Simulating Real-Time Aspects of Wireless Sensor Networks

Pagano, Paolo; Chitnis, Mangesh; Lipari, Giuseppe; Nastasi, Christian; Liang, Yao

doi:10.1155/2010/107946

Cited by 16 publications

(7 citation statements)

References 2 publications

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“…A simulator for traffic flow monitoring was developed using Matlab based on discrete-event simulation formalization [26,27]. It simulates the proposed SDA algorithm and other wireless sensor events.…”

Section: Simulationmentioning

confidence: 99%

Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

Fimbombaya

Mvungi

Ndyetabura

et al. 2018

Modelling and Simulation in Engineering

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Traffic flow monitoring involves the capturing and dissemination of real-time traffic flow information for a road network. When a vehicle, a ferromagnetic object, travels along a road, it disturbs the ambient Earth’s magnetic field, causing its distortion. The resulting distortion carries vehicle signature containing traffic flow related information such as speed, count, direction, and classification. To extract such information in chaotic cities, a novel algorithm based on the resulting magnetic field distortion was developed using nonintrusive sensor localization. The algorithm extracts traffic flow information from resulting magnetic field distortions sensed by magnetic wireless sensor nodes located on the sides of the road. The model magnetic wireless sensor networks algorithm for local Earth’s magnetic field performance was evaluated through simulation using Dar es Salaam City traffic flow conditions. Simulation results for vehicular detection and count showed 93% and 87% success rates during normal and congested traffic states, respectively. Travel Time Index (TTI) was used as a congestion indicator, where different levels of congestion were evaluated depending on the traffic state with a performance of 87% and 88% success rates during normal and congested traffic flow, respectively.

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Section: Simulationmentioning

confidence: 99%

Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

Fimbombaya

Mvungi

Ndyetabura

et al. 2018

Modelling and Simulation in Engineering

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“…Table 1 shows that (1) scalable techniques (analysis and performance prediction) are not able to model heterogeneous, multithreaded devices, and (2) none of thetechniques extends network simulators to increase their accuracy. Existing efforts that address the latter are limited to modeling specialized systems such as routers [5] or sensors [12], do not consider multi-threading [16] or parallel execution [15], or do not provide any method to relate the models to any real, existing system [15]. Extensions of network simulators that use machine simulators [13] suffer from scalability issues, large modeling effort and/or lack of abstractions.…”

Section: H H H H H H H H H H Hmentioning

confidence: 99%

Modeling communication software execution for accurate simulation of distributed systems

Kristiansen

Plagemann

Goebel

2013

Proceedings of the 1st ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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Network simulation is commonly used to evaluate the performance of distributed systems, but these approaches do not account for the performance impact that protocol execution on nodes has on performance, which may be significant. We propose a methodology to capture execution models from communication software running on real devices where the execution models can be integrated with discrete event network simulators to improve their accuracy. We provide a set of rules to instrument the software to obtain the events of importance, and present techniques to create executable models based on the obtained traces. To make the models scalable, processing stages are reduced to statistical distributions. When the resulting models are executed in a device model with a scheduler simulator, we are able to model the dynamics of multithreading and parallel execution. Our initial results from a proof-of-concept extension to Ns-3 show that our models are able to accurately model protocol execution on the Google Nexus One with low simulation overhead.

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“…Support for the IEEE 802.11 and IEEE 802.15.4 standards is provided by external contribution modules. An extension of the NS-2 simulator is the Real-Time Network Simulator (RTNS) [9], which extends the support for the IEEE 802.15.4 (the de-facto standard for WSNs) and adds support for modeling local processing (simulating operating systems).…”

Section: A Network Simulatorsmentioning

confidence: 99%

WiSE-MNet: an experimental environment for wireless multimedia sensor networks

Nastasi

Cavallaro

2011

Sensor Signal Processing for Defence (SSPD 2011)

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We propose a simulation environment for networks for Wireless Multimedia Sensor Networks (WMSNs), i.e. networks with sensors capturing complex vectorial data, such as for example video and audio. The proposed simulation environment allows us to model the communication layers, the sensing and distributed applications of a WMSN. This Wireless Simulation Environment for Multimedia Networks (WiSE-MNet) is based on Castalia/Omnet++ and is available as open source to the research community [1]. The environment is designed to be flexible and extensible, and has a simple camera model that enables the simulation of distributed computer-vision algorithms at a high level of abstraction. We demonstrate the effectiveness of WiSE-MNet with a distributed tracking application.

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Simulating Real-Time Aspects of Wireless Sensor Networks

Cited by 16 publications

References 2 publications

Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

Modeling communication software execution for accurate simulation of distributed systems

WiSE-MNet: an experimental environment for wireless multimedia sensor networks

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