As vehicle autonomy increases, human operators become more susceptible to distractions and a loss of situational awareness (SA) due to cognitive limitations. Rapidly enhancing human SA in hazardous situations is, therefore, critical for timely hazard perception and collision avoidance, particularly in human-vehicle teaming contexts that demand fast, accurate hazard reasoning. This study evaluates the efficiency of a low-cost vibrotactile interface for enhancing hazard SA of human operators when teaming with heterogeneous autonomous vehicles, including both ground and aerial autonomous vehicles. To do so, we evaluate the effectiveness of a vibrotactile interface in challenging time-critical scenarios considering adversarial attacks to better understand the cognitive constraints faced by human operators. Our quantitative analysis, based on the data collected from 39 participants, demonstrates that: first, haptic cues can significantly enhance human hazard SA across various metrics for the ground and aerial scenarios; second, perception of aerial attacks in a 3-D environment is more challenging than ground risk perception.

Time-series modelling (TSM) is a critical task for intelligent vehicles (IVs), covering areas like fault detection, health monitoring, and inference of road user intentions. In this study, we present a novel TSM approach for enhancing the accuracy of multi-variate signal forecasting in intelligent vehicles. Our method leverages advanced Transformer networks within a relativistic generative adversarial network (RGAN) training framework. The RGAN training framework efficiently improves the accuracy of vehicle states forecasting for IV, demonstrating effective learning of long-time dependencies for more accurate predictions over extended sequences. Additionally, we introduce a high-dimensional extension (HDE) built-in block for the time-series Transformer to explore the impact of higher-dimensional features on representing long-term sequences. The experimental data is collected from a real-world electric vehicle testing bed. We evaluate the proposed RGANFormer framework and the HDE block on two popular time-series models, namely, Autoformer and FiLM. The results demonstrate that the RGANFormer, along with the built-in HDE block, significantly enhances long-term sequential forecasting accuracy for both multivariate and univariate tasks.

Unmanned Aerial Vehicle (UAV) Networks have recently attracted great attention as being able to provide convenient and fast wireless connections. One central question is how to allocate a limited number of UAVs to provide wireless services across a large number of regions, where each region has dynamic arriving flows and flows depart from the system once they receive the desired amount of service (referred to as the flow-level dynamic model). In this article, we propose a MaxWeight-type scheduling algorithm taking into account sharp flow-level dynamics that efficiently redirect UAVs across a large number of regions. However, in our considered model, each flow experiences an independent fading channel and will immediately leave the system once it completes its service, which makes its evolution quite different from the traditional queueing model for wireless networks. This poses significant challenges in our performance analysis. Nevertheless, we incorporate sharp flow-dynamic into the Lyapunov-drift analysis framework, and successfully establish both throughput and heavy-traffic optimality of the proposed algorithm. Extensive simulations are performed to validate the effectiveness of our proposed algorithm.

We consider the load-balancing design for forwarding incoming flows to access points (APs) in high-density wireless networks with both channel fading and flow-level dynamics, where each incoming flow has a certain amount of service demand and leaves the system once its service request is complete. The efficient load-balancing design is strongly needed for supporting high-quality wireless connections in high-density areas. In this work, we propose a Joint Load-Balancing and Scheduling (JLBS) Algorithm that always forwards the incoming flows to the AP with the smallest workload in the presence of flow-level dynamics and each AP always serves the flow with the best channel quality. Our analysis reveals that our proposed JLBS Algorithm not only achieves maximum system throughput, but also minimizes the total system workload in the heavy-traffic regime. Moreover, we observe from both our theoretical and simulation results that the mean total workload performance under the proposed JLBS Algorithm does not degrade as the number of APs increases, which is strongly desirable in high-density wireless networks.

In recent years, virtual/augmented reality (VR/AR) technology has received great attention due to its capability of creating various levels of immersive experiences. However, current wireless VR/AR devices are quite expensive, which hinders its large-scale deployment in practice. In this demo, we present a wireless interactive VR/AR teaching system based on popular Android phones. In such a demo, when a teacher explains a 3D model, multiple students can see it from exactly the same perspective as the teacher does through VR/AR glasses. When one student has a concern or question regarding a particular part of the 3D model, he/she can point it out, and a corresponding blue cursor will appear on screens of all users. Moreover, in the absence of 3D models in Android phones, we broadcast 3D models based on their visual priorities.

Recent years, more and more enterprises migrate their applications to the cloud for cost saving. During the application migration, an application usually needs to be re-designed from a workflow to an execution plan on a cloud platform. Based on the execution plan, many scheduling optimization approaches have been proposed to improve the performance of the system. In this paper, we argue that the concurrency optimization of each task in the workflow can significantly improve the system efficiency under delay or resource constraint. To generate an efficient execution plan, we optimize the concurrency of the task based on the critical path of the workflow. Furthermore, we deal with the scheduling problem in two different scenarios respectively. To save the costs under a certain deadline, we propose a delay constrained task scheduling approach. To shorten the completion time under certain resources, we propose a resource constrained task scheduling approach. Finally, by deploying a video analysis application on a cloud platform testbed, we validate the proposed two scheduling approaches.