###### Abstract
Real-time catastrophic incidents in densely populated, commercial, and industrial zones particularly needed local [11] early warning systems, particularly for mitigating an earthquake that is aligned with strategic facilities [12] and lifelines of buildings in form of structural health monitoring (SHM) and indigenous structural conditions sensing (ISCS) nodes. The desideratum of low noise, high resolution, and a sampling rate ubiquitous unit solution capable of long-range error compensating communication was observed in [4; 5; 6; 7; 8; 9; 10; 11; 12], accommodating for early warning [1; 2; 3] and orientation error compensation. ISCS aimed to provide prognosis [13] in a smart way to make a non-destructive evaluation (NDE), precise long-haul monitoring of angular displacements, and critical frequencies. Geo-seismic event detection falls in the category of NDE [14] that follows four basic strategies: time series, a condition triggered, load effect, and sensors orientation schemes. Almost all the physical variables [15] are keystones in the NDE of structures, i.e., temperature, pressure, humidity, displacement, and vibration using cameras, Bragg grating fiber optic sensors, electrochemical(corrosion), laser Doppler vibrometer (LVD), accelerometers [16], strain gauges, linear variable differential transformers (LVDT), piezo-electric load cells, and thermocouples. A simple, low cost, diligent, and robust high resolution \(\pm 0.0000\)X angular displacement monitoring solution with 12-24-bit configurable analog to digital converter (ADC) was needed to accommodate [1; 2; 3; 1; 13; 1] in a plethora of NDE sensors [14; 15; 16] only for fundamental geo-mechanics early warnings and least computation assessments. Recent review work by [17] concluded that the five generic approaches to NDE are (1) wavelet transform approach, (2) empirical mode decomposition (EMD) approach, (3) Hilbert-Huang transform (HHT) approach, (4) parametric system identification (PSI) approach, (5) the peak picking approach, and (6) damage identification. Detection of abrupt changes [18] in structural integrity using peak picking methods was more hardware implementable [19] for pre-evaluation or NDE. The semi-automatic peak picking technique SegNet [20] based on convolutional neural networks (CNNs) was successful in peak picking. SegNet needed training of ANNs for image processing resulting in high computation cost that limited its applicability for real-time detection applications with hardware calibration. The machine learning-based earthquake early warning (EEW) approach [21] based on generative adversarial network (GAN) critic, as an automatic feature extractor-based discriminator, could recognize 99.2% of the earthquake P waves and 98.4% of the noise signals. The EEW had extreme pre-processing computation costs and real-time detection limitations on physically embedded systems. A very valuable work [22], illustrating the weighted genetic feature search algorithm, had gaps in the real-time application of the five techniques using wired and wireless schemes. The work [23] with (1) vibration-based method, (2) impedance-based method, (3) data fusion method, and (4) inverse methods used in wired techniques and smart sensing technology comprised of multi-sensor motes utilized in wireless techniques and served an excellent ISCS directive. The crave for time series-based least-cost applied computation was needed for ISCS or any geo-mechanics sensing unit due to the lapse in real-time applicability [17; 18; 19; 20; 21; 22; 23] of complex methods. In the light of the study carried out in this work from ISCS for [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13] requirements, strategies, methods, and techniques, the importance of positioning and orientation [24] of data acquisition points was the vital factor for implementing any algorithm for detailed feature extraction and study.