A Wireless Sensor Network can be represented as a graph with nodes representing sensors and edges representing communication channels, with Euclidean distance and communication range serving as important metrics. The Weighted Connected Vertex Cover (WCVC) is optimized by the suggested Hybrid Genetic Algorithm (HGA) to improve energy efficiency and provide robust Wireless Sensor Network (WSN) connectivity. This paper contains a detailed comparison between the operating architectures of IP cameras and WSNs, two important technologies utilized in security surveillance and environmental monitoring. WSNs consist of autonomous sensor nodes that are capable of sensing, processing, and transmitting the data over a wireless network. The self-organizing ability and functionality of these nodes in harsh environments makes them particularly valuable in hazardous or hard-to-reach locales. IP cameras, in turn, are vital for video surveillance, as they capture digital video streams that can be sent to the Internet for recorded or live monitoring. This research examines key elements enhancing scalable and effective monitoring systems for smart cities, industrial sites, and more are promised by integrating WSN and IP camera. However, challenges exist, such as the high cost of large-scale deployments, network latency in IP cameras, and energy limitations in WSNs. Future studies should tackle these issues by improving graph-based optimization methods for resource efficiency, creating deterministic placement models for IP cameras to maximize coverage, and developing energy-harvesting technologies to increase sustainability. These developments direct to reliable, affordable, and expandable surveillance and monitoring systems that take advantage of the complementing qualities of IP cameras and WSNs.