Wireless Sensor Network-Based Environmental Monitoring for Smart Cities

Abstract

Rapid urbanization increases the need for continuous, spatially distributed information about air quality, microclimate, noise, water conditions, and other environmental variables. Conventional monitoring stations provide high-quality measurements but are expensive and usually too sparse to characterize neighborhood-scale variations. This methodology paper proposes a wireless sensor network (WSN)-based environmental monitoring framework for smart cities that combines low-power sensor nodes, energy-aware communication, gateway connectivity, cloud storage, data-quality controls, and decision-oriented visualization. The proposed method uses heterogeneous sensing for particulate matter, gases, temperature, relative humidity, noise, and selected water-quality parameters; nodes perform local filtering and health checks before transmitting observations through short-range IEEE 802.15.4/ZigBee or low-power wide-area communication such as LoRa. A gateway timestamps, validates, and forwards data to a cloud database where calibration functions, missing-data rules, threshold detection, and spatial-temporal analysis are applied. The framework is evaluated through a simulation-based prototype scenario rather than claimed field measurements. The illustrative evaluation indicates that a well-planned WSN can achieve high packet delivery, low missing-data rates, and useful alert latency while substantially increasing spatial coverage compared with a few centralized stations. The methodology therefore provides a practical research design for scalable smart-city environmental sensing while explicitly addressing energy consumption, calibration, security, maintenance, and data governance.