5,159 research outputs found
Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systems
Mobile edge computing (MEC) and wireless power transfer (WPT) are two
promising techniques to enhance the computation capability and to prolong the
operational time of low-power wireless devices that are ubiquitous in Internet
of Things. However, the computation performance and the harvested energy are
significantly impacted by the severe propagation loss. In order to address this
issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless powered system is
studied in this paper. The computation rate maximization problems in a
UAV-enabled MEC wireless powered system are investigated under both partial and
binary computation offloading modes, subject to the energy harvesting causal
constraint and the UAV's speed constraint. These problems are non-convex and
challenging to solve. A two-stage algorithm and a three-stage alternative
algorithm are respectively proposed for solving the formulated problems. The
closed-form expressions for the optimal central processing unit frequencies,
user offloading time, and user transmit power are derived. The optimal
selection scheme on whether users choose to locally compute or offload
computation tasks is proposed for the binary computation offloading mode.
Simulation results show that our proposed resource allocation schemes
outperforms other benchmark schemes. The results also demonstrate that the
proposed schemes converge fast and have low computational complexity.Comment: This paper has been accepted by IEEE JSA
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions
The Internet of Things (IoT) paradigm is being rapidly adopted for the
creation of smart environments in various domains. The IoT-enabled
Cyber-Physical Systems (CPSs) associated with smart city, healthcare, Industry
4.0 and Agtech handle a huge volume of data and require data processing
services from different types of applications in real-time. The Cloud-centric
execution of IoT applications barely meets such requirements as the Cloud
datacentres reside at a multi-hop distance from the IoT devices. \textit{Fog
computing}, an extension of Cloud at the edge network, can execute these
applications closer to data sources. Thus, Fog computing can improve
application service delivery time and resist network congestion. However, the
Fog nodes are highly distributed, heterogeneous and most of them are
constrained in resources and spatial sharing. Therefore, efficient management
of applications is necessary to fully exploit the capabilities of Fog nodes. In
this work, we investigate the existing application management strategies in Fog
computing and review them in terms of architecture, placement and maintenance.
Additionally, we propose a comprehensive taxonomy and highlight the research
gaps in Fog-based application management. We also discuss a perspective model
and provide future research directions for further improvement of application
management in Fog computing
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning in communications and networking. Modern networks, e.g.,
Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become
more decentralized and autonomous. In such networks, network entities need to
make decisions locally to maximize the network performance under uncertainty of
network environment. Reinforcement learning has been efficiently used to enable
the network entities to obtain the optimal policy including, e.g., decisions or
actions, given their states when the state and action spaces are small.
However, in complex and large-scale networks, the state and action spaces are
usually large, and the reinforcement learning may not be able to find the
optimal policy in reasonable time. Therefore, deep reinforcement learning, a
combination of reinforcement learning with deep learning, has been developed to
overcome the shortcomings. In this survey, we first give a tutorial of deep
reinforcement learning from fundamental concepts to advanced models. Then, we
review deep reinforcement learning approaches proposed to address emerging
issues in communications and networking. The issues include dynamic network
access, data rate control, wireless caching, data offloading, network security,
and connectivity preservation which are all important to next generation
networks such as 5G and beyond. Furthermore, we present applications of deep
reinforcement learning for traffic routing, resource sharing, and data
collection. Finally, we highlight important challenges, open issues, and future
research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper
Multi-tier Drone Architecture for 5G/B5G Cellular Networks: Challenges, Trends, and Prospects
Drones (or unmanned aerial vehicles [UAVs]) are expected to be an important
component of fifth generation (5G)/beyond 5G (B5G) cellular architectures that
can potentially facilitate wireless broadcast or point-to-multipoint
transmissions. The distinct features of various drones such as the maximum
operational altitude, communication, coverage, computation, and endurance impel
the use of a multi-tier architecture for future drone-cell networks. In this
context, this article focuses on investigating the feasibility of multi-tier
drone network architecture over traditional single-tier drone networks and
identifying the scenarios in which drone networks can potentially complement
the traditional RF-based terrestrial networks. We first identify the challenges
associated with multi-tier drone networks as well as drone-assisted cellular
networks. We then review the existing state-of-the-art innovations in drone
networks and drone-assisted cellular networks. We then investigate the
performance of a multi-tier drone network in terms of spectral efficiency of
downlink transmission while illustrating the optimal intensity and altitude of
drones in different tiers numerically. Our results demonstrate the specific
network load conditions (i.e., ratio of user intensity and base station
intensity) where deployment of drones can be beneficial (in terms of spectral
efficiency of downlink transmission) for conventional terrestrial cellular
networks
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey
With the Internet of Things (IoT) becoming part of our daily life and our
environment, we expect rapid growth in the number of connected devices. IoT is
expected to connect billions of devices and humans to bring promising
advantages for us. With this growth, fog computing, along with its related edge
computing paradigms, such as multi-access edge computing (MEC) and cloudlet,
are seen as promising solutions for handling the large volume of
security-critical and time-sensitive data that is being produced by the IoT. In
this paper, we first provide a tutorial on fog computing and its related
computing paradigms, including their similarities and differences. Next, we
provide a taxonomy of research topics in fog computing, and through a
comprehensive survey, we summarize and categorize the efforts on fog computing
and its related computing paradigms. Finally, we provide challenges and future
directions for research in fog computing.Comment: 48 pages, 7 tables, 11 figures, 450 references. The data (categories
and features/objectives of the papers) of this survey are now available
publicly. Accepted by Elsevier Journal of Systems Architectur
Resource Management of energy-aware Cognitive Radio Networks and cloud-based Infrastructures
The field of wireless networks has been rapidly developed during the past
decade due to the increasing popularity of the mobile devices. The great demand
for mobility and connectivity makes wireless networking a field whose
continuous technological development is very important as new challenges and
issues are arising. Many scientists and researchers are currently engaged in
developing new approaches and optimization methods in several topics of
wireless networking. This survey paper study works from the following topics:
Cognitive Radio Networks, Interactive Broadcasting, Energy Efficient Networks,
Cloud Computing and Resource Management, Interactive Marketing and
Optimization
Internet of Things: An Overview
As technology proceeds and the number of smart devices continues to grow
substantially, need for ubiquitous context-aware platforms that support
interconnected, heterogeneous, and distributed network of devices has given
rise to what is referred today as Internet-of-Things. However, paving the path
for achieving aforementioned objectives and making the IoT paradigm more
tangible requires integration and convergence of different knowledge and
research domains, covering aspects from identification and communication to
resource discovery and service integration. Through this chapter, we aim to
highlight researches in topics including proposed architectures, security and
privacy, network communication means and protocols, and eventually conclude by
providing future directions and open challenges facing the IoT development.Comment: Keywords: Internet of Things; IoT; Web of Things; Cloud of Thing
A Survey on Modeling Energy Consumption of Cloud Applications: Deconstruction, State of the Art, and Trade-off Debates
Given the complexity and heterogeneity in Cloud computing scenarios, the
modeling approach has widely been employed to investigate and analyze the
energy consumption of Cloud applications, by abstracting real-world objects and
processes that are difficult to observe or understand directly. It is clear
that the abstraction sacrifices, and usually does not need, the complete
reflection of the reality to be modeled. Consequently, current energy
consumption models vary in terms of purposes, assumptions, application
characteristics and environmental conditions, with possible overlaps between
different research works. Therefore, it would be necessary and valuable to
reveal the state-of-the-art of the existing modeling efforts, so as to weave
different models together to facilitate comprehending and further investigating
application energy consumption in the Cloud domain. By systematically
selecting, assessing and synthesizing 76 relevant studies, we rationalized and
organized over 30 energy consumption models with unified notations. To help
investigate the existing models and facilitate future modeling work, we
deconstructed the runtime execution and deployment environment of Cloud
applications, and identified 18 environmental factors and 12 workload factors
that would be influential on the energy consumption. In particular, there are
complicated trade-offs and even debates when dealing with the combinational
impacts of multiple factors.Comment: in pres
Mobile Cloud Computing with a UAV-Mounted Cloudlet: Optimal Bit Allocation for Communication and Computation
Mobile cloud computing relieves the tension between compute-intensive mobile
applications and battery-constrained mobile devices by enabling the offloading
of computing tasks from mobiles to a remote processors. This paper considers a
mobile cloud computing scenario in which the "cloudlet" processor that provides
offloading opportunities to mobile devices is mounted on unmanned aerial
vehicles (UAVs) to enhance coverage. Focusing on a slotted communication system
with frequency division multiplexing between mobile and UAV, the joint
optimization of the number of input bits transmitted in the uplink by the
mobile to the UAV, the number of input bits processed by the cloudlet at the
UAV, and the number of output bits returned by the cloudlet to the mobile in
the downlink in each slot is carried out by means of dual decomposition under
maximum latency constraints with the aim of minimizing the mobile energy
consumption. Numerical results reveal the critical importance of an optimized
bit allocation in order to enable significant energy savings as compared to
local mobile execution for stringent latency constraints.Comment: 21 pages, 3 figures, 1 Table, accepted in IET Communication
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