2,156 research outputs found
Understanding Security Requirements and Challenges in Internet of Things (IoTs): A Review
Internet of Things (IoT) is realized by the idea of free flow of information
amongst various low power embedded devices that use Internet to communicate
with one another. It is predicted that the IoT will be widely deployed and it
will find applicability in various domains of life. Demands of IoT have lately
attracted huge attention and organizations are excited about the business value
of the data that will be generated by the IoT paradigm. On the other hand, IoT
have various security and privacy concerns for the end users that limit its
proliferation. In this paper we have identified, categorized and discussed
various security challenges and state of the art efforts to resolve these
challenges
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
A Survey of Fog Computing and Communication: Current Researches and Future Directions
In this survey, we discuss the evolution of distributed computing from the
utility computing to the fog computing, various research challenges for the
development of fog computing environments, the current status on fog computing
research along with a taxonomy of various existing works in this direction.
Then, we focus on the architectures of fog computing systems, technologies for
enabling fog, fog computing features, security and privacy of fog, the QoS
parameters, applications of fog, and give critical insights of various works
done on this domain. Lastly, we briefly discuss about different fog computing
associations that closely work on the development of fog based platforms and
services, and give a summary of various types of overheads associated with fog
computing platforms. Finally, we provide a thorough discussion on the future
scopes and open research areas in fog computing as an enabler for the next
generation computing paradigm
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
Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks
The next generation wireless networks (i.e. 5G and beyond), which would be
extremely dynamic and complex due to the ultra-dense deployment of
heterogeneous networks (HetNets), poses many critical challenges for network
planning, operation, management and troubleshooting. At the same time,
generation and consumption of wireless data are becoming increasingly
distributed with ongoing paradigm shift from people-centric to machine-oriented
communications, making the operation of future wireless networks even more
complex. In mitigating the complexity of future network operation, new
approaches of intelligently utilizing distributed computational resources with
improved context-awareness becomes extremely important. In this regard, the
emerging fog (edge) computing architecture aiming to distribute computing,
storage, control, communication, and networking functions closer to end users,
have a great potential for enabling efficient operation of future wireless
networks. These promising architectures make the adoption of artificial
intelligence (AI) principles which incorporate learning, reasoning and
decision-making mechanism, as natural choices for designing a tightly
integrated network. Towards this end, this article provides a comprehensive
survey on the utilization of AI integrating machine learning, data analytics
and natural language processing (NLP) techniques for enhancing the efficiency
of wireless network operation. In particular, we provide comprehensive
discussion on the utilization of these techniques for efficient data
acquisition, knowledge discovery, network planning, operation and management of
the next generation wireless networks. A brief case study utilizing the AI
techniques for this network has also been provided.Comment: ITU Special Issue N.1 The impact of Artificial Intelligence (AI) on
communication networks and services, (To appear
Security and Privacy Issues in Cloud Computing
Cloud computing transforms the way information technology (IT) is consumed
and managed, promising improved cost efficiencies, accelerated innovation,
faster time-to-market, and the ability to scale applications on demand
(Leighton, 2009). According to Gartner, while the hype grew exponentially
during 2008 and continued since, it is clear that there is a major shift
towards the cloud computing model and that the benefits may be substantial
(Gartner Hype-Cycle, 2012). However, as the shape of the cloud computing is
emerging and developing rapidly both conceptually and in reality, the
legal/contractual, economic, service quality, interoperability, security and
privacy issues still pose significant challenges. In this chapter, we describe
various service and deployment models of cloud computing and identify major
challenges. In particular, we discuss three critical challenges: regulatory,
security and privacy issues in cloud computing. Some solutions to mitigate
these challenges are also proposed along with a brief presentation on the
future trends in cloud computing deployment.Comment: 42 pages, 2 Figures, and 5 Tables. The book chapter is accepted for
publication and is expected to be published in the second half of 201
Applying mathematical models in cloud computing: A survey
As more and more information on individuals and companies are placed in the
cloud, concerns are beginning to grow about just how safe an environment it is.
It is better to prevent security threats before they enter into the systems and
there is no way how this can be prevented without knowing where they come from.
The issue of resource allocation and revenue maximization is also equally
important especially when it comes to cloud security. This brings about the
necessity of different modelling techniques including but not limited; security
threat, resource allocation and revenue maximization models. This survey paper
will try to analyse security threats and risk mitigation in cloud computing. It
gives introduction of how viral attack can invade the virtual machines on the
cloud, discusses the top security threats and countermeasures by providing the
viral threat modelling in virtual machines and risk mitigation. Resource
allocation models and revenue maximization techniques are also discussed
Software Defined Optical Networks (SDONs): A Comprehensive Survey
The emerging Software Defined Networking (SDN) paradigm separates the data
plane from the control plane and centralizes network control in an SDN
controller. Applications interact with controllers to implement network
services, such as network transport with Quality of Service (QoS). SDN
facilitates the virtualization of network functions so that multiple virtual
networks can operate over a given installed physical network infrastructure.
Due to the specific characteristics of optical (photonic) communication
components and the high optical transmission capacities, SDN based optical
networking poses particular challenges, but holds also great potential. In this
article, we comprehensively survey studies that examine the SDN paradigm in
optical networks; in brief, we survey the area of Software Defined Optical
Networks (SDONs). We mainly organize the SDON studies into studies focused on
the infrastructure layer, the control layer, and the application layer.
Moreover, we cover SDON studies focused on network virtualization, as well as
SDON studies focused on the orchestration of multilayer and multidomain
networking. Based on the survey, we identify open challenges for SDONs and
outline future directions
Cognitive Internet of Things: A New Paradigm beyond Connection
Current research on Internet of Things (IoT) mainly focuses on how to enable
general objects to see, hear, and smell the physical world for themselves, and
make them connected to share the observations. In this paper, we argue that
only connected is not enough, beyond that, general objects should have the
capability to learn, think, and understand both physical and social worlds by
themselves. This practical need impels us to develop a new paradigm, named
Cognitive Internet of Things (CIoT), to empower the current IoT with a `brain'
for high-level intelligence. Specifically, we first present a comprehensive
definition for CIoT, primarily inspired by the effectiveness of human
cognition. Then, we propose an operational framework of CIoT, which mainly
characterizes the interactions among five fundamental cognitive tasks:
perception-action cycle, massive data analytics, semantic derivation and
knowledge discovery, intelligent decision-making, and on-demand service
provisioning. Furthermore, we provide a systematic tutorial on key enabling
techniques involved in the cognitive tasks. In addition, we also discuss the
design of proper performance metrics on evaluating the enabling techniques.
Last but not least, we present the research challenges and open issues ahead.
Building on the present work and potentially fruitful future studies, CIoT has
the capability to bridge the physical world (with objects, resources, etc.) and
the social world (with human demand, social behavior, etc.), and enhance smart
resource allocation, automatic network operation, and intelligent service
provisioning
State of the Software Development Life-Cycle for the Internet-of-Things
Software has a longstanding association with a state of crisis considering
its success rate. The explosion of Internet-connected devices,
Internet-of-Things, adds to the complexity of software systems. The particular
characteristics of these systems, such as being large-scale and its
heterogeneity, pose increasingly new challenges. In this paper, we first
briefly introduce the IoT paradigm and the current state of art of software
development. Then, we delve into the particularities of developing software for
IoT systems and systems of systems, given an overview of what are the current
methodologies and tools for design, develop and test such systems. The findings
are discussed, revealing open issues and research directions, and reveal that
the nowadays IoT software development practices are still lagging behind of
what are the current best practices.Comment: 38 page
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