Sensing coverage is a fundamental design issue in wireless sensor networks(WSNs),while sensor scheduling ensures coverage degree to the monitored event and extends the network lifetime.In this paper,we address k-coverage scheduling problem in dense WSNs,we maintain a connected k-coverage energy efficiently through a novel Hard-Core based Coordinated Scheduling(HCCS),in which hardcore is a thinning process in stochastic geometry that inhibits more than one active sensor covering any area redundantly in a minimum distance. As compared with existing coordinated scheduling,HCCS allows coordination between sensors with little communication overhead.Moreover,due to the traditional sensing models in k-coverage analysis is unsuitable to describe the characteristic of transmit channel in dense WSNs,we propose a novel sensing model integrating Rayleigh Fading and Distribution of Active sensors(RFDA),and derive the coverage measure and k-coverage probability for the monitored event under RFDA. In addition,we analyze the influence factors,i.e. the transmit condition and monitoring degree to the k-coverage probability. Finally,through Monte Carlo simulations,it is shown that the k-coverage probability of HCCS outperforms that of its random scheduling counterpart. Sensing coverage is a fundamental design issue in wireless sensor networks (WSNs), while sensor scheduling ensures coverage coverage to the monitored event and extends the network lifetime. This paper, we address k-coverage scheduling problem in dense WSNs, we maintain a connected k-coverage energy efficiently through a novel Hard-Core based Coordinated Scheduling (HCCS), in which hardcore is a thinning process in stochastic geometry that inhibits more than one active sensor covering any area redundantly in a minimum distance. , HCCS allows coordination between sensors with little communication overhead. Moreover, due to the traditional sensing models in k-coverage analysis is unsuitable to describe the characteristic of transmit channel in dense WSNs, we propose a novel sensing model integrating Rayleigh Fading and Distribution of Active sensors (RFDA), and derive the coverage measure and k-coverage probability for the monitored event under RFDA. In Finally, through Monte Carlo simulations, it is shown that the k-coverage probability of HCCS outperforms that of its random scheduling counterpart.