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Stay time is important for understanding people's travel behavior and mobility motivation. In this paper, by leveraging private car trajectory data, we propose a novel systematic approach for implementing stay behavior detection and stay time prediction. Specifically, we first propose a fuzzy logicbased stay detection method for detecting stay behavior in a large-scale private car trajectory dataset. Then, we design a spatiotemporal feature extraction method called clustering and kernel (CaK) by considering the spatial similarity, temporal periodicity and spatiotemporal correlation of stay behavior data. Furthermore, we propose a stay time predictor (STP) based on gradient-boosting regression trees and a long short-term memory network that can estimate the future durations of private car users' stays in various scenarios. We perform extensive experiments based on two real-life trajectory datasets. The experimental results demonstrate that the STP achieves a predictive accuracy (specifically, the root-mean-square error) of 123.94 and R 2 of 0.893 for stay time prediction of individual stays. This study provides a new perspective for understanding people's stay behavior.INDEX TERMS Machine learning, data mining, regression analysis, prediction methods. I. INTRODUCTION A. BACKGROUND AND MOTIVATION

Developing a 3D topology control algorithm to reduce interference is important. Because wireless sensor networks are often deployed in 3D space (such as multi-floor building, forest or underwater) and interference imposes a negative impact on prolonging network lifetime in wireless sensor networks. In this paper, we analyze existing 3D topology control algorithms, and the discussion shows that existing 3D topology control algorithms can not reduce interference effectively. We propose an Interference-Reducing Topology Control (IRTC) for 3D wireless sensor networks. In IRTC, each node adjusts its transmitting range to choose proper neighbors according to the cost of distance and interference to construct the local topology. We prove that the graph induced by IRTC is an energy-t-spanner of the original graph, which is extremely useful for prolonging the lifetime of the network. Simulations results show that IRTC performs 2∼5 times better than existing 3D topology control algorithms in interference reduction.

Power assignment in wireless ad hoc networks is an important issue of topology control which assigns power for each wireless node so that the induced communication graph satisfies some desired properties such as the connectivity and the energy spanner. In this paper, we study the problem of power assignment in order that its induced communication graph meets the following properties: (1) it is an energy-t-spanner which is energy efficient; (2) it is k-fault resistant which can withstand up to k − 1 node failures where k 1; (3) the interference is minimal. We propose algorithms to address this problem. Both the theoretic analysis and the simulations in the paper prove that our algorithms can induce a k-fault resistant energy spanner and furthermore the interference is minimized. To the best of our knowledge, this is the first paper to study the power assignment problem simultaneously considering spanner properties, the fault tolerance, and the interference reduction.