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Vehicular Edge Computing (VEC) is a transportation-specific version of Mobile Edge Computing (MEC) designed for vehicular scenarios. Task offloading allows vehicles to send computational tasks to nearby Roadside Units (RSUs) in order to reduce the computation cost for the overall system. However, the state-of-the-art solutions have not fully addressed the challenge of large-scale task result feedback with low delay, due to the extremely flexible network structure and complex traffic data. In this paper, we explore the joint task offloading and resource allocation problem with result feedback cost in the VEC. In particular, this study develops a VEC computing offloading scheme, namely, a Lagrange multipliers-based adaptive computing offloading with prediction model, considering multiple RSUs and vehicles within their coverage areas. First, the VEC network architecture employs GAN to establish a prediction model, utilizing the powerful predictive capabilities of GAN to forecast the maximum distance of future trajectories, thereby reducing the decision space for task offloading. Subsequently, we propose a real-time adaptive model and adjust the parameters in different scenarios to accommodate the dynamic characteristic of the VEC network. Finally, we apply Lagrange Multiplier-based Non-Uniform Genetic Algorithm (LM-NUGA) to make task offloading decision. Effectively, this algorithm provides reliable and efficient computing services. The results from simulation indicate that our proposed scheme efficiently reduces the computation cost for the whole VEC system. This paves the way for a new generation of disruptive and reliable offloading schemes.

The rapid advancement in consumer technology has led to an exponential increase in the connected devices, resulting in an enormous and continuous flow of data, particularly the image data. This data needs to be processed, managed, and secured efficiently, especially in the quantum-enabled consumer technology era. This paper, in this regards, presents a quantum image encryption scheme featuring a novel two-phase chaotic confusion-diffusion architecture. The proposed architecture consists of four distinct confusion-diffusion modules that perform a simultaneous qubit and pixel-level encryption on both the position and intensity of quantum encoded pixels. Moreover, quantum circuits for $^{\prime }$ qubit-level chaotic transformation $^{\prime }$ and $^{\prime }$ chaos-based selective perfect shuffle operation $^{\prime }$ have been implemented, which collectively enhance the encryption strength of the proposed scheme. Extensive evaluation has been performed based on various statistical security parameters, such as entropy and correlation. When subjected to differential attacks, the proposed scheme proved its resilience exhibiting ideal results of average 99.6% NPCR (Number of Pixels Change Rate) and 33.5% UACI (Unified Average Changing Intensity). Besides, the proposed scheme also demonstrated resilience against occlusion attacks. Tests involving 50% data occlusion from encrypted images validated the proposed scheme’s capability to successfully decrypt the tampered images, recovering maximum information.

LoRa networks represent a promising technology for Internet of Things applications due to their long range, low cost, and energy efficiency. However, their ALOHA-based access method and duty cycle restrictions can limit their scalability and reliability in high-density networks. To address such a challenge, this article proposes the autonomous time-slotted LoRa (ATS-LoRa), protocol that allows LoRaWAN nodes to autonomously determine their optimal transmission parameters without extensive downlink transmissions from the gateway. ATS-LoRa utilizes the geographical coordinates of the nodes and their gateway in a novel way to allow them to determine their appropriate transmission parameters, such as the spreading factor, the channel frequency, and the slot ID. ATS-LoRa performance is evaluated through extensive simulations under different operating conditions showing an average throughput of around 47 times better than the adaptive data rate algorithm of LoRaWAN protocol.

It is estimated that by 2050 approximately one in ten individuals globally will experience disabling hearing impairment. In the presence of everyday reverberant noise, a substantial proportion of individual users encounter challenges in speech comprehension. This study introduces a novel application of neuro-fuzzy modeling that synergizes and fuses audio-visual speech enhancement (AV SE) with an initial user preference learning based framework. Specifically, our approach uniquely integrates multimodal AV speech data with innovative SE methods and fuzzy inferencing techniques. This integration is further enriched by incorporating a user-preference learning model that adapts to environmental and user-specific contexts, including signal-to-noise ratios, sound power, and the quality of visual information. The proposed framework facilitates the incorporation of clinical measures such as user cognitive load (or listening effort) with real-world uncertainty to steer the system outputs. We employ an adaptive fuzzy neural network to derive the most effective Sugeno fuzzy inference model, employing particle swarm optimization to ensure optimal SE by considering sound power, ambient noise levels, and visual quality. Experimental results utilize our new benchmark AV multitalker challenge dataset to demonstrate the superiority of our user preference-informed, context-aware AV SE approach in enhancing speech intelligibility and quality in challenging noisy conditions, marking a significant advancement over conventional methods while reducing energy consumption. The conclusion supports the ecological scalability of our approach and its potential for real-world applications, setting a new benchmark in AV SE research, paving the way for future assistive hearing and communication technologies.

Intelligent transportation systems (ITS) utilize advanced technologies to enhance traffic safety and efficiency, contributing significantly to modern transportation. The integration of Vehicle-to-Everything (V2X) further elevates road safety and fosters the progress of ITS through enabling direct vehicle communication and interaction with infrastructure. However, the penetration rate of V2X vehicles is advancing gradually. Consequently, there will be mixed scenarios on the road, involving both on-board units (OBUs)-equipped and non-equipped vehicles. This results in disparities in communication capabilities, highlighting the need to ensure the efficient and safe operation of vehicles in such mixed scenarios. This paper addresses this challenge by presenting a feasibility analysis and prediction method for lane-changing overtaking maneuvers in mixed scenarios, specifically for vehicles equipped with OBUs. This method assists vehicles in completing overtaking maneuvers by offering a non-binary lane-changing overtaking feasibility index along with corresponding speed guidance. First, vehicle sensors are used to sense the state of surrounding vehicles, addressing any missing sensor data due to occlusions. Moreover, the future driving behavior of the vehicle is taken into account to more accurately predict the future state of the vehicle. Then, a deep reinforcement learning algorithm is deployed to process the hybrid action space to train a lane-changing overtaking model, which also takes into account the influence of the flow of each lane in front of the vehicle, and finally predicts the feasibility of the vehicle performing lane-changing overtaking. Experimental results demonstrate that our method can accurately predict the vehicle’s future state and effectively assist the vehicle in completing lane-changing overtaking maneuvers. This research provides strong support for the integration of ITS and V2X technologies.

Agriculture 4.0 offers the potential to revolutionize the agriculture sector through improved productivity and efficiency. However, adopting Agriculture 4.0 requires a period of transition and effort. Satellite-airborne sensing technologies may become an opening enabler technology of this new paradigm due to its fast deployment process and flexible infrastructure. This article provides an overview of the technology, trends, challenges, and opportunities in agriculture and climate-resilient sensing technologies. The research covers critical enabling technologies such as low-altitude platforms (LAPs) (i.e., drones and tethered balloons), high-altitude platforms (HAPs) (i.e., airships, HAPs balloons, and aircraft), and satellites, as well as recent advancements in data processing and digital twins (DT), with some examples from agricultural research projects. Furthermore, this article explores some challenges in agriculture and the technological deployment of satellite-airborne sensing technologies. Finally, this article provides some potential opportunities for satellite-airborne sensing technologies for agricultural purposes. This article may become a guide for adopting Industry 4.0 by leveraging satellite-airborne network technologies.

Generative adversarial networks (GANs) have been advancing and gaining tremendous interests from both academia and industry. With the development of wireless technologies, a huge amount of data generated at the network edge provides an unprecedented opportunity to develop GANs applications. However, due to the constraints such as bandwidth, privacy, and legal issues, it is inappropriate to collect and send all data to the cloud or servers for analysis, training, and mining. Thus, deploying and training GANs at the edge becomes a promising alternative solution. The instability of GANs introduced by non-independent and identical data (Non-IID) poses significant challenges to training GANs. To address these challenges, this paper presents a novel federated learning framework for GANs, namely, Collaborated gAme Parallel Learning (CAP). CAP supports parallel training of data and models for GANs, breaking the isolated training among generators that exists in the previous distributed algorithms, and achieving collaborative learning among cloud, edge servers, and devices. Then, to further enhance the ability of CAP-GAN for addressing Non-IID issues, we propose a Mix-Generator module (Mix-G) which divides a generator into the sharing layer and personalizing layer. The Mix-G module extracts the generic and personalization features and improves the performance of CAP-GAN on extremely personalizing datasets. Experimental results and analysis substantiate the usefulness and superiority of our proposed CAP-GAN scheme which can achieve better results in the Non-IID scenarios compared with the state-of-the-art algorithms.