Efficient and refined modeling of wireless sensor network nodes using SystemC-AMS

Vasilevski, Michel; Beilleau, N.; Aboushady, Hassan; Pêcheux, François

doi:10.1109/rme.2008.4595730

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…A survey of modelling techniques is undertaken [18] detailing the type and scope of modelling available for IoT networks. The models range from modelling node components [19,20,21], to channel modelling [22], to whole system modelling [23]. The node based techniques aim to model all components to present a layer of abstraction from particular architecture or operating systems upon which applications can be designed.…”

Section: State Of the Artmentioning

confidence: 99%

A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection

Delaney

O’Hare

2016

Sensors

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No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks.

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Section: State Of the Artmentioning

confidence: 99%

A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection

Delaney

O’Hare

2016

Sensors

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“…SystemC-AMS has also been used to model and simulate WSNs. The focus is mainly on the transceiver circuits and communication [23,24,25]. The work in [7] describes the design methodology of the AMS part of embedded systems.…”

Section: Related Workmentioning

confidence: 99%

A hardware/software simulation environment for energy harvesting wireless sensor networks

Hormann

Glatz

Hein

et al. 2012

Proceedings of the 9th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks

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Wireless sensor networks (WSNs) consist of wirelessly communicating nodes with an autarkic power supply for each node. Typically, the consumable energy of these nodes is very limited. Energy harvesting systems (EHSs) can be used to extend the lifetime or even enable perpetual operation of the sensor nodes. Applicable energy-aware WSN protocols and applications usually raise the complexity such that rough calculations are not sufficient any more. Simulationbased analysis is needed to cope with the complexity of hardware/software interaction and its implications.This work presents a simulation environment which enables combined simulation and performance evaluation of complete WSNs including the sensor nodes' application software, the energy harvesting enhanced hardware, the wireless network communication, and the environment of the sensor nodes.

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“…SystemC-AMS has also been used to model WSNs. However, the focus is mainly on the transceiver circuits and communication [17]- [19] and not on the power supply of nodes. The work presented in [20] describes a framework for testing different power-management approaches in WSNs implemented in SystemC.…”

Section: Related Workmentioning

confidence: 99%

State-of-charge measurement error simulation for power-aware wireless sensor networks

Hormann

Glatz

Hein

et al. 2012

2012 IEEE Wireless Communications and Networking Conference (WCNC)

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Wireless sensor networks (WSNs) are power critical systems, because they are used in application areas without wired infrastructure. Each sensor node needs a dedicated power supply. Today's protocols and applications for WSNs are often poweraware. However, the state-of-charge (SoC) estimation of the energy storage component (e.g. rechargeable battery) influences the decisions of the power-aware software. A measurement error may cause wrong decisions which would reduce the lifetime of the network. This work presents the simulation results of the impact of an SoC measurement error on the network lifetime running a simple and an enhanced power-aware WSN application. A simulation environment written in SystemC-AMS is used, which enables the combined simulation of the sensor node's application software and its hardware. The simulation shows a reduction of the network lifetime of 3.13% caused by an erroneous measurement. However, the enhanced software is able to increase the network lifetime by 1.66%.

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Efficient and refined modeling of wireless sensor network nodes using SystemC-AMS

Cited by 9 publications

References 8 publications

A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection

A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection

A hardware/software simulation environment for energy harvesting wireless sensor networks

State-of-charge measurement error simulation for power-aware wireless sensor networks

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