By fully exploiting the cooperative communication capacities among mobile terminals (MTs), the MTs can adapt the offloading designs well to the practical network with dynamic features. In this paper, joint multi-user cooperative partial offloading, transmission scheduling and computation allocating is discussed for device-to-device (D2D) underlay mobile edge computing (MEC). By considering stochastic application requests, unpredictable MTs states, time-varying channel states and computation resources, a customized application offloading model, which aims to minimize the network-wide response latency and energy consumption simultaneously, is formulated. In order to solve this non-convex and non-smooth optimization problem, an online resource coordinating and allocating scheme (ORCAS) is proposed by exploiting Lyapunov optimization theory, variable substitution technique and resource provisioning priority mechanism. Both theoretical analyses and simulation results demonstrate that the proposed ORCAS can 1) drive the application response cost converge to the minimum; 2) achieve superior performance (e.g., the average network-wide response cost under ORCAS is approximately 19.14% lower than that under partial offloading directly); 3) adapt to dynamic situations in terms of stochastic user demands and channel states.

To address the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this article, a self-supervised learning-based anomalous node detection method based on an autoencoder is designed. This method integrates the extraction of temporal WSN data flow feature, spatial position feature, and intermodal WSN correlation feature into the autoencoder design to make full use of the spatial and temporal information of the WSN for anomaly detection. First, a fully connected network is used to extract the temporal features of nodes by considering a single mode from a local spatial perspective. Second, a graph neural network is used to introduce the WSN topology from a global spatial perspective for anomaly detection and to extract the spatial and temporal features of the data flows of nodes and their neighbors by considering a single mode. Then, an adaptive fusion method involving weighted summation is used to extract the relevant features between different modes. In addition, the gated recurrent unit network structure is introduced to solve the long-term dependence problem in the time dimension. Eventually, the reconstructed output of the decoder and the hidden layer representation of the autoencoder are fed into a fully connected network to calculate the anomaly probability for the current system. Since the spatial feature extraction operation is performed first, the designed method can be applied for anomaly detection in large-scale networks with the addition of a clustering operation. Experiments show that the designed method outperforms baseline methods, and the F1 score reaches 90.6%, at least 5.2% higher than those of existing anomaly detection methods based on unsupervised reconstruction and prediction.

Nowadays, urban noise emerges as a distinct threat to people’s physiological and psychological health. Previous works mainly focus on the measurement and mapping of the noise by using Wireless Acoustic Sensor Networks (WASNs) and further propose some methods that can effectively reduce the noise pollution in urban environments. In addition, the research on the combination of environmental noise measurement and acoustic events recognition are rapidly progressing. In a real-life application, there still exists the challenges on the hardware design with enough computational capacity, the reduction of data amount with a reasonable method, the acoustic recognition with CNNs, and the deployment for the long-term outdoor monitoring. In this paper, we develop a novel system that utilizes the WASNs to monitor the urban noise and recognize acoustic events with a high performance. Specifically, the proposed system mainly includes the following three stages: (1) We used multiple sensor nodes that are equipped with various hardware devices and performed with assorted signal processing methods to capture noise levels and audio data; (2) the Convolutional Neural Networks (CNNs) take such captured data as inputs and classify them into different labels such as car horn, shout, crash, explosion; (3) we design a monitoring platform to visualize noise maps, acoustic event information, and noise statistics. Most importantly, we consider how to design effective sensor nodes in terms of cost, data transmission, and outdoor deployment. Experimental results demonstrate that the proposed system can measure the urban noise and recognize acoustic events with a high performance in real-life scenarios.

With the increasing demand for location awareness, indoor positioning has gradually become one of the research hotspots. In recent years the indoor positioning research field generally introduced dilution of precision (DOP) in the field of satellite navigation to the optimization of base station (BS) configuration, but in practice, the distribution of DOP and the distribution of errors is far from the same. This paper analyzes the applicability of DOP in the indoor time difference of arrival (TDOA) positioning systems from various aspects. Then, according to the ultrawideband (UWB) ranging error model, the TDOA time-difference measurement error model is derived and used as the BS configuration optimization metric. Finally, the experiments are conducted in 8 m $\ast ~8$ m $\ast ~3$ m small-scale scene and 100 m $\ast ~30$ m $\ast ~10$ m large-scale scene respectively to compare the positioning accuracy of the three BS configurations under different receiving signal-to-interference ratio (SIR). At the same time, the experiment is conducted in outdoor standard football field to compare the influence of multipath on the positioning accuracy of the three BS configurations under the conditions of two SIR. The experimental results show that the optimized BS configuration with DOP as the optimized target is not the best BS configuration, and the positioning accuracy is not as good as the commonly used cuboid 8-BS configuration in low SIR environment. It proves that DOP cannot be used as a decisive metric for the BS configuration optimization of UWB indoor positioning, but can be used as a reference index, and the optimized BS configuration with TDOA time-difference measurement error as the optimization target has higher positioning accuracy.

Background: Gouty arthritis is the most frequently diagnosed inflammatory arthritis worldwide. Dynamin-related protein 1 (Drp1), a regulator of mitochondrial fission, contributes to various inflammatory disorders via activating NLRP3 inflammasome. However, the biological role of Drp1 in gouty arthritis remains undefined. Methods: A mouse model of monosodium urate (MSU)-induced gouty arthritis and MSU-stimulated macrophages were established as in vivo and in vitro models, respectively. Histological changes were assessed by H&E and IHC analysis. RT-qPCR and western blot were used to detect the expression of Drp1 and the key molecules in joint tissues and macrophages. Cytokine secretion was measured by ELISA assay, and antioxidant enzymes activities and LDH release were monitored using commercial kits. Mitochondrial structure and functions were assessed by transmission electron microscopy (TEM) and MitoSOX staining. Co-IP and GST pull-down assay were used to detect the direct interaction between USP16 and Drp1, as well as the ubiquitination of Drp1. Results: Drp1 was elevated in MSU-induced gouty arthritis model, and it induced gouty arthritis via NF-κB pathway and NLRP3 inflammasome activation. In addition, Drp1 activated NF-κB/NLRP3 signaling via modulating mitochondrial fission. Mechanistically, USP16 mediated deubiquitination and stabilization of Drp1 through its direct interaction with Drp1. Functional studies further showed that USP16 was highly expressed in MSU-stimulated macrophages and induced gouty arthritis via Drp1-dependent NLRP3 inflammasome activation. Conclusion: Deubiquitinase USP16 induced gouty arthritis via Drp1-dependent mitochondrial fission and NF-κB/NLRP3 signaling.