Empirical Study of Internet of Things Platform Performance for Large-Scale Environmental Sensor Networks in the Niger Delta Region

Large-scale environmental monitoring sensor networks deployed across the Niger Delta region for oil spill detection, air quality surveillance, and water pollution tracking generate substantial data volumes requiring reliable, low-latency IoT communication and cloud processing infrastructure. This study presents an empirical performance evaluation of three IoT platform architectures, a cloud-centralized platform using AWS IoT Core, a fog computing architecture with edge processing at regional gateway nodes, and a hybrid cloud-fog configuration, for supporting a simulated 500-node environmental sensor network in the Niger Delta. Network traffic, latency, data loss rate, and cloud processing cost metrics are evaluated using a purpose-built IoT testbed with 30 physical sensor nodes and Mininet-WiFi network emulation representing the remaining 470 nodes. Sensor payloads representative of hydrocarbon sensor, weather station, and water quality meter data streams are generated at realistic 5-minute reporting intervals. Results demonstrate that the fog computing architecture reduces average end-to-end latency from 2,140 milliseconds on the cloud-centralized platform to 280 milliseconds, while also reducing cloud data transmission cost by 67 percent through edge-level data aggregation. The hybrid platform achieves a balance of 410 milliseconds latency and 48 percent cost reduction, with the added benefit of raw data archiving to cloud for retrospective analysis. Data loss rate was below 0.8 percent for all three architectures during normal network simulation conditions, rising to 3.2 percent for the cloud-centralized platform under simulated cellular network congestion. The study provides architecture selection guidance for environmental monitoring network designers in the Niger Delta context. Keywords: IoT platform, fog computing, environmental monitoring, Niger Delta, sensor network