Fog computing is used to process a large amount of data produced by terminal devices where fog nodes are the closest acquirers to the terminal devices. In fog computing, the processed data may be tampered with or illegally captured by some malicious nodes while the data is transferred or stored. So, when some applications need to require data process with high security, fog computing must provide a security approach to secure and check the final results. In this paper, we propose a secure data sequence query framework based on multiple fog servers, where we use multiple fog servers to store and provide data hashing values to verify corresponding data sequence. In the proposed scheme, the cloud server needs to pre-designate some Merkle hashing tree topologies to the fog network, then the fog server directly acquires related data from leaf-nodes (fog nodes) according to one of the pre-designated Merkle hashing tree topologies; at the same time, an actual Merkle hashing tree is constructed according to the selected Merkle hashing tree topology in the fog network. In fog computing, the related fog nodes participate in computing and transferring the hashing values on data, then the corresponding root node uses the $(t,n)$ threshold secret sharing scheme to compute and distribute the sub-secrets of the root value to other fog servers. Therefore, when a user requests a data sequence from a fog server, the fog server may immediately respond to the request and the other fog servers can provide the sub-secrets of hashing root value to verify the corresponding data sequence. Our proposed framework can ensure that users may obtain required data fast, accurately and reliably. Additionally, based on the data query security requirements of fog computing, we analyze the security of our proposed scheme. Our proposed scheme not only guarantees the reliability of data but also effectively protects data against various attacks.

Many environmental monitoring applications that are based on the Internet of Things (IoT) require robust and available systems. These systems must be able to tolerate the hardware or software failure of nodes and communication failure between nodes. However, node failure is inevitable due to environmental and human factors, and battery depletion in particular is a major contributor to node failure. The existing failure detection algorithms seldom consider the problem of node battery consumption. In order to rectify this, we propose a low-power failure detector (LP-FD) that can provide an acceptable failure detection service and can save on the battery consumption of nodes. From simulation experiments, results show that the LP-FD can provide better detection speed, accuracy, overhead and battery consumption than other failure detection algorithms.

An advanced source number estimation (SNE) algorithm based on both fuzzy C‐means clustering (FCM) and data local density (DLD) is proposed in this paper. The DLD of an eigenvalue refers to the number of eigenvalues within a specific neighborhood of this eigenvalue belonging to the data covariance matrix. This local density essentially as the one‐dimensional sample feature of the FCM is extracted into the SNE algorithm based on FCM and can enable to improve the probability of correct detection (PCD) of the SNE algorithm based on the FCM especially for low signal‐to‐noise ratio (SNR) environment. Comparison experiment results demonstrate that compared to the SNE algorithm based on the FCM and other similar algorithms, our proposed algorithm can achieve highest PCD of the incident source number in both cases of spatial white noise and spatial correlation noise.

Sparse arrays based on the concept of a sum-difference coarray (SDCA) have increased degrees of freedom and enlarged effective array aperture compared to those only considering a difference coarray. Nevertheless, there still exist a number of overlapping virtual sensors between the difference coarray and the sum coarray, yielding high coarray redundancy. In this paper, we propose a k-level extended sparse array configuration consisting of one sparse subarray with k-level expansion and one uniform linear subarray. By systematically analyzing the inherent structure of the k-level extended sparse array, the closed-form expressions for sensor locations, uniform DOF and coarray redundancy ratio (CARR) are derived. Moreover, with the utilization of a k-level extended strategy, the proposed array remains a hole-free property and achieves low coarray redundancy. According to the proposed sparse array, the spatial and temporal information of the incident sources are jointly exploited for underdetermined direction-of-arrival estimation. The theoretical propositions are proven and numerical simulations are performed to demonstrate the superior performance of the proposed array.

The hybrid teaching mode, combining online and offline approaches, utilizes Internet technology to integrate ideological and political courses in colleges. This integration effectively enhances the teaching outcomes and expands the practical scope of these course. To fully leverage the advantages of this hybrid mode in integrating ideological and political education, this study focuses on the “Advanced Language Programming” course as an example. It examines the ideological and political design of this course under the hybrid teaching mode by considering aspects such as enhancing students’ ideological and political literacy, designing effective teaching methods, constructing a comprehensive curriculum resource library for ideology and politics, as well as ensuring its dynamic updates. Emphasis is placed on improving students’ ideological and political literacy within the context of this course while exploring various strategies to achieve it. Additionally, this paper presents a systematic approach for identifying ideological and political elements within professional courses which serves as a guide for deeply excavating relevant content within the curriculum. Furthermore, it establishes a curriculum resource library dedicated to ideology and politics with mechanisms in place for continuous updates. These proposed methodologies provide valuable support for seamlessly integrating ideology education into curriculum content under the online-offline mixed teaching mode.

A localization system is essential for providing crucial position information in various applications, such as three-dimensional (3D) warehousing, smart cities, uncrewed aerial vehicle (UAV) control, and other services that heavily rely on accurate localization. However, the transmission of wireless signals can be impacted by diverse environmental factors, leading to decreased accuracy in determining localization in scenarios involving multiple signal paths, None Line of Sight (NLOS) situations, and different types of interference. In some cases, this may render the localization system unsuitable for subsequent applications. To enhance the localization accuracy, we propose a 3D localization method using an optimization selection strategy. With this method, we make the following innovations: (1) We utilize an evaluation of feature points to minimize the negative impact of NLOS. (2) Through the backward assessment and the optimal selection of distance estimations, we obtain a more accurate localization result. In more detail, our approach implements a specific strategy for distance estimation, followed by defining the feature points within the localization field and selecting the most optimized one. Subsequently, using the chosen feature points, we evaluate the quality of the distances in reverse. We then select suitable distance estimation outcomes for further localization calculations. Ultimately, by employing the proposed 3D localization technique, we achieve a highly precise localization result. We perform simulations and experiments to assess the presented localization system. More specifically, compared with certain strategies, we improve the localization accuracy by 58.33% and 43.83% using the selection strategy. Compared with the other methods, we enhance the localization accuracy from 17.94% to 32.54%. The results from these evaluations demonstrate that our method significantly enhances 3D localization accuracy.