Modern smart factories accommodate numerous heterogeneous devices, making co-scheduling of Ultra-Reliable Low-Latency Communications (URLLC) and massive MachineType Communications (mMTC) services on shared physical resources are a critical challenge. This paper proposes a resource allocation mechanism for this convergence scenario, combining Signal-to-Noise Ratio (SNR) boosting strategy, flexible numerology configuration, and device priority control. By formulating a multi-objective optimization model that integrates delay constraints, channel conditions, and resource utilization, we design a low-complexity adaptive scheduling algorithm. The simulation results demonstrate that our proposed method improves URLLC schedulability by 19.8% while achieving an average latency of 0.843ms, substantially outperforming the baseline algorithms. This paper provides effective technical support for the establishment of industrial wireless communication systems capable of supporting coexisting Quality of Service (QoS) requirements.

Resource allocation in 6G digital twin-enabled Internet of Robotic Things (DT-IoRT) faces critical challenges from limited resources, dynamic demands, and heterogeneous service requirements, while existing solutions lack adaptability, incur high complexity, or fail to capture hierarchical pricing interactions. This paper proposes a learning-driven Stackelberg resource allocation framework for 6G DT-IoRT, where an Edge Base Station (EBS) dynamically optimizes bandwidth pricing using Proximal Policy Optimization (PPO), and digital twin agents optimize bandwidth demands through logarithmic utility maximization in response to announced prices. The pricing problem is formulated as a Markov Decision Process, enabling real-time adaptation without requiring knowledge of follower utility parameters. Simulation results show that the proposed approach improves follower utility by 12.4% and leader profit by 9.7% compared to the best static pricing baseline, while maintaining a Jain’s fairness index of approximately 0.80 across varying system scales, demonstrating its effectiveness and scalability for industrial IoRT scenarios.

Scheduling mission-critical tasks in sixth-generation (6G) vehicular networks over millimeter-wave (mmWave) links is challenging due to dynamic topology, fast-varying channels, and strict latency constraints. This paper addresses these challenges by proposing a lightweight cross-layer heuristic scheduling framework that integrates channel state information (CSI), task priority, and beamforming gain into a unified scoring function. The framework comprises two modules: (i) edge selection to balance load across nodes via link and queue metrics, and (ii) task scheduling to prioritize collision-warning tasks under favorable links. Unlike optimization-based methods, the proposed design enables real-time use with sub-millisecond execution and linear per-slot complexity. Overall, simulations show that 90% of tasks meet 5 ms deadlines, and the Packet Loss Rate (PLR) stays below 20%. The proposed framework reduces latency by 80% and 60%, and PLR by 27.5% and 57.5%, compared to the strategies based on Equal and CSI schemes, respectively. These results confirm the suitability of the framework for vehicular latency and 6G reliability requirements.

With the rapid advancement of autonomous driving technology, enhancing the intelligence of individual vehicles and Vehicle-to-Everything (V2X) networking is crucial for improving traffic efficiency and safety. Intelligent Transportation Systems (ITS) face challenges in evolving traditional traffic models, transitioning the focus from human reaction times to vehicle communication and system delays, as exemplified by scenarios like intersection startup delays. This paper presents a comprehensive validation of simultaneous control in Vehicle-to-Infrastructure (V2I) communication. It provides problem mathematical modeling, validation, and quantitative analysis specifically for connected autonomous vehicle lanes at 100% penetration. The real experiment showed that even with identical scenarios, hardware, and communication delays, startup times still varied due to complex internal and external factors affecting command execution. These factors cause cumulative errors over time, diminishing command execution precision despite advanced control algorithms and sensor calibrations. The investigation underscores the imperative for dynamic modifications and systematic recalibrations in control systems of autonomous vehicles, facilitated by fixed infrastructure, to sustain long-term accuracy and effectively navigate real-world complexities.

Ultra-reliable low-latency communication (URLLC) has become essential for mission-critical applications such as autonomous systems, remote surgery, and industrial automation. This growing demand has been a key driver behind the development and deployment of fifth-generation (5G) networks and is influencing the design of future 6G systems. However, dynamic traffic patterns and limited bandwidth, particularly during peak load conditions, lead to network congestion, packet loss, and reduced reliability, undermining URLLC performance. This paper proposes ALPHA-NET (Agentic Latency and Prioritization for High-Availability Networks), an Agentic AI framework that dynamically prioritizes bandwidth allocation to reduce latency and improve reliability in mission-critical 5G/6G networks. The proposed system outperforms traditional bandwidth management strategies by leveraging a decentralized multi-agent architecture and real-time monitoring execution. ALPHA-Net consists of three collaborative agents: a latency monitoring agent, a bandwidth slicing agent, and a traffic forecasting agent. These agents operate through an on-demand, distributed infrastructure to enable real-time decision-making with minimal overhead. Simulation results demonstrate a 62.8% reduction in packet loss and a 22% improvement in critical bandwidth allocation compared to baseline approaches. These findings highlight the potential of intelligent, adaptive coordination for resilient next-generation network management.

This study presents a machine learning (ML)-based localization method for improving location estimation accuracy in wireless networks, especially in challenging environments where traditional techniques often fall short. Conventional methods rely on a limited number of multipath components (MPCs), leading to inaccurate localization in complex environments. By leveraging a novel dataset generated from ray-tracing simulations in urban and campus environments, we propose a deep neural network (DNN)-based method that incorporates rich channel metrics such as angle of arrival (AoA), time of arrival (ToA), and received signal strength (RSS). The DNN is trained on diverse scenarios, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, and outperforms traditional MPC-based methods, reducing localization error by up to 20%. Our approach challenges the conventional use of only 3 MPCs for localization and demonstrates that a larger number of MPCs enhances accuracy, particularly in urban and obstructed environments. This research provides important insights into the potential of ML-driven solutions for improving localization accuracy in next-generation wireless systems , such as 5G and beyond.

As vehicle intelligence technology advances rapidly, the need for unrestricted, fully autonomous driving intensifies. Relying solely on single vehicle intelligence is no longer sufficient to meet the requirements high-level autonomous driving. Issues such as sensor range and occluded areas urgently require the assistance of roadside infrastructure. However, the lack of consensus or clear solutions and testing methods on how to unify heterogeneous sensors and information amplifies the urgency of our proposed solution. Integrated sensing and communication (ISAC) has already become a key candidate technology for 6G, and integrated sensing, communication, and computing (ISCC) has also been frequently mentioned recently. Therefore, this paper aims to coordinate the intelligence of vehicles and roads to establish and validate integrated intelligence, including integrated sensing, communication, computing and control (IS3C). Our research takes a safety-centric approach, using hazard degree as a unifying feature for heterogeneous devices and information. We integrate sensors, roadside units (RSUs), and mobile edge computers (MECs) into roadside infrastructure. By combining the sensor-set, local dynamic maps (LDMs), Vehicle-to-Everything (V2X), Infrastructure-to-Everything (I2X), edge computing, and centralized control, we have developed a practical IS3C structure for intelligent transportation systems (ITS). This structure has been modeled and deployed step-by-step on real robots, and its feasibility has been demonstrated through experiments, meeting the latency requirements for ITS. Additionally, our research leverages the advantages of IS3C, particularly “resource aggregation” by utilizing real-time environmental judgments and maximizing the potential of short-term historical information. We propose a new topic for future researchers studying ITS integrated control based on these integrated advantages.

Vehicle-to-grid (V2G) energy trading based on distributed ledger technologies (DLT), such as blockchains, has attracted much attention due to its promising features, including ease of deployment, decentralization, transparency, and security. However, existing DLT-based models do not support microtransactions due to the low value of such transactions relative to the incentives offered to transaction verifiers. To address this issue, we propose an IOTA DLT-based efficient and secure energy trading model for V2G networks, where electric vehicles (EVs) and grids negotiate energy prices in an off-chain manner. The proposed model utilizes a privacy-preserving protocol to prevent real-time tracking of EV locations. We develop a Stackelberg game model to represent the interactions between the EVs and grids, from which we derive a pricing scheme and propose a deposit mechanism to prevent fake energy trading between the EVs and grids. Extensive simulations demonstrate that our proposed scheme outperforms existing V2G energy trading mechanisms regarding transaction efficiency, provides enhanced EV privacy, and improves resilience against fake energy trading. Offering robust computational performance and addressing computational complexity (time, space, and message), our model presents a comprehensive V2G energy trading solution, balancing efficiency, security, and privacy.

Gesture-based teleoperation is a complex and essential task that enables remote object manipulation. Recent advancements in 3D human hand pose estimation, driven by affordable depth cameras, have proven its aptitude for this task. However, while previous vision-based approaches focus on mapping hand posture to end-effectors, they overlook the interaction between the robot and the object. This leaves a challenge in interpreting these hand joint estimates into practical robotic behaviour. In this paper, we propose a method that leverages the geometric information of the human hand to enable robots to perform human-like grasping and manipulation. Our approach incorporates a pointcloud-based hand joint regressor and the grasping direction analysis (GDA) to control the robot. The joint-wise regressor showed an improved mean joint error of 7.8mm on the MSRA dataset compared to the 8.5mm baseline. We demonstrate that the GDA-based teleoperation can successfully perform real-time robotic manipulator controlling and grasping for various tasks.

The rise of connected vehicular networks (CVNs) holds promise for future intelligent transport systems, offering improvements in safety and road efficiency. CVNs face challenges due to data-driven perception and driving models, requiring extensive knowledge to navigate complex scenarios. In vehicular networks, federated learning (FL) is vital for privacy-preserving machine learning (ML). It allows collaborative training of a single ML model across edge devices while keeping data locally, preserving privacy. However, scalability remains a challenge, especially for large ML models, and can yield suboptimal results when local data distributions diverge. We present a robust and efficient Fed-aided multi-task temporal clustering (FeMTC) knowledge-sharing framework tailored to the demands of highly distributed vehicular networks. Our approach quantifies the temporal similarity between a pair of client vectors to group clients with higher similarity at the edge-base server and trains independently on single and multi-task cluster learning. Experiments show that FeMTC achieves faster convergence and up to 15% better performance than existing methods in some scenarios. It easily combines with other methods for improved performance and exhibits robust gains in various non-independent and identically distributed (non-IID) scenarios.

Cellular communication standards have been established to ensure connectivity across most urban environments, complemented by deployment hardware and facilities tailored for city life. At the same time, numerous initiatives seek to broaden connectivity to rural and developing areas. However, with nearly half the global population still offline, there is an urgent need to drive research toward enhancing connectivity in areas and conditions that deviate from the norm. This article delves into innovative communication solutions not only for hard-to-reach and extreme environments but also introduces “hard-to-serve” areas as a crucial, yet underexplored, category within the broader spectrum of connectivity challenges.We explore the latest advancements in communication systems designed for environments subject to extreme temperatures, harsh weather, excessive dust, or even disasters such as fires. Our exploration spans the entire communication stack, covering communications on isolated islands, sparsely populated regions, mountainous terrains, and even underwater and underground settings. We highlight system architectures, hardware, materials, algorithms, and other pivotal technologies that promise to connect these challenging areas. Through case studies, we explore the application of 5G for innovative research, long range (LoRa) for audio messages and emails, LoRa wireless connections, free-space optics, communications in underwater and underground scenarios, delay-tolerant networks, satellite links, and the strategic use of shared spectrum and TV white space (TVWS) to improve mobile connectivity in secluded and remote regions. These studies also touch on prevalent challenges such as power outages, regulatory gaps, technological availability, and human resource constraints, where we introduce the concept of peri-urban hard-to-serve areas where populations might struggle with affordability or lack the skills for traditional connectivity solutions. This article provides an exhaustive summary of our research, showcasing how 6G and future networks will play a crucial role in delivering connectivity to areas that are hard-to-reach, hard-to-serve, or subject to extreme conditions (ECs).

6G is expected to revolutionize the Internet of things (IoT) applications toward a future of completely intelligent and autonomous systems. Conventional machine-learning approaches involve centralizing training data in a data center, where the algorithms can be used for data analysis and inference. To promote green computing in IoT applications, Machine-2-Machine (M2M) technologies are largely focused on lowering energy consumption and creating effective IT infrastructure. In this paper, we introduce an AI-enabled One-Shot Interference(O-SI) Knowledge-Driven unified model block sharing (K-Dumbs) framework in which actionable knowledge is aggregated from the training perception robots to facilitate others at the Edge in the vicinity. To demonstrate the practicality of the proposed concept, we explore a K-Dumb Fed-Average (FedAvg) algorithm to meet the massively distributed and unbalanced pattern and privacy requirement of the Internet of Robotic Things(IoRT). Simulation results show that, when compared to traditional Federated Learning (FL) systems, the proposed K-Dumb FedAvg architecture delivers higher information-sharing and learning quality. In addition, we validate our method using MNIST handwritten digits for training image processing with an accuracy that is close to the centralized solution for up to 80% reduction in the amount of exchange data with the O-SI method. Furthermore, the suggested solution reduces IoRT energy consumption by up to 10 times and protects privacy.

The spatial distribution of cellular networks has made them very promising to use for localization. By knowing the location of a user, cellular networks can provide context-aware services customized to that user. Objects and the dynamic nature of indoor locations result in lots of multipath and non-line-of-sight (NLOS) propagations. In this work, we carry out a novel experimental investigation to improve indoor localization using a grid approach with channel state information (CSI) fingerprinting and artificial intelligence (AI)/ machine learning (ML) methods for determining the location of a mobile device. Experiments are conducted in a standard indoor setting. This paper compares a method for indoor positioning based on received signal strength identifier (RSSI), phase, and CSI using ML to show how the accuracy of indoor localization can be improved. Compared to heuristic approaches like DOA estimation, the precision of ML is superior.

In this work, we design an infrared protocol (IRP) for light emitting diode (LED) based indoor positioning. The designed IRP compensates for the shortcomings of other existing protocols when applied to the multiple LED estimation indoor positioning model (MLEM). MLEM uses overlap of LED beams to increase accuracy of positioning. The overlap sets up a multipoint-to-point optical communication channel. The existing protocols which are designed for point-to-point links, when modified to suit the MLEM overlapping region, show a high positioning time between 3 s and 4.5 s. These values are not desirable for real time tracking. A new protocol is therefore designed to reduce the positioning time. The protocol is implemented in an experimental MLEM design using ATmega 328 microcontroller hardware. The experimental results show the new protocol reduces the positioning time to 0.5 s.