Abstract:
Efficient sanitation system management relies on vigilant sewage surveillance to uphold environmental hygiene. The absence of robust monitoring infrastructure jeopardizes unimpeded
conduit flow, leading to floods and contamination. The accumulation of harmful gases in sewer
chambers, coupled with tampered lids, compounds sewer network challenges, resulting in
structural damage, disruptions, and safety risks from accidents and gas inhalation. Notably, even
vehicular transit is vulnerable, facing collisions due to inadequately secured manholes. The core
objective of this research was to deconstruct and synthesize a prototype blueprint for a live-feed
sewer monitoring framework (LSMF). This involves creating a data gathering nexus (DGN) and
empirically assessing diverse wireless sensing implements (WSI) for precision. Simultaneously, a
geographic information matrix (GIM) was developed with algorithms to detect sewer surges,
blockages, and missing manhole covers. Three scrutinized sensors—the LiDar TF-Luna, laser
TOF400 VL53L1X, and ultrasonic JSN-SR04T—were evaluated for their ability to measure water
levels in sewer vaults. The results showed that the TF-Luna LiDar sensor performed favorably
within the 1.0–5.0 m range, with a standard deviation of 0.44–1.15. The TOF400 laser sensor
ranked second, with a more variable standard deviation of up to 104 as obstacle distance
increased. In contrast, the JSN-SR04T ultrasonic sensor exhibited lower standard deviation but
lacked consistency, maintaining readings of 0.22–0.23 m within the 2.0–5.0 m span. The insights
from this study provide valuable guidance for sustainable solutions to sewer surveillance challenges. Moreover, employing a logarithmic function, TF-Luna Benewake exhibited reliability at
approximately 84.5%, while TOF400 VL53L1X adopted an exponential equation, boasting reliability approaching approximately 89.6%. With this navigational tool, TF-Luna Benewake
maintained accuracy within ±10 cm for distances ranging from 8 to 10 m, showcasing its
exceptional performance.
