14,308 research outputs found
Recommended from our members
A classification of emerging and traditional grid systems
The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings.
However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area
Mobile Broadband Possibilities considering the Arrival of IEEE 802.16m & LTE with an Emphasis on South Asia
This paper intends to look deeper into finding an ideal mobile broadband
solution. Special stress has been put in the South Asian region through some
comparative analysis. Proving their competency in numerous aspects, WiMAX and
LTE already have already made a strong position in telecommunication industry.
Both WiMAX and LTE are 4G technologies designed to move data rather than voice
having IP networks based on OFDM technology. So, they aren't like typical
technological rivals as of GSM and CDMA. But still a gesture of hostility seems
to outburst long before the stable commercial launch of LTE. In this paper
various aspects of WiMAX and LTE for deployment have been analyzed. Again, we
tried to make every possible consideration with respect to south Asia i.e. how
mass people of this region may be benefited. As a result, it might be regarded
as a good source in case of making major BWA deployment decisions in this
region. Besides these, it also opens the path for further research and in depth
thinking in this issue.Comment: IEEE Publication format, ISSN 1947 5500,
http://sites.google.com/site/ijcsis
Calling Dick Tracy! Or, Cellphone Use, Progress, and a Racial Paradigm
The hero and phone-watch from Dick Tracy are evoked regularly in news and studies of cellphone use. This paper argues that the racial paradigm of White law enforcer and Dark law-breaker in the comic strip resonates in contemporary evocations and in discussions of cellphone use and crime. Representations of mobile communication and racialized criminality in Dick Tracy were inspired by the 1930s “war on crime” that intersected with wireless innovations and with lynching. This paper interprets that repeated evocation of the comic strip is a “perverse nostalgia” for an old-fashioned form of law and order premised on racialized violence and viewing
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
- …