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Typical applications of wireless sensor networks include infrastructure monitoring and surveillance, where in many cases the geometry of the monitored object determines the topology of the deployed network. For example, important applications like pipeline monitoring and border surveillance feature a linear arrangement of wireless sensors. In this paper, we address the scalable optimization of linear sensor networks for serial distributed detection applications. In serial distributed detection, signal detection is performed collaboratively by multiple sensors arranged in serial until a final detection result is reached. By locally maximizing the Chernoff information at each sensor in the serial network, scalable solutions are obtained which only rely on local information. By considering the problem of detecting a deterministic signal in the presence of Gaussian noise, a detailed numerical study reveals interesting trade-offs and dependencies between communication constraints and detection performance.