Semantic communications (SEMCOM) offer a paradigm shift by transmitting the intended meaning of messages using neural networks and shared knowledge, rather than relying on raw data. This is essential for next-generation applications such as autonomous driving and holographic telepresence, where accurate semantic interpretation is critical. However, the open and knowledge-centric nature of SemCom makes it susceptible to adversarial attacks, especially during knowledge sharing and decoding. To enhance security, we introduce SECO, a robust learning framework designed to defend against both targeted and untargeted adversarial threats. SECO combines sensitivity-aware noise detection with Huber loss-based filtering to identify and suppress adversarial perturbations, while preserving the integrity of natural signals. Experimental results on speech datasets under Rayleigh and Rician fading channels show that SECO boosts speech-to-distortion ratios by up to 2.2× and 2.5×, respectively, and reduces semantic disruption by up to 17%. It also demonstrates resilience to natural noise and mitigates fixed-pattern attacks by exploiting unique voice characteristics.

Unmanned Aerial Vehicles (UAVs) have been widely adopted in various applications, including surveillance, search and rescue, cartography, and damage assessment in remote areas. This study aims to enhance the safe navigation of multiple UAVs in adverse environments by integrating computer vision with deep reinforcement learning (DRL) and a route planning algorithm known as REAP. REAP incorporates a real-time object detection module that identifies obstacle positions from surveillance drone footage or satellite imagery. Additionally, A* and tangent point search algorithms are employed to compute the shortest paths while maintaining connectivity and safe distances from threats, such as dynamic obstacles (e.g., birds). Notably, this study also introduces an optimized DRL approach to further refine flight paths and reduce the latency in search result retrieval. Evaluation results show that the proposed method can compute optimal paths in under 10 milliseconds-three times faster than state-of-the-art (SOTA) methods-while ensuring UAV safety from collisions with flying obstacles, tall structures, or bird swarms.

Currently, online map navigation applications are essential to our daily demands, such as route finding in a big city or booking a taxi trip. Further, the applications also play a pivotal role in finding access to the nearest medical aid in big cities with complex traffic networks. However, existing commercial map programs are unable to provide route suggestions in areas where a continuous network connection is assured, which is essential for distant emergency medical services. This study introduces a highly effective personalized shortest-path algorithm for finding the fastest path of an emergency vehicle (EV) while also ensuring stable network connections for potential remote robotic surgery. By using a customized A-star algorithm, the experimental findings demonstrate that this technique can outperform current search approaches with a 5% shorter path length per 5km. For rescue missions in a large city, the enhancement is remarkable and has the potential to save lives. Due to high compatibility with the existing search algorithms, this method can easily be included as a critical feature for existing commercial map apps.

WiFi and cellular networks have become essential components of our modern lives. On the other hand, these connectivity technologies also enable many radio localization techniques and even user monitoring that offer convenience and valuables, such as object tracking in augmented reality or patient tracking in emergency calls. This paper describes a novel approach for recreating a restricted-access architectural drawing that takes advantage of passive radio localization on multiple users. Initially, we produce data points in an empty building drawing from the data points provided by our built-in signal-to-image-based localization. Next, we propose a few-shot drawing reconstruction model, DRNet, for regenerating the raw structure based on collected data points from localization. Finally, we combine the time-series monitoring data to predict the functionality of the rooms. The assessment findings indicate that the precision of accessible area regeneration can reach a maximum of 83.4%. The methodology used in this study exhibits versatility in its applicability to a wide range of prospective applications. For instance, law enforcement authorities possess the capacity to effectively ascertain the ingress/egress pathways or accessible locations for potential intrusion during a narcotics enforcement operation. Another scenario involves the disclosure of frequently visited locations by criminals or elucidating the purposes served by certain areas inside a restricted-access facility.

Connected and automated vehicles already are the main force in realizing the vision of intelligent transportation in smart cities. However, enabling broadband connectivity for vehicles brings up new threats of spreading fake information. By broadcasting false sharing data, an attack vehicle has the ability to cause nearby vehicles to get confused or possibly collide in catastrophic accidents. The present study presents a resilient fusion-based multimodal abnormal detection technique, referred to as FMAD. FMAD facilitates a fusion model based on Dempster- Shafer's theory to strengthen confidence in the final detection assessment of detection results from multiple vehicles. FMAD can determine whether a vehicle is spreading false maneuver information with up to 96.18 percent accuracy of confidence. Meanwhile, our method outperforms all existing approaches in terms of the reliability of the detection decision.

Autonomous cars are already the driving force behind achieving the goal of intelligent mobility in smart cities. However, providing internet access for automobiles introduces additional risks of distributing false information. By broadcasting fake sharing data, an attack vehicle has the potential to mislead surrounding cars and trigger catastrophic accidents. This work introduces a novel incremental learning model for misbehavior detection in vehicular communications. Unlike previous methods which mostly relied on the pre-trained model, in this work, we develop accumulative learning capability for the misbehavior detection engines at vehicles or distributed roadside units. The model can assist in obtaining new knowledge from continuous learning. The simulation results indicate that the proposed model outperforms the state-of-the-art studies by up to 12% in terms of detection accuracy and 45% faster in continuous processing, particularly in the single detector mode.

Vehicular networks are expected to be the key technologies in the age of intelligent transportation and connected intelligence. However, by broadcasting false maneuver information in vehicular networks (emergency brake, merging/changing lane), an attacker can cause many vehicles to be disoriented or even crash in severe accidents. This work introduces a robust misbehavior detection scheme, namely TRIMO, by exploiting multimodal learning from various independent data sources (e.g., camera, joint radar and communications in the sixth-generation (6G) mobile networks). TRIMO can determine whether a car is lying about its sharing data with up to 92.7 percent accuracy by examining the consistency of data from numerous sources.

For critical applications such as emergency medical rescue missions or telehealth in remote areas, stable network connectivity is vital for patient state monitoring and proper temporary care. Unexpected connection interruption or network lag can cause trouble for skilled doctors in remote care centers to predict the progress of a patient’s condition. Network quality is variable in many areas because of signal power degradation (zones without purple coverage) in rural areas with many building obstacles. As a result, many current emergency services still rely on on-site first aid efforts. The idea of unmanned aerial vehicles (UAVs) serving as aerial relaying stations to provide connectivity for ground users has received much attention over the years. However, controlling UAVs via cellular networks is still a challenging issue. In this work, we consider the mission of dispatching UAVbased relaying stations as a path-planning scheme, where the UAVs go to planned locations and serve the EVs with a certain connectivity requirement. The core novelty of this work is a novel searching scheme that can suggest a deployment plan for the swarm of UAVs at the time of the EVs’ departure. The search is also robust for path planning with real-time applications or dynamic environments.

Directional antenna technologies are crucial to enhance high-speed data transmission in emerging wireless communications such as mmWave. These technologies can enable high-accuracy radio positioning by exploiting spatial-temporal signal processing in wideband beamforming space. However, the radio positioning technique potentially poses surveillance risks to mobile users, particularly being tracked illegally. This work presents a novel scheme to track a user in a building based on passively received signals. The scheme includes a Deep Convolutional Neural Network (DCNN) localization module to train on the accumulated channel impulse responses (CIR) and corresponding Angle-Delay profiles. The user's estimated locations from the DCNN localization are then refined with an Unscented Kalman filter (UKF) data fusion module to eliminate outlier data points. Simulations indicate that the proposed scheme can accurately regenerate the user trajectory, even without the attacker's physical intrusion into the building. This poses a new concern of surveillance risks in directional wireless communications, given their expected popularity in 5G and beyond.

Vehicle platooning is a promising technology to bring up significant benefits of improved fuel economy and fewer traffic collisions. However, many security attacks such as beacon message falsification have been exposed, creating grave concerns about maintaining a vehicle platoon stably. This work introduces a robust positioning-based verification scheme, namely PVS, to enhance reliability of vehicle platoon control in vehicular networks. By exploiting geographic and maneuver information from 5G radio-based positioning, PVS can detect whether a vehicle is honest in reporting its location for platoon joining preparation or collision avoidance, with up to 96% accuracy.

Recently, autopilot-like vehicles have become more pervasive. To maintain inter-vehicle distance stably, Cooperative Adaptive Cruise Control (CACC) is then proposed to make each autonomous vehicle exchange its dynamic state with neighboring vehicles via vehicle-to-vehicle (V2V) communication. However, longitudinal platooning via CACC systems alone is not enough to improve traffic throughput since each vehicle has its own desired speed and only considers itself to optimize its traveling. Therefore, we develop a novel framework MANA to determine the suitable platoon-merge maneuver, lane-change maneuver, and space-reserve maneuver. Extensive simulation results manifest that MANA can avoid collisions effectively, converge fast, and save fuel consumption and CO2emissions by 24% and 20%.