# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 2 Related Works  
This section discusses various cluster-based routing protocols that can be used by the Internet of Things to increase the lifespan of the network. [PERSON] et al. [37] proposed an energy-efficient heterogeneous ring cluster routing (E2 HRC) protocol in WSNs, where a ring clustering is performed with event-driven CH rotation principle-based cluster head selection. However, E2 HRC-RPL initially takes more time to form the clusters in the network. [PERSON] et al. [38] created a set of RPL, resulting in the extension of the packet delivery ratio by 25%, increasing the scalability and data communication by reducing the latency and packet loss in IoT networks. On the other hand, this creates node congestion for unbalanced clusters in the network. A content-centric RPL (CCR-RPL) was proposed by [PERSON] et al. [39] that reduced the data traffic across the network, using a higher data aggregation ratio in the routing process. Even though their proposed protocol outperformed the standard RPL in terms of latency and packet delivery ratio, it supported limited content being highly resource-constrained in the nodes. [PERSON] et al. [40] proposed an energy-efficient region source routing (ER-SR) protocol for WSN. The protocol distributed the energy utilization among the nodes to increase the lifespan while overcoming the limitations of resource constrain. ER-SR offered a substantial performance with respect to the energy efficiency, lifespan, and latency as compared to other RPL in WSN, but it required extra time for picking up a suitable parent for selecting CH in the cluster. [PERSON] et al. [41] proposed an energy-efficient region-based RPL (ER-RPL) for LLN. The protocol improved the latency and energy consumption but faced node congestion due to uneven region formation. A hierarchical-distributed management clustering (HDMC) protocol for WSN was designed by [PERSON] [42] that surpassed the performance of LEACH and hierarchical-distributed management clustering (HEED) in terms of latency and lifetime. The model had limited applications, unlike the previously mentioned protocols. An adaptive distributed game theory-based congestion control in RPL for WSN was proposed by [PERSON] and [PERSON] [43] to solve the congestion issue of RPL. The protocol supported both congestion detection and moderation during the route establishment, increasing the network lifespan and decreasing the latency. However, the threshold value was not well-defined for the protocol. [PERSON] and [PERSON] [44] proposed an energy-efficient clustering protocol using dual cluster head and game theory (ECGD) in WSN. The protocol's effectiveness was compared to LEACH-ERE, threshold-sensitive energy-efficient sensor network (TEEN), and power-efficient gathering in sensor information systems (PEGASIS). The ECGD protocol achieved superior performance through the network's energy and lifespan of the network. [PERSON] et al. [45] proposed a game theoretic approach for power management (GTSPM) in the IoT. The selection of CH played an important part in the IoT network. It considered the game theoretical approach for selecting the cluster's appropriate CH nodes. The effectiveness of GTSPM was compared with the LEACH and game theory-based energy-efficient clustering (GEEC) routing protocol. However, it provided less reliability compared to other protocols. [PERSON] et al. [46] proposed two novel algorithms, namely the genetic algorithm-based optimized clustering (GAOC) protocol and data sink-based GAOC (MS-GAOC), for electing the CH node in WSN. It addressed the hotspot problem in the network. The GAOC protocol was compared with the dynamic cluster head selection based on the genetic algorithm (DCH-GA), genetic algorithm-based distance-aware LEACH (GADA-LEACH), and threshold-sensitive energy-efficient delay-aware routing protocol (TEDRP).