Calibrating Wireless Sensor Network Simulation Models with Real-World Experiments

Hurni, Philipp; Braun, Torsten

doi:10.1007/978-3-642-01399-7_1

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…For example Marchiori et al traced their testbed and developed their own simulator called WsnSimPy to replay these traces [22]. There are also quite some other works that tried to reproduce testbed results based on generic network simulators like NS2 or OMNeT++ [14,17,23] or the WSN Simulator Castalia [3,19,25]. As opposed to our approach, all of them only focus on simulating the network layer.…”

Section: Related Workmentioning

confidence: 94%

DrySim

Strübe

Lukas

et al. 2014

Proceedings of the 17th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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Despite intensive research in the field of mote-class Wireless Sensor Networks in recent years, real-life deployments are still challenging and systems are prone to failures. This can typically be attributed to fragile hardware or misbehaving software. Issues caused by software, often induced by the inherent constraints of resources, can be countered using simulations. However the simulation results often do not reflect those of the specific deployment.We suggest analyzing the actual environment conditions of a deployed network and map them to a simulator. Then, based on simulations, software and parameters can be tailored to the specific deployment.We developed two tool chains, RealSim and DryRun, and compared results from simulation runs to those acquired from two different testbeds using Tmote Sky nodes. This was done in two campaigns, each altering 2 configuration parameters from the hardware to the application layer. The presented data is based on over 1100 experiments, respectively over 270 h, on real hardware and almost 7000 simulations. The close relation of simulation and real measurements shows that our DrySim approach is feasible.

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Section: Related Workmentioning

confidence: 94%

DrySim

Strübe

Lukas

et al. 2014

Proceedings of the 17th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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“…In this Section, we describe the implementation of Fig. 9: Lifetimes of ESB nodes different states [25] WiseMAC and both broadcast techniques on real-world sensor hardware and illustrate our measurement methodology. In a small experiment conducted in both simulation and real-world implementation, we validate the simulation model applied in Section IV-A.…”

Section: Model Validation On Real Sensorsmentioning

confidence: 99%

“…We measured the time until the voltage drops below 3 V as the node's lifetime (cf. [25] for more details on the evaluation methodology). By applying the GoldCap methodology, we obtained robust and stable results with low variance.…”

Section: Model Validation On Real Sensorsmentioning

confidence: 99%

An Energy-Efficient broadcasting scheme for unsynchronized wireless sensor MAC protocols

Hurni

Braun

2010

2010 Seventh International Conference on Wireless on-Demand Network Systems and Services (WONS)

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In the past couple of years, many Energy-Efficient Medium Access Control (E 2-MAC) protocols for all kinds of wireless networks have been proposed. Many of them are based on Preamble Sampling (also referred-to as Low Power Listening) with asynchronous wake-up schemes and brief periodic polls for channel activity. Although these protocols have proven to almost reach the theoretic lower bounds of energy-efficiency in case of unicast point-to-point transmissions, they yet lack an efficient solution to implement a one-hop broadcast and a networkwide broadcast mechanism. This paper proposes an energyefficient broadcasting scheme for use in E 2-MAC protocols with asynchronous duty cycles. We integrate the technique into the asynchronous wireless sensor MAC protocol WiseMAC and show that the technique is superior to the existing WiseMAC broadcast mechanism with respect to packet delivery rate, latency and energy consumption through simulation study and experiments with a real-world prototype implementation.

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“…In that approach, dependability metrics are often skewed by hidden assumptions that are added by the simulator, the libraries, the runtime environment, the random number generator, or the postprocessing of simulation traces. In the wireless sensor community, there is a growing awareness [1], [24], [26] for this problem, and this paper constitutes a step towards a solution.…”

Section: Introductionmentioning

confidence: 99%

Bounded Fairness for Probabilistic Distributed Algorithms

Crouzen

Hahn

Hermanns

et al. 2011

2011 Eleventh International Conference on Application of Concurrency to System Design

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This paper investigates quantitative dependability metrics for distributed algorithms operating in the presence of sporadic or frequently occurring faults. In particular, we investigate necessary revisions of traditional fairness assumptions in order to arrive at useful metrics, without adding hidden assumptions that may obfuscate their validity. We formulate faulty distributed algorithms as Markov decision processes to incorporate both probabilistic faults and non-determinism arising from concurrent execution. We lift the notion of bounded fairness to the setting of Markov decision processes. Bounded fairness is particularly suited for distributed algorithms running on nearly symmetric infrastructure, as it is common for sensor network applications. Finally, we apply this fairness notion in the quantitative model-checking of several case studies.

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Calibrating Wireless Sensor Network Simulation Models with Real-World Experiments

Cited by 7 publications

References 9 publications

DrySim

DrySim

An Energy-Efficient broadcasting scheme for unsynchronized wireless sensor MAC protocols

Bounded Fairness for Probabilistic Distributed Algorithms

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