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This book provides a timely and comprehensive study of developing
efficient network slicing frameworks in both 5G wireless and core
networks. It also presents protocol stack layer perspectives, which
includes virtual network topology design, end-to-end delay
modeling, dynamic resource slicing, and link-layer and
transport-layer protocol customization. This book provides basic
principles, concepts and technologies for communication, computing
and networking. Optimization and queueing analysis techniques are
applied to solving different problems for network slicing
illustrated in this book as well. Researchers working in the area
of network slicing in 5G networks and beyond, and advanced-level
students majoring in electrical engineering, computer engineering
and computer science will find this book useful as a reference or
secondary textbook. Professionals in industry seeking solutions to
resource management for 5G networks and beyond will also want to
purchase this book.
This book covers connectivity and edge computing solutions for
representative Internet of Things (IoT) use cases, including
industrial IoT, rural IoT, Internet of Vehicles (IoV), and mobile
virtual reality (VR). Based on their unique characteristics and
requirements, customized solutions are designed with targets such
as supporting massive connections or seamless mobility and
achieving low latency or high energy efficiency. Meanwhile, the
book highlights the role of artificial intelligence (AI) in future
IoT networks and showcases AI-based connectivity and edge computing
solutions. The solutions presented in this book serve the overall
purpose of facilitating an increasingly connected and intelligent
world. The potential benefits of the solutions include increased
productivity in factories, improved connectivity in rural areas,
enhanced safety for vehicles, and enriched entertainment
experiences for mobile users. Featuring state-of-the-art research
in the IoT field, this book can help answer the question of how to
connect billions of diverse devices and enable seamless data
collection and processing in future IoT. The content also provides
insights regarding the significance of customizing use
case-specific solutions as well as approaches of using various AI
methods to empower IoT. This book targets researchers and graduate
students working in the areas of electrical engineering, computing
engineering, and computer science as a secondary textbook or
reference. Professionals in industry who work in the field of IoT
will also find this book useful.
This book provides a timely and comprehensive study of dynamic
resource management for network slicing in service-oriented
fifth-generation (5G) and beyond core networks. This includes the
perspective of developing efficient computation resource
provisioning and scheduling solutions to guarantee consistent
service performance in terms of end-to-end (E2E) data delivery
delay. Network slicing is enabled by the software defined
networking (SDN) and network function virtualization (NFV)
paradigms. For a network slice with a target traffic load, the E2E
service delivery is enabled by virtual network function (VNF)
placement and traffic routing with static resource allocations.
When data traffic enters the network, the traffic load is dynamic
and can deviate from the target value, potentially leading to QoS
performance degradation and network congestion. Data traffic has
dynamics in different time granularities. For example, the traffic
statistics (e.g., mean and variance) can be non-stationary and
experience significant changes in a coarse time granularity, which
are usually predictable. Within a long time duration with
stationary traffic statistics, there are traffic dynamics in small
timescales, which are usually highly bursty and unpredictable. To
provide continuous QoS performance guarantee and ensure efficient
and fair operation of the network slices over time, it is essential
to develop dynamic resource management schemes for the embedded
services in the presence of traffic dynamics during virtual network
operation. Queueing theory is used in system modeling, and
different techniques including optimization and machine learning
are applied to solving the dynamic resource management problems.
Based on a simplified M/M/1 queueing model with Poisson traffic
arrivals, an optimization model for flow migration is presented to
accommodate the large-timescale changes in the average traffic
rates with average E2E delay guarantee, while addressing a
trade-off between load balancing and flow migration overhead. To
overcome the limitations of Poisson traffic model, the authors
present a machine learning approach for dynamic VNF resource
scaling and migration. The new solution captures the inherent
traffic patterns in a real-world traffic trace with non-stationary
traffic statistics in large timescale, predicts resource demands
for VNF resource scaling, and triggers adaptive VNF migration
decision making, to achieve load balancing, migration cost
reduction, and resource overloading penalty suppression in the long
run. Both supervised and unsupervised machine learning tools are
investigated for dynamic resource management. To accommodate the
traffic dynamics in small time granularities, the authors present a
dynamic VNF scheduling scheme to coordinate the scheduling among
VNFs of multiple services, which achieves network utility
maximization with delay guarantee for each service. Researchers and
graduate students working in the areas of electrical engineering,
computing engineering and computer science will find this book
useful as a reference or secondary text. Professionals in industry
seeking solutions to dynamic resource management for 5G and beyond
networks will also want to purchase this book.
The book presents the proceedings of the 2nd International
Conference on 5G for Ubiquitous Connectivity (5GU 2018), which took
place on December 4-5, 2018 in Nanjing, People's Republic of China.
The aim of this conference is to bring together researchers and
developers as well as regulators and policy makers to present their
latest views on 5G, including new networking, new wireless
communications, resource control & management, future access
techniques, new emerging applications, and latest findings in key
research activities on 5G. The book is applicable to researchers,
academics, students, and professionals. Features practical, tested
applications in 5G for ubiquitous connectivity; Includes discussion
of 5G for ubiquitous connectivity in relation to wireless
communications, resource control & management, and future
access techniques; Applicable to researchers, academics, students,
and professionals.
The book presents the proceedings of the 2nd International
Conference on 5G for Ubiquitous Connectivity (5GU 2018), which took
place on December 4-5, 2018 in Nanjing, People's Republic of China.
The aim of this conference is to bring together researchers and
developers as well as regulators and policy makers to present their
latest views on 5G, including new networking, new wireless
communications, resource control & management, future access
techniques, new emerging applications, and latest findings in key
research activities on 5G. The book is applicable to researchers,
academics, students, and professionals. Features practical, tested
applications in 5G for ubiquitous connectivity; Includes discussion
of 5G for ubiquitous connectivity in relation to wireless
communications, resource control & management, and future
access techniques; Applicable to researchers, academics, students,
and professionals.
This timely book provides broad coverage of vehicular ad-hoc
network (VANET) issues, such as security, and network selection.
Machine learning based methods are applied to solve these issues.
This book also includes four rigorously refereed chapters from
prominent international researchers working in this subject area.
The material serves as a useful reference for researchers, graduate
students, and practitioners seeking solutions to VANET
communication and security related issues. This book will also help
readers understand how to use machine learning to address the
security and communication challenges in VANETs. Vehicular ad-hoc
networks (VANETs) support vehicle-to-vehicle communications and
vehicle-to-infrastructure communications to improve the
transmission security, help build unmanned-driving, and support
booming applications of onboard units (OBUs). The high mobility of
OBUs and the large-scale dynamic network with fixed roadside units
(RSUs) make the VANET vulnerable to jamming. The anti-jamming
communication of VANETs can be significantly improved by using
unmanned aerial vehicles (UAVs) to relay the OBU message. UAVs help
relay the OBU message to improve the
signal-to-interference-plus-noise-ratio of the OBU signals, and
thus reduce the bit-error-rate of the OBU message, especially if
the serving RSUs are blocked by jammers and/or interference, which
is also demonstrated in this book. This book serves as a useful
reference for researchers, graduate students, and practitioners
seeking solutions to VANET communication and security related
issues.
This book presents the current research on safety message
dissemination in vehicular networks, covering medium access control
and relay selection for multi-hop safety message broadcast. Along
with an overall overview of the architecture, characteristics, and
applications of vehicular networks, the authors discuss the
challenging issues in the research on performance improvement for
safety applications, and provide a comprehensive review of the
research literature.A cross layer broadcast protocol is included to
support efficient safety message broadcast by jointly considering
geographical location, physical-layer channel condition, and moving
velocity of vehicles in the highway scenario. To further support
multi-hop safety message broadcast in a complex road layout, the
authors propose an urban multi-hop broadcast protocol that utilizes
a novel forwarding node selection scheme. Additionally, a busy tone
based medium access control scheme is designed to provide strict
priority to safety applications in vehicle-to-infrastructure
communications.This book offers useful insights into protocol
design and inspires a new line of thinking in performance
improvements for safety applications in vehicular networks. It is a
valuable resource for professionals, researchers, or advanced-level
students working in vehicular networks or quality of service.
This book presents link-layer cooperative frameworks to improve
transmission reliability and network throughput of distributed TDMA
MAC protocols in vehicular ad hoc networks (VANETs). The main
objective of this book is to present link-layer node cooperation
schemes to enhance the link-layer performance of vehicular
networks, in terms of reliability and system throughput. The
authors present approaches proposed for the medium access control
(MAC) and node cooperation in VANETs. The authors also cover
cooperative ADHOC MAC for point-to-point communication between a
pair of source and destination nodes, and cooperative relay
broadcasting for broadcast services in this book. The performance
of node cooperation frameworks is evaluated via mathematical
analysis and computer simulations, in comparison with distributed
TDMA MAC protocols without cooperation. The proposed node
cooperation frameworks enhance the performance of distributed TDMA
MAC and make it more robust to tackle the dynamic networking
conditions in VANETs. Furthermore, with cooperation enabled
transmission, the performance of distributed TMDA MAC is more
suitable to support the wide range of mobile applications and their
strict service requirements which is discussed in this book. The
proposed node cooperation schemes and distributed cooperation
decisions can be extended to wireless systems other than
distributed TDMA MAC, such as cellular communication, for vehicular
communications introduced in this book. This book is useful for
researchers from academia and industry, as well as advanced level
students interested in vehicular communication networks. It is also
useful for professionals and engineers developing applications that
use cooperative wireless communication systems.
This book presents the current research on safety message
dissemination in vehicular networks, covering medium access control
and relay selection for multi-hop safety message broadcast. Along
with an overall overview of the architecture, characteristics, and
applications of vehicular networks, the authors discuss the
challenging issues in the research on performance improvement for
safety applications, and provide a comprehensive review of the
research literature.A cross layer broadcast protocol is included to
support efficient safety message broadcast by jointly considering
geographical location, physical-layer channel condition, and moving
velocity of vehicles in the highway scenario. To further support
multi-hop safety message broadcast in a complex road layout, the
authors propose an urban multi-hop broadcast protocol that utilizes
a novel forwarding node selection scheme. Additionally, a busy tone
based medium access control scheme is designed to provide strict
priority to safety applications in vehicle-to-infrastructure
communications.This book offers useful insights into protocol
design and inspires a new line of thinking in performance
improvements for safety applications in vehicular networks. It is a
valuable resource for professionals, researchers, or advanced-level
students working in vehicular networks or quality of service.
This brief presents a stochastic microscopic mobility model that
describes the temporal changes of intervehicle distances. The model
is consistent with simulated and empirical vehicle traffic
patterns. Using stochastic lumpability methods, the proposed
mobility model is mapped into an aggregated mobility model that
describes the mobility of a group of vehicles. In addition, the
proposed mobility model is used to analyze the spatiotemporal VANET
topology. Two metrics are proposed to characterize the impact of
vehicle mobility on VANET topology: the time period between
successive changes in communication link state (connection and
disconnection) and the time period between successive changes in
node's one-hop neighborhood. Using the proposed lumped group
mobility model, the two VANET topology metrics are
probabilistically characterized for different vehicular traffic
flow conditions. Furthermore, the limiting behavior of a system of
two-hop vehicles and the overlap-state of their coverage ranges is
modeled, and the steady-state number of common vehicle neighbors
between the two vehicles is approximately derived. The proposed
mobility model will facilitate mathematical analysis in VANETs. The
spatiotemporal VANET topology analysis provides a useful tool for
the development of mobility-aware vehicular network protocols.
Mobility Modeling for Vehicular Communication Networks is designed
for researchers, developers, and professionals involved with
vehicular communications. It is also suitable for advanced-level
students interested in communications, transport infrastructure,
and infotainment applications.
This brief focuses on medium access control (MAC) in vehicular ad
hoc networks (VANETs), and presents VeMAC, a novel MAC scheme based
on distributed time division multiple access (TDMA) for VANETs. The
performance of VeMAC is evaluated via mathematical analysis and
computer simulations in comparison with other existing MAC
protocols, including the IEEE 802.11p standard. This brief aims at
proposing TDMA as a suitable MAC scheme for VANETs, which can
support the quality-of-service requirements of high priority VANET
applications.
This Springer Brief investigates the voice and elastic/interactive
data service support over cognitive radio networks (CRNs), in terms
of their delay requirements. The increased demand for wireless
communication conflicts with the scarcity of the radio spectrum,
but CRNS allow for more efficient use of the networks. The authors
review packet level delay requirements of the voice service and
session level delay requirements of the elastic/interactive data
services, particularly constant-rate and on-off voice traffic
capacities in CRNs with centralized and distributed network
coordination. Some generic channel access schemes are considered as
the coordination mechanism, and call admission control algorithms
are developed for non-fully-connected CRNs. Other key topics
include the advantages of supporting voice traffic flows with
different delay requirements, the mean response time of the elastic
data traffic over a centralized CRN, and effects of the traffic
load at the base station and file length (service time requirement)
distribution on the mean response time. The brief is designed for
professionals and researchers working with wireless networks,
cognitive radio, and communications. It is also a helpful reference
for advanced-level students interested in efficient wireless
communications.
This brief focuses on radio resource allocation in a heterogeneous
wireless medium. It presents radio resource allocation algorithms
with decentralized implementation, which support both
single-network and multi-homing services. The brief provides a set
of cooperative networking algorithms, which rely on the concepts of
short-term call traffic load prediction, network cooperation,
convex optimization, and decomposition theory. In the proposed
solutions, mobile terminals play an active role in the resource
allocation operation, instead of their traditional role as passive
service recipients in the networking environment.
This brief investigates distributed medium access control (MAC)
with QoS provisioning for both single- and multi-hop wireless
networks including wireless local area networks (WLANs), wireless
ad hoc networks, and wireless mesh networks. For WLANs, an
efficient MAC scheme and a call admission control algorithm are
presented to provide guaranteed QoS for voice traffic and, at the
same time, increase the voice capacity significantly compared with
the current WLAN standard. In addition, a novel token-based
scheduling scheme is proposed to provide great flexibility and
facility to the network service provider for service class
management. Also proposed is a novel busy-tone based distributed
MAC scheme for wireless ad hoc networks and a collision-free MAC
scheme for wireless mesh networks, respectively, taking the
different network characteristics into consideration. The proposed
schemes enhance the QoS provisioning capability to real-time
traffic and, at the same time, significantly improve the system
throughput and fairness performance for data traffic, as compared
with the most popular IEEE 802.11 MAC scheme.
This book demonstrates that the reliable and secure communication
performance of maritime communications can be significantly
improved by using intelligent reflecting surface (IRS) aided
communication, privacy-aware Internet of Things (IoT)
communications, intelligent resource management and location
privacy protection. In the IRS aided maritime communication system,
the reflecting elements of IRS can be intelligently
controlled to change the phase of signal, and finally enhance the
received signal strength of maritime ships (or sensors) or jam
maritime eavesdroppers illustrated in this book. The power and
spectrum resource in maritime communications can be jointly
optimized to guarantee the quality of service (i.e., security and
reliability requirements), and reinforcement leaning is adopted to
smartly choose the resource allocation strategy. Moreover, learning
based privacy-aware offloading and location privacy protection are
proposed to intelligently guarantee the privacy-preserving
requirements of maritime ships or (sensors). Therefore, these
communication schemes based on reinforcement learning algorithms
can help maritime communication systems to improve the information
security, especially in dynamic and complex maritime environments.
This timely book also provides broad coverage of the maritime
wireless communication issues, such as reliability, security,
resource management, and privacy protection. Reinforcement learning
based methods are applied to solve these issues. This book includes
four rigorously refereed chapters from prominent international
researchers working in this subject area. The material serves as a
useful reference for researchers, graduate students. Practitioners
seeking solutions to maritime wireless communication and security
related issues will benefit from this book as well.
This book provides a timely and comprehensive study of developing
efficient network slicing frameworks in both 5G wireless and core
networks. It also presents protocol stack layer perspectives, which
includes virtual network topology design, end-to-end delay
modeling, dynamic resource slicing, and link-layer and
transport-layer protocol customization. This book provides basic
principles, concepts and technologies for communication, computing
and networking. Optimization and queueing analysis techniques are
applied to solving different problems for network slicing
illustrated in this book as well. Researchers working in the area
of network slicing in 5G networks and beyond, and advanced-level
students majoring in electrical engineering, computer engineering
and computer science will find this book useful as a reference or
secondary textbook. Professionals in industry seeking solutions to
resource management for 5G networks and beyond will also want to
purchase this book.
This book covers connectivity and edge computing solutions for
representative Internet of Things (IoT) use cases, including
industrial IoT, rural IoT, Internet of Vehicles (IoV), and mobile
virtual reality (VR). Based on their unique characteristics and
requirements, customized solutions are designed with targets such
as supporting massive connections or seamless mobility and
achieving low latency or high energy efficiency. Meanwhile, the
book highlights the role of artificial intelligence (AI) in future
IoT networks and showcases AI-based connectivity and edge computing
solutions. The solutions presented in this book serve the overall
purpose of facilitating an increasingly connected and intelligent
world. The potential benefits of the solutions include increased
productivity in factories, improved connectivity in rural areas,
enhanced safety for vehicles, and enriched entertainment
experiences for mobile users. Featuring state-of-the-art research
in the IoT field, this book can help answer the question of how to
connect billions of diverse devices and enable seamless data
collection and processing in future IoT. The content also provides
insights regarding the significance of customizing use
case-specific solutions as well as approaches of using various AI
methods to empower IoT. This book targets researchers and graduate
students working in the areas of electrical engineering, computing
engineering, and computer science as a secondary textbook or
reference. Professionals in industry who work in the field of IoT
will also find this book useful.
This book provides a timely and comprehensive study of dynamic
resource management for network slicing in service-oriented
fifth-generation (5G) and beyond core networks. This includes the
perspective of developing efficient computation resource
provisioning and scheduling solutions to guarantee consistent
service performance in terms of end-to-end (E2E) data delivery
delay. Network slicing is enabled by the software defined
networking (SDN) and network function virtualization (NFV)
paradigms. For a network slice with a target traffic load, the E2E
service delivery is enabled by virtual network function (VNF)
placement and traffic routing with static resource allocations.
When data traffic enters the network, the traffic load is dynamic
and can deviate from the target value, potentially leading to QoS
performance degradation and network congestion. Data traffic has
dynamics in different time granularities. For example, the traffic
statistics (e.g., mean and variance) can be non-stationary and
experience significant changes in a coarse time granularity, which
are usually predictable. Within a long time duration with
stationary traffic statistics, there are traffic dynamics in small
timescales, which are usually highly bursty and unpredictable. To
provide continuous QoS performance guarantee and ensure efficient
and fair operation of the network slices over time, it is essential
to develop dynamic resource management schemes for the embedded
services in the presence of traffic dynamics during virtual network
operation. Queueing theory is used in system modeling, and
different techniques including optimization and machine learning
are applied to solving the dynamic resource management problems.
Based on a simplified M/M/1 queueing model with Poisson traffic
arrivals, an optimization model for flow migration is presented to
accommodate the large-timescale changes in the average traffic
rates with average E2E delay guarantee, while addressing a
trade-off between load balancing and flow migration overhead. To
overcome the limitations of Poisson traffic model, the authors
present a machine learning approach for dynamic VNF resource
scaling and migration. The new solution captures the inherent
traffic patterns in a real-world traffic trace with non-stationary
traffic statistics in large timescale, predicts resource demands
for VNF resource scaling, and triggers adaptive VNF migration
decision making, to achieve load balancing, migration cost
reduction, and resource overloading penalty suppression in the long
run. Both supervised and unsupervised machine learning tools are
investigated for dynamic resource management. To accommodate the
traffic dynamics in small time granularities, the authors present a
dynamic VNF scheduling scheme to coordinate the scheduling among
VNFs of multiple services, which achieves network utility
maximization with delay guarantee for each service. Researchers and
graduate students working in the areas of electrical engineering,
computing engineering and computer science will find this book
useful as a reference or secondary text. Professionals in industry
seeking solutions to dynamic resource management for 5G and beyond
networks will also want to purchase this book.
This timely book provides broad coverage of vehicular ad-hoc
network (VANET) issues, such as security, and network selection.
Machine learning based methods are applied to solve these issues.
This book also includes four rigorously refereed chapters from
prominent international researchers working in this subject area.
The material serves as a useful reference for researchers, graduate
students, and practitioners seeking solutions to VANET
communication and security related issues. This book will also help
readers understand how to use machine learning to address the
security and communication challenges in VANETs. Vehicular ad-hoc
networks (VANETs) support vehicle-to-vehicle communications and
vehicle-to-infrastructure communications to improve the
transmission security, help build unmanned-driving, and support
booming applications of onboard units (OBUs). The high mobility of
OBUs and the large-scale dynamic network with fixed roadside units
(RSUs) make the VANET vulnerable to jamming. The anti-jamming
communication of VANETs can be significantly improved by using
unmanned aerial vehicles (UAVs) to relay the OBU message. UAVs help
relay the OBU message to improve the
signal-to-interference-plus-noise-ratio of the OBU signals, and
thus reduce the bit-error-rate of the OBU message, especially if
the serving RSUs are blocked by jammers and/or interference, which
is also demonstrated in this book. This book serves as a useful
reference for researchers, graduate students, and practitioners
seeking solutions to VANET communication and security related
issues.
The next-generation of wireless communications are envisioned to be
supported by heterogeneous networks by using various wireless
access technologies. The popular cellular networks and wireless
local area networks (WLANs) present perfectly complementary
characteristics in terms of service capacity, mobility support, and
quality-of-service (QoS) provisioning. The cellular/WLAN
interworking is an effective way to promote the evolution of
wireless networks. Interworking of Wireless LANs and Cellular
Networks focuses on three aspects, namely access selection, call
admission control and load sharing to investigate heterogeneous
interworking for cellular/WLAN integrated networks. It not only
reveals important observations but also offers useful tools for
performance evaluation. The unique traffic and network
characteristics are exploited to enhance interworking
effectiveness. Theoretical analysis and simulation validation
demonstrate benefits of cellular/WLAN interworking in real
networks. Last but not the least, this brief highlights promising
future research directions to guide interested readers.
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