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Recent years have witnessed the deployments of wireless sensor networks for mission-critical applications such as battlefield monitoring and security surveillance. These applications often impose stringent Quality of Surveillance (QoSv) requirements including low false alarm rate and short detection delay. In practice, collaborative data fusion techniques that can deal with sensing uncertainty and enable sensor collaboration have been widely employed in sensor systems to achieve stringent QoSv requirements. However, most previous analytical studies on the surveillance performance of wireless sensor networks are based on simplistic models (such as the disc model) that cannot capture the stochastic and collaborative nature of sensing. In this paper, we systematically analyze the fundamental relationship between QoSv, network density, sensing parameters, and target properties. The results show that data fusion is effective in achieving stringent QoSv requirements, especially in the scenarios with low signal-to-noise ratios (SNRs). In contrast, the disc model is only suitable when the SNR is sufficiently high. Our results help understand the limitations of disc model and provide insights into improving QoSv of sensor networks using data fusion.