Metaphorical auditory displays are increasingly recognized for data presentation and self-reflection. This article presents the design and evaluation of RainMind, a web-based soundscape application that represents physiological data with dynamic natural sounds for daily stress reflection. Based on different combinations of auditory display with visualization, we identified three modes: the Visual-Aided Mode (VAM), the Audio-Aided Mode (AAM), and the Audio-Visual Mode (AVM). Through a within-subject study involving 30 participants, we conducted a mixed-methods evaluation to assess the task load, engagement, and user experience among the three modes. The findings indicated that the combination of dynamic natural soundscapes with visualization (AVM) contributes to a lower task load compared to the other two modes. Moreover, dynamic natural soundscapes as metaphors for stress data significantly enhanced engagement and user experience of self-reflection compared to static natural sounds. Based on our study, we discuss the potential of leveraging dynamic natural soundscapes as a new way of data-driven self-reflection.

As more users seek generative AI (GAI) models to enhance work efficiency, GAI and Model-as-a-Service will drive transformative changes and upgrades across all industries. However, when users utilize GAI models provided by the service provider, they cannot be certain that the model’s quality matches the provider’s claims. Considering the need to protect intellectual property, the service provider will not disclose model details for user verification. To this end, we take the Internet of Vehicles as research background, proposing a zero knowledge model proof architecture based on UAVs. We also introduce a multiagent reinforcement learning algorithm to optimize the verification process. In specific, we first propose a verification scheme for the key operations of generative adversarial networks based on noninteractive zero knowledge proof. The zero knowledge proof architecture ensures that model parameters cannot be stolen during the verification process. After that, we propose an Age of Verification (AoV) metric to ensure the timeliness and freshness of zero knowledge proof. We also construct a tradeoff optimization problem between the energy consumption of UAV as a verifier and the AoV of edge servers as service providers, and transform the problem based on Lyapunov optimization theory. Following that, we propose an enhanced multiagent proximal policy optimization algorithm to enable the collaborative verification of edge servers by multiple UAVs. The algorithm simulation results demonstrate that the reward value of our proposed algorithm is over 10% higher than that of the standard algorithm, with a faster and more stable overall convergence speed. Additionally, the zero knowledge proof performance test results indicate that the verification delay in our proposed architecture is less than 500 ms during the verification phase, meeting practical requirements.

Blockchain has been considered as a critical solution to handle the privacy and security concerns for data sharing in vehicular networks. However, deploying vehicular blockchain onboard vehicles is constrained by the sophisticated communication environments of vehicular networks, the restricted resources of vehicles, and self-interested property of vehicles. In this paper, a Cellular Vehicle-to-Everything (C-V2X) based vehicular blockchain sharding framework is presented in vehicular edge computing. To motivate vehicles to assist in validating block data in vehicular shard, a contract-based incentive mechanism is presented to efficiently solve the joint moral hazard and adverse selection problem. Considering packet sensing ratio, half-duplex effect, and successful sensing probability, a dual PC5/Uu interface based block consensus delay model is formulated during the consensus process. To achieve two objectives of capability-discrimination and effort-motivation, we aim at enhancing the saved delay utility of blockchain service requester (BSR) while ensuring complex conditions of vehicles. Simulation outcomes demonstrate that the proposed mechanism successfully fulfills capability-discrimination and effort-motivation, and offers a 52% and 7% increase in BSR’s utility compared to the linear pricing scheme and uniform scheme, respectively.

Abstract Scene risk identification is essential for the traffic safety of Internet of Vehicles. However, the performance of existing risk identification approaches is heavily limited by the imbalanced historical data and the poor model interpretability. Meanwhile, the large processing delay and the potential privacy leakage threat also restrict their application. In this paper, a novel risk identification model is proposed that leverages the synthetic minority over‐sampling technique nearest neighbor (SMOTEENN) method to balance between high‐risk and low‐risk data. The risk identification model has fine interpretability by using recursive feature elimination cross validation (RFECV) with the Shapley additive explanation (SHAP) to analyze the importance of different features, and further elaborately design the Focal Loss function to tackle the disparity between the difficult and easy sample learning. The proposed interpretability scene risk identification framework, named iScene, is built on the infrastructure of 6G space‐air‐ground integrated networks (SAGINs) with blockchain assistance. The model updata efficiency and privacy preservation are effectively enhanced. An elastic computing offloading algorithm is applied to minimize the system overhead under the hierarchical edge service architecture. The experimental evaluation is carried out to verify the effectiveness of the proposed risk identification framework. The results indicate that the G‐Mean value is increased by 23.4%, while the task average response delay is reduced by 21.2%, compared to that in the traditional risk identification approaches with local computing services.

Traditional optical positioning techniques employing visible light signals or infrared light signals require line-of-sight links between transmitters and receivers. The wireless positioning techniques using ultraviolet (UV) signals can enjoy both non-line-of-sight (NLOS) positioning ability and immunity to electromagnetic jamming. In this work, we focus on NLOS UV positioning techniques using linearly-arrayed photon-counting receivers. We first derive the geometrical and physical constrains for the NLOS UV positioning using linearly-arrayed receivers. We then derive the analytical relation between location parameters and pointing parameters of unknown transmitter and propose a NLOS UV positioning method with acceptable computational complexity. We further derive the Cramér-Rao bounds for the positioning method when the separate distance between adjacent receivers equals zero. Numerical results demonstrate that the proposed NLOS UV positioning methods using photon-counting receivers can achieve a distance error less than 2 m when the transmitting elevation angle is greater than 30 degrees and the separate distance is greater than 2 m. Besides, we demonstrate that at least three receivers are required to avoid multiple solution problem; and three receivers are enough for achieving an acceptable positioning error for NLOS UV positioning using photon-counting receivers.

Optical positioning techniques have inherent ad-vantages such as high precision, low power consuming and immunity to the electromagnetic interference. However, it is challenging to estimate the non-line-of-sight (NLOS) targets using current optical positioning methods. In this work, we propose an NLOS ultraviolet positioning method based on a simplified single-scattering channel model, which can obtain both the location and the pointing direction of the transmit-ter. Numerical results demonstrated that the proposed NLOS ultraviolet positioning method can achieve a positioning error less than 2 meters and an azimuth error less than 2 degrees by using two photon-counting receivers under typical transceiver geometries within 100 meters. Besides, we found that the positioning performance can be improved by increasing the gap between two receiving elevation angles.

Due to the prominent advantages of decentralization, transparency, security, and traceability, blockchain technologies have attracted ever-increasing attention from academia and industry, which can be applied to establish secure and reliable resource sharing platforms for future networks and applications. Especially, with the promising 6G technology which has large bandwidth and space-air-ground integrated coverage, blockchains have been evolved into 6G-enabled blockchain and envisioned to build various decentralized data and resource management systems. However, for 6G-enabled wireless blockchain networks, there still exist many challenges for their development and prosperity, e.g., large block propagation delay and propagation incentive. Therefore, this paper focuses on addressing the block propagation challenges. Firstly, inspired by epidemic models, we classify consensus nodes into five different states and establish a block propagation model for public blockchains that depicts block propagation laws. Then, considering consensus nodes are limited rational, we propose an Incentive Mechanism based on evolutionary game for Block Propagation (marked as BPIM) to minimize the block propagation delay. Numerical results demonstrate that compared with traditional routing algorithms, BPIM has better block propagation efficiency and greater incentive strength.

The 5th generation (5G) mobile network technology is accelerating the development of autonomous vehicles by significantly shortening the communication latency and improving the reliability of network connection and transmission. However, as the number of vehicles increases, neither cloud servers nor multi-access edge computing (MEC) servers alone could sufficiently meet the Quality-of-Service (QoS) requirements for computing-intensive vehicle tasks. In this paper, we consider a hierarchical offloading scenario, where vehicle tasks are allowed to execute in MEC servers, convergence servers or cloud servers. To reduce the cost of latency and energy, we optimize the communication and computation resource allocation problem. The optimization problem is converted to a Markov decision process, and deep reinforcement learning is used to tackle the resource slicing and scheduling problem. Simulation results show that the proposed scheme is more resilient and efficient than that of single cloud server offloading or single MEC server offloading.

The fast penetration of Intelligent Connected Vehicles (ICVs) has become the primary growth engine of the automotive industry in recent years. Urban vehicular network consisting of ICVs is evolving towards a distributed intelligent platform for pervasive sensing, connecting and computing in Intelligent Transportation System (ITS) and smart cities. In this paper, we propose that parked vehicles (PVs) could be exploited for environment perception and model inference. We describe the system architecture and its typical application scenarios of distributed environment perception for city roads, parking lots, as well as for commercial and residential buildings. PVs are motivated to assist in deep learning model inference for the captured image data in such applications. Regarding the diversity of PVs in deep learning capability, a differential incentive mechanism is elaborately designed based on contract theory to emulate PVsparticipation. The experiment on the dataset of German Traffic Sign Recognition Benchmark is conducted to verify the effectiveness and efficiency of the proposed approach.

With the repaid development of Internet of Vehicles (IoV), more available resources and energy-efficient optimizations in resources scheduling are exactly required for large-scale network implementation for sustainable development. We observe that parked vehicles (PVs) have rich and underutilized resources for task execution. By scheduling them as general computing nodes to undertake computation tasks, we introduce a new computing paradigm, named by parked vehicular computing (PVC). There exists some challenging issues to be addressed for the facilitation of PVC. In particular, an incentive mechanism is needed to offer optimized rewards for PVs with the consideration of their parking time and energy consumption. In this paper, we investigate an energy-efficient PVC paradigm, and we design a contract-based incentive mechanism to motivate PVs to contribute their idle on-board resources. The PVs are classified into different types according to their parking time. Then, the designed contracts are assigned to different types of PVs. To realize the incentive mechanism, the optimization problem with the contract design is formulated to maximize the utility of the service provider. For optimal contract design, we solve the simplified problem by using Lagrangian multiplier method. Numerical results indicate that the proposed PVC with optimal contract design outperforms existing work in improving social welfare of resource scheduling, which takes quality-of-service and overall energy consumption into consideration. We also demonstrate that the contract-based incentive mechanism is energy-efficient and effective.