The relationship between node speed and the average localization errors is presented in Fig. 5. Figure 5 shows that, with the increase of speed, the average localization errors of all three algorithms do not change significantly.
On this basis, we (i) develop an analytical framework that allows us to analyze the relationship between network parameters and the average localization accuracy obtained in ad hoc sensor networks with a spread-spectrum physical layer, and (ii) show that, for such networks, minimizing the average localization error is equivalent to maximizing the average effective throughput.
The relationship between the communication radius and the average localization errors is presented in Fig. 4.
The relationship between the anchor node density and the average localization errors, when the node communication radius is 30 m, is shown in Fig. 3.
Table 4 Comparative experimental results of the average localization error between fuzzy and non-fuzzy algorithms Localization algorithm Average localization error (m) IT2FLS 0.8 K-nearest neighbor (KNN) 1.1 Lateration 1.9.
Thus, the strong coupling between the MWCNTs and the PPy chains enhances the average localization length and hence conductivity increases for the nanocomposites.
The conductivity temperature dependence test showed a strong interaction between the MWCNTs and PPy chains which increases the average localization length so that conductivity increases for the nanocomposite samples.
Bounds on the average localization accuracy in a packet-based sensor network with time-of-arrival (TOA) based distance estimation are used to investigate the connection between the average effective throughput of packets and the average localization accuracy.
Further experiments were conducted on the R&D room (8 m × 5.5 m) to calculate the average localization error (The localization error is measured as the Euclidean distance between the actual and the estimated locations).
The average localization error and total rate are averaged over 100 node configurations.
