4,640 research outputs found
Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems
Intelligent transportation systems (ITSs) will be a major component of
tomorrow's smart cities. However, realizing the true potential of ITSs requires
ultra-low latency and reliable data analytics solutions that can combine, in
real-time, a heterogeneous mix of data stemming from the ITS network and its
environment. Such data analytics capabilities cannot be provided by
conventional cloud-centric data processing techniques whose communication and
computing latency can be high. Instead, edge-centric solutions that are
tailored to the unique ITS environment must be developed. In this paper, an
edge analytics architecture for ITSs is introduced in which data is processed
at the vehicle or roadside smart sensor level in order to overcome the ITS
latency and reliability challenges. With a higher capability of passengers'
mobile devices and intra-vehicle processors, such a distributed edge computing
architecture can leverage deep learning techniques for reliable mobile sensing
in ITSs. In this context, the ITS mobile edge analytics challenges pertaining
to heterogeneous data, autonomous control, vehicular platoon control, and
cyber-physical security are investigated. Then, different deep learning
solutions for such challenges are proposed. The proposed deep learning
solutions will enable ITS edge analytics by endowing the ITS devices with
powerful computer vision and signal processing functions. Preliminary results
show that the proposed edge analytics architecture, coupled with the power of
deep learning algorithms, can provide a reliable, secure, and truly smart
transportation environment.Comment: 5 figure
Security for Cyber-Physical Systems: Leveraging Cellular Networks and Fog Computing
The reach and scale of Cyber Physical Systems (CPS) are expanding to many
aspects of our everyday lives. Health, safety, transportation and education are
a few areas where CPS are increasingly prevalent. There is a pressing need to
secure CPS, both at the edge and at the network core. We present a hybrid
framework for securing CPS that leverages the computational resources and
coordination of Fog networks, and builds on cellular connectivity for low-power
and resource constrained CPS devices. The routine support for cellular
authentication, encryption, and integrity protection is enhanced with the
addition of a cellular cloud controller to take over the management of the
radio and core security contexts dedicated to CPS devices. Specialized cellular
cloudlets liaison with core network components to implement localized and
network-wide defense for denial-or-service, smart jamming, or unauthorized CPS
tracking attacks. A comparison between our framework and recent cellular/fog
solutions is provided, together with a feasibility analysis for operational
framework deployment. We conclude with future research directions that we
believe are pivotal to the proliferation of secure and scalable CPS.Comment: IEEE CNS 201
Energy and Information Management of Electric Vehicular Network: A Survey
The connected vehicle paradigm empowers vehicles with the capability to
communicate with neighboring vehicles and infrastructure, shifting the role of
vehicles from a transportation tool to an intelligent service platform.
Meanwhile, the transportation electrification pushes forward the electric
vehicle (EV) commercialization to reduce the greenhouse gas emission by
petroleum combustion. The unstoppable trends of connected vehicle and EVs
transform the traditional vehicular system to an electric vehicular network
(EVN), a clean, mobile, and safe system. However, due to the mobility and
heterogeneity of the EVN, improper management of the network could result in
charging overload and data congestion. Thus, energy and information management
of the EVN should be carefully studied. In this paper, we provide a
comprehensive survey on the deployment and management of EVN considering all
three aspects of energy flow, data communication, and computation. We first
introduce the management framework of EVN. Then, research works on the EV
aggregator (AG) deployment are reviewed to provide energy and information
infrastructure for the EVN. Based on the deployed AGs, we present the research
work review on EV scheduling that includes both charging and vehicle-to-grid
(V2G) scheduling. Moreover, related works on information communication and
computing are surveyed under each scenario. Finally, we discuss open research
issues in the EVN
Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities
The ever-increasing mobile data demands have posed significant challenges in
the current radio access networks, while the emerging computation-heavy
Internet of things (IoT) applications with varied requirements demand more
flexibility and resilience from the cloud/edge computing architecture. In this
article, to address the issues, we propose a novel air-ground integrated mobile
edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and
assist the communication, caching, and computing of the edge network. In
specific, we present the detailed architecture of AGMEN, and investigate the
benefits and application scenarios of drone-cells, and UAV-assisted edge
caching and computing. Furthermore, the challenging issues in AGMEN are
discussed, and potential research directions are highlighted.Comment: Accepted by IEEE Communications Magazine. 5 figure
Differential Privacy Techniques for Cyber Physical Systems: A Survey
Modern cyber physical systems (CPSs) has widely being used in our daily lives
because of development of information and communication technologies (ICT).With
the provision of CPSs, the security and privacy threats associated to these
systems are also increasing. Passive attacks are being used by intruders to get
access to private information of CPSs. In order to make CPSs data more secure,
certain privacy preservation strategies such as encryption, and k-anonymity
have been presented in the past. However, with the advances in CPSs
architecture, these techniques also needs certain modifications. Meanwhile,
differential privacy emerged as an efficient technique to protect CPSs data
privacy. In this paper, we present a comprehensive survey of differential
privacy techniques for CPSs. In particular, we survey the application and
implementation of differential privacy in four major applications of CPSs named
as energy systems, transportation systems, healthcare and medical systems, and
industrial Internet of things (IIoT). Furthermore, we present open issues,
challenges, and future research direction for differential privacy techniques
for CPSs. This survey can serve as basis for the development of modern
differential privacy techniques to address various problems and data privacy
scenarios of CPSs.Comment: 46 pages, 12 figure
Fog Computing in IoT Aided Smart Grid Transition- Requirements, Prospects, Status Quos and Challenges
Due to unfolded developments in both the IT sectors viz. Intelligent
Transportation and Information Technology contemporary Smart Grid (SG) systems
are leveraged with smart devices and entities. Such infrastructures when
bestowed with the Internet of Things (IoT) and sensor network make a universe
of objects active and online. The traditional cloud deployment succumbs to meet
the analytics and computational exigencies decentralized, dynamic cum
resource-time critical SG ecosystems. This paper synoptically inspects to what
extent the cloud computing utilities can satisfy the mission-critical
requirements of SG ecosystems and which subdomains and services call for fog
based computing archetypes. The objective of this work is to comprehend the
applicability of fog computing algorithms to interplay with the core centered
cloud computing support, thus enabling to come up with a new breed of real-time
and latency free SG services. The work also highlights the opportunities
brought by fog based SG deployments. Correspondingly, we also highlight the
challenges and research thrusts elucidated towards the viability of fog
computing for successful SG Transition.Comment: 13 Pages, 1 table, 1 Figur
Fog Computing: Focusing on Mobile Users at the Edge
With smart devices, particular smartphones, becoming our everyday companions,
the ubiquitous mobile Internet and computing applications pervade people daily
lives. With the surge demand on high-quality mobile services at anywhere, how
to address the ubiquitous user demand and accommodate the explosive growth of
mobile traffics is the key issue of the next generation mobile networks. The
Fog computing is a promising solution towards this goal. Fog computing extends
cloud computing by providing virtualized resources and engaged location-based
services to the edge of the mobile networks so as to better serve mobile
traffics. Therefore, Fog computing is a lubricant of the combination of cloud
computing and mobile applications. In this article, we outline the main
features of Fog computing and describe its concept, architecture and design
goals. Lastly, we discuss some of the future research issues from the
networking perspective.Comment: 11 pages, 6 figure
Big Data and Fog Computing
Fog computing serves as a computing layer that sits between the edge devices
and the cloud in the network topology. They have more compute capacity than the
edge but much less so than cloud data centers. They typically have high uptime
and always-on Internet connectivity. Applications that make use of the fog can
avoid the network performance limitation of cloud computing while being less
resource constrained than edge computing. As a result, they offer a useful
balance of the current paradigms. This article explores various aspects of fog
computing in the context of big data.Comment: To Appear as a contribution in Encyclopedia of Big Data Technologies,
Sherif Sakr and Albert Zomaya eds., Springer Nature, 201
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
A Survey on the Security of Pervasive Online Social Networks (POSNs)
Pervasive Online Social Networks (POSNs) are the extensions of Online Social
Networks (OSNs) which facilitate connectivity irrespective of the domain and
properties of users. POSNs have been accumulated with the convergence of a
plethora of social networking platforms with a motivation of bridging their
gap. Over the last decade, OSNs have visually perceived an altogether
tremendous amount of advancement in terms of the number of users as well as
technology enablers. A single OSN is the property of an organization, which
ascertains smooth functioning of its accommodations for providing a quality
experience to their users. However, with POSNs, multiple OSNs have coalesced
through communities, circles, or only properties, which make
service-provisioning tedious and arduous to sustain. Especially, challenges
become rigorous when the focus is on the security perspective of cross-platform
OSNs, which are an integral part of POSNs. Thus, it is of utmost paramountcy to
highlight such a requirement and understand the current situation while
discussing the available state-of-the-art. With the modernization of OSNs and
convergence towards POSNs, it is compulsory to understand the impact and reach
of current solutions for enhancing the security of users as well as associated
services. This survey understands this requisite and fixates on different sets
of studies presented over the last few years and surveys them for their
applicability to POSNs...Comment: 39 Pages, 10 Figure
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