IOT-Enabled Real-Time Monitoring Framework for sustainable Management of Aquatic Resource in Lake Victoria, Kenya

Sustainable management of aquatic resources within the blue economy requires timely, accurate, and continuous environmental data to support informed decision-making. Lake Victoria, Kenya, supports fisheries, aquaculture, transport, tourism, biodiversity, and the livelihoods of many riparian communities. However, the lake continues to face growing pressure from pollution, climate variability, invasive species, habitat degradation, and unsustainable resource use. Conventional monitoring approaches often rely on periodic field sampling and manual observations, which may be costly, time-consuming, irregular, and limited in spatial and temporal coverage. These limitations create a need for innovative digital solutions that can support real-time surveillance and early response to environmental changes.

This paper proposes an Internet of Things-enabled monitoring framework for real-time management of aquatic resources in Lake Victoria, Kenya. The proposed framework will integrate distributed water-quality sensors, wireless communication technologies, cloud-based data storage, and dashboard visualization to support continuous monitoring of key aquatic environmental parameters. The study will focus on indicators such as water temperature, dissolved oxygen, pH, turbidity, conductivity, and water level, which are important for assessing water quality, fisheries productivity, aquaculture suitability, pollution risks, and general ecosystem health.

The proposed system is expected to provide timely environmental information that can support early detection of water-quality deterioration, improve decision-making by lake managers, reduce dependence on delayed manual reporting, and enhance evidence-based planning for aquatic resource management. The paper will also examine key implementation considerations, including sensor calibration, connectivity challenges, data accuracy, maintenance costs, local technical capacity, and integration with existing institutional monitoring systems.