33,971 research outputs found
Distributed Hybrid Simulation of the Internet of Things and Smart Territories
This paper deals with the use of hybrid simulation to build and compose
heterogeneous simulation scenarios that can be proficiently exploited to model
and represent the Internet of Things (IoT). Hybrid simulation is a methodology
that combines multiple modalities of modeling/simulation. Complex scenarios are
decomposed into simpler ones, each one being simulated through a specific
simulation strategy. All these simulation building blocks are then synchronized
and coordinated. This simulation methodology is an ideal one to represent IoT
setups, which are usually very demanding, due to the heterogeneity of possible
scenarios arising from the massive deployment of an enormous amount of sensors
and devices. We present a use case concerned with the distributed simulation of
smart territories, a novel view of decentralized geographical spaces that,
thanks to the use of IoT, builds ICT services to manage resources in a way that
is sustainable and not harmful to the environment. Three different simulation
models are combined together, namely, an adaptive agent-based parallel and
distributed simulator, an OMNeT++ based discrete event simulator and a
script-language simulator based on MATLAB. Results from a performance analysis
confirm the viability of using hybrid simulation to model complex IoT
scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487
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
Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model
Multi-agent geographical models integrate very large numbers of spatial
interactions. In order to validate those models large amount of computing is
necessary for their simulation and calibration. Here a new data processing
chain including an automated calibration procedure is experimented on a
computational grid using evolutionary algorithms. This is applied for the first
time to a geographical model designed to simulate the evolution of an early
urban settlement system. The method enables us to reduce the computing time and
provides robust results. Using this method, we identify several parameter
settings that minimise three objective functions that quantify how closely the
model results match a reference pattern. As the values of each parameter in
different settings are very close, this estimation considerably reduces the
initial possible domain of variation of the parameters. The model is thus a
useful tool for further multiple applications on empirical historical
situations
Robustness and Adaptiveness Analysis of Future Fleets
Making decisions about the structure of a future military fleet is a
challenging task. Several issues need to be considered such as the existence of
multiple competing objectives and the complexity of the operating environment.
A particular challenge is posed by the various types of uncertainty that the
future might hold. It is uncertain what future events might be encountered; how
fleet design decisions will influence and shape the future; and how present and
future decision makers will act based on available information, their personal
biases regarding the importance of different objectives, and their economic
preferences. In order to assist strategic decision-making, an analysis of
future fleet options needs to account for conditions in which these different
classes of uncertainty are exposed. It is important to understand what
assumptions a particular fleet is robust to, what the fleet can readily adapt
to, and what conditions present clear risks to the fleet. We call this the
analysis of a fleet's strategic positioning. This paper introduces how
strategic positioning can be evaluated using computer simulations. Our main aim
is to introduce a framework for capturing information that can be useful to a
decision maker and for defining the concepts of robustness and adaptiveness in
the context of future fleet design. We demonstrate our conceptual framework
using simulation studies of an air transportation fleet. We capture uncertainty
by employing an explorative scenario-based approach. Each scenario represents a
sampling of different future conditions, different model assumptions, and
different economic preferences. Proposed changes to a fleet are then analysed
based on their influence on the fleet's robustness, adaptiveness, and risk to
different scenarios
Robustness and Adaptability Analysis of Future Military Air Transportation Fleets
Making decisions about the structure of a future military fleet is challenging. Several issues need to be considered, including multiple competing objectives and the complexity of the operating environment. A particular challenge is posed by the various types of uncertainty that the future holds. It is uncertain what future events might be encountered and how fleet design decisions will influence these events. In order to assist strategic decision-making, an analysis of future fleet options needs to account for conditions in which these different uncertainties are exposed. It is important to understand what assumptions a particular fleet is robust to, what the fleet can readily adapt to, and what conditions present risks to the fleet. We call this the analysis of a fleet’s strategic positioning. Our main aim is to introduce a framework that captures information useful to a decision maker and defines the concepts of robustness and adaptability in the context of future fleet design. We demonstrate our conceptual framework by simulating an air transportation fleet problem. We account for uncertainty by employing an explorative scenario-based approach. Each scenario represents a sampling of different future conditions and different model assumptions. Proposed changes to a fleet are then analysed based on their influence on the fleet’s robustness, adaptability, and risk to different scenarios
The Quest for Scalability and Accuracy in the Simulation of the Internet of Things: an Approach based on Multi-Level Simulation
This paper presents a methodology for simulating the Internet of Things (IoT)
using multi-level simulation models. With respect to conventional simulators,
this approach allows us to tune the level of detail of different parts of the
model without compromising the scalability of the simulation. As a use case, we
have developed a two-level simulator to study the deployment of smart services
over rural territories. The higher level is base on a coarse grained,
agent-based adaptive parallel and distributed simulator. When needed, this
simulator spawns OMNeT++ model instances to evaluate in more detail the issues
concerned with wireless communications in restricted areas of the simulated
world. The performance evaluation confirms the viability of multi-level
simulations for IoT environments.Comment: Proceedings of the IEEE/ACM International Symposium on Distributed
Simulation and Real Time Applications (DS-RT 2017
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