The use of energy-harvesting technologies in mobile wireless sensor networks (MWSN) delivers a promising opportunity to mitigate the limitations that irreplaceable energy sources impose over conventional MWSN. We propose Leontief-Data Cleaning Distribution Strategy (Leontief-DCD), an economic model-based method designed to distribute the data cleaning workload in energy harvesting MWSN powered by predictable energy sources, such as solar energy. Leontief-DCD creates interdependencies among sensor nodes to predict the required cooperation from each node in the data cleaning process. Different from existing task allocation methods, the interdependencies in Leontief-DCD allows for to plan a workload distribution that benefits the network as a whole, rather than only individual sensors, which consequently benefit the overall system performance. Our results show that when employing our method to distribute data cleaning workload in highly dirty, real-world datasets in scenarios with high and low energy, our method increased the number of data samples engaged in data cleaning processes by up to 25.57%, the count of active sensor nodes by up to 44.01%, and the network overall well-being by up to 55.42% compared to data cleaning performed by each node individually.
