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Open Access

ARTICLE

IoT-Enabled Middleware for Smart City Environmental Monitoring with Integrated Analytics and Visualization

Zulfiqar Ali, Azhar Mahmood*, Shaheen Khatoon, Seyed Ali Ghorashi
School of Architecture, Computing and Engineering, Department of Computer Science and Digital Technologies, University of East London, University Way, London, UK
* Corresponding Author: Azhar Mahmood. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.084386

Received 21 April 2026; Accepted 26 June 2026; Published online 30 July 2026

Abstract

Smart city systems increasingly depend on data analytics and visualization to support informed and timely decision making in complex urban environments. However, existing middleware solutions predominantly focus on data acquisition and communication, while analytical processing and visualization are typically delegated to external applications, resulting in increased development complexity, reduced reusability, and fragmented system architectures. This study presents analytics and visualization-centric middleware named “Service-Oriented Middleware for Smart City Applications” (SOMSCA), in which these capabilities are embedded directly within the middleware layer. SOMSCA adopts a service-oriented approach, exposing analytics and visualization functionalities as reusable platform services, and incorporates a data-type-oriented visualization strategy along with a template-assisted dashboarding mechanism to enable dynamic and flexible application development. To validate the proposed approach, a prototype implementation is developed using React, FastAPI, MySQL, and TimescaleDB and evaluated using air-quality data collected from the London Air Quality Network, comprising more than one million observations across multiple pollutants and monitoring locations. The implementation supports real-time, historical, and aggregated analytical services together with dynamic dashboard generation. The results demonstrate the practical feasibility of middleware-integrated analytics and visualization through reusable service creation, flexible dashboard configuration, and interactive environmental monitoring capabilities. These findings highlight the potential of middleware-level intelligence to simplify application development and support data-driven decision making in smart city environment.

Keywords

Middleware; smart city; internet of things (IoT); data analytics; data visualization; service-oriented architecture; dashboarding; environmental monitoring; air quality; decision support

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