Seamless positioning services are of a critical concern in building smart cities. In a multisource fusion indoor positioning system, providing the guidance information for the deployment of positioning sources is a key technology, which can optimize the infrastructure resources to provide higher positioning accuracy. The error models of single-source positioning such as the received signal strength (RSS) fingerprint and the pedestrian dead reckoning (PDR) should be extended to meet the requirement of multisource indoor positioning for positioning error estimation. This paper proposes a model that combines the RSS fingerprint and PDR positioning error models for fusion positioning error simulation, which weights the PDR and RSS fingerprint positioning results and calculates the mean square error for the fusion positioning according to their positioning variances. This model is also used to establish an indoor positioning simulation system. To validate the proposed model, an experiment is performed which compared the actual positioning errors using the fusion positioning with the errors of the simulate model. The results show that the actual positioning error curves and the error curve predicted by the model are consistent. As a result, the proposed error model provides a solution for optimizing the deployment of positioning sources.

With recent advances in cloud computing, mobile devices are increasingly being used to record patient physiological parameters, and transfer them to a cloud-based hospital information system, for access control mediation over a variety of stakeholders. In such a cloud-based architecture, the patient must specify an access policy for a group of authorized parties towards its outsourced data. Multiauthority ciphertext-policy attribute-based encryption (CP-ABE) was provided as an innovative cloud-based access control cryptographic primitive to tackle the key escrow issue in a centralized architecture, and boost flexibility through cross-domain attributes management. Existing works, however, still have glaring drawbacks. First, they still rely on a trusted authority to generate and distribute user secret keys. Second, they do not simultaneously provide encryption, decryption, or revocation outsourcing, resulting in high processing and communication cost for both the data sender and the data receiver. Third, they do not support both user and attribute revocation, and the integrity of ciphertext downloaded from the cloud is not always verified at the user end. As a result, this paper exploits the dummy attribute technique and introduces a novel, efficient, and secure multiauthority ciphertext-policy ABE method for mediating access control over medical data, in the mobile cloud. The ciphertext access policy enforcement, partial ciphertext decryption, and both the user and attribute indirect revocation updates are safely outsourced to the cloud server in this study. Theoretical analysis demonstrates that our scheme is efficient and verifiable, and we prove that our construction is secure under the decisional bilinear Diffie-Hellman assumption.

This erratum replaces the corresponding author Liang Tiancai with Ai Shan. In the article cited above, the authors wish to change the corresponding author from Liang Tiancai to Ai Shan as shown below.1 Shan Ai School of Computer Science and Cyberspace Security, Hainan University, Hainan, China Email address: [email protected] ORCID ID: 0000-0002-1784-0220

Graph neural networks (GNNs) can be effectively applied to solve many real-world problems across widely diverse fields. Their success is inseparable from the message-passing mechanisms evolving over the years. However, current mechanisms treat all node features equally at the macro-level (node-level), and the optimal aggregation method has not yet been explored. In this paper, we propose a new GNN called Graph Decipher (GD), which transparentizes the message flows of node features from micro-level (feature-level) to global-level and boosts the performance on node classification tasks. Besides, to reduce the computational burden caused by investigating message-passing, only the relevant representative node attributes are extracted by graph feature filters, allowing calculations to be performed in a category-oriented manner. Experiments on 10 node classification data sets show that GD achieves state-of-the-art performance while imposing a substantially lower computational cost. Additionally, since GD has the ability to explore the representative node attributes by category, it can also be applied to imbalanced node classification on multiclass graph data sets.

The application of intelligent computing in Internet of Things (IoTs) makes IoTs systems such as telemedicine, in-vehicle IoT, and smart home more intelligent and efficient. Secure communication and secure resource sharing among intelligent terminals are essential. A secure communication channel for intelligent terminals can be established through group key agreement (GKA), thereby ensuring the security communication and resource sharing for intelligent terminals. Taking into account the confidentiality level of the shared resources of each terminal, and the different permissions of the resource sharing of each terminal, a GKA protocol for intelligent IoTs is proposed. Compared with previous work, this protocol mainly has the following advantages: (1) The hidden attribute identity authentication technology can achieve the security of identity authentication and protect personal privacy from being leaked; (2) Only intelligent terminals satisfying the threshold required of the GKA can participate in the GKA, which increases the security of group communication; (3) Low-level group terminals can obtain new permissions to participate in high-level group communication if they meet certain conditions. High-level group terminals can participate in low-level group communication through permission authentication, which increases the flexibility and security of group communication; (4) The intelligent terminals in the group can use their own attribute permission parameters to calculate the group key. They can verify the correctness of the calculated group key through a functional relationship, and does not need to exchange information with other members in the same group. Under the hardness assumption of inverse computational Diffie-Hellman problem and discrete logarithm problem, it is proven that the protocol has high security, and compared with the cited literatures, it has good advantages in terms of computational complexity, time cost and communication energy cost.

Rumors mislead judgments of people, affect economic development, and the stability of social order. The research on the rule of spreading rumors is significant and meaningful. This paper improves the traditional Barabási–Albert scale-free network and proposes a network topology model that conforms to the characteristics of sharing social networks based on the complex network theory and the actual characteristics of sharing social networks. In addition, the credulous spider rational taciturn rumor propagation model is proposed by improving the credulous spider rational model, which solves the overspread problem of the traditional rumor propagation model. This paper further studies the influence of anxiety on the spread of rumors, and finds that the anxiety of audience is increasing with the spread degree of rumors.

When making regression predictions, the traditional random forest (RF) algorithm can only make predictions within the training set, which can easily lead to overfitting when modeling data have some specific noise. To solve the problem of over-fitting, an improved RF method is proposed in this paper for wind pressure prediction. With the aim to verify the prediction performance of the improved RF algorithm, this paper predicts the wind pressure coefficients of a high-rise building model without wind pressure measurement points. The results show that the improved RF can achieve good results in predicting the mean and fluctuating wind pressure coefficients of high-rise buildings, and its relative error for each measurement point is basically controlled at 5%, which is acceptable in engineering terms. Further applications show that this improved RF can be used for wind pressure distribution prediction in other large-span building type wind tunnel tests.