# Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications
## 4 Proposed Hybrid PSOGA
### Proposed Hybrid-Based PSOGA Routing
In this model, after the formation of grids, the optimal selection of the CH using a trust model among the stationary nodes enables reliable data transmission from the sensor nodes to the cluster head. After forming the

Figure 10: Results of BS at edge, 2 \(\times\) 4 100 nodes. (**a**) Network structure BS at edge. (**b**) Packets reaching the BS. (**c**) Total alive nodes. (**d**) Total residual energy. (**e**) PDR. (**f**) Throughput. initial random paths using a zigzag strategy, the optimization algorithm helps in finding the best routes that offer the quickest delivery of data packets to the sink node. The consideration of metrics, such as route score, number of received packets, number of delayed packets, residual energy, power consumed, and total hop counts, enables the optimal selection of routes among the random paths. Such routes are reliable enough to carry out the packet transmission, allowing for higher residual energy. The use of the PSOGA ensures the faster selection of routes than the existing LEACH mechanism; the proposed PSOGA obtained increased network lifetimes in all five different case studies. The percentages of dead nodes in the network after several iterations were significantly lower than those of the LEACH protocol, which shows the efficacy of the present system. Currently, the authors are engaged in extending the presented work in the direction of data reduction, coupled with security, to further enhance the network efficiency and privacy. In the future, deep learning algorithms can be deployed for bufferless systems that match the speed of input data acquisition from several IoT devices. In addition to this, further research will consider a cross-layer approach to increase the network efficiency and lifetime when faced with node failures. [PERSON] conducted the simulation, coding, and performance analysis. [PERSON] conceived the scenario impact and design of the manuscript. [PERSON] performed the scenario impact and simulation and coding. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. No new data were created or analyzed in this study. Data sharing is not applicable in this article. The authors declare no conflict of interest. The statements made herein are solely the responsibility of the authors. ## References

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