Scalable and Energy-Efficient Routing Protocol Using Adaptive Unequal Clustering for IoT
Ranjit Kumar, Suchismita Chinara · IETE Journal of Research · 2025
Most Internet of Things (IoT) devices use IPv6’s Routing Protocol for Low-power and Lossy Networks (RPL). However, its performance endures due to high network congestion and the limited energy capacity of nodes, especially in large-scale networks. Clustering is a widely used technique for better energy utilization. The existing cluster-based RPL routing protocols are not efficient in balancing energy consumption and are not fit for a scalable network. So, this work proposed a novel unequal clustering-based method that extends the network’s life of RPL by forming clusters of variable size based on the node and base station (BS) separation. This is accomplished in a tri-stage cluster setup, routing, and maintenance. The cluster head selection is based on the connectivity and residual energy, and then an unequal transmission range is given to CH. This paper emphasized data transfer in the direction of the base station. To achieve this, we have defined a grey region. This grey region contains the data forwarder node, which transfers data to the Base station. The selection of data forwarder nodes is based on composite metrics, namely, Expected Transmission Count(ETX), Congestion Score (Cs), and Residual Energy (RE). The simulation is performed using the COOJA simulator. When confronted with a clustered additive approach (CA-RPL) and the minimum rank with hysteresis objective function (MHROF-RPL), the proposed algorithm (SER-UC) delivers significant 8.10% and 27.21% improvement in packet delivery rate (PDR), 9.17% and 13.9% decrement in energy consumption and 11.16% and 19.62% decrement in end-to-end delay concerning CA-RPL and MHROF-RPL. Along with this, the proposed algorithm also shows improvement in network lifetime. The network lifetime is measured in terms of First node die (FND), Half node die (HND), and Last node die (LND). The proposed algorithms show 22.67% and 14.13% enhancement in FND, 29.44% and 17.08% in HND, and 30.27% and 16.16% in LND concerning CA-RPL and MHROF-RPL.