Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of low-latency connectivity, which is defined as the end-to-end delay between the action and the reaction, is very crucial to leverage these technologies for a high-quality immersive experience. This paper provides a comprehensive survey and detailed insight into various advantageous approaches from the hardware and software perspectives, as well as the integration of 5G technology, towards 5GB, in enabling a low-latency environment for AR and VR applications. The contribution of 5GB systems as an outcome of several cutting-edge technologies, such as massive multiple-input, multiple-output (mMIMO) and millimeter wave (mmWave), along with the utilization of artificial intelligence (AI) and machine learning (ML) techniques towards an ultra-low-latency communication system, is also discussed in this paper. The potential of using a visible-light communications (VLC)-guided beam through a learning algorithm for a futuristic, evolved immersive experience of augmented and virtual reality with the ultra-low-latency transmission of multi-sensory tracking information with an optimal scheduling policy is discussed in this paper.

In the absence of depth-centric sensors, 3D object detection using only conventional cameras becomes ill-posed and inaccurate due to the lack of depth information in the RGB image. We propose a multi-camera perception solution to predict the 3D properties of the vehicle obtained from the aggregated information from multiple static infrastructure-installed cameras. While a multi-bin regression loss has been adopted to predict the orientation of a 3D bounding box using a convolutional neural network, combining it with the geometrical constraints of a 2D bounding box to form a 3D bounding box is not accurate enough for all the driving scenarios and orientations. This paper leverages a vision transformer that overcomes the drawbacks of convolutional neural networks when there are no external LiDAR or pseudo-LiDAR pre-trained datasets available for depth map estimation, particularly in occluded regions. By combining the predicted 3D boxes from various cameras using an average weighted score algorithm, we determine the best bounding box with the highest confidence score. Comprehensive simulations for performance analysis are shown from the results obtained by utilizing the KITTI standard data generated from the CARLA simulator.

Underwater acoustic communication faces significant challenges including limited bandwidth, high propagation delays, severe multipath fading, and stringent energy constraints. While integrated sensing and communication (ISAC) has shown promise in radio frequency systems, its adaptation to underwater environments remains challenging due to the unique acoustic channel characteristics and the inadequacy of traditional delay-based performance metrics that fail to capture the spatio-temporal value of information in dynamic underwater scenarios. This paper presents a comprehensive underwater ISAC framework centered on a novel Spatio-Temporal Information-Theoretic Freshness metric that fundamentally transforms resource allocation from delay minimization to value maximization. Unlike conventional approaches that treat all data equally, our spatio-temporal framework enables intelligent prioritization by recognizing that obstacle detection data directly ahead of an autonomous underwater vehicle (AUV) require immediate processing. Our framework addresses key underwater ISAC challenges through spatio-temporal-guided power allocation, adaptive beamforming, waveform optimization, and cooperative sensing strategies. Multi-agent reinforcement learning algorithms enable coordinated resource allocation and mission-critical information prioritization across heterogeneous networks comprising surface buoys, AUVs, and static sensors. Extensive simulations in realistic Munk profile acoustic environments demonstrate significant performance improvements. The spatio-temporal framework successfully filters spatially irrelevant data, resulting in substantial energy savings for battery-constrained underwater nodes.

In the rapidly evolving field of uncrewed aerial vehicles (UAVs), these miniaturized platforms are increasingly being designed for intelligent aerial data acquisition, enabling dynamic target detection and tracking to facilitate the deployment of new use cases. This article presents a novel integrated sensing and communications framework for UAV networks to enhance latency, reliability, and resource allocation efficiency. A novel multidimensional freshness metric, the age of valid sensing (AVS), is introduced as a measure of actionable intelligence to quantify and prioritize the sensing data, accurately capturing the quality and relevance of information in dynamic environments, leading to improved UAV coordination and efficient resource allocation. The effectiveness of AVS is strengthened by the presence of Frechet distance, which performs the behavioral analysis of moving targets to enable spatiotemporal clustering based on their trajectory similarity for efficient sensing. Intelligence is being added to each UAV through a robust multiagent reinforcement learning (MARL) framework to regularly update their target sensing and communications information, dynamically balancing data freshness and the likelihood of successful information gathering. This approach allows for the efficient integration and processing of sensing data from multiple geographically dispersed targets, significantly improving real-time tracking and decision-making capabilities in complex environments. Our simulation results demonstrate the superior performance of the proposed framework in achieving lower latency, higher detection accuracy, and improved resource efficiency compared to existing methods.

As autonomous vehicles become more prevalent in urban settings, the need for accurate perception systems capable of high-precision localization and object detection in complex urban environments becomes increasingly critical. Traditional methods often struggle in underground structures or densely built urban zones where sensor data is obstructed or noisy. To address these challenges, we propose a novel integration of multiple cameras and radars, leveraging their complementary strengths to enhance overall perception accuracy. This approach moves beyond the limitations of single-sensor systems, combining the detailed visual data from cameras with the robust distance and velocity measurements from radars. Our innovative framework, MultiFusionNet, incorporates deep learning, adaptive multimodal fusion, and multiscale attention mechanisms to dynamically adjust to varying environmental conditions and complex sensor data interactions. Additionally, we introduce the scene-aware spatiotemporal network (SA-STNet) to refine radar data and improve its resolution and temporal coherence. By optimizing sensor placement with an uncertainty-aware probabilistic metric, we achieve comprehensive environmental coverage and significantly enhance the reliability of autonomous navigation. By optimizing sensor placement with an uncertainty-aware probabilistic metric, we achieve comprehensive environmental coverage, leading to improved detection accuracy (e.g., 0.88 AP and 0.90 recall) in simulated urban scenarios, paving the way for enhanced autonomous navigation reliability pending real-world validation. Validated through extensive simulations in the CARLA-generated datasets, our framework demonstrates notable improvements in detection accuracy, offering a robust solution for navigating complex and challenging urban landscapes.