IoT and Cloud Computing in Biological Water Monitoring: A Systematic Review of Challenges, Architectures, and Emerging Trends

The integration of Internet of Things (IoT) and cloud computing technologies has revolutionized biological water quality monitoring by enabling high-resolution, real-time sensing and data analytics. Despite the potential, various challenges persist in deployment, data management, scalability, and security. This systematic review investigates the current landscape of IoT and cloud-enabled biological water monitoring, identifying commonly used technologies, architectural models, and recurring limitations while highlighting opportunities for advancement. A total of 17,872 records were screened from Google Scholar, Web of Science, and Scopus, of which 80 studies met inclusion criteria. The review adhered to PRISMA guidelines, and data were extracted and categorized across themes including cloud platforms, microcontrollers, communication protocols, system architectures, and security mechanisms. Most studies were published between 2020 and 2023, with Google Scholar contributing 60% of the included records. Custom/private cloud servers (45%) were the most used backend platforms, while ThingSpeak (13.75%) and AWS IoT (8.75%) were notable open/cloud-based solutions. Hardware trends favored Arduino-based (16.25%) and general microcontroller-based systems (15%), with ESP32-based and Raspberry Pi platforms also widely adopted. Major implementation barriers included connectivity issues (15.05%), GSM/Zigbee congestion (11.83%), and deployment cost (8.6%). HTTP (28.75%) and API (22.5%) were dominant communication methods, with MQTT used in 17.5% of cases. Architecture-wise, over half of the systems followed a cloud-only model, while hybrid and embedded systems remained underutilized. Alarmingly, 55% of studies did not report security mechanisms, and 50% lacked explicit privacy measures; when reported, encryption (17.5%) and data anonymization (30%) were the most common. The integration of IoT and cloud technologies in biological water monitoring is maturing, yet significant challenges remain—particularly in standardization, energy efficiency, and security. The review underscores the urgent need for context-aware architectures, transparent reporting, and stronger emphasis on privacy-by-design. Future work should leverage edge computing, AI integration, and standardized frameworks to enhance scalability, accuracy, and sustainability in aquatic monitoring systems.