Multiple-Mobile-Sink-Assisted Data Collection in Wireless Sensor Networks: A Survey of Coordination, Optimization, and Intelligent Approaches

Mobile sinks have been widely studied to mitigate the energy-hole problem and improve data collection in wireless sensor networks. Although existing surveys have primarily examined sink mobility, routing, and energy efficiency, they provide limited analysis of the interdependent coordination and optimization problems arising from multiple mobile sinks. To address this gap, this survey presents a coordination-centered review of multiple-mobile-sink-assisted data collection, organizing existing studies into five research dimensions within three complementary analytical levels: decision problems, decision coupling, and solution paradigms. These dimensions encompass task assignment and network partitioning, coordinated trajectory planning, multi-sink routing and data collection, cross-layer coupling and joint optimization, and intelligent multi-sink coordination. Beyond conventional classification, we analyze decision dependencies, distinguish sequentially coupled approaches from explicit joint optimization, and compare representative solution methodologies and their operational trade-offs. We further examine reinforcement learning and multi-agent reinforcement learning alongside alternative learning paradigms, distinguishing learning-based adaptation, federated parameter sharing, and explicit cooperation among multiple physical mobile sinks. We also identify open challenges involving learning scalability and heterogeneous multi-sink coordination, as well as security and practical deployment constraints.