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Location monitoring systems are used to detect human activities and provide monitoring services, e.g., aggregate queries. In this paper, we consider an aggregate location monitoring system where wireless sensor nodes are counting sensors that are only capable of detecting the number of objects within their sensing areas. As traditional query processors rely on the knowledge of users' exact locations, they cannot provide any monitoring services based on the readings reported from counting sensors. To this end, we propose an adaptive spatio-temporal histogram to enable monitoring services without the need of users' exact locations. The main idea of the histogram is to keep statistics about the distribution of moving objects. At the core of the histogram, we propose three techniques, memorization, locality awareness and packing, to improve monitoring accuracy and efficiency. Furthermore, the histogram is designed in a way that achieves a trade-off between the energy and bandwidth consumption of the sensor network and the accuracy of monitoring services. Experimental results show that the proposed histogram provides high-quality location monitoring services (i.e., 90% accuracy for both skewed and uniform mobility patterns) and outperforms a basic histogram and the state-of-the-art spatio-temporal histogram by two orders of magnitude in most cases.