(a) Queueing delay (DL).
0 5 10 15 20
Throughput (Mbps) 0%
20%
40%
60%
80%
100%
Likelihood (%)
μQoSQoS1
(1%) BE 3
BE 4 QoS 1
(b) Throughput (DL).
0 5 10 15 20
Throughput (Mbps) 0%
20%
40%
60%
80%
100%
Likelihood (%)
μQoSQoS2(1%) BE 1 BE 2 QoS 2
(c) Throughput (UL).
10−210−1100 101 102 103 104 105 Queueing delay (ms) 0%
20%
40%
60%
80%
100%
Likelihood (%)
DQoSQoS1 (93%)
BE 3BE 4 QoS 1
(d) Queueing delay (DL).
0 5 10 15 20
Throughput (Mbps) 0%
20%
40%
60%
80%
100%
Likelihood (%)
μQoSQoS1(66%)
BE 3 BE 4 QoS 1
(e) Throughput (DL).
0 5 10 15 20
Throughput (Mbps) 0%
20%
40%
60%
80%
100%
Likelihood (%)
μQoS2QoS (76%) BE 1 BE 2 QoS 2
(f) Throughput (UL).
Figure 16.Experiment 3 CDFs. (a) Queueing delay of slices (DL), (b) throughput of slices (DL), and (c) throughput of flows from STAs (UL) with Gómez et al. [5]; (d) queueing delay of slices (DL), (e) throughput of slices (DL), and (f) throughput of flows from STAs (UL) with our approach.
QoS 1 BE 3 BE 4
1010101010101010−10123456
Queueing Dela (ms)
DQoSQoS1
(5ms)
Gómez et al. Proposed
(a) Queueing delay (DL).
QoS 1 BE 3 BE 4
0 5 10 15 20 25
Throughput (Mbps) μQoS1
(10Mbps)QoS
Gómez et al. Proposed
(b) Throughput (DL).
BE 1 BE 2 QoS 2
0 5 10 15 20 25 25
Throughput (Mbps) μQoS2
(5Mbps)QoS
Gómez et al. Proposed
(c) Throughput (UL).
Figure 17.Experiment 3 box-and-whiskers plots. (a) Queueing delay of slices (DL); (b) throughput of slices (DL); (c through-put of flows from STAs (UL).
6. Conclusions and Future Work
In this new era of 5G communication, MCAs are imposing stricter QoS requirements on coexisting technologies, such as IEEE 802.11 networks. Because traditional IEEE 802.11 networks are known to achieve insufficient reliability and non-deterministic latency, dy-namic, and precise RA mechanisms are essential. In this paper, we proposed a delay-aware approach for MAC management via airtime-based network slicing and traffic shaping, as well as user association while using MCDA in IEEE 802.11 SD-RANs. With the centralized view of the network, our approach performs traffic shaping on the STAs to prevent BE flows from degrading the QoS requirements of others. Differently from most work in the literature, our approach considers QoS and BE flows in both the UL and DL directions.
Focusing on MCAs, we designed our experiments based on the QoS requirements from the use case of process automation and remote control in future digital factories. Through experimentation in a real-world testbed, our results show that our approach enhances the QoS support at runtime, drawing closer to the MCA requirements, as compared to a state-of-the-art user association algorithm [5].
As future work, we plan to address the design of a more deterministic scheduling algo-rithms for precise QoS service differentiation on the AP, the design of decentralized control algorithms for airtime-shaping and local decision-making, and the use of monitoring information with finer granularity for network management in general.
Author Contributions:Conceptualization, all authors; methodology, P.H.I., D.J.K., and V.R.S.; soft-ware, P.H.I. and D.J.K.; validation, P.H.I., D.J.K., and V.R.S.; formal analysis, P.H.I.; investigation, P.H.I.; resources, all authors; writing—original draft preparation, P.H.I., D.J.K., and V.R.S.; writing—
review and editing, all authors; visualization, P.H.I.; supervision, J.M.M.-B., L.Z.G., S.L., and V.R.S.
All authors have read and agreed to the published version of the manuscript
Funding:This research received partial funding from a research project of The Flemish Research Fund (FWO), from The European Union’s Horizon 2020 Research and innovation program, under grant agreement No. 826284 (ProTego), from The U.S. National Science Foundation NeTS Award 1813451, and was also supported by the FLEXNET project: “Flexible IoT Networks for Value Creators”
(Celtic 2016/3), in the Eureka Celtic-Next Cluster.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are openly available in FigShare at https://doi.org/10.6084/m9.figshare.13607276, reference number 13607276.
Conflicts of Interest:The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Abbreviations
The following abbreviations are used in this manuscript:
A-MSDU Aggregated MAC Service Data Unit ADWRR Airtime Deficit Weighted Round Robin AHP Analytic Hierarchy Process
AP Access Point
API Application Programming Interface ARP Address Resolution Protocol
BE Best-Effort
CDF Comulative Ditribution Function CSA Channel Switch Annoucement CUPS Control/User Plane Split
CW Contention Window
DCF Distributed Coordination Function
DL Downlink
DSCP Differentiated Services Code Point
E2E End-to-End
EDCA Enhanced Distributed Channel Access HD High-Definition
HT High Throughput
LVAP Light Virtual Access Point MAC Medium Access Control MCA Mission-Critical Application MCDA Multi-Criteria Decision Analysis MCS Modulation and Coding Scheme NIC Network Interface Card
QoS Quality of Service QoSS Quality of Service Slicing RA Resource Allocation RAN Radio Access Network RAT Radio Access Technology
RSSI Received Signal Strength Indicator SD-RAN Software-Defined Radio Access Network SDN Software-Defined Networking
SLA Service Level Agreement SMA Simple Moving Average SMM Simple Moving Median SSID Service Set Identifier
STA Station
TCP Transmission Control Protocol
TOPSIS Technique for Order of Preference by Similarity to Ideal Solution TXOP Transmission Opportunity
UDP User Datagram Protocol
UL Uplink
URLLC Untral-Reliably Low Latency Communication VHT Very High Throughput
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