## 6 Proposed Approach
### Multi-Objective Area Coverage Optimization Based on IAPSO
First, we set up a multi-objective area coverage optimization model in order to improve coverage ratio and reduce redundancy ratio. Then, we set up an energy efficiency cluster head selection optimization model and proposed a cluster formation algorithm based on weight function. We utilized IAPSO-MOACO to achive high coverage ratio, low redundancy ratio by sensing direction rotation. We also utilized IAPSO-CHSO to achieve energy consumption balance by reasonable cluster head selection. We conducted simulation experiments to demonstrate the advantages of our proposed approach. For area coverage optimization, the comparison with existing approaches showed that our proposed approach could effectively improve coverage ratio and reduce redundancy ratio when the round of data transmission was no more than 700. The IAPSO-MOACO had better optimization performance in most of

Figure 7: Comparison in terms of data packets received by BS with 100 to 200 nodes. (**a**) 100 nodes with 6 cluster heads; (**b**) 150 nodes with 9 cluster heads; (**c**) 200 nodes with 12 cluster heads. time because the IAPSO could effectively avoid local optima to some degree. For energy consumption optimization, the comparison with existing approach showed that the IAPSO-CHSO could effectively achieve energy consumption balance because it could guarantee higher number of alive nodes and data packets received by BS. During the early 700 rounds, 70% sensor nodes was still alive when we ran the IAPSO-CHSO in networks with different sizes. The number of data packets received by BS was much higher than other approches after the round was over 500. The proposed approach could effectively guarantee energy consumption balance. Conceptualization, Y.X. ; methodology, Y.X. ; software, S.P. ; validation, S.P. ; formal analysis, S.P. ; investigation, Y.X. ; resources, S.P. ; data curation, Y.X. ; writing--original draft preparation, S.P. ; writing--review and editing, Y.X. ; visualization, S.P. ; supervision, Y.X. ; project administration, S.P. ; funding acquisition, Y.X. This work was supported in part by the National Nature Science Foundation of China under Grant No. 61873249. The authors declare no conflict of interest. ## References

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