Recent research in Coverage and Connectivity in Wireless Sensor Networks (WSNs) focuses on optimizing sensor deployment, energy utilization, and communication efficiency to ensure reliable monitoring and robust network performance. Advanced metaheuristic algorithms such as Improved Chaotic Grey Wolf Optimization (ICGWO) and hybrid PSO-based approaches are being applied to enhance coverage rates and connectivity with minimal sensor nodes. Studies also emphasize adaptive placement strategies, probabilistic sensing models, and mobility-assisted node repositioning to maintain continuous coverage under dynamic conditions. These developments aim to achieve an optimal trade-off between network lifetime, sensing accuracy, and communication stability, enabling efficient and scalable WSN designs for real-world applications like environmental surveillance, smart cities, and disaster management.