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In cognitive radios, in-band spectrum sensing is essential for the protection of legacy spectrum users, enabling secondary users to vacate channels immediately upon detection of primary users. For in-band sensing, it is important to meet detectability requirements, such as the maximum allowed detection latency and the probability of misdetection and false alarm. In this paper, we propose key techniques for efficient in-band sensing. We first advocate the use of clustered sensor networks, and propose a periodic in-band sensing algorithm that optimizes sensing period and sensing time to meet the detectability requirements while minimizing sensing overhead. The scheme also determines the better of energy or feature detection incurring less sensing overhead at each SNR level, and derives the threshold aRSSthreshold on the average received signal strength of a primary signal above which energy detection is preferred to feature detection. We consider two key factors affecting aRSSthreshold noise uncertainty and inter-CRN interference. aRSSthreshold appears to lie between -114.6 dBm and -109.9 dBm with noise uncertainty ranging from 0.5 dB to 2 dB, and between -112.9 dBm and -110.5 dBm with 1-6 interfering CRNs. We also investigate how strict the detection requirement must be for efficient reuse of idle channels without incurring unnecessary channel switches due to false detection of primaries.