## 3 Proposed Methods
### The Continuous Object Area Computation
#### 3.2.2 Refine the Enclosed Area with Mobile Sensors
The conclusion is that more sensors are required to adjust their locations and more time is required to make the sensors achieve a stable state. ## 5 Conclusions

This paper proposed two algorithms to detect the area of intangible continuous objects (ICO), with mobile sensors named Delaunay triangulation with moving sensors (MDT) and convex hull with moving sensors (MCH). First, MDT and MCH classified sensors into ICO-covered ones and ICO-uncovered ones. Then, they applied the Delaunay triangulation and the convex hull techniques on the steady sensors to estimate the rough area of the continuous object, respectively. Next, MDT and MCH used the proposed moving mechanism to adjust the positions of the sensors to refine the enclosed region of the ICO. Simulation results show that MDT can achieve a precision of 135-157% of the actual area of the ICO. MCH can reach 102-145% of that area in ICO. They are better than GG and SDT, which can achieve 137-160% and 145-164% of the actual area of the ICO. The experiments also concluded that moving the step size to \(\gamma/4\) allows for a precision similar to the one obtained with \(\gamma/3\). However, the number of termination rounds spent by using \(\gamma/4\) is about twice as high. Finally, MCH compared the number of moving sensors with the method that uses a steady number of mobile sensors to travel the boundary line for area refinement. Although the proposed MCH moves more sensors than the existing method, the area precision of the proposed MCH can increase 1% to 2.5% by using only one-third to one-fifth of the time spent by the algorithm proposed in [15]. Conceptualization, S.-C.H. ; methodology, S.-C.H. ; software, C.-H.H. ; validation, S.-C.H. and C.-H.H. ; formal analysis, S.-C.H. ; investigation, C.-H.H. ; resources, S.-C.H. and C.-H.H. ; data curation, C.-H.H. ; writing--original draft preparation, S.-C.H. ; writing--review and editing, S.-C.H. ; visualization, S.-C.H. ; supervision, S.-C.H. ; project administration, S.-C.H. ; funding acquisition, S.-C.H. All authors have read and agreed to the published version of the manuscript. This research was funded by Ministry of Science and Technology (MOST), Taiwan, grant number MOST 110-2637-E-150-015. Not applicable. Authors are grateful for the financial assistance provided by the Ministry of Science and Technology, Taiwan and National Formosa University. The authors declare no conflict of interest. ## References

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