The existing coverage hole boundary detection methods cannot detect large-scale coverage hole boundary in wireless sensor network quickly and efficiently. Aiming at this problem, a boundary detection method for large-scale coverage holes in wireless sensor network based on minimum critical threshold constraint is proposed. Firstly, the optimization problem of minimum critical threshold is highlighted, and its formulaic description is constructed according to probabilistic sensing model. On the basis of this, the distributed gradient information is used to approximately solve the optimization problem. After that, local-scale rough boundary detection algorithm incorporating the minimum critical threshold and its iterative thinning algorithm are proposed according to blocking flow theory. The experimental results show that the proposed method has low computational complexity and network overhead when detecting large-scale coverage hole boundary in wireless sensor network.

Up to date, model and parameter uncertainties are generally overlooked by majority of researchers in the field of battery study. As a consequence, accuracy of the SOC estimation is dominated by the model fidelity and may vary from cell-to-cell. This paper proposes a systematic framework with associated methodologies to quantify the battery model and parameter uncertainties for more effective battery SOC estimation. Such a framework is also generally applicable for estimating other battery performances of interest (e.g. capacity and power capability). There are two major benefits using the proposed framework: i) consideration of the cell-to-cell variability, and ii) accuracy improvement of the low fidelity model comparable to the high fidelity without scarifying computational efficiency. One case study is used to demonstrate the effectiveness of the proposed framework.

Data sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above issues, this paper proposes a personalized POI recommendation using an improved graph convolutional network (PPR_IGCN) model, which integrates collaborative influence and social influence into POI recommendations. On the one hand, a user-POI interaction graph, a POI-POI graph, and a user–user graph are constructed based on check-in data and social data in a location-based social network (LBSN). The improved graph convolutional network (GCN) is used to mine the higher-order collaborative influence of users and POIs in the three types of relationship graphs and to deeply extract the potential features of users and POIs. On the other hand, the social influence of the user’s higher-order social friends and community neighbors on the user is obtained according to the user’s higher-order social embedding vector learned in the user–user graph. Finally, the captured user and POI’s higher-order collaborative influence and social influence are used to predict user preferences. The experimental results on Foursquare and Yelp datasets indicate that the proposed model PPR_IGCN outperforms other models in terms of precision, recall, and normalized discounted cumulative gain (NDCG), which proves the effectiveness of the model.

Double-chained visual mechanism on a circular orbit is a novel binocular visual platform which the length, location and direction of the baseline can be adjusted within a circular region. Measuring structure parameters and pulse equivalents of motors is a key question to ensure the accuracy of metric reconstruction of the scene. This paper describes an indirect strategy based on vision measurement for it. Firstly, the geometric model of the mechanism is build. Secondly, the optimal estimation model of the camera center from a target image is proposed. Then the method to compute structure parameters and pulse Equivalents from the fitted trajectory of the camera center is presented. Finally, experiment results show the effectiveness of the method.

In order to solve the connectivity problem between multiple islands in the wireless sensor network (WSN) due to large‐scale damage, a multirobot WSN island alliance method based on the local priority of the optimal anchor point is proposed. This method introduces the idea of priority restoration of local connectivity in weighted networks and uses the optimal anchor point election mechanism to construct connectivity paths. Compared with existing methods, LPRMA’s local preferential recovery thinking makes WSN island alliances more adaptable. And LPRMA’s trunk deployment strategy based on optimal anchor can significantly reduce the path length and the number of trunk deployments for connected recovery. Finally, through the comparison and simulation experiments with the existing methods, the results show that the proposed method can effectively improve the efficiency of WSN island alliance while satisfying the constraints of lower network delay and higher robustness.