Individual trajectory data play a pivotal role in various application fields, such as urban planning, traffic control, and epidemic simulation. Despite the diverse means for data collection in current times, the real-world trajectory data in practical application remains severely limited due to concerns over personal privacy. In this study, we designed a Spatiotemporal-knowledge enhanced multi-TAsk GEnerative adversarial network (GAN), named STAGE, to generate synthetic trajectories that statistically resemble the real data without recycling personal information. In STAGE, we designed a multi-task generator with three stages of spatio-temporal generation tasks, i.e. activity-sequence generation task, township-level trajectory generation task, and neighborhood-level trajectory generation task, with the last one as the main task while the other two as auxiliary tasks. Meanwhile, we designed a spatial consistency loss in the adversarial training process to assess the spatial consistency of generated trajectories at different spatial scales. Experiment results show that compared to the baselines, trajectories generated by our method have closer data distributions to the real ones. We argued that the designs of spatiotemporal-knowledge enhanced generation tasks and training loss benefit the spatiotemporal generation processes, which help reproduce the temporal patterns of human daily activities and spatial distribution of human movements.

This paper presents the first distributed algorithm to construct a spanner for arbitrary ad hoc networks under the physical signal-to-interference-and-noise-ratio (SINR) interference model. Spanner construction is one of the most important techniques for topology control in wireless networks, which intends to find a sparse topology in which only a small number of links need to be maintained, without substantially degrading the path connecting any pair of the nodes in the network. Due to the non-local property of interference, constructing a spanner is challenging under the SINR model, especially when a local distributed algorithm is desired. We meet this challenge by proposing an efficient randomized distributed algorithm that can construct a spanner in O(log n log Γ) timeslots with a high probability, where n is the total number of nodes and Γ describes the ratio of the maximum distance to the minimum distance between nodes. The constructed spanner concurrently satisfies two most desirable properties: constant stretch and linear sparseness. Our algorithm employs a novel maximal independent set (MIS) procedure as a subroutine, which is crucial in achieving the time efficiency of spanner construction. The MIS algorithm improves the best known result of O(log2n) [33] to O(log n) and is of independent interest as the algorithm is applicable also to many other applications. We conduct simulations to verify the proposed spanner construction algorithm, and the results show that our algorithm also performs well in realistic environments.

In a seminal STOC 1995 paper, Arya et al. conjectured that spanners for low-dimensional Euclidean spaces with constant maximum degree, hop-diameter $O(\log n)$, and lightness $O(\log n)$ (i.e., weight $O(\log n) \cdot w({MST}))$ can be constructed in $O(n \log n)$ time. This conjecture, which became a central open question in this area, was resolved in the affirmative by Elkin and Solomon in STOC 2013. In fact, Elkin and Solomon proved that the conjecture of Arya et al. holds even in doubling metrics. However, Elkin and Solomon's spanner construction is complicated. In this work we present a significantly simpler construction of spanners for doubling metrics with the same guarantees as above. Our construction is based on the basic net-tree spanner framework. However, by employing well-known properties of the net-tree spanner in conjunction with numerous new ideas, we managed to get significantly stronger results. First and foremost, our construction extends in a simple and natural way to provide $k$-fault tolerant spanners with maximum degree $O(k^2)$, hop-diameter $O(\log n)$, and lightness $O(k^2 \log n)$. This is the first construction of fault-tolerant spanners (even for Euclidean metrics) that achieves good bounds (polylogarithmic in $n$ and polynomial in $k$) on all the involved parameters simultaneously. Second, we show that the lightness bound of our construction can be improved to $O(k^2)$ (with high probability), for random points in $[0,1]^D$, where $2 \le D = O(1)$.

In this article, we study the convergence time required to achieve consensus in dynamic networks. In each timestep, a node's value is updated to some weighted average of its neighbors and its old values. We study the case when the underlying network is dynamic and investigate different averaging models. Both our analysis and experiments show that dynamic networks exhibit fast convergence behavior, even under very mild connectivity assumptions.

The conventional methods of improving the performance of wireless sensor network focus on proposed algorithm mechanism to increase the packet delivery ratio, reduce the delay and so on. In this paper, firstly we propose an adaptive sleep scheduled scheme to reduce the energy consumption of the whole network. Based on this, we introduce weight w to measure the importance of data. We assume that the faster important the data is transmitted to sink node, the better the performance of whole network is. Applied the scheduling policy, system sleep optimum is impossible. The simulation shows that the performance has increased compared with full connect network.