3,351 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
An Investigation into the Performance Evaluation of Connected Vehicle Applications: From Real-World Experiment to Parallel Simulation Paradigm
A novel system was developed that provides drivers lane merge advisories, using vehicle trajectories obtained through Dedicated Short Range Communication (DSRC). It was successfully tested on a freeway using three vehicles, then targeted for further testing, via simulation. The failure of contemporary simulators to effectively model large, complex urban transportation networks then motivated further research into distributed and parallel traffic simulation. An architecture for a closed-loop, parallel simulator was devised, using a new algorithm that accounts for boundary nodes, traffic signals, intersections, road lengths, traffic density, and counts of lanes; it partitions a sample, Tennessee road network more efficiently than tools like METIS, which increase interprocess communications (IPC) overhead by partitioning more transportation corridors. The simulator uses logarithmic accumulation to synchronize parallel simulations, further reducing IPC. Analyses suggest this eliminates up to one-third of IPC overhead incurred by a linear accumulation model
Efficient Domain Decomposition Algorithms and Applications in Transportation and Structural Engineering
Domain decomposition is a divide-and-conquer strategy. In the first part of this dissertation, a new/simple/efficient domain decomposition partitioning algorithm is proposed to break a large domain into smaller sub-domains, in such a way as to minimize the number of system boundary nodes and to balance the work load for each sub-domain. This new domain decomposition algorithm is based on the network’s shortest path solution. Numerical results indicate that the new Shortest Distance Decomposition Algorithm outperformed the most widely used METIS algorithm in 21 out of 27 tested (transportation) examples. In the second part of this dissertation, another new/simple and highly efficient shortest path algorithm is described for finding the shortest path from all-to-all (all source nodes to all destination nodes). This new Domain Decomposition-based Shortest Path algorithm basically finds the SP from all-to-all for each sub-domain, and assembles each sub-domains’ shortest path solution to correctly obtain the original (un-partitioned) network’s shortest path solution. Numerical results for real-life transportation networks have shown that the algorithm is much faster than the existing Dijkstra’s shortest path algorithm. Finally, the Shortest Distance Decomposition Algorithm has also been shown to perform better than METIS when minimizing the non-zero fill-in terms of structural engineering stiffness matrices used during the finite element simultaneous linear equation solution process
Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach
Prediction of taxi service demand and supply is essential for improving
customer's experience and provider's profit. Recently, graph neural networks
(GNNs) have been shown promising for this application. This approach models
city regions as nodes in a transportation graph and their relations as edges.
GNNs utilize local node features and the graph structure in the prediction.
However, more efficient forecasting can still be achieved by following two main
routes; enlarging the scale of the transportation graph, and simultaneously
exploiting different types of nodes and edges in the graphs. However, both
approaches are challenged by the scalability of GNNs. An immediate remedy to
the scalability challenge is to decentralize the GNN operation. However, this
creates excessive node-to-node communication. In this paper, we first
characterize the excessive communication needs for the decentralized GNN
approach. Then, we propose a semi-decentralized approach utilizing multiple
cloudlets, moderately sized storage and computation devices, that can be
integrated with the cellular base stations. This approach minimizes
inter-cloudlet communication thereby alleviating the communication overhead of
the decentralized approach while promoting scalability due to cloudlet-level
decentralization. Also, we propose a heterogeneous GNN-LSTM algorithm for
improved taxi-level demand and supply forecasting for handling dynamic taxi
graphs where nodes are taxis. Extensive experiments over real data show the
advantage of the semi-decentralized approach as tested over our heterogeneous
GNN-LSTM algorithm. Also, the proposed semi-decentralized GNN approach is shown
to reduce the overall inference time by about an order of magnitude compared to
centralized and decentralized inference schemes.Comment: 13 pages, 10 figures, LaTeX; typos corrected, references added,
mathematical analysis adde
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