The integration of space, air, and ground segments into unified Space-Air-Ground Integrated Networks (SAGINs) enables low-latency, ubiquitous, and scalable computing. However, such systems face critical challenges: ground terminals suffer from weak satellite links, UAV-based edge nodes have limited resources, and highly dynamic environments make it difficult to make efficient offloading and resource allocation decisions. Prior approaches often optimize either communication or computation in isolation and lack adaptability to real-time environmental feedback. This paper presents a novel perception-aware hybrid-action deep reinforcement learning (DRL) framework for joint optimization of task offloading, beamforming, and resource allocation in SAGINs. To improve tractability, the original non-convex problem is first decomposed using Block Coordinate Descent (BCD) and approximated with Successive Convex Approximation (SCA), generating a structured feasible action space. A Soft Actor-Critic (SAC) agent then learns policies over this space, informed by real-time UAV perception via mmWave radar and vision sensors that detect user density, link quality, and environmental blockages. The DRL agent operates over a hybrid action space, combining discrete offloading decisions with continuous controls such as beamforming weights, CPU frequency, and transmission power. We employ a constraint-aware action masking mechanism that prunes infeasible hybrid actions violating delay, power, or SNR limits, thereby accelerating learning while respecting SAGIN-specific constraints. Extensive simulations show that the proposed framework significantly outperforms greedy, no-perception DRL, and state-of-the-art DRL offloading algorithms in reducing latency and energy consumption, while improving offloading success and resource stability. These results highlight the effectiveness of combining analytical optimization structure with adaptive perception-driven learning for robust and scalable control in future SAGINs.

Deploying Convolutional Neural Network (CNN) models on ubiquitous Internet of Things (IoT) devices in a cloud-assisted manner to provide users with a variety of high-quality services has become mainstream. Most existing studies speed up model cloud training/on-device inference by reducing the number of convolution (Conv) parameters and floating-point operations (FLOPs). However, they usually employ two or more lightweight operations (e.g., depthwise Conv,$1\times 1$cheap Conv) to replace a Conv, which can still affect the model's speedup even with fewer parameters and FLOPs. To this end, we propose the Grouped NonLinear transformation generation method (GroupNL), leveraging data-agnostic, hyperparameters-fixed, and lightweight Nonlinear Transformation Functions (NLFs) to generate diversified feature maps on demand via grouping, thereby reducing resource consumption while improving the robustness of CNNs. First, in a GroupNL Conv layer, a small set of feature maps, i.e., seed feature maps, are generated based on the seed Conv operation. Then, we split seed feature maps into several groups, each with a set of different NLFs, to generate the required number of diversified feature maps with tensor manipulation operators and nonlinear processing in a lightweight manner without additional Conv operations. We further introduce a sparse GroupNL Conv to speed up by reasonably designing the seed Conv groups between the number of input channels and seed feature maps. Experiments conducted on benchmarks and on-device resource measurements demonstrate that the GroupNL Conv is an impressive alternative to Conv layers in baseline models. Specifically, on Icons-50 dataset, the accuracy of GroupNL-ResNet-18 is 2.86% higher than ResNet-18; on ImageNet-C dataset, the accuracy of GroupNL-EfficientNet-ES achieves about 1.1% higher than EfficientNet-ES. In addition, we verified the efficiency of GroupNL-based models in terms of cloud training and on-device inference.

The integration of reconfigurable intelligent surfaces (RIS) into unmanned aerial vehicle (UAV) networks presents a transformative solution for achieving energy-efficient and reliable communication, particularly within the rapidly expanding low-altitude economy (LAE). As UAVs facilitate diverse aerial services—spanning logistics to smart surveillance—their limited energy reserves create significant challenges. RIS effectively addresses this issue by dynamically shaping the wireless environment to enhance signal quality, blackuce power consumption, and extend UAV operation time, thus enabling sustainable and scalable deployment across various LAE applications. This survey provides a comprehensive review of RIS-assisted UAV networks, focusing on energy-efficient design within LAE applications. We begin by introducing the fundamentals of RIS, covering its operational modes, deployment architectures, and roles in both terrestrial and aerial environments. Next, advanced energy efficiency (EE)-driven strategies for integrating RIS and UAVs. Techniques such as trajectory optimization, power control, beamforming, and dynamic resource management are examined. Emphasis is placed on collaborative solutions that incorporate UAV-mounted RIS, wireless energy harvesting (EH), and intelligent scheduling frameworks. We further categorize RIS-enabled schemes based on key performance objectives relevant to LAE scenarios. These objectives include sum rate maximization, coverage extension, quality of service (QoS) guarantees, secrecy rate improvement, latency blackuction, and age of information (AoI) minimization. The survey also delves into RIS-UAV synergy with emerging technologies like multi-access edge computing (MEC), non-orthogonal multiple access (NOMA), vehicle-to-everything (V2X) communication, and wireless power transfer (WPT). Finally, we outline open research challenges and future directions, emphasizing the critical role of energy-aware, RIS-enhanced UAV networks in shaping scalable, sustainable, and intelligent infrastructures within the LAE.

Unmanned aerial vehicles (UAVs) have become essential in advancing multi-access edge computing (MEC), providing flexible platforms that enhance network capacity, coverage, and efficiency while reducing latency and improving communication quality. Integrating reconfigurable intelligent surfaces (RIS) with UAV-based MEC systems further elevates these capabilities, delivering significant gains in computational power, energy efficiency (EE), and physical layer security (PLS). However, managing the complexity of RIS within UAV networks requires sophisticated optimization strategies. This survey offers a comprehensive analysis of the fundamentals of RIS, UAVs, and MEC, followed by an in-depth examination of RIS configurations in UAV-based MEC systems, including static, dynamic, and hybrid models. We evaluate the benefits and challenges of RIS integration, such as improved communication, enhanced computational efficiency, optimized energy use, better task management, and strengthened security. In addition, the survey explores the latest advancements in RIS-assisted UAVs for MEC, focusing on boosting computational capacity, minimizing delay, maximizing EE, and enhancing security. To provide a thorough exploration of these topics, detailed summary tables are included, offering a comparative analysis of methodologies, performance metrics, and scenarios from recent studies. Furthermore, the survey presents key lessons learned from current research and identifies future research directions crucial for fully realizing the potential of RIS-enhanced UAV-based MEC systems in next-generation networks.

This survey provides an in-depth examination of the role of reconfigurable intelligent surfaces (RIS) in enhancing physical layer security (PLS) within unmanned aerial vehicle (UAV)-assisted networks, which are essential for the secure and efficient operation of sixth-generation (6G) wireless communications. The study covers various types of RIS—passive, active, and hybrid—and their applications in both terrestrial and aerial environments to strengthen PLS. Key focus areas include advanced PLS techniques such as optimizing UAV trajectory, beamforming, and RIS phase-shift configurations, all aimed at improving secrecy rates (SRs) while mitigating the risks of eavesdropping and jamming. Moreover, the survey also addresses strategies for enhancing energy-efficient SRs and implementing anti-jamming mechanisms within UAV-assisted networks. Additionally, it explores the integration of RIS-UAV systems with emerging technologies such as non-orthogonal multiple access (NOMA), mobile edge computing (MEC), cognitive radio, and THz networks, demonstrating how security can be enhanced in such networks. Through detailed performance analysis, the paper highlights the transformative potential of RIS-equipped UAVs in overcoming the potential security challenges for future 6G networks. Finally, the survey presents lessons learned and identifies critical future research directions and open challenges, offering insights that will guide the development of robust and secure RIS-assisted UAV systems in next-generation wireless networks.

The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks’ traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading’s category, we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.

Ensuring dependable quality of service (QoS) and quality of experience (QoE) for computation-intensive and delay-sensitive applications in vehicles can be a challenging task that impacts performance. While multi-access edge computing (MEC) based vehicular edge computing network (VECN) and vehicular cloudlets (VC) enable task offloading, but their prompt and optimal accessibility is another challenge. The conventional wireless technologies may not suffice to meet the stringent ultra-low latency and cost constraints of such applications. Nonetheless, the combination of different wireless technologies can enhance network performance and satisfy these requirements. Focusing on the computational efficacy of VECN, this paper proposes a mobility, contact, and computational load-aware (MCLA) task offloading scheme for heterogeneous VECN. The MCLA scheme dynamically considers the mobility, contact, and computational load of vehicles for making task offloading decisions. To optimize the performance, the MCLA scheme integrates the Mode-1 and Mode-2 of the 5G-NR-V2X standard, along with mmWave communications. The MCLA scheme provides an opportunistic switching mechanism between these modes and heterogeneous radio access technologies (RATs) to reduce communication delays and costs. Moreover, the MCLA scheme leverages public vehicles (i.e., public buses), in proximity by using their computational power to manage computational latency and cost. Furthermore, it also considers the shareable computations from passengers’ mobile equipment within the public vehicle to improve the computation capacity of the public vehicles. Extensive evaluations and numerical results show that the proposed MCLA scheme significantly improves the task turnover ratio by 4%–15% with 4.7%–29.8% lower transmission and computation costs.

Multiaccess edge computing (MEC) is considered as a backbone for the 5G network. The successive MEC network combines the networking and computation at the edge of the network to achieve the Quality of Services (QoS) with ultralow latency. The devices with mobility feature, whether hand-held devices or vehicles move from one edge server (ES) location to another ES, creates a nonoptimal environment in the long run. To maintain QoS and avoid service disruptions, existing network topologies do not fulfill the requirement. Hence, a unique traffic steering with dynamic path selection is required for live service migration of time-sensitive applications. In this article, we are the first to introduce a distributed traffic steering through the differentiation of two different types of network elements (i.e., ESs and routers), in a large MEC system. Using this concept, we, for the first time, resolve the scalability problem of a large MEC network into a partitioned MEC network. The proposed framework bounds the path-finding procedure with a filter strategy based on the network distance to eliminate the excess of nonrelated network elements. With a decentralized framework for MEC, we propose matrix-based dynamic shortest path selection and matrix-based dynamic multipath searching algorithms for dynamic path selection under the proposed autonomous network boundary discovery and must connect node block benchmarks. Our proposed dynamic traffic steering system works under two most important metric measurements (time delay and available bandwidth). Experimental results validate the effectiveness of dynamic and adaptive path searching in a partitioned controlled MEC network that significantly outperforms the centralized approaches with 35%–70% efficiency in QoS.

Vehicular edge computing (VEC) is an innovative computing paradigm with an exceptional ability to improve the vehicles’ capacity to manage computation-intensive applications with both low latency and energy consumption. Vehicles require to make task offloading decisions in dynamic network conditions to obtain maximum computation efficiency. In this article, we analyze computation efficiency in a VEC scenario, where a vehicle offloads its tasks to maximize computation efficiency as a tradeoff between computation time and energy consumption. Although, it is quite a challenge to ensure the quality of experience of the vehicle due to diverse task requirements and the dynamic wireless conditions caused by vehicle mobility. To tackle this problem, a computation efficiency problem is formulated by jointly optimizing task offloading decision and computation resource allocation. We propose a mobility-aware computational efficiency-based task offloading and resource allocation (MACTER) scheme and develop a distributed MACTER algorithm that provides the near-optimal solution. We further consider the fifth-generation new-radio vehicle-to-everything communication model, i.e., cellular link and millimeter wave, to enhance the system performance. The simulation outcomes demonstrate that the proposed algorithm can efficiently enhance computation efficiency while satisfying computing time and energy consumption constraints.

As more investors adopt to enter the field of blockchain investment, the Ponzi scheme, a traditional investment scam, has emerged as a hidden fraud in smart contracts. Although some proposed solutions have paid attention to detecting Ponzi schemes in the blockchain, two problems remain: features for detecting Ponzi schemes are incomplete, and algorithms for detecting Ponzi schemes are not sufficiently efficient. Therefore, we innovatively extract the bytecode feature and combine it with user transaction and opcode frequencies to get more comprehensive features. With these features, we propose a smart contract Ponzi scheme identification method based on the improved LightGBM algorithm. Experiments conducted on the real data set of Ethereum prove that our proposed method has improved accuracy dramatically in terms of the$F$-score index and the AUC index compared with the state-of-the-art methods. In addition, model training speed is improved significantly. Therefore, our method more accurately identifies Ponzi schemes in smart contracts, thus reducing investment risk.

A new networking paradigm, Vehicular Edge Computing (VEC), has been introduced in recent years to the vehicular network to augment its computing capacity. The ultimate challenge to fulfill the requirements of both communication and computation is increasingly prominent, with the advent of ever-growing modern vehicular applications. With the breakthrough of VEC, service providers directly host services in close proximity to smart vehicles for reducing latency and improving quality of service (QoS). This paper illustrates the VEC architecture, coupled with the concept of the smart vehicle, its services, communication, and applications. Moreover, we categorized all the technical issues in the VEC architecture and reviewed all the relevant and latest solutions. We also shed some light and pinpoint future research challenges. This article not only enables naive readers to get a better understanding of this latest research field but also gives new directions in the field of VEC to the other researchers.

A huge amount of data, generated by Internet of Things (IoT), is growing up exponentially based on nonstop operational states. Those IoT devices are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of IoT. Fog computing structure confronts those disruptions, with powerful complement functionality of cloud framework, based on deployment of micro clouds (fog nodes) at proximity edge of data sources. Particularly big IoT data analytics by fog computing structure is on emerging phase and requires extensive research to produce more proficient knowledge and smart decisions. This survey summarizes the fog challenges and opportunities in the context of big IoT data analytics on fog networking. In addition, it emphasizes that the key characteristics in some proposed research works make the fog computing a suitable platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g., health care monitoring, smart cities, connected vehicles, and smart grid) will be discussed here to create a well‐organized green computing paradigm to support the next generation of IoT applications.