Design and Deployment of Lightweight IoT Systems for Real-Time Environmental Monitoring Using Fog-Based Computation Models

Abstract

The increasing need for real-time monitoring of environmental parameters (e.g. air quality, temperature, pollution) demands lightweight, energy-efficient IoT solutions. Conventional cloud-centric IoT systems face challenges in terms of high latency, bandwidth constraints, and energy costs. Fog computing addresses these limitations by performing in-situ processing closer to the sensors. In this study, we design and simulate a fog-based IoT architecture for environmental monitoring. Our system uses low-power sensor nodes (e.g. LoRa-enabled air quality and temperature sensors) that send data to a local fog gateway for preprocessing (filtering, anomaly detection) before forwarding aggregated results to the cloud. We present architecture and deployment models (sensor layers, fog gateway, cloud backend, alert module) and evaluate performance via simulation. Compared to cloud-only deployment, the fog-based model significantly reduces end-to-end latency (e.g. ~60 ms vs 250 ms at 100 nodes) and improves data throughput while lowering overall energy usage. Tables summarize device specifications and system performance under varying parameters (node type, packet size, sampling rate). Our results demonstrate that fog-enabled IoT can provide real-time, scalable environmental sensing with much lower latency and power consumption than traditional cloud systems. We conclude with recommendations for incorporating AI at the edge, renewable-powered sensors, and fault-tolerant designs to further enhance future IoT environmental monitoring systems.