LOA-RPL: Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

Abstract

Energy conservation is a significant task in the Internet of Things (IoT) because IoT involves highly resource-constrained devices. Clustering is an effective technique for saving energy by reducing duplicate data. In a clus-tering protocol, the selection of a cluster head (CH) plays a key role in prolong-ing the lifetime of a network. However, most cluster-based protocols, including routing protocols for low-power and lossy networks (RPLs), have used fuzzy logic and probabilistic approaches to select the CH node. Consequently, early battery depletion is produced near the sink. To overcome this issue, a lion optimization algorithm (LOA) for selecting CH in RPL is proposed in this study. LOA-RPL comprises three processes: cluster formation, CH selection, and route establishment. A cluster is formed using the Euclidean distance. CH selection is performed using LOA. Route establishment is implemented using residual energy information. An extensive simulation is conducted in the network simulator ns-3 on various parameters, such as network lifetime, power consumption, packet delivery ratio (PDR), and throughput. The performance of LOA-RPL is also compared with those of RPL, fuzzy rule-based energy -efficient clustering and immune-inspired routing (FEEC-IIR), and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm (RISA-RPL). The performance evaluation metrics used in this study are network lifetime, power consumption, PDR, and throughput. The proposed LOA-RPL increases network lifetime by 20% and PDR by 5%-10% compared with RPL, FEEC-IIR, and RISA-RPL. LOA-RPL is also highly energy-efficient compared with other similar routing protocols.