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A Novel Congestion Control Scheme Using Fuzzy Logic Systems to Enhance the Path Selection Criteria in Routing Protocols for Low-Power and Lossy Networks on the Internet of Things

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Abstract

In routing protocols for low-power and lossy (RPL)-based Internet of Things (IoT) networks, congestion control is essential for ensuring efficient and dependable communications with energy awareness. As the number of devices and the amount of data traffic increase, congestion typically occurs, resulting in degraded network performance, i.e., increased packet losses and delays with decreased energy efficiency. Many parameters relating to congestion behaviors have been considered and dynamically changed, as have the possible transmission paths either within a single destination-oriented directed acyclic graph (DoDAG) or across different DoDAGs. Thus, this research investigates congestion mitigation methods for RPL-IoT networks based on a fuzzy logic system (FLS) by proposing a novel FLS scheme to determine the selection criteria for conducting path management in three FLS components: (1) inside/outside the DoDAG selection process, (2) inside the path selection process within a single DoDAG (the so-called inside DoDAG) for high or to-be-high loads, and (3) inside the path selection process across the DoDAGs (the so-called outside DoDAG) during severe congestion applying the concept of a multi-DoDAG node (MDN) to determine both a suitable MDN and data traffic for alternate path selection. Simulation results demonstrate that the proposed method effectively controls congestion levels, decreases packet losses and latency, and attains improved energy efficiency in comparison with the state-of-the-art congestion control schemes in RPL-IoT networks.

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Acknowledgements

This research is funded by the Young Researcher Development Project of Khon Kaen University Year 2022 and the College of Computing, Khon Kaen University.

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Authors and Affiliations

  1. Applied Network Technology (ANT), Department of Computer Science, College of Computing, Khon Kaen University, Khon Kaen, 40002, Thailand

    Phet Aimtongkham & Chakchai So-In

  2. Advanced Intelligent Interdisciplinary Integration (AIII), Department of Computer Science, College of Computing, Khon Kaen University, Khon Kaen, 40002, Thailand

    Pakarat Musikawan & Yanika Kongsorot

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Appendices

Appendix A

Fuzzy Logic System Design for the FNO and FLO

Membership Function

After determining the two variables, the NR and EDR for the FLO and the PFR and LQW for the FNO, these variables are first normalized by using max/min normalization within the interval from 0 to 1. Note that creating a membership function based on a preliminary experiment by using MF models is similar to the process described in “Fuzzifier”. Figures 14 and 15 also depict the MFs for the FNO and FLO (i.e., triangular) with respect to the NR, EDR, PFR, LQW and output weight (namely, the node metric weight (NMW) and link metric weight (LMW)).

Fig. 14
figure 14

FNO membership functions, i.e., the a node rank (NR), b energy drainage rate (EDR), and c output weight (NMW)

Full size image
Fig. 15
figure 15

FLO membership functions, i.e., the a packet flowing ratio (PFR), b link quality weight (LQW), and c output weight (LMW)

Full size image

Fuzzy Rules

Considering the second component, rules are formulated to determine the membership level of the output MF. Rules are assigned to the crisp input for which the rule conditions are derived from hypothesized experiments involving all probability values (probability distribution model). The number of rules varies with the input variables and MF stage. Two crisp input variables are used in this research, with three MF stages (L, M, and H) for each variable. Nine fuzzy rules are employed in total, and they are shown in Tables 9 and 10.

Table 9 Examples of 9 NMW fuzzy rules
Full size table
Table 10 Examples of 9 LMW fuzzy rules
Full size table

Fuzzy Logic System Design for the FIO

Membership Function

The two variables, the NMW and LMW, are then normalized by using max/min normalization within the interval from 0 to 1. Again, the membership function creation process is based on intensive experiments conducted using different MFs, as explained in “Fuzzifier”. Figure 16 depicts the MF of the FIO membership functions (i.e., triangular) for the NMW, LMW and output weight (namely, the integration weight (IW)).

Fig. 16
figure 16

FIO membership functions, i.e., the a node metric weight (NMW), b link metric weight (LMW), and c output weight (IW)

Full size image

Fuzzy Rules

In the second component, rules are formulated to determine the membership level of the output MF. The number of rules varies with the input variables and MF stage. Two crisp input variables are used, i.e., the NMW and LMW, with three MF stages (L, M, and H) for each variable. Nine fuzzy rules are employed in total, and they are shown in Table 11. Similarly, the designs of the last two components, i.e., the fuzzy interference engine and defuzzifier, follow the same design principles as those in “Fuzzy Inference Engine” and “Defuzzifier” but differ in terms of the number of variables and their final interpretations. The result (i.e., the IW) is finally used to determine the path within a single DoDAG.

Table 11 Examples of 9 IW fuzzy rules
Full size table

Appendix B

Fuzzy Logic System Design for the MDN-UT

Membership Function

Two variables, CS and MDN-RE, are normalized by using max/min normalization within the interval from 0 to 1. Again, the membership function creation process is based on intensive experiments conducted using different MFs, as explained in “Fuzzifier”. Figure 17 depicts the MFs of the MDN-UT membership functions (i.e., trapezoidal and triangular) for the CS, MDN-RE, and output weight (namely, the MDN utilization weight (MDNUW)).

Fig. 17
figure 17

FL-MDN-SS membership functions, i.e., the a congestion stage (CS), b remaining MDN energy (MDN-RE), and c output weight (MDNUW)

Full size image

Fuzzy Rules

In the second component, rules are formulated to determine the membership level of the output MF. The number of rules varies with the input variables and MF stage. Two crisp input variables are used, i.e., the CS and MDN-RE, with three MF stages (L, M, and H) for each variable. Nine fuzzy rules are applied in total, and they are shown in Table 12. The designs of the last two components, i.e., the fuzzy interference engine and defuzzifier, follow the same design principles as those in “Fuzzy Inference Engine” and “Defuzzifier” but differ in terms of the number of variables and their final interpretations.

Table 12 Examples of 9 MDNUW fuzzy rules
Full size table

Fuzzy Logic System Design for the FL-MDN-SS

Membership Function

Three variables, the LSM, QU, and RE, are then normalized by using max/min normalization within the interval from 0 to 1. Again, the membership function creation process is based on intensive experiments conducted using different MFs, as explained in “Fuzzifier”. Figure 18 depicts the MF of the FL-MDN-SS membership functions (i.e., triangular) for the LSM, QU, RE and output weight (namely, the MDN selection weight (MDNSW)).

Fig. 18
figure 18

FL-MDN-UT membership functions, i.e., the a link stage metric (LSM), b queue utilization (QU), c remaining energy (RE), and d output weight (MDNSW)

Full size image

Fuzzy Rules

In the second component, rules are formulated to determine the membership level of the output MF. The number of rules varies with the input variables and MF stage. Three crisp input variables are used, i.e., the LSM, QU, and RE, with three MF stages (L, M, and H) for each variable. There are 27 fuzzy rules in total, and they are shown in Table 13. The designs of the last two components, i.e., the fuzzy interference engine and defuzzifier, follow the same design principles as those in “Fuzzy Inference Engine” and “Defuzzifier” but differ in terms of the number of variables and their final interpretations. Note that the result (i.e., the highest MDNSW) is finally used to determine the selected MDN (e.g., outside DoDAG).

Table 13 Examples of 27 FL-MDN-SS fuzzy rules
Full size table

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Aimtongkham, P., Musikawan, P., Kongsorot, Y. et al. A Novel Congestion Control Scheme Using Fuzzy Logic Systems to Enhance the Path Selection Criteria in Routing Protocols for Low-Power and Lossy Networks on the Internet of Things. SN COMPUT. SCI. 5, 610 (2024). https://doi.org/10.1007/s42979-024-02940-z

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