## 5 Results  
Two identical SHM systems with two heterogeneous node sets, each was used at two different geo-locations as discussed in Section 2. The transmission from the SHM nodes to the gateway was started, and the packets P\({}_{\text{N}}\) were sent by the SGBINs to gateway(from Equation (6)). In Figure 16, a very rich source of data with 16,00,000 x-axis and y-axis tilt angle samples has been shown, with each dataset containing 80,000 samples. These data have been collected from our high-precision nodes placed at two geospatial positions, i.e., Lab106A and C05 Bridge Site at Qatar University, Doha. Two clusters of two tiltmeter nodes with specifications given in Figures 10-12 were networked and their data were collected in our database. For a detailed analysis, data of only two nodes with x and y-axis tilts were included. The data in Figure 17 will probably give false alarms as it has off-sets, we can see that data-centered at (0.23\({}^{\circ}\), \(-\)0.23\({}^{\circ}\)) in x-axis and y-axis for node A at bridge, (\(-\)0.39\({}^{\circ}\), \(-\)0.61\({}^{\circ}\)) in x-axis and y-axis for  
Figure 16: Bi-axial tilt angles data gathered at two unique geolocations in five calendar months (80,000 samples). node B at bridge, (0.5\({}^{\circ}\), \(-\)0.07\({}^{\circ}\)) in x-axis and y-axis for node A lab, (0.8\({}^{\circ}\), 0.23\({}^{\circ}\)) in x-axis and y-axis for node A lab at bridge. In Figure 13, we can see that SWEDA had used the relative gradient method to programmatically calibrate all the axis to 0\({}^{\circ}\) to avoid false alarms and ensure accurate performance of micro-steps otherwise all the calculation is useless. Furthermore, various data processing techniques like averaging, mean, frequency analysis and histogram. Focusing on the embedded devices for minimum computation cost histogram was selected. In Figure 18, the histogram of the entire dataset is shown. The most frequent tilt angle values (MFTAVs) on SHM Node A--Bridge were an x-axis tilt angle of 0.67\({}^{\circ}\) and a y-axis tilt angle of \(-\)0.23\({}^{\circ}\). The SHM Node B--Bridge had an x-axis tilt angle of \(-\)0.42\({}^{\circ}\) and a y-axis tilt angle of \(-\)0.61\({}^{\circ}\). SHM OSB A had an x-axis tilt angle of 0.51\({}^{\circ}\) and a y-axis tilt angle of 0.295\({}^{\circ}\). SHM OSB B had an x-axis tilt angle of 0.8\({}^{\circ}\) and a y-axis tilt angle of 0.24\({}^{\circ}\). Furthermore, the results of various data processing techniques, like averaging, mean, frequency analysis, and histogram, are shown. Focusing on the embedded devices for minimum computation cost, a histogram was the least costly operation. The most frequent values are automatically assigned to thresholding counters in SWEDA. MFTAV contributes to LTA triggering due to the maximum occurrence in the dataset. Figure 19 shows the effectiveness in reducing the computation cost of SWEDA in real sense for embedded systems by using the histogram after gradient which shows that the population of zeros is  
Figure 17: Bi-axial tilt angles data gathered at two unique geolocations in five calendar months (80,000 samples). Figure 18: Histogram of data gathered at 2 unique geolocations in 5 calendar months (80,000 samples). very favorable for floating-point operations by resource-constrained hardware like field-programmable gate arrays (FPGA), system-on chip (SoC), application-specific integrated circuits (ASICs), Arduino, and Raspberry Pi 3. SWEDA enabled 62,000 zeros that improved the real-time efficiency by 77.5% (62,000/80,000) in any domain, be it time or frequency. In Figure 20, the acceptable range of frequencies is shown that assisted the switching bandwidth of our Butterworth band-pass filter with a lower limit of 2 Hz and an upper limit of 48 Hz for optimized feature extraction in SWEDA. SWEDA uses Nyquist criteria that are at least twice the actual frequency of the signal. For detailing, SWEDA operated at five times the frequency of the node signals. The efficiency of the FFT was also increased by 77.5%.