CAFOR: congestion avoidance using fuzzy logic to find an optimal routing path in 6LoWPAN networks


Internet of Things is an increasing technology that has recently seen growth in the field of research. In a 6LoWPAN network, congestion at nodes is caused by huge network traffic, which reduces the overall performance, as well as the quality of routing metrics. Congestion in IoT network affects various performance measures such as energy inefficiency, decrease in throughput and loss of data packets. Therefore, in this paper, congestion avoidance using fuzzy logic algorithm has been proposed to avoid congestion by selecting the best parent in a tree-structured IoT network to find the optimal routing path. Fuzzy weighted sum model is used to model the problem of parent selection into multi-attribute decision making based problem. Routing metrics used are buffer occupancy, expected transmission count and routing metric (RtMetric). The objective function selects, the parent based on the combination of these routing metrics. The proposed algorithm’s dynamic nature can identify the congestion and then selects the non-congested path by selecting the best parent and thus creates a best routing path for the packets. The algorithm has been implemented and simulated on Contiki OS and a comparison of performance is carried out against optimization-based hybrid congestion alleviation (OHCA) and queue utilization-based RPL (QU-RPL) algorithms. Simulation results indicate that the proposed algorithm has 15% more throughput, 10% more goodput, 4.5% less packets loss, 10.1% less energy use, and 19% less one-way delay over OHCA and QU-RPL algorithms.

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Shreyas, J., Singh, H., Tiwari, S. et al. CAFOR: congestion avoidance using fuzzy logic to find an optimal routing path in 6LoWPAN networks. J Reliable Intell Environ (2021).

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  • Fuzzy logic (FL)
  • Internet of things (IoT)
  • Multi attribute decision making (MADM)
  • Optimization based hybrid congestion alleviation (OHCA)
  • Soft computing (SC)