Aug 2026· Sustainability· Vol 18, pp. 8126· 0 citations· 52 references
Abstract
The digitalization of photovoltaic (PV) systems can support sustainable energy deployment by improving operational efficiency, system visibility, and energy extraction. However, many existing Internet of Things (IoT)-enabled solutions address monitoring and maximum power point tracking (MPPT) separately or depend on proprietary platforms, remote cloud services, and relatively costly hardware, which may restrict their accessibility and replication in small-scale and resource-constrained applications. This study presents the implementation and laboratory-scale experimental evaluation of an edge-IoT architecture that integrates real-time PV monitoring, embedded adaptive MPPT control, local data management, and visualization using low-cost hardware and open-source software. The proposed architecture combines an ESP32 microcontroller with a Raspberry Pi (RPi) local server to enable environmental and electrical sensing, edge-based control, message queuing telemetry transport (MQTT) communication, local data storage, and interactive visualization through the open-source Node-RED, InfluxDB, and Grafana platforms. An adaptive perturb-and-observe (AP&O) algorithm is implemented on the ESP32 to dynamically adjust the duty cycle of a DC–DC boost converter in response to changing operating conditions. The system is experimentally evaluated using a PV test bench equipped with a custom boost converter and sensing modules measuring eleven electrical and environmental parameters. The architecture achieved an average communication latency of 193 ± 23 ms and an average MPPT efficiency of 97.3 ± 0.54%. It also provided a power gain of 0.7 ± 0.5% compared with the conventional fixed-step perturb-and-observe method. By combining local processing, open-source software, low-cost components, and integrated monitoring and control, the proposed system reduces dependence on external cloud infrastructure while supporting responsive and accessible PV energy management. These results demonstrate its potential as a replicable technological framework for improving the operational sustainability and digital management of small-scale PV installations.
Continuous monitoring of water quality is essential for supporting environmental management and enabling timely decision-making in wastewater treatment facilities. Although numerous Internet of Things (IoT) solutions have been proposed for environmental monitoring, many rely on proprietary cloud platforms or commercial gateways that limit flexibility, scalability, and integration with customized applications. This paper presents the design, implementation, and field validation of a modular IoT architecture for real-time water quality monitoring based on distributed sensor nodes, long-range LoRa communication, and a self-hosted web platform. The proposed architecture integrates sensor nodes equipped with calibrated pH, dissolved oxygen, and turbidity sensors, a hybrid LoRa/Wi-Fi Main Controller implementing a custom master–slave communication protocol, and a Python-based back-end with a PostgreSQL database for data acquisition, storage, visualization, and historical analysis. The complete system was deployed and experimentally validated in a real coupled constructed wetland located at the Universidad del Atlántico, Colombia, where three monitoring stations continuously acquired and transmitted water quality measurements over a one-month evaluation period. During the experimental deployment, the system generated more than 4.5 million measurement records (297 MB) while recording average RSSI values between −55.7 and −58.1 dBm (standard deviation: 1.6–2.2 dB). The developed web platform successfully supported real-time visualization and historical analysis of all acquired measurements. These results demonstrate the feasibility of the proposed architecture as a practical, scalable, and modular solution for continuous environmental monitoring that can be readily adapted to other distributed water quality monitoring applications.
Abstract
The Internet of Things (IoT) has emerged as one of the most significant technological advancements in recent years, enabling seamless communication and interaction among physical devices through the Internet. IoT technology has transformed traditional monitoring and control systems by providing real-time data collection, remote accessibility, intelligent decision-making, and automated control mechanisms. The increasing demand for smart environments in sectors such as smart homes, healthcare, agriculture, industrial automation, and smart cities has accelerated the adoption of IoT-based solutions. This dissertation presents the design and implementation of an IoT-based smart system for real-time monitoring and automation using low-cost hardware components, cloud computing platforms, and mobile applications.
The primary objective of this research is to develop an efficient, reliable, and scalable IoT-based monitoring system capable of collecting environmental data, transmitting information to cloud servers, and enabling remote monitoring and automated control. The proposed system integrates NodeMCU ESP8266 as the central processing unit with sensors such as the DHT11 temperature and humidity sensor and the PIR motion sensor. These sensors continuously monitor environmental conditions and transmit the collected data to cloud platforms through wireless communication. ThingSpeak is used as the cloud platform for data storage, visualization, and analysis, while the Blynk mobile application provides users with real-time monitoring and remote control capabilities.
The development process involved hardware configuration, software programming, cloud integration, and system testing. The NodeMCU microcontroller was programmed using Arduino IDE to acquire sensor readings, establish wireless connectivity, and communicate with cloud services. The collected data was uploaded to the cloud platform and displayed through graphical dashboards for real-time observation. Furthermore, automation functionalities were incorporated through relay modules that enable automatic control of connected devices based on predefined threshold values and environmental conditions.
Experimental investigations were conducted to evaluate the performance and effectiveness of the proposed system. The results demonstrated successful real-time monitoring of temperature, humidity, and motion detection parameters. Sensor data was accurately transmitted to the cloud platform and displayed on the mobile application with minimal delay. The automation features successfully triggered control actions whenever predefined conditions were satisfied. The system also demonstrated reliable communication between sensors, cloud servers, and end users, thereby validating the feasibility of the proposed approach.
A comparative analysis between traditional monitoring systems and the proposed IoT-based solution revealed significant improvements in terms of automation, accessibility, data management, operational efficiency, and remote monitoring capabilities. The implementation confirmed that IoT technology can substantially reduce manual intervention, improve response time, and enhance system effectiveness. Despite challenges such as network dependency, security concerns, and sensor limitations, the developed system proved to be a cost-effective and practical solution for intelligent monitoring and automation applications.
Zarreen Fatima, A. Farooqi· International Scientific Jou...· 0 citations
The findings indicate that integrating IoT technology with cloud communication and MATLAB analytics provides a practical, low-cost, and scalable solution for intelligent energy monitoring.
I. M. Danjuma, M. Asih, S. S. Garba et al.· International Journal of App...· 0 citations
Smart buildings increasingly depend on dense, distributed sensing infrastructures to improve energy efficiency, indoor environmental quality and operational flexibility. However, large-scale IoT/WSN deployment is still constrained by wiring effort, battery maintenance and limited access to sensing locations. Energy harvesting (EH) offers a promising approach toward low-maintenance and partly autonomous sensing, but its practical value in building automation depends on more than the output of individual transducers. This article presents a structured review of EH for IoT/WSN and edge-enabled building automation, focusing on smart-building, Building Management System (BMS) and Building Automation and Control System (BACS) contexts. Light-based, thermoelectric, mechanical, RF/wireless-power-transfer and hybrid harvesting technologies are interpreted through a system-oriented chain linking energy sources, power management, storage, communication, adaptive operation, gateways, diagnostics and edge intelligence. The synthesis shows that EH is most promising for low-duty-cycle environmental monitoring, envelope and façade sensing, occupancy and human–building interaction, airflow-related sensing, technical monitoring and retrofit automation. The main challenges concern the transition from device autonomy to sensing-service autonomy, complete-node evaluation under real building conditions, interoperability with supervisory systems and diagnostic interpretation of intermittent operation. Further research is also needed on lifecycle value assessment and safe transferability toward remote, temporary, resilient and closed ecological infrastructure applications.
Traditional energy management at universities is characterised by manual monitoring, static control systems, and lack of real-time data, resulting in excessive energy consumption and high operational costs. This study presents the design, implementation, and evaluation of a Smart Energy Management System (SEMS) at the University of Calabar, Cross River State, Nigeria, with the objective of reducing energy consumption and operational costs. The SEMS integrates Internet of Things (IoT) sensors, real-time data analytics, and automated control mechanisms to monitor and manage energy consumption dynamically. Key hardware components include motion sensors, smart meters, and environmental monitors, all connected to a centralised dashboard that provides actionable insights for energy optimisation. A six-month pilot deployment using a three-tier IoT architecture demonstrated energy savings of 30–40%, improved operational efficiency, with automated response times of 2–4 seconds and system uptime exceeding 95%. Comparative analysis confirmed that the SEMS outperforms traditional energy management in responsiveness (automated response time of 2.5–3.5 seconds versus delayed manual response), cost-effectiveness (30–40% reduction in energy expenditure with low long-term operational costs), and data visibility (real-time, room-level consumption data versus monthly, incomplete utility bills). The study provides a scalable framework for implementing smart energy solutions in university settings.
Ofem Ajah Ofem, Iniobong Ime, Osowomuabe Njama-Abang et al.· Global Journal of Pure and A...· 0 citations
Findings indicate that the proposed framework serves as an innovative prototype for Smart Health management within higher education institutions, aligned with the global Smart Campus paradigm.
S. Janpla, Thanakorn Uiphanit· International Journal of Int...· 0 citations
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