166,581 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
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
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
Allocation of Heterogeneous Resources of an IoT Device to Flexible Services
Internet of Things (IoT) devices can be equipped with multiple heterogeneous
network interfaces. An overwhelmingly large amount of services may demand some
or all of these interfaces' available resources. Herein, we present a precise
mathematical formulation of assigning services to interfaces with heterogeneous
resources in one or more rounds. For reasonable instance sizes, the presented
formulation produces optimal solutions for this computationally hard problem.
We prove the NP-Completeness of the problem and develop two algorithms to
approximate the optimal solution for big instance sizes. The first algorithm
allocates the most demanding service requirements first, considering the
average cost of interfaces resources. The second one calculates the demanding
resource shares and allocates the most demanding of them first by choosing
randomly among equally demanding shares. Finally, we provide simulation results
giving insight into services splitting over different interfaces for both
cases.Comment: IEEE Internet of Things Journa
The Internet of Simulation, a Specialisation of the Internet of Things with Simulation and Workflow as a Service (SIM/WFaaS)
Abstract: A trend seen in many industries is the increasing reliance on modelling and simulation to facilitate design, decision making and training. Previously, these models would operate in isolation but now there is a growing need to integrate and connect simulations together for co-simulation. In addition, the 21st century has seen the expansion of the Internet of Things (IoT) enabling the interconnectivity of smart devices across the Internet. In this paper we propose that an important, and often overlooked, domain of IoT is that of modelling and simulation. Expanding IoT to encompass interconnected simulations enables the potential for an Internet of Simulation whereby models and simulations are exposed to the wider internet and can be accessed on an "as-a-service" basis. The proposed IoS would need to manage simulation across heterogeneous infrastructures, temporal and causal aspects of simulations, as well as variations in data structures. Via the proposed Simulation as a Service (SIMaaS) and Workflow as a Service (WFaaS) constructs in IoS, highly complex simulation integration could be performed automatically, resulting in high fidelity system level simulations. Additionally, the potential for faster than real-time simulation afforded by IoS opens the possibility of connecting IoS to existing IoT infrastructure via a real-time bridge to facilitate decision making based on live data
ADN: An Information-Centric Networking Architecture for the Internet of Things
Forwarding data by name has been assumed to be a necessary aspect of an
information-centric redesign of the current Internet architecture that makes
content access, dissemination, and storage more efficient. The Named Data
Networking (NDN) and Content-Centric Networking (CCNx) architectures are the
leading examples of such an approach. However, forwarding data by name incurs
storage and communication complexities that are orders of magnitude larger than
solutions based on forwarding data using addresses. Furthermore, the specific
algorithms used in NDN and CCNx have been shown to have a number of
limitations. The Addressable Data Networking (ADN) architecture is introduced
as an alternative to NDN and CCNx. ADN is particularly attractive for
large-scale deployments of the Internet of Things (IoT), because it requires
far less storage and processing in relaying nodes than NDN. ADN allows things
and data to be denoted by names, just like NDN and CCNx do. However, instead of
replacing the waist of the Internet with named-data forwarding, ADN uses an
address-based forwarding plane and introduces an information plane that
seamlessly maps names to addresses without the involvement of end-user
applications. Simulation results illustrate the order of magnitude savings in
complexity that can be attained with ADN compared to NDN.Comment: 10 page
CRUC: Cold-start Recommendations Using Collaborative Filtering in Internet of Things
The Internet of Things (IoT) aims at interconnecting everyday objects
(including both things and users) and then using this connection information to
provide customized user services. However, IoT does not work in its initial
stages without adequate acquisition of user preferences. This is caused by
cold-start problem that is a situation where only few users are interconnected.
To this end, we propose CRUC scheme - Cold-start Recommendations Using
Collaborative Filtering in IoT, involving formulation, filtering and prediction
steps. Extensive experiments over real cases and simulation have been performed
to evaluate the performance of CRUC scheme. Experimental results show that CRUC
efficiently solves the cold-start problem in IoT.Comment: Elsevier ESEP 2011: 9-10 December 2011, Singapore, Elsevier Energy
Procedia, http://www.elsevier.com/locate/procedia/, 201
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