Recent research on coverage and connectivity issues in heterogeneous wireless sensor networks (HWSNs) emphasizes optimizing node deployment, communication reliability, and energy efficiency across diverse sensor types. Advanced metaheuristic algorithms such as improved wild horse optimization, hybrid particle swarm optimization, and evolutionary computation have been used to maximize network coverage while maintaining strong inter-node connectivity. Studies also integrate mathematical programming and AI-based models to dynamically adapt node placement and routing based on environmental changes and node heterogeneity. Multi-sink and multi-tier architectures are being explored to enhance data flow reliability and reduce latency, ensuring that HWSNs can sustain large-scale monitoring applications with minimal coverage gaps and network partitions.