• Corpus ID: 6449934

Stable Desynchronization for Wireless Sensor Networks: (II) Performance Evaluation

@article{Choochaisri2017StableDF,
  title={Stable Desynchronization for Wireless Sensor Networks: (II) Performance Evaluation},
  author={Supasate Choochaisri and Kittipat Apicharttrisorn and Chalermek Intanagonwiwat},
  journal={ArXiv},
  year={2017},
  volume={abs/1704.07007}
}
In this paper, we evaluate M-DWARF performance by experimentation and simulation. We validate its functionalities on TelosB motes and compare its performance with EXT-DESYNC, and LIGHTWEIGHT on TOSSIM. On simulation, we test the algorithms on several multihop topologies and discuss both the average and problematic cases. In addition, we investigate the impacts of period length (T) and compare channel utilization fairness among the desynchronization algorithms. Finally, we propose a method to… 
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References

SHOWING 1-10 OF 15 REFERENCES
A Localized Multi-Hop Desynchronization Algorithm for Wireless Sensor Networks
TLDR
A new desynchronization algorithm aimed at providing collision-free transmission scheduling for single-hop and acyclic multi-hop wireless sensor networks and is resilient to the hidden terminal problem and topology changes is presented.
Extended Desynchronization for Multi-Hop Topologies
TLDR
This paper recapitulates the single-hop desynchronization algorit hm DESYNC for Wireless Sensor Networks (WSNs) and identifies its lack of handling hidden nodes in multi-hop envi ronments, and explains in detail its decentralized and selforganizing multi-Hop extension EXTENDED-DESYNC, solving the hidden node problem although it uses just locally available information.
Convergence of Desynchronization Primitives in Wireless Sensor Networks: A Stochastic Modeling Approach
TLDR
A novel framework to estimate the required iterations for convergence to fair TDMA scheduling is presented, and the proposed estimates are shown to characterize the desynchronization convergence iterations significantly better than existing conjectures or bounds.
TOSSIM: accurate and scalable simulation of entire TinyOS applications
TLDR
TOSSIM, a simulator for TinyOS wireless sensor networks can capture network behavior at a high fidelity while scaling to thousands of nodes, by using a probabilistic bit error model for the network.
A novel MAC scheduler to minimize the energy consumption in a Wireless Sensor Network
Lightweight Coloring and Desynchronization for Networks
TLDR
A randomized algorithm for network desynchronization that uses an extremely lightweight model of computation, while being robust to link volatility and node failure is presented.
Telos: enabling ultra-low power wireless research
TLDR
Telos is the latest in a line of motes developed by UC Berkeley to enable wireless sensor network (WSN) research, a new mote design built from scratch based on experiences with previous mote generations, with three major goals to enable experimentation: minimal power consumption, easy to use, and increased software and hardware robustness.
Towards Desynchronization of Multi-hop Topologies
  • Julius Degesys, R. Nagpal
  • Computer Science
    2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems
  • 2008
TLDR
The results suggest that DESYNC has significant potential as a lightweight method for providing non-overlapping variable-sized slots in ad-hoc multi-hop settings and the performance and efficiency of resource allocation in simulation.
WARP: a flexible platform for clean-slate wireless medium access protocol design
TLDR
This demo demonstrates the flexibility of the interaction between the the WARP PHY and MAC layers by showing the capability to instantaneously change the modulation scheme, disabling/enabling MAC features such as carrier sensing or RTS/CTS 4-way handshake, and different multi-rate schemes.
TDMA scheduling algorithms for wireless sensor networks
TLDR
A distributed algorithm based on the distributed coloring of the nodes, that increases the delay by a factor of 10–70 over centralized algorithms for 1000 nodes, and obtain upper bound for these schedules as a function of the total number of packets generated in the network.
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