24,214 research outputs found
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
Digital curation and the cloud
Digital curation involves a wide range of activities, many of which could benefit from cloud
deployment to a greater or lesser extent. These range from infrequent, resource-intensive tasks
which benefit from the ability to rapidly provision resources to day-to-day collaborative activities
which can be facilitated by networked cloud services. Associated benefits are offset by risks
such as loss of data or service level, legal and governance incompatibilities and transfer
bottlenecks. There is considerable variability across both risks and benefits according to the
service and deployment models being adopted and the context in which activities are
performed. Some risks, such as legal liabilities, are mitigated by the use of alternative, e.g.,
private cloud models, but this is typically at the expense of benefits such as resource elasticity
and economies of scale. Infrastructure as a Service model may provide a basis on which more
specialised software services may be provided.
There is considerable work to be done in helping institutions understand the cloud and its
associated costs, risks and benefits, and how these compare to their current working methods,
in order that the most beneficial uses of cloud technologies may be identified. Specific
proposals, echoing recent work coordinated by EPSRC and JISC are the development of
advisory, costing and brokering services to facilitate appropriate cloud deployments, the
exploration of opportunities for certifying or accrediting cloud preservation providers, and
the targeted publicity of outputs from pilot studies to the full range of stakeholders within the
curation lifecycle, including data creators and owners, repositories, institutional IT support
professionals and senior manager
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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