prune'
method can reduce the training time for certain models by up to 6x without
affecting the accuracy when compared against a compression-aware split-learning
approach
Performance Assessment of Aggregation and Deaggregation Algorithms in Vehicular Delay-Tolerant Networks
Vehicular Delay-Tolerant Networks (VDTNs) are a new approach for vehicular
communications where vehicles cooperate with each other, acting as the
communication infrastructure, to provide low-cost asynchronous opportunistic
communications. These communication technologies assume variable delays
and bandwidth constraints characterized by a non-transmission control protocol/
internet protocol architecture but interacting with it at the edge of the
network.
VDTNs are based on the principle of asynchronous communications, bundleoriented
communication from the DTN architecture, employing a store-carryand-
forward routing paradigm. In this sense, VDTNs should use the tight network
resources optimizing each opportunistic contact among nodes.
At the ingress edge nodes, incoming IP Packets (datagrams) are assembled
into large data packets, called bundles. The bundle aggregation process plays
an important role on the performance of VDTN applications. Then, this paper
presents three aggregation algorithms based on time, bundle size, and a hybrid
solution with combination of both. Furthermore, the following four aggregation
schemes with quality of service (QoS) support are proposed: 1) single-class bundle
with N = M, 2) composite-class bundle with N = M, 3) single-class bundle
with N > M, and 4) composite-class bundle with N > M, where N is the number
of classes of incoming packets and M is the number of priorities supported
by the VDTN core network. The proposed mechanisms were evaluated through
a laboratory testbed, called VDTN@Lab. The adaptive hybrid approach and the
composite-class schemes present the best performance for different types of
traffic load and best priorities distribution, respectively
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Communication systems to date primarily aim at reliably communicating bit
sequences. Such an approach provides efficient engineering designs that are
agnostic to the meanings of the messages or to the goal that the message
exchange aims to achieve. Next generation systems, however, can be potentially
enriched by folding message semantics and goals of communication into their
design. Further, these systems can be made cognizant of the context in which
communication exchange takes place, providing avenues for novel design
insights. This tutorial summarizes the efforts to date, starting from its early
adaptations, semantic-aware and task-oriented communications, covering the
foundations, algorithms and potential implementations. The focus is on
approaches that utilize information theory to provide the foundations, as well
as the significant role of learning in semantics and task-aware communications.Comment: 28 pages, 14 figure
Lecture Notes on Network Information Theory
These lecture notes have been converted to a book titled Network Information
Theory published recently by Cambridge University Press. This book provides a
significantly expanded exposition of the material in the lecture notes as well
as problems and bibliographic notes at the end of each chapter. The authors are
currently preparing a set of slides based on the book that will be posted in
the second half of 2012. More information about the book can be found at
http://www.cambridge.org/9781107008731/. The previous (and obsolete) version of
the lecture notes can be found at http://arxiv.org/abs/1001.3404v4/
Unified Power Management in Wireless Sensor Networks, Doctoral Dissertation, August 2006
Radio power management is of paramount concern in wireless sensor networks (WSNs) that must achieve long lifetimes on scarce amount of energy. Previous work has treated communication and sensing separately, which is insufficient for a common class of sensor networks that must satisfy both sensing and communication requirements. Furthermore, previous approaches focused on reducing energy consumption in individual radio states resulting in suboptimal solutions. Finally, existing power management protocols often assume simplistic models that cannot accurately reflect the sensing and communication properties of real-world WSNs. We develop a unified power management approach to address these issues. We first analyze the relationship between sensing and communication performance of WSNs. We show that sensing coverage often leads to good network connectivity and geographic routing performance, which provides insights into unified power management under both sensing and communication performance requirements. We then develop a novel approach called Minimum Power Configuration that ingegrates the power consumption in different radio states into a unified optimization framework. Finally, we develop two power management protocols that account for realistic communication and sensing properties of WSNs. Configurable Topology Control can configure a network topology to achieve desired path quality in presence of asymmetric and lossy links. Co-Grid is a coverage maintenance protocol that adopts a probabilistic sensing model. Co-Grid can satisfy desirable sensing QoS requirements (i.e., detection probability and false alarm rate) based on a distributed data fusion model
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, thereby providing avenues for novel design insights. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications, covering the foundations, algorithms and potential implementations. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications
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