# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 6 Conclusions  
In I-UWSN and smart environment, establishing reliable links is a major requirement. Therefore, we have investigated the parameters that are affected due to poor reliability of links. The significant factor of the poor link reliability is dynamic topology and the effected parameters are transmission loss, packet delivery ratio, network lifetime, and throughput. Dynamic topology occurs due to the continuous flow of water. Therefore, establishing a reliable link for a long time is not possible. Short time reliability can be achieved, therefore, in this article, we have proposed TBRL for dynamic topology routing protocol. Initially, TBRL discovers the approximate location of each sensor node using a topology discovery algorithm. To determine nodes location, only nodes depth is considered as an input parameter. After that, TBRL determines the link reliability of each next possible forwarder by taking their distance, ETX and residual energy level. TBRL continues to establish a reliable link until the sink is reached. In this way, multiple reliable paths from source to sinks are established. On the basis of maximum number of nodes participation, the most reliable path has been selected. For assigning the time interval, average time calculation is considered and that average time is assigned to two nodes reliability model to select the next forwarder. We have evaluated the performance of our proposed protocols with DBR and R-ER\(P^{2}\)R in terms of packet delivery ratio, end to end delay, throughput, and energy consumption of network. We also tested 2N-RM against 2H-ACK and 3H-RM in terms of packet delivery ratio. The simulation results show that TBRL performs better as compared to the state of the art techniques. It is observed from the results that TBRL is a better protocol for underwater sensor communication and sensor monitoring to support smart cities' vision. In the future, we will use machine learning techniques for more optimized path selection.  
The research conceptualization and methodology are done by [PERSON] and [PERSON]. ([PERSON]). The technical and simulations are performed by [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]). The technical review and improvements have been performed by [PERSON] ([PERSON]) and [PERSON] ([PERSON]). The overall technical support, guidance and project administration is done by [PERSON], [PERSON] ([PERSON]) and [PERSON] Finally, responses to the queries of the reviewers were done by [PERSON] ([PERSON]) and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research work was funded by the Ministry of Education, Research and Development Office, Saudi Arabia. The APC was funded by the Ministry of Education Saudi Arabia in cooperation with Najran University Saudi Arabia in the form of a post-doctoral fellowship.  
The authors acknowledge the support from the Research and Development Office, Ministry of Education, Saudi Arabia in cooperation with Najran University Saudi Arabia in the form of post-doctoral fellowship.  
There is no conflict of interest.  
Figure 15: Energy Consumption of TBRL, DBR and R-ER\(P^{2}\)R.