143 research outputs found
Applications of Game Theory in Vehicular Networks: A Survey
In the Internet of Things (IoT) era, vehicles and other intelligent
components in an intelligent transportation system (ITS) are connected, forming
Vehicular Networks (VNs) that provide efficient and secure traffic and
ubiquitous access to various applications. However, as the number of nodes in
ITS increases, it is challenging to satisfy a varied and large number of
service requests with different Quality of Service and security requirements in
highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for
limited network resources to achieve either an individual or a group's
objectives. Game Theory (GT), a theoretical framework designed for strategic
interactions among rational decision-makers sharing scarce resources, can be
used to model and analyze individual or group behaviors of communicating
entities in VNs. This paper primarily surveys the recent developments of GT in
solving various challenges of VNs. This survey starts with an introduction to
the background of VNs. A review of GT models studied in the VNs is then
introduced, including its basic concepts, classifications, and applicable
vehicular issues. After discussing the requirements of VNs and the motivation
of using GT, a comprehensive literature review on GT applications in dealing
with the challenges of current VNs is provided. Furthermore, recent
contributions of GT to VNs integrating with diverse emerging 5G technologies
are surveyed. Finally, the lessons learned are given, and several key research
challenges and possible solutions for applying GT in VNs are outlined.Comment: It has been submitted to "IEEE communication surveys and
tutorials".This is the revised versio
End-to-End Design for Self-Reconfigurable Heterogeneous Robotic Swarms
More widespread adoption requires swarms of robots to be more flexible for
real-world applications. Multiple challenges remain in complex scenarios where
a large amount of data needs to be processed in real-time and high degrees of
situational awareness are required. The options in this direction are limited
in existing robotic swarms, mostly homogeneous robots with limited operational
and reconfiguration flexibility. We address this by bringing elastic computing
techniques and dynamic resource management from the edge-cloud computing domain
to the swarm robotics domain. This enables the dynamic provisioning of
collective capabilities in the swarm for different applications. Therefore, we
transform a swarm into a distributed sensing and computing platform capable of
complex data processing tasks, which can then be offered as a service. In
particular, we discuss how this can be applied to adaptive resource management
in a heterogeneous swarm of drones, and how we are implementing the dynamic
deployment of distributed data processing algorithms. With an elastic drone
swarm built on reconfigurable hardware and containerized services, it will be
possible to raise the self-awareness, degree of intelligence, and level of
autonomy of heterogeneous swarms of robots. We describe novel directions for
collaborative perception, and new ways of interacting with a robotic swarm
Game Theoretic Approaches in Vehicular Networks: A Survey
In the era of the Internet of Things (IoT), vehicles and other intelligent
components in Intelligent Transportation System (ITS) are connected, forming
the Vehicular Networks (VNs) that provide efficient and secure traffic,
ubiquitous access to information, and various applications. However, as the
number of connected nodes keeps increasing, it is challenging to satisfy
various and large amounts of service requests with different Quality of Service
(QoS ) and security requirements in the highly dynamic VNs. Intelligent nodes
in VNs can compete or cooperate for limited network resources so that either an
individual or group objectives can be achieved. Game theory, a theoretical
framework designed for strategic interactions among rational decision-makers
who faced with scarce resources, can be used to model and analyze individual or
group behaviors of communication entities in VNs. This paper primarily surveys
the recent advantages of GT used in solving various challenges in VNs. As VNs
and GT have been extensively investigate34d, this survey starts with a brief
introduction of the basic concept and classification of GT used in VNs. Then, a
comprehensive review of applications of GT in VNs is presented, which primarily
covers the aspects of QoS and security. Moreover, with the development of
fifth-generation (5G) wireless communication, recent contributions of GT to
diverse emerging technologies of 5G integrated into VNs are surveyed in this
paper. Finally, several key research challenges and possible solutions for
applying GT in VNs are outlined
Energy-Efficient Offloading in Mobile Edge Computing with Edge-Cloud Collaboration
Multiple access mobile edge computing is an emerging technique to bring
computation resources close to end mobile users. By deploying edge servers at
WiFi access points or cellular base stations, the computation capabilities of
mobile users can be extended. Existing works mostly assume the remote cloud
server can be viewed as a special edge server or the edge servers are willing
to cooperate, which is not practical. In this work, we propose an edge-cloud
cooperative architecture where edge servers can rent for the remote cloud
servers to expedite the computation of tasks from mobile users. With this
architecture, the computation offloading problem is modeled as a mixed integer
programming with delay constraints, which is NP-hard. The objective is to
minimize the total energy consumption of mobile devices. We propose a greedy
algorithm as well as a simulated annealing algorithm to effectively solve the
problem. Extensive simulation results demonstrate that, the proposed greedy
algorithm and simulated annealing algorithm can achieve the near optimal
performance. On average, the proposed greedy algorithm can achieve the same
application completing time budget performance of the Brute Force optional
algorithm with only 31\% extra energy cost. The simulated annealing algorithm
can achieve similar performance with the greedy algorithm.Comment: Accepted by the 18th International Conference on Algorithms and
Architectures for Parallel Processing (ICA3PP 2018
Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities
Smart cities demand resources for rich immersive sensing, ubiquitous
communications, powerful computing, large storage, and high intelligence
(SCCSI) to support various kinds of applications, such as public safety,
connected and autonomous driving, smart and connected health, and smart living.
At the same time, it is widely recognized that vehicles such as autonomous
cars, equipped with significantly powerful SCCSI capabilities, will become
ubiquitous in future smart cities. By observing the convergence of these two
trends, this article advocates the use of vehicles to build a cost-effective
service network, called the Vehicle as a Service (VaaS) paradigm, where
vehicles empowered with SCCSI capability form a web of mobile servers and
communicators to provide SCCSI services in smart cities. Towards this
direction, we first examine the potential use cases in smart cities and
possible upgrades required for the transition from traditional vehicular ad hoc
networks (VANETs) to VaaS. Then, we will introduce the system architecture of
the VaaS paradigm and discuss how it can provide SCCSI services in future smart
cities, respectively. At last, we identify the open problems of this paradigm
and future research directions, including architectural design, service
provisioning, incentive design, and security & privacy. We expect that this
paper paves the way towards developing a cost-effective and sustainable
approach for building smart cities.Comment: 32 pages, 11 figure
An Efficient Mechanism for Computation Offloading in Mobile-Edge Computing
Mobile edge computing (MEC) is a promising technology that provides cloud and
IT services within the proximity of the mobile user. With the increasing number
of mobile applications, mobile devices (MD) encounter limitations of their
resources, such as battery life and computation capacity. The computation
offloading in MEC can help mobile users to reduce battery usage and speed up
task execution. Although there are many solutions for offloading in MEC, most
usually only employ one MEC server for improving mobile device energy
consumption and execution time. Instead of conventional centralized
optimization methods, the current paper considers a decentralized optimization
mechanism between MEC servers and users. In particular, an assignment mechanism
called school choice is employed to assist heterogeneous users to select
different MEC operators in a distributed environment. With this mechanism, each
user can benefit from minimizing the price and energy consumption of executing
tasks while also meeting the specified deadline. The present research has
designed an efficient mechanism for a computation offloading scheme that
achieves minimal price and energy consumption under latency constraints.
Numerical results demonstrate that the proposed algorithm can attain efficient
and successful computation offloading.Comment: 36 page
Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks
As an emerging decentralized secure data management platform, blockchain has
gained much popularity recently. To maintain a canonical state of blockchain
data record, proof-of-work based consensus protocols provide the nodes,
referred to as miners, in the network with incentives for confirming new block
of transactions through a process of "block mining" by solving a cryptographic
puzzle. Under the circumstance of limited local computing resources, e.g.,
mobile devices, it is natural for rational miners, i.e., consensus nodes, to
offload computational tasks for proof of work to the cloud/fog computing
servers. Therefore, we focus on the trading between the cloud/fog computing
service provider and miners, and propose an auction-based market model for
efficient computing resource allocation. In particular, we consider a
proof-of-work based blockchain network. Due to the competition among miners in
the blockchain network, the allocative externalities are particularly taken
into account when designing the auction mechanisms. Specifically, we consider
two bidding schemes: the constant-demand scheme where each miner bids for a
fixed quantity of resources, and the multi-demand scheme where the miners can
submit their preferable demands and bids. For the constant-demand bidding
scheme, we propose an auction mechanism that achieves optimal social welfare.
In the multi-demand bidding scheme, the social welfare maximization problem is
NP-hard. Therefore, we design an approximate algorithm which guarantees the
truthfulness, individual rationality and computational efficiency. Through
extensive simulations, we show that our proposed auction mechanisms with the
two bidding schemes can efficiently maximize the social welfare of the
blockchain network and provide effective strategies for the cloud/fog computing
service provider.Comment: 15 page
Mobile Edge Intelligence and Computing for the Internet of Vehicles
The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent
advancements in vehicular communications and networking. Advances in research
can now provide reliable communication links between vehicles, via
vehicle-to-vehicle communications, and between vehicles and roadside
infrastructures, via vehicle-to-infrastructure communications. Meanwhile, the
capability and intelligence of vehicles are being rapidly enhanced, and this
will have the potential of supporting a plethora of new exciting applications,
which will integrate fully autonomous vehicles, the Internet of Things (IoT),
and the environment. These trends will bring about an era of intelligent IoV,
which will heavily depend upon communications, computing, and data analytics
technologies. To store and process the massive amount of data generated by
intelligent IoV, onboard processing and Cloud computing will not be sufficient,
due to resource/power constraints and communication overhead/latency,
respectively. By deploying storage and computing resources at the wireless
network edge, e.g., radio access points, the edge information system (EIS),
including edge caching, edge computing, and edge AI, will play a key role in
the future intelligent IoV. Such system will provide not only low-latency
content delivery and computation services, but also localized data acquisition,
aggregation and processing. This article surveys the latest development in EIS
for intelligent IoV. Key design issues, methodologies and hardware platforms
are introduced. In particular, typical use cases for intelligent vehicles are
illustrated, including edge-assisted perception, mapping, and localization. In
addition, various open research problems are identified.Comment: 18 pages, 6 figures, submitted to Proceedings of the IEE
Vehicular Edge Computing and Networking: A Survey
As one key enabler of Intelligent Transportation System (ITS), Vehicular Ad
Hoc Network (VANET) has received remarkable interest from academia and
industry. The emerging vehicular applications and the exponential growing data
have naturally led to the increased needs of communication, computation and
storage resources, and also to strict performance requirements on response time
and network bandwidth. In order to deal with these challenges, Mobile Edge
Computing (MEC) is regarded as a promising solution. MEC pushes powerful
computational and storage capacities from the remote cloud to the edge of
networks in close proximity of vehicular users, which enables low latency and
reduced bandwidth consumption. Driven by the benefits of MEC, many efforts have
been devoted to integrating vehicular networks into MEC, thereby forming a
novel paradigm named as Vehicular Edge Computing (VEC). In this paper, we
provide a comprehensive survey of state-of-art research on VEC. First of all,
we provide an overview of VEC, including the introduction, architecture, key
enablers, advantages, challenges as well as several attractive application
scenarios. Then, we describe several typical research topics where VEC is
applied. After that, we present a careful literature review on existing
research work in VEC by classification. Finally, we identify open research
issues and discuss future research directions
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