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Efficient wireless sensor nodes have significantly motivated the usage of wireless sensor networks for intrusion detection and surveillance. A passive wireless surveillance network has the ability to detect humans by analyzing only the variations of the signal strength with respect to distance and alignment between nodes. When a human passes through an area covered by radio network, his/her body interferes with radio signals resulting in signal strength variations due to absorption, reflection and diffraction. In this paper, we analyze the signal strength variation induced by human presence, as a reliable method for passive surveillance. The proposed method analyzes principal components from a covariance matrix composed of samples that present signal strength variations gathered from wireless nodes. By using smart wireless outlets and inter-outlets communication signals, the original environment is not visually modified, but a certain level of sensorial intelligence is introduced without additional sensors. Principal component analysis enhances the detection accuracy level and improves the overall robustness of the surveillance method. Compared to conventional sensor networks, the use of smart wireless outlets and signal strength analysis preserves the transparency of the surveillance system and supports high level of sensorial intelligence, retaining low installation costs.