Abstract
The Routing Protocol for Low Power Lossy Networks (RPL) is prone to congestion under high traffic. The single-path routing strategy and single-parent selection make RPL energy and resource-efficient only when the traffic is low and uniform. Two Objective Functions (OFs) are defined for RPL, which use single routing metrics-Expected Transmission Count (ETX) and hop count, to select the best parent and path toward the root. However, considering a single metric for OFs is unsuitable for detecting congestion in Lossy Networks (LLNs) applications as each metric has limitations. The current study proposes a novel Multi-Metric Objective Function (MMOF) that combines these two metrics and removes the weakness of the existing OFs. The proposed MMOF works under the nodes' varying transmission ranges (Tx ranges) to reduce the congestion. By changing Tx ranges, we show that the congestion in a fixed topology RPL network reduces, and MMOF can detect this congestion state more accurately than the existing OFs. The research introduces a successful transmission probability metric that makes MMOF more efficient in detecting congestion than ETX and Hop-Count. We prove that considering these two parameters individually is misleading and cannot contribute 100% to detect congestion state. Increasing transmission range can decrease congestion, and MMOF can detect this state transition with 100% accuracy. Simulation results in Cooja show that MMOF outperforms these two metrics and that the robust metric shows a linear relationship with the Tx range. Finally, two quality of service (QoS) parameters are derived to prove the method's efficiency and novelty.
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"All authors contributed to the literature study. Somnath Sinha and Aditi Paul designed the Model and Implementation; Vikas Srivastava performed the result analysis. All the authors read and approved the final manuscript."
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Srivastava, V., Paul, A. & Sinha, S. MMOF: A Multi-Metric Objective Function for Congestion Detection Under Varying Transmission Ranges in RPL-Based WSN. SN COMPUT. SCI. 5, 1112 (2024). https://doi.org/10.1007/s42979-024-03391-2
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DOI: https://doi.org/10.1007/s42979-024-03391-2