The metaverse aims to build an immersive virtual reality world to support the daily life, work, and recreation of people. In this survey, the status quo of the metaverse is investigated, and the technical framework of the metaverse is introduced from three aspects: 1) the generation of virtual worlds; 2) the connection of virtual and real objects; and 3) the transmission of data. Specifically, this survey first discusses the development and challenges of the related technologies for virtual world generation methods from three aspects: 1) the 3-D world generation; 2) immersive human–computer interaction experience; and 3) ecosystem. Second, we investigate the status quo of extended reality (XR), motion capture, and brain–computer interface technologies and evaluate the potential and research directions of these entrance technologies for the metaverse. Finally, network and data transmission technologies for the metaverse are reviewed from the Internet of Things (IoT), 5G/6G wireless, and edge computing aspects, the demand side of the metaverse in virtual-reality interpromotion, big data processing, and low-latency networking is discussed, and promising research hotspots are identified.

The high-speed movement of nodes and the burstiness of interactions in the Internet of Vehicles pose huge challenges to the trusted vehicle collaboration and data sharing. Aiming at the disadvantages of existing authentication mechanisms and trust management models for connected vehicles, this article proposes a trust management model enabled by blockchain to ensure the traceability, nontampering, unforgeability, and transparency of vehicular interactions. The proposed trust management model leverages Dirichlet distribution, reputation regression, and revocation punishment to objectively and accurately reflect the trust status of vehicles. Simulation results on real-world data sets show that the proposed trust management model advantageously improves the accuracy of malicious vehicle detection and the attack resistance of connected vehicles.

cellular D2D networks consist of numerous D2D user equipments (UEs) carried by human beings with multiple social attributes, which accordingly connotes an overlapping community (OC) structure. Accurately detecting OC D2D UEs can effectively improve the efficiency of multi-hop D2D communication. Existing relay selection methods overlook the OC structure characteristic of cellular D2D networks, which results in limited relay efficiency. Therefore, we propose an overlapping community deep exploring based relay selection method. First, we build the social tie matrix between D2D UEs and then based on deep learning theory we extract the features of social tie matrix to further precisely detect OC D2D UEs. Moreover, by reasonably utilizing detected OC D2D UEs, we design the effective relay selection method. Simulation results demonstrate that the proposed method can largely enhance the delivery rate and power consumption performances of cellular D2D networks.

The state of physical entity in the Internet of Things (IoT) has an obvious time-varying characteristic. Preliminarily selecting candidate entities by predicting their current state when searching match entities from massive ones can effectively reduce the communication overhead of IoT search system. The existing methods are all based on shallow learning theories whose performances are very limited. Thus, a high-accuracy entity state prediction method (HESPM) based on deep learning theory is proposed. The model of HESPM is built by utilizing the deep belief network (DBN). Then the contrastive divergence (CD) algorithm is adopted to train the model. Therefore, the dynamic evolution trend of entity state can be accurately perceived and the future entity state can be precisely predicted. Simulation results demonstrate the effectiveness of HESPM in enhancing the prediction accuracy and communication overhead performances.

Federated Learning (FL) is a distributed machine learning paradigm that ensures data do not leave local devices. Data sharing problems can be addressed by FL in untrusted environments, e.g., the Internet of Vehicles (IoV). However, FL needs to frequently exchange massive parameters to achieve preset model goals. In addition, the change in bandwidths and the delay of data communications due to user mobility challenge the synchronization of model parameters. In this paper, an Efficient Hierarchical Asynchronous Federated Learning (EHAFL) algorithm is proposed to adjust the encoding length dynamically according to the bandwidth and reduce the communication cost substantially. A dynamic hierarchical asynchronous aggregation mechanism is proposed leveraging gradient sparsification and asynchronous aggregation techniques to further reduce the communication costs and improve the aggregation efficiency of the global model. Simulation results on MNIST and real-world datasets show that our proposed solution can reduce the communication costs by 98% while only compromising the model accuracy by 1%.

With the widespread adaptation of the Internet of things (IoT), we are already witnessing a deluge of IoT data analytics applications. IoT data analytics can be defined as a process to control and optimize decision making in real time, by analyzing huge chunks of IoT sensor data. Moreover, data analytics stands on the shoulders of sensor data aggregation that includes data pre-processing and routing. This paper envisions two open problems of data aggregation, first, raw IoT sensor data is highly uncertain, second, the traditional algorithms are not fit for processing highly uncertain sensor data. This is formally known as data veracity problem. This paper proposes a data aggregation scheme for highly uncertain raw IoT sensor data collected using the device to device communication (D2D). The approach initially reconstructs the subspace using sample data and then it iteratively tracks down the low-rank approximation of the dominant subspace in the presence of high uncertainties at the fog server. Later the robust dominant subspace is used to estimate a more reliable true sensor data matrix from the highly uncertain raw IoT sensor data traffic matrix. Moreover, the proposed scheme achieves the aforementioned tasks while processing the raw IoT sensor data without any prior information, i.e. in a fully unsupervised fashion. The existing literature based on sampling, approximation and data reduction either causes random data reduction or destruction of global characteristics of the raw data. However, unlike the existing solutions, the proposed method removes the uncertainties while preserving the global characteristics of the raw data. Performance evaluations conducted using both the real world sensor data and synthetic data injected with noise, outliers and missing values. Experimental results show that the proposed approach can estimate a reliable true sensor data matrix in the presence of high uncertainties and the energy efficient device to device communication-based data delivery mechanism can accommodate a large number of IoT devices.