11,779 research outputs found
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
A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process
Scalability is one of the major issues for real-world Vehicle-to-Vehicle
network realization. To tackle this challenge, a stochastic hybrid modeling
framework based on a non-parametric Bayesian inference method, i.e.,
hierarchical Dirichlet process (HDP), is investigated in this paper. This
framework is able to jointly model driver/vehicle behavior through forecasting
the vehicle dynamical time-series. This modeling framework could be merged with
the notion of model-based information networking, which is recently proposed in
the vehicular literature, to overcome the scalability challenges in dense
vehicular networks via broadcasting the behavioral models instead of raw
information dissemination. This modeling approach has been applied on several
scenarios from the realistic Safety Pilot Model Deployment (SPMD) driving data
set and the results show a higher performance of this model in comparison with
the zero-hold method as the baseline.Comment: This is the accepted version of the paper in 2018 IEEE 88th Vehicular
Technology Conference (VTC2018-Fall) (references added, title and abstract
modified
A predefined channel coefficients library for vehicle-to-vehicle communications
It is noticeable that most of VANETs communications tests are assessed through simulation. In a majority of simulation results, the physical layer is often affected by an apparent lack of realism. Therefore, vehicular channel model has become a critical issue in the field of intelligent transport systems (ITS). To overcome the lack of realism problem, a more robust channel model is needed to reflect the reality. This paper provides an open access, predefined channel coefficients library. The library is based on 2x2 and 4x4 Multiple – Input – Multiple – Output (MIMO) systems in V2V communications, using a spatial channel model extended SCME which will help to reduce the overall simulation time. In addition, it provides a more realistic channel model for V2V communications; considering: over ranges of speeds, distances, multipath signals, sub-path signals, different angle of arrivals, different angle departures, no line of sight and line of sight. An intensive evaluation process has taken place to validate the library and acceptance results are produced. Having an open access predefined library, enables the researcher at relevant communities to test and evaluate several complicated vehicular communications scenarios in a wider manners with less time and efforts
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 and Design of Millimeter-Wave Networks for Highway Vehicular Communication
Connected and autonomous vehicles will play a pivotal role in future
Intelligent Transportation Systems (ITSs) and smart cities, in general.
High-speed and low-latency wireless communication links will allow
municipalities to warn vehicles against safety hazards, as well as support
cloud-driving solutions to drastically reduce traffic jams and air pollution.
To achieve these goals, vehicles need to be equipped with a wide range of
sensors generating and exchanging high rate data streams. Recently, millimeter
wave (mmWave) techniques have been introduced as a means of fulfilling such
high data rate requirements. In this paper, we model a highway communication
network and characterize its fundamental link budget metrics. In particular, we
specifically consider a network where vehicles are served by mmWave Base
Stations (BSs) deployed alongside the road. To evaluate our highway network, we
develop a new theoretical model that accounts for a typical scenario where
heavy vehicles (such as buses and lorries) in slow lanes obstruct Line-of-Sight
(LOS) paths of vehicles in fast lanes and, hence, act as blockages. Using tools
from stochastic geometry, we derive approximations for the
Signal-to-Interference-plus-Noise Ratio (SINR) outage probability, as well as
the probability that a user achieves a target communication rate (rate coverage
probability). Our analysis provides new design insights for mmWave highway
communication networks. In considered highway scenarios, we show that reducing
the horizontal beamwidth from to determines a minimal
reduction in the SINR outage probability (namely, at
maximum). Also, unlike bi-dimensional mmWave cellular networks, for small BS
densities (namely, one BS every m) it is still possible to achieve an
SINR outage probability smaller than .Comment: Accepted for publication in IEEE Transactions on Vehicular Technology
-- Connected Vehicles Serie
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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