In this section, we present some experimental results to verify the effectiveness of the proposed sensor deployment algorithm. We design six kinds of sensing fields, as shown in Fig. 5.1. We consider four cases: (rs, rc) = (7, 5), (5, 5), (3.5, 5), and (2, 5) to reflect the relationships of rs > rc, rs = rc, rs < rc ≤ √
3rs, and √ 3rs <
rc, respectively. We mainly compare our algorithm and two deployment methods discussed in Section 2.3 (namely coverage-first and connectivity-first methods). The comparison metric is the number of sensors being used.
Fig. 5.2 compares the number of sensors being used when rc ≤√
3rs in different sensing fields. The connectivity-first method is dominated by the value of rc, so the number of sensors is fixed when rc ≤ √
3rs. Thus, when rs ≥ rc, this method uses the most sensors because the overlapping in coverage is very large. On the contrary, when rs < rc ≤ √
3rs, the coverage-first method uses the most sensors, because it needs many extra sensors to maintain connectivity between neighboring sensors. The proposed method uses the least sensors because it can adjust the distance between two adjacent rows according to the relationship of rs and rc.
Fig. 5.3 makes a similar comparison whenrc >√
3rs. Our algorithm still uses the least sensors in all cases. Note that when rc >√
3rs, our algorithm works the same as the coverage-first method in each individual region, so we omit its performance in Fig. 5.3.
(a) (b) (c) Non-convex polygon
Figure 5.1: Sensing fields used in the simulations.
376 408
(c) Non-convex polygon (d) H-shape
(e) Office 1 (f) Office 2
Figure 5.2: Comparison on the number of sensors used when rc ≤ √
3rs under different shapes of sensing fields.
Figure 5.3: Comparison on the number of sensors used when rc > √
3rs under different shapes of sensing fields.
Chapter 6 Conclusions
In this work, we have proposed a systematical solution for sensor deployment. The sensing field is modeled as an arbitrary polygon with possible obstacles. Thus, the result may be used in an indoor environment. The result can be applied to sensors with arbitrary relationships of communication ranges and sensing ranges. Fewer sensors are required to ensure fully coverage of the sensing field and connectivity of the network as compared to other methods. Note that in this work we assume that sensors have predictable communication distance rc and sensing distance rs. This may result in fragile networks when the terrain factor is concerned. To resolve this problem, we can substitute rc and rs by rc and rs which are slightly smaller than rc and rs, respectively. This should result in a stronger network. Also, in our solution in 3.2.1, we can add more columns of sensors among adjacent rows to improve the reliability of the network.
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