The deployment of Unmanned Aerial Vehicles (UAVs) has increasingly demonstrated potential in addressing the overload of Base Stations (BSs) by enabling efficient data dissemination during peak traffic hours. However, the operational endurance of UAVs in such scenarios is constrained by their limited onboard energy, making efficient data dissemination strategies essential to extend UAV service duration. To address this challenge, this study proposes a UAV-assisted vehicular edge computing data dissemination framework in the software-defined Internet of Vehicles, leveraging centralized control and UAV-enabled cooperative services to improve dissemination efficiency. Specifically, we formulate a UAV-Assisted Cooperative Scheduling (UACS) problem that synergizes network coding, vehicular caching, and UAV caching to minimize service delay and extend UAV endurance. Furthermore, a graph-based model is introduced to capture the interactions among vehicles, UAVs, and the BS. The NP-hardness of the UACS problem is established through a polynomial-time reduction from the minimum clique cover problem. In addition, a Clique Search-based Cooperative Scheduling (CSCS) algorithm is developed to determine optimal coding-based broadcasting and cooperative operation strategies, and its computational complexity is analyzed to confirm its practical applicability. Extensive simulations using real-world traffic datasets further demonstrate the superiority and effectiveness of the proposed solution.

Vehicular Edge Computing (VEC) is widely regarded as a promising paradigm for enhancing the Quality of Service in the Internet of Vehicles (IoV). However, due to limited computing resources and increasing computational demands, especially during peak traffic hours in urban environments, VEC faces significant challenges. Meanwhile, the idle computing resources of nearby Parked Vehicles (PVs) remain largely underutilized. To address this issue, we propose a PV-enhanced IoV framework that models hierarchical interactions among multiple Task Vehicles (TVs), Service Providers (SPs), and PVs in task offloading and resource allocation, efficiently leveraging the computing resources of PVs to complement VEC servers. Specifically, we first design a Game-based Pre-offloading Assignment (GPA) algorithm to determine the optimal SP selection for each TV. Once TVs have selected their respective SPs, we model the interaction between each TV and SP as a Stackelberg game, where the SP acts as the leader by setting the service price, and each TV responds by determining its offloading workload. Furthermore, we prove the existence of a Nash equilibrium through backward induction and develop a Gradient-based Stackelberg Pricing (GSP) algorithm to compute this equilibrium. Finally, to emphasize the role of PVs, we propose an Optimized Recruitment Allocation (ORA) algorithm, which enables SPs to efficiently recruit PVs and allocate their computing resources, while ensuring that each PV selects the SP that maximizes its utility. Extensive simulation results demonstrate that the proposed scheme outperforms baseline methods in terms of offloading performance and overall utility improvement.

Vehicular Edge Computing (VEC) has emerged as a promising paradigm to enhance the Quality of Service (QoS) in the Internet of Vehicles (IoV). However, the limited computing capabilities of Task Vehicles (TVs), coupled with increasing computational demands during peak periods, pose significant challenges. Meanwhile, the unused computing resources of nearby Parked Vehicles (PVs) are often underutilized. To address these challenges, we propose a parked vehicle-assisted VEC architecture that integrates multiple TVs, Service Providers (SPs), and PVs. Specifically, we first design a Game-based Preoffloading Assignment (GPA) algorithm to determine the optimal SP for each TV. After SP selection, the interaction between each TV and its selected SP is modeled as a Stackelberg game, where the SP acts as the leader by setting the pricing strategy, and the TV acts as the follower by determining its offloading strategy. Moreover, we demonstrate the existence of a unique Nash equilibrium and propose a Gradient-based Stackelberg Pricing (GSP) algorithm to derive the equilibrium strategies. Additionally, to enhance the utilization of PV resources, we introduce an Optimized Recruitment Allocation (ORA) algorithm, enabling each PV to select the SP that maximizes its utility. Finally, extensive simulations demonstrate that the proposed scheme significantly improves offloading efficiency.

In recent years, visible light positioning (VLP) techniques have been gaining popularity in research. Among them, the scheme of using a camera as a receiver is more popular, and the technology provides low-cost, high-precision positioning capability and easy integration with existing multimedia devices and robots. However, receiver pose changes can lead to image distortion and light source displacement, significantly increasing positioning errors. Addressing these errors is crucial for enhancing the accuracy of VLP technology. Most current solutions rely on gyroscopes or Inertial Measurement Units (IMUs) for error optimization, but these approaches often add complexity and cost to the system. To overcome these limitations, we propose a positioning algorithm based on an attention mechanism convolutional neural network (CNN), aimed at reducing the errors caused by angles. We designed experiments and comparisons within a rotation angle range of ±15 degrees. The results demonstrate that algorithm maintains an average positioning error within 5 cm.

Location information plays an important role in many applications of the Internet of Things (IoT). The low cost and ease of scalability of range-free localization algorithms have attracted the attention of many researchers, but the performance of many localization algorithms available in the literature varies greatly in different networks. Specifically, algorithms designed for anisotropic networks may not perform well in isotropic networks, and vice versa. To improve localization accuracy in both isotropic and anisotropic networks, a novel range-free localization algorithm named LSAE is proposed in this article, oriented to the network positioning without ranging over the IoT. The proposed algorithm utilizes the known information in the network, namely, the hop counts and distances between anchor nodes, to train the stacked autoencoders (SAE) model. In this way, it achieves accurate prediction of the distances between unknown nodes and anchor nodes. To further improve the localization accuracy, the disadvantage of the least square method is analyzed, and a novel coordinate estimation method based on the statistical results of distance estimation errors is proposed. We conducted a huge number of numerical simulations with and without the impact of multiple anisotropic factors in three different types of networks. The results indicate that the proposed algorithm outperforms other state-of-the-art algorithms treating the impact of multiple anisotropic factors, and demonstrates the high accuracy and robustness.

It is essential to acquire the location of sensor nodes in the wireless sensor networks (WSNs), since the data collected with nodes would become meaningless without their location. In the existing studies, range-free localization schemes have been proved suitable to large-scaled WSNs due to the low cost in hardware implementation. However, the defect of those schemes is their poor accuracy in anisotropic networks with coverage holes. To tackle this problem, we propose a range-free localization scheme that combines the advantages of geometric constraint and hop progress-based methods. The geometric information provided by the combination of anchor pairs and unknown nodes is used to design the discrimination conditions, and each node divides the anchor pairs into one of three proposed categories. For different categories of anchor pairs, corresponding methods are proposed to estimate the distancse between sensor nodes. In this way, the trade-off between distance estimation accuracy and anchor utilization can be achieved. Simulation results indicate that the proposed scheme outperforms other schemes in terms of localization accuracy and proportion of outliers with acceptable computational and time complexity, where the localization accuracy and proportion of outliers in the proposed scheme are improved by up to 47% and 61% compared to DV-maxHop.

This study presents a high‐efficient Reed–Solomon (RS) decoder based on the recursive enhanced parallel inversionless Berlekamp–Massey algorithm architecture. Compared with the conventional enhanced parallel inversionless Berlekamp–Massey algorithm architecture, the proposed architecture consists of a single processing element and has very low hardware complexity. It also employs a new initialisation to reduce the latency. This architecture uses pipelined Galois–Field multipliers to improve the clock frequency. In addition, the proposed architecture also has the dynamic power saving feature. The proposed RS (255, 239) decoder has been developed and implemented with SMIC 0.18‐μm CMOS technology. The synthesis results show that the decoder requires about 13K gates and can operate at 575 MHz to achieve the data rate of 4.6 Gb/s. The proposed RS (255, 239) decoder is at least 28.15% more efficient than the previously related designs.