# Swarm-Intelligence-Centric Routing Algorithm for Wireless Sensor Networks
## 5 Conclusions  
The proposed routing protocol overcomes the disadvantages of AODV and improves routing performance via the incorporation of collision avoidance, link-quality prediction, and maintenance techniques that mimic the ACO algorithm. SI can operate reliably in accordance with simple rules of behavior, while providing reliable solutions to given problems in complex environments.  
In this study, the performance of the routing protocol was improved by replacing the periodic \"Hello\" message with an interrupt message capable of detecting and predicting link disconnections. As observed, routing performance can be further improved via the addition of processing procedures for each message type. In addition, when the signal strength of a received data packet approaches the residual-energy threshold prior to energy loss, an increase in path-loss tendency can be observed. In contrast, when the signal strength of a received packet falls below the minimum threshold, a pre-warning packet is generated. Simulation results reveal that the proposed method is superior to the existing AODV and DSR routing protocols. The trade-off between the acquisition time of the sensing information and the energy consumed by the nodes is well resolved. In addition to providing a highly reliable and robust path for information transmission, the proposed method improves source-to-destination data latency, thereby reducing the frequency of the link disconnections and unnecessary control packet transmissions within the network.  
In view of its above-mentioned features and advantages, the proposed biomimetic algorithm is expected to be effectively utilized in large-scale communication networks. This requires sophisticated  
Figure 8: Changes in node survival rate with an increase in the number of nodes.  
mathematical modeling of various biological systems, as well as rigorous performance verification based on several system-environment variables. In future, further research is required on techniques to maintain a continuous alternative path in the proposed algorithm and to increase the reliability of the path in one-way links that often occur in wireless network environments. In addition, some path improvements can be made, such as using the results of the proposed method to compare with other methods (e.g., recursive neural networks).  
Methodology, writing (review and editing), and software, M.L.; validation, project administration, and funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript. This research was supported by the Korea Institute of Planning and Evaluation for Technology (IPET) in Food, Agriculture, Forestry, and Fisheries through the Advanced Production Technology Development Program, funded by the Ministry of Agriculture, Food, and Rural Affairs (MAFRA; grant no. 315001-5). The study also received support from \"Research and Development of Standard-based Test Elements Related to Communication in Smart Greenhouses\" (project No. PJ01548002) from the Rural Development Administration, Republic of Korea. It was also supported by the Ministry of Science and ICT (MSIT), Korea, under the Grand Information Technology Research Center support program (IITP-2020-0-01489), supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP). The authors declare no conflict of interest.