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The K-coverage configuration is widely exploited to monitor critical applications in wireless sensor networks. A major challenge here is how to maximize the system lifetime while preserving high-quality coverage. The existing sleep scheduling algorithms, classified into time-synchronized and self-organized approaches, either generate many redundant active sensors or incur high computation cost. In this paper, we propose KGS and DKEA algorithms to settle all essential problems of these two approaches respectively. KGS adopts an appropriate scheduling granularity to minimize the number of active sensors. DKEA efficiently determines whether a sensor should stay active by tracing only some decision areas. We further analyzed which approach maximizes the system lifetime of the K-coverage configuration. Experimental results show that, (i) KGS minimizes the average coverage degree among several popular time-synchronized algorithms, (ii) the computation cost of DKEA is only 11% of that of a well-known self-organized algorithm, and (iii) DKEA outperforms KGS in most cases.