Recent research in sink mobility for wireless sensor networks emphasizes optimizing the movement and placement of mobile sinks to enhance energy efficiency, data delivery reliability, and network longevity. Modern approaches use intelligent algorithms such as ant colony optimization, particle swarm optimization, and reinforcement learning to design adaptive mobility patterns that minimize communication overhead and balance energy consumption among nodes. These studies also explore trajectory optimization, dynamic clustering, and delay-tolerant mechanisms to improve data aggregation and minimize packet loss in large-scale or dynamic environments, ultimately ensuring more scalable and sustainable WSN deployments.