ORIGINAL RESEARCH article
Front. Commun. Netw.
Sec. IoT and Sensor Networks
Volume 6 - 2025 | doi: 10.3389/frcmn.2025.1672617
Modular Middleware for IoT: Scalability, Interoperability and Energy Efficiency in Smart Campus
Provisionally accepted
- University of the Americas, Quito, Ecuador
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The rapid expansion of IoT devices has led to increasingly complex networks, such as Smart Campuses, where ensuring interoperability, scalability, and energy efficiency becomes crucial. Existing middleware solutions, such as Z-Wave and LoRaWAN, have proven effective in specific applications but fail to address the diverse demands of dense and heterogeneous Internet of Things (IoT) environments. The limited scalability of Z-Wave (232 devices) and the high latency of LoRaWAN (150 ms) highlight the need for a more comprehensive solution. This study presents a middleware designed to overcome these limitations through a modular, microservices-based architecture. The system enables dynamic protocol translation and adaptive resource management, demonstrat-ing robust performance with 120 devices deployed and validated in a Smart Campus scenario. Additionally, simulations using NS-3 extended the evaluation to 500 virtual devices, supporting scalability analysis under varying traffic and heterogeneity condi-tions. The middleware incorporates optimization strategies, such as data compression and adaptive task prioritization, to improve energy efficiency and operational performance. Experimental validation in a controlled environment demonstrated a 26.7 % reduction in power consumption for optimized nodes, achieving an average of 60 W compared to 80 W for non-optimized nodes. Response times averaged 130 ms on opti-mized nodes, outperforming LoRaWAN while achieving a 94 % interoperability success rate. Deployment in a real Smart Campus confirmed the robustness of the middleware, maintaining consistent performance under dynamic conditions and in the presence of external interference.
Keywords: IoT middleware, Scalability and interoperability, energy efficiency, Smart campus, machine learning
Received: 24 Jul 2025; Accepted: 08 Sep 2025.
Copyright: © 2025 Villegas, Gutiérrez and Govea. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: William Villegas, University of the Americas, Quito, Ecuador
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