The nature of mathematical modeling
The nature of mathematical modeling
WISE Design of Indoor Wireless Systems: Practical Computation and Optimization
IEEE Computational Science & Engineering
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
TOSSIM: accurate and scalable simulation of entire TinyOS applications
Proceedings of the 1st international conference on Embedded networked sensor systems
Wireless Communications
TinyOS: An Open Operating System for Wireless Sensor Networks (Invited Seminar)
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Telos: enabling ultra-low power wireless research
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Improving wireless simulation through noise modeling
Proceedings of the 6th international conference on Information processing in sensor networks
The β-factor: measuring wireless link burstiness
Proceedings of the 6th ACM conference on Embedded network sensor systems
On the scaling properties of low power wireless links
Proceedings of the 6th ACM conference on Embedded network sensor systems
Burstiness and scaling in the structure of low-power wireless links
ACM SIGMOBILE Mobile Computing and Communications Review
M&M: multi-level Markov model for wireless link simulations
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Realistic performance analysis of WSN protocols through trace based simulation
Proceedings of the 7th ACM workshop on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks
Improving wireless link simulation using multilevel markov models
ACM Transactions on Sensor Networks (TOSN)
Discrete-time Markov Model for Wireless Link Burstiness Simulations
Wireless Personal Communications: An International Journal
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We propose deriving wireless simulation models from experimental traces of radio signal strength. Because experimental traces have holes due to packet losses, we explore two algorithms for filling the gaps in lossy experimental traces. Using completed traces, we apply the closest-fit pattern matching (CPM) algorithm, originally designed for modeling external interference, to model signal strength. We compare the observed link behavior using our models with that of the experimental packet trace. Our approach results in more accurate packet reception ratios than current simulation methods, reducing the absolute error in PRR by up to about 30%. We also find that using CPM for signal strength improves simulation of packet burstiness, reducing the Kantorovich-Wasserstein (KW) distance of conditional packet delivery functions (CPDFs) by a factor of about 3 for intermediate links. These improvements give TOSSIM, a standard sensor network simulator, a better capability to capture real-world dynamics and edge conditions that protocol designers typically must wait until deployment to detect.