# REERP: A Region-Based Energy-Efficient Routing Protocol for IoT Wireless Sensor Networks
## 5 Conclusions and Future Scope  
A routing protocol is developed that addresses the drawbacks of conventional routing protocols. The issue of choosing CH using a probabilistic method was resolved by the proposed approach. The primary purpose of the proposed protocol is to minimize energy utilization by transmitting the data from the source to the BS. To accomplish data transmission, the data must be forwarded from source to destination via one of the intermediate nodes. This work is compared with the state-of-the-art approaches in which bandwidth, acquired packets, remaining energy of the network, and dead nodes are considered while performing the simulations. The proposed routing protocols achieve 10.45% with respect to the stable election protocol, the homogeneity of the gateway, and the heterogeneous routing protocol. The method ensures that residual energy nodes are chosen as heads and are capable of successfully transmitting data to the BS. In addition to using multi-hop communication amongst nodes throughout all regions, the new approach also created diverse nodes in regions far away from the BS. The proposed approach has minimized the depletion of energy of the far-reaching nodes as soon as they send their information to the BS, as shown in Figure 6. Every node-sparing energy approach has been discussed in the proposed approach, which resulted in the least utilization of energy. The simulations are evaluated and show that the proposed protocol was executed with better results than the baseline studies with regard to the stability period, remaining energy, and the useful life of the network.  
The following can be concluded:  
1. The WSN is one of the most demanding needs in today's time due to its ubiquitous nature. In the near future, WSNs can be deployed as underwater acoustic sensor systems, cognitive sensing and spectrum management, and security and privacy management.
2. The sophisticated algorithms based on machine learning techniques will be integrated to obtain a more energy-efficient and cost-effective protocol.
3. Due to the arithmetic bottleneck and energy consumption limitation of WSNs, machine learning algorithms cannot be deployed at scale in sensors with small computational power and limited energy. However, distributed learning methods require less computational capacity, less energy consumption, and smaller memory footprints than centralized learning algorithms (i.e., they do not need to consider the entire network information). Distributed cooperative learning breaks the arithmetic bottleneck and achieves ML-based green routing with less energy consumption, which is very suitable for WSNs.  
Conceptualization, R.D. and S.R.; methodology, R.D. and S.R.; software, R.D. and S.R.; validation, G.G. and S.R.; resources, G.G. and R.D.; writing--original draft preparation, R.D. and S.R.; writing--review and editing, S.R. and G.G.; supervision, S.R. and G.G.; funding acquisition G.G. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Energy consumption.  
Funding: This work was partially supported by the MUSA project, Multilayered Urban Sustainability Action-project, funded by the European Union--NextGenerationEU, under the National Recovery and Resilience Plan (NRRP) Mission 4 Component 2 Investment Line 1.5: Strengthening research structures and creation of R&D \"innovation ecosystems\", set up of \"territorial leaders in R&D\" (CUP G43C22001370007, Code ECS00000037). The work was also partially supported by the SERICS project (PE0000014) under the NRRP MUR program funded by the EU--NextGenerationEU.