Replicating Low-Cost IoT Solutions for Environmental Monitoring in Urban Slums of Togo

{ "background": "The urban slums of Togo face significant environmental challenges such as poor waste management leading to pollution and health risks.", "purposeandobjectives": "To replicate a previous study's low-cost Internet of Things (IoT) solutions for monitoring air quality, water contamination, and waste levels in these communities.", "methodology": "A replication of the original IoT setup was conducted with sensors calibrated using a linear regression model to predict pollutant concentrations based on historical data ($y = \beta0 + \beta1x + \epsilon$, where $R^2=0.85$ and robust standard errors are reported).", "findings": "The replicated system detected an average of 7% higher levels of particulate matter in urban slums compared to the original study, indicating potential calibration discrepancies.", "conclusion": "While replication confirms the viability of IoT solutions for monitoring environmental conditions in Togolese urban slums, it also highlights the importance of local calibration.", "recommendations": "Further studies should focus on calibrating sensors locally and incorporating feedback mechanisms to improve accuracy.", "keywords": "IoT, Urban Slums, Environmental Monitoring, Calibration, Replication Study", "contributionstatement": "This study demonstrates that with slight modifications in sensor calibration, IoT solutions can be successfully replicated for monitoring environmental conditions in urban slums." } --- Structured Abstract Background The urban slums of Togo face significant environmental challenges such as poor waste management leading to pollution and health risks. Purpose and Objectives To replicate a previous study's low-cost Internet of Things (IoT) solutions for monitoring air quality, water contamination, and waste levels in these communities. Methodology A replication of the original IoT setup was conducted with sensors calibrated using a linear regression model to predict pollutant concentrations based on historical data ($y = \beta0 + \beta1x + \epsilon$, where $R^2=0.85$ and robust standard errors are reported). Findings