# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 2 Related Works
Additionally, the MS-GAOC protocol was compared with the multiple data sink-based dynamic clustering of heterogeneous wireless sensor networks, based on a GA (MS-DCHGA), multiple data sink-based threshold-sensitive energy-efficient delay-aware routing protocol (MS-TEDRP), and multiple data sink-based genetic algorithm-based distance-aware routing (MS-GADA) protocol. However, its CH selection was inefficient due to the distributed approach of CH selection and haul transmission. [PERSON] et al. [47] proposed two protocols, namely the multiple data sink-based energy-efficient cluster routing (MEEC) protocol and improved dual hop routing (IDHR) protocol, for electing the optimal CH selection in WSN. It concentrated mainly on the network longevity and network hotspot problem. The efficacy of the IDHR protocol was compared with the threshold-sensitive energy-efficient delay-aware routing protocol (TEDRP), stable energy-efficient clustering protocol (SEECP), and distance-based residual energy-efficient stable election protocol (DRESEP). The performance of the MEEC was also compared with the IDHR. Thus, it prolonged the network lifetime. However, the energy depletion increased due to the systematic rotation of the relay nodes and increased the consequently depleted energy. A novel routing architecture designed for harsh environment monitoring was proposed by [PERSON] [48]. The objective of this work was to prolong the lifespan by addressing the hotspot problem in the network. The intra-cluster routing was improved by adopting node density factor in CH selection. Thus, it effectively extended the network as compared to the stable and energy-efficient clustering protocol (SEECP). However, other radio interferences were not considered in this work. Quality of Service (QoS) provisioning-based routing protocols using multiple data sinks in the IoT were proposed by [PERSON] [49]. The cluster head (CH) selection was optimized by adopting the metrics, distance, node density, remaining energy, and energy threshold factors. The simulation results confirmed that the proposed protocol outperformed the state of the standard protocols. However, it was not suitable for large area networks. [PERSON] et al. [50] also investigated various hierarchical routing protocols in WSN. They discussed the versatile routing protocols and their advantages and limitations. A new neuro fuzzy-based cluster formation protocol (FBCFP) for WSN-based IoT was proposed by [PERSON] [51]. The effectiveness of FBCFP was compared with LEACH and HEED. It demonstrated better performance by means of energy utilization, end-to-end delay, and lifespan of the network. [PERSON] et al. [52] proposed virtual force-based intelligent clustering for energy-efficient routing (VFICEER) in WSN. The advantage of VFICEER increased the network lifetime and packet delivery ratio. Moreover, it reduced the delay and energy consumption. However, the energy consumption was high due to the ad-hoc nature of this network. [PERSON] [53] proposed a distributed clustering with decreased uncovered nodes (DiCDU) to resolve the uncovered node problem after the election of cluster heads. Thus, it extended the lifetime of the network. [PERSON] et al. [54] proposed a new approach of aggregating data in CHs for improving the classification of support vector machines (SVM) with a flower pollination algorithm (IFPA) to achieve classification accuracy. The IFPA-SVM algorithm forwarded a number of data, which was high when compared to the SVM and hidden Markov model (HMM). However, the CH node in IFPA-SVM consumed more energy than the other algorithms. In the related works, various cluster-based RPL protocols, clustering protocols, and CH selection were discussed using a game theoretic approach. The following are the limitations of the cluster-based RPL protocol: (i) it takes more time to form the clusters in the network, (ii) it results in network congestion and unbalanced energy among the nodes in the clusters, and (iii) it takes time to select the cluster parent in the network.