Mini-Mesh: Practical assessment of a miniaturized IEEE 802.11n/s mesh testbed

Rethfeldt, Michael; Beichler, Benjamin; Raddatz, Hannes; Uster, Felix; Danielis, Peter; Haubelt, Christian; Timmermann, Dirk

doi:10.1109/wcnc.2018.8377247

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…The previously most frequently generated cluster constellation (see Figure 13a) could still be reproduced in nine of ten measurements. The parameter values listed in Table 9 are specific to our testbed Mini-Mesh and its device platform [42]. It can be assumed that even lower times can be achieved with more powerful hardware.…”

Section: Results For Different Grid Sizesmentioning

confidence: 99%

“…Figure 10 shows the grid geometry and node numbering. Based on our miniaturization approach Mini-Mesh [42], using RF attenuators and a fixed 802.11 data rate and TX power combination, we achieve a substantial reduction of transmission range. As a result, only grid neighbors communicate directly, whereas multi-hop paths are established between more faraway nodes by the 802.11s routing protocol HWMP.…”

Section: Experimental Validation 61 Testbed Setupmentioning

confidence: 99%

“…In [42], we introduced this scaling approach and validated its applicability via comparative measurements in outdoor setups without RF attenuators. The miniaturized 5 × 5-node grid topology in our lab corresponds to an un-miniaturized wireless mesh backbone covering an area of more than 300,000 m 2 .…”

Section: Experimental Validation 61 Testbed Setupmentioning

confidence: 99%

“…All cards operate at a fixed 802.11 data rate and start on a default channel, not used otherwise in our institute building. In [42], we conducted a general performance assessment of our device platform (Intel Galileo Gen. 1, 400 MHz single-core CPU) for the 802.11n HT data rates MCS 0-15 (i.e., from BPSK 1/2 to 64-QAM 5/6). Generating saturated TCP/UDP traffic with MTU-sized data frames, MCS 3 was determined as the highest data rate that could be fully utilized before our devices ran into CPU performance limitations.…”

Section: Experimental Validation 61 Testbed Setupmentioning

confidence: 99%

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Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks

Rethfeldt

Brockmann

Beichler

et al. 2021

Sensors

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WLAN mesh networks are one of the key technologies for upcoming smart city applications and are characterized by a flexible and low-cost deployment. The standard amendment IEEE 802.11s introduces low-level mesh interoperability at the WLAN MAC layer. However, scalability limitations imposed by management traffic overhead, routing delays, medium contention, and interference are common issues in wireless mesh networks and also apply to IEEE 802.11s networks. Possible solutions proposed in the literature recommend a divide-and-conquer scheme that partitions the network into clusters and forms smaller collision and broadcast domains by assigning orthogonal channels. We present CHaChA (Clustering Heuristic and Channel Assignment), a distributed cross-layer approach for cluster formation and channel assignment that directly integrates the default IEEE 802.11s mesh protocol information and operating modes, retaining unrestricted compliance to the WLAN standard. Our concept proposes further mechanisms for dynamic cluster adaptation, including subsequent cluster joining, isolation and fault detection, and node roaming for cluster balancing. The practical performance of CHaChA is demonstrated in a real-world 802.11s testbed. We first investigate clustering reproducibility, duration, and communication overhead in static network scenarios of different sizes. We then validate our concepts for dynamic cluster adaptation, considering topology changes that are likely to occur during long-term network operation and maintenance.

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Section: Results For Different Grid Sizesmentioning

confidence: 99%

Section: Experimental Validation 61 Testbed Setupmentioning

confidence: 99%

Section: Experimental Validation 61 Testbed Setupmentioning

confidence: 99%

Section: Experimental Validation 61 Testbed Setupmentioning

confidence: 99%

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Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks

Rethfeldt

Brockmann

Beichler

et al. 2021

Sensors

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“…On the other hand, the Java programming language is suitable for rapid prototyping in practical testbeds as it is relatively easy to debug and platform independent. Therefore, we extensively use Java applications in our testbeds (e.g., in the Mini-Mesh testbed [8] or for the evaluation of the PSPI-Sync approach [4]). Moreover, Java is even suitable for real-time applications in the Industrial Internet of Things (IIoT) as shown in [6].…”

Section: Introductionmentioning

confidence: 99%

jUDPWrapper: A Lightweight Approach to Access the OMNeT++/INET UDP Functionality from Java

Puttnies

Danielis

Thiele³

et al.

EPiC Series in Computing

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There are three methods to evaluate networked systems during the design phase: by means of simulation, a practical testbed, and mathematical analysis. In this paper, we propose the jUDPWrapper, which enables to access the OMNeT++/INET UDP functionality from Java using the Java Extensions for OMNeT++. The jUDPWrapper provides a socket-based API corresponding to the standard API defined in the java.net package. Consequently, it enables to develop application layer simulation models in Java and here- after easily derive a Java prototype implementation. Therefore, the jUDPWrapper eases the evaluation of Java UDP applications using both simulation and practical testbed. We evaluate the jUDPWrapper using two distinct versions of OMNeT++/INET, provide the Java Extensions for OMNeT++ 5.4, and for the first time quantify the execution time of Java models versus C++ models. C++ models are approx. twice as fast as Java models in long simulation runs, if the release mode is used.

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