Role Assignment for Spatially-Correlated Data Aggregation Using Multi-Sink Internet of Underwater Things

In this paper, we consider a multi-sink underwater data aggregation network, in which a set of Internet-of-Underwater-Things devices survey an underwater area of interest and upload their data to a set of data gathering stations. A device-role assignment framework is provided, which captures the network topology and allows multi-hop data aggregation. In this framework, an optimization problem is formulated with the objective of maximizing the uncorrelated data at the gathering stations with minimal energy consumption. The optimization problem is constrained over binary coupled role assignment, inter-device, and device-station association decision variables. An ant colony optimization (ACO) algorithm is developed to tackle the complexity of the optimization problem and find optimized solutions. Simulation results illustrate that the proposed ACO algorithm provides performance close to the optimal solution, which is obtained through exhaustive search. Results also show that the proposed framework aggregates more uncorrelated data and preserves more energy compared to a baseline approach, where the devices transmit raw data to the stations directly.