Despite of continuous evolution of Wireless Sensor Networks, Energy exhaustion issue of wireless sensors is still remained. Thus, it is difficult to guarantee the self-sustainability of each sensor. Researchers in areas of energy conservation and energy harvesting have been consecutively developing new methods to increase the lifetime of a sensor. One of the methods is the Adaptive Sampling Algorithm (ASA). This is an effective algorithm to reduce the wasted sampling energy by using optimal sampling rate for monitoring. In this paper, we propose two advanced Adaptive Sampling Algorithms, Resuscitation Adaptive Sampling Algorithm (RASA), and Compensation Adaptive Sampling Algorithm (CASA). And also we propose the Adaptive Sensor Management Scheme (ASMS) to apply ASA, CASA and RASA according to the energy state of sensors. RASA is the algorithm to set low sampling rate and guarantee the self-sustainability when energy sate of sensors is too low. Sensor nodes in CASA can be recharged some energy by saving the consumption energy when the harvesting quality is good. ASMS scheme combines with these two algorithms. ASMS scheme classifies the sensors of WSN into three classes according to the Current energy state and Current energy harvesting quality. Nodes in each class can be applied different optimal sampling rate to achieve the self-sustainability. To prove the efficiency of proposed algorithms, we setup the micro dust air pollution application consisted of Arduino Uno, Zigbee and dust sensor and simulate through MATLAB. Simulation shows that ASMS can save consumption energy maximum 50% and guarantee the genuine self-sustainability of nodes. And also we compare ASMS with existing ASA.