TY - GEN
T1 - Congestion Aware Algorithm using Fuzzy Logic to Find an Optimal Routing Path for IoT Networks
AU - Shreyas, J.
AU - Singh, Hemant
AU - Bhutani, Jatin
AU - Pandit, Sanjay
AU - Srinidhi, N. N.
AU - Kumar, DIlip S.M.
N1 - Funding Information:
This research work has been funded by the Science and Engineering Research Board (SERB-DST) Project File No: EEQ/2017/000681. Authors sincerely thank SERB-DST for intellectual generosity and research support provided.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Internet of Things (IoT) is a rapidly expanding technology that has recently got significant recognition in the field of studies. In IoT networks, huge traffic in network causes congestion at nodes that influences the quality of routing metrics the overall performance of the network. Therefore in this paper, congestion aware algorithm using fuzzy logic (CAUF) has been proposed to avoid congestion by selecting the best parent in a tree structured IoT network to find the optimal routing path. It models the problem of parent selection into multi attribute decision making (MADM) based problem using fuzzy weighted sum model. CAUF has been implemented and simulated on cooja simulator and a comparison of performance is carried out with queue utilization based RPL (QU-RPL) and optimization based hybrid congestion alleviation (OHCA) algorithms. Simulation results indicate that proposed has 15% more throughput and 4.5% packets less dropped over OHCA and QU-RPL algorithms.
AB - Internet of Things (IoT) is a rapidly expanding technology that has recently got significant recognition in the field of studies. In IoT networks, huge traffic in network causes congestion at nodes that influences the quality of routing metrics the overall performance of the network. Therefore in this paper, congestion aware algorithm using fuzzy logic (CAUF) has been proposed to avoid congestion by selecting the best parent in a tree structured IoT network to find the optimal routing path. It models the problem of parent selection into multi attribute decision making (MADM) based problem using fuzzy weighted sum model. CAUF has been implemented and simulated on cooja simulator and a comparison of performance is carried out with queue utilization based RPL (QU-RPL) and optimization based hybrid congestion alleviation (OHCA) algorithms. Simulation results indicate that proposed has 15% more throughput and 4.5% packets less dropped over OHCA and QU-RPL algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85080927435&partnerID=8YFLogxK
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U2 - 10.1109/ICCIKE47802.2019.9004351
DO - 10.1109/ICCIKE47802.2019.9004351
M3 - Conference contribution
AN - SCOPUS:85080927435
T3 - Proceedings of 2019 International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2019
SP - 141
EP - 145
BT - Proceedings of 2019 International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2019
Y2 - 11 December 2019 through 12 December 2019
ER -