9,028 research outputs found
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
Modeling the Internet of Things: a simulation perspective
This paper deals with the problem of properly simulating the Internet of
Things (IoT). Simulating an IoT allows evaluating strategies that can be
employed to deploy smart services over different kinds of territories. However,
the heterogeneity of scenarios seriously complicates this task. This imposes
the use of sophisticated modeling and simulation techniques. We discuss novel
approaches for the provision of scalable simulation scenarios, that enable the
real-time execution of massively populated IoT environments. Attention is given
to novel hybrid and multi-level simulation techniques that, when combined with
agent-based, adaptive Parallel and Distributed Simulation (PADS) approaches,
can provide means to perform highly detailed simulations on demand. To support
this claim, we detail a use case concerned with the simulation of vehicular
transportation systems.Comment: Proceedings of the IEEE 2017 International Conference on High
Performance Computing and Simulation (HPCS 2017
CERN Storage Systems for Large-Scale Wireless
The project aims at evaluating the use of CERN computing infrastructure for next generation sensor networks data analysis. The proposed system allows the simulation of a large-scale sensor array for traffic analysis, streaming data to CERN storage systems in an efficient way. The data are made available for offline and quasi-online analysis, enabling both long term planning and fast reaction on the environment
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