A Review of IoT and Cloud Computing: Issues, Challenges, and Opportunities in Eutrophication Monitoring

Eutrophication remains a significant ecological threat to freshwater and coastal environments, often resulting in algal blooms, oxygen depletion, and biodiversity loss. Traditional monitoring methods—such as periodic manual sampling and laboratory analysis—lack the spatial and temporal resolution required for real-time assessment and management. Recent advances in Internet of Things (IoT) and cloud computing offer promising alternatives for continuous, automated water quality monitoring. This review aims to systematically evaluate current research on the integration of IoT and cloud platforms for eutrophication monitoring, focusing on system architectures, communication protocols, storage mechanisms, deployment strategies, and security practices. A total of 5,744 articles were identified through Google Scholar, Web of Science, and Scopus databases. After title and abstract screening and full-text evaluation, 81 articles published between 2015 and 2024 met the inclusion criteria. Descriptive analysis and categorization were performed on various system components, including cloud usage, communication protocols, data storage, deployment platforms, and microcontroller adoption.Research output on IoT-based eutrophication monitoring has steadily increased, peaking in 2023. The majority of studies originated from Asia-Pacific regions, particularly India (33.33%) and China (13.58%). Most papers were indexed in Google Scholar (57%), with fewer in Scopus (29%) and Web of Science (14%). ThingSpeak (30.86%) and custom platforms (9.88%) were the most used cloud services, while 23.46% of studies did not specify the platform. HTTP (33.33%) and MQTT (12.35%) were the most common communication protocols. In terms of storage, 35.80% employed miscellaneous or unclassified mechanisms, followed by real-time databases (20.99%) and SQL (13.58%). Hybrid cloud-edge deployment was dominant (55.56%), reflecting a need for both scalability and real-time responsiveness. Security practices were inconsistently reported; 49.38% of studies did not specify any mechanism, while encryption (24.69%) and authentication (16.05%) were the most mentioned. The Arduino family (34.57%) and ESP series (23.45%) were the most adopted microcontrollers. Although IoT integration into eutrophication monitoring systems is growing, there are persistent gaps in cloud analytics integration, standardized security implementation, and system documentation. The widespread use of hybrid cloud-edge models suggests an evolving architecture aimed at balancing performance and scalability. However, underutilization of advanced cloud services like predictive analytics (used in only ~ 30% of studies) and inconsistent protocol/reporting standards call for unified frameworks to guide future research and deployment.