34,237 research outputs found
Big Data Analytics for Dynamic Energy Management in Smart Grids
The smart electricity grid enables a two-way flow of power and data between
suppliers and consumers in order to facilitate the power flow optimization in
terms of economic efficiency, reliability and sustainability. This
infrastructure permits the consumers and the micro-energy producers to take a
more active role in the electricity market and the dynamic energy management
(DEM). The most important challenge in a smart grid (SG) is how to take
advantage of the users' participation in order to reduce the cost of power.
However, effective DEM depends critically on load and renewable production
forecasting. This calls for intelligent methods and solutions for the real-time
exploitation of the large volumes of data generated by a vast amount of smart
meters. Hence, robust data analytics, high performance computing, efficient
data network management, and cloud computing techniques are critical towards
the optimized operation of SGs. This research aims to highlight the big data
issues and challenges faced by the DEM employed in SG networks. It also
provides a brief description of the most commonly used data processing methods
in the literature, and proposes a promising direction for future research in
the field.Comment: Published in ELSEVIER Big Data Researc
A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions
The fifth generation (5G) wireless network technology is to be standardized
by 2020, where main goals are to improve capacity, reliability, and energy
efficiency, while reducing latency and massively increasing connection density.
An integral part of 5G is the capability to transmit touch perception type
real-time communication empowered by applicable robotics and haptics equipment
at the network edge. In this regard, we need drastic changes in network
architecture including core and radio access network (RAN) for achieving
end-to-end latency on the order of 1 ms. In this paper, we present a detailed
survey on the emerging technologies to achieve low latency communications
considering three different solution domains: RAN, core network, and caching.
We also present a general overview of 5G cellular networks composed of software
defined network (SDN), network function virtualization (NFV), caching, and
mobile edge computing (MEC) capable of meeting latency and other 5G
requirements.Comment: Accepted in IEEE Communications Surveys and Tutorial
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
The Convergence of Machine Learning and Communications
The areas of machine learning and communication technology are converging.
Today's communications systems generate a huge amount of traffic data, which
can help to significantly enhance the design and management of networks and
communication components when combined with advanced machine learning methods.
Furthermore, recently developed end-to-end training procedures offer new ways
to jointly optimize the components of a communication system. Also in many
emerging application fields of communication technology, e.g., smart cities or
internet of things, machine learning methods are of central importance. This
paper gives an overview over the use of machine learning in different areas of
communications and discusses two exemplar applications in wireless networking.
Furthermore, it identifies promising future research topics and discusses their
potential impact.Comment: 8 pages, 4 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
Internet of Things for Residential Areas: Toward Personalized Energy Management Using Big Data
Intelligent management of machines, particularly in a residence area, has
been of interest for many years. However, such system design has always been
limited to simple control of machines from a local area or remotely from the
Internet. In this report, for the first time, an intelligent system is
proposed, where not only provides intelligent control ability of machines to
user, but also utilizes big data and optimization techniques to provide
promotional offers to the user to optimize energy consumption of machines.
Since a high traffic communication is involved among the machines and the
optimization-big data core of system, the communication core of the proposed
system is designed based on cloud, where many challenging issues such as
spectrum assignment and resource management are involved. To deal with that,
the communication network in the home area network (HAN) is designed based on
the cognitive radio system, where a new spectrum assignment method based on the
ant colony optimization (ACO) algorithm is proposed to perform spectrum
assignment to the machines in the HAN. Performance evaluation of the proposed
spectrum assignment method shows its performance in fair spectrum assignment
among machines.Comment: Draft of technical report. Limited version under preparation for
submissio
Adaptive Power Management for Wireless Base Station in Smart Grid Environment
The growing concerns of a global environmental change raises a revolution on
the way of utilizing energy. In wireless industry, green wireless
communications has recently gained increasing attention and is expected to play
a major role in reduction of electrical power consumption. In particular,
actions to promote energy saving of wireless communications with regard to
environmental protection are becoming imperative. To this purpose, we study a
green communication system model where wireless base station is provisioned
with a combination of renewable power source and electrical grid to minimize
power consumption as well as meeting the users' demand. More specifically, we
focus on an adaptive power management for wireless base station to minimize
power consumption under various uncertainties including renewable power
generation, power price, and wireless traffic load. We believe that demand side
power management solution based on the studied communication architecture is a
major step towards green wireless communications.Comment: IEEE Wireless Communication (17 pages, 6 figures.
Payload-size and Deadline-aware Scheduling for Upcoming 5G Networks: Experimental Validation in High-load Scenarios
High data rates, low latencies, and a widespread availability are the key
properties why current cellular network technologies are used for many
different applications. However, the coexistence of different data traffic
types in the same 4G/5G-based public mobile network results in a significant
growth of interfering data traffic competing for transmission. Particularly in
the context of time-critical and highly dynamic Cyber Physical Systems (CPS)
and Vehicle-to-Everything (V2X) applications, the compliance with deadlines and
therefore the efficient allocation of scarce mobile radio resources is of high
importance. Hence, scheduling solutions are required offering a good trade-off
between the compliance with deadlines and a spectrum-efficient allocation of
resources in mobile networks. In this paper, we present the results of an
experimental validation of the Payload-size and Deadline-aware (PayDA)
scheduling algorithm using a Software-Defined Radio (SDR)-based eNodeB. The
results of the experimental validation prove the high efficiency of the
proposed PayDA scheduling algorithm for time-critical applications in both
miscellaneous and homogeneous data traffic scenarios
On Green Energy Powered Cognitive Radio Networks
Green energy powered cognitive radio (CR) network is capable of liberating
the wireless access networks from spectral and energy constraints. The
limitation of the spectrum is alleviated by exploiting cognitive networking in
which wireless nodes sense and utilize the spare spectrum for data
communications, while dependence on the traditional unsustainable energy is
assuaged by adopting energy harvesting (EH) through which green energy can be
harnessed to power wireless networks. Green energy powered CR increases the
network availability and thus extends emerging network applications. Designing
green CR networks is challenging. It requires not only the optimization of
dynamic spectrum access but also the optimal utilization of green energy. This
paper surveys the energy efficient cognitive radio techniques and the
optimization of green energy powered wireless networks. Existing works on
energy aware spectrum sensing, management, and sharing are investigated in
detail. The state of the art of the energy efficient CR based wireless access
network is discussed in various aspects such as relay and cooperative radio and
small cells. Envisioning green energy as an important energy resource in the
future, network performance highly depends on the dynamics of the available
spectrum and green energy. As compared with the traditional energy source, the
arrival rate of green energy, which highly depends on the environment of the
energy harvesters, is rather random and intermittent. To optimize and adapt the
usage of green energy according to the opportunistic spectrum availability, we
discuss research challenges in designing cognitive radio networks which are
powered by energy harvesters
Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey
Future buildings will offer new convenience, comfort, and efficiency
possibilities to their residents. Changes will occur to the way people live as
technology involves into people's lives and information processing is fully
integrated into their daily living activities and objects. The future
expectation of smart buildings includes making the residents' experience as
easy and comfortable as possible. The massive streaming data generated and
captured by smart building appliances and devices contains valuable information
that needs to be mined to facilitate timely actions and better decision making.
Machine learning and big data analytics will undoubtedly play a critical role
to enable the delivery of such smart services. In this paper, we survey the
area of smart building with a special focus on the role of techniques from
machine learning and big data analytics. This survey also reviews the current
trends and challenges faced in the development of smart building services
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