The proliferation of Unmanned Aerial Vehicles (UAVs) in 6G-enabled edge computing presents a dual challenge of ensuring secure data transmission and performing accurate object detection on resource-constrained platforms. This paper proposes a Synergistic Encryption and Detection framework (SED-UAV) that integrates a lightweight chaotic image encryption algorithm, LB-ICA, and an enhanced small object detector, YOLO-LFP. The LB-ICA algorithm leverages an improved chaotic map and a plaintext-aware key mechanism using SHA-256. It achieves a key space of over 2100 and an average encryption speed of 20.3 ms per block, providing robust security against differential and chosen-plaintext attacks with high efficiency suitable for UAVs. For detection, the YOLO-LFP model enhances the YOLOv8 baseline with a hybrid attention module, an adaptive feature fusion strategy, and a dedicated small object head. Experimental results on the VisDrone2019 benchmark show YOLO-LFP achieves a state-of-the-art performance of 44.7% [email protected], significantly outperforming existing models. This work provides a comprehensive solution for deploying secure, real-time computer vision applications on UAV platforms.

The Segment Anything Model 2 (SAM2) has revolutionized prompt-driven visual segmentation, yet its integration with Neural Radiance Field (NeRF) for object segmentation remains constrained by static scene assumptions and Low Dynamic Range (LDR) input limitations. This paper introduces SA4D-HDR, a novel framework that lifts SAM2’s ability to 4D HDR scenes, enabling accurate 4D segmentation of arbitrary objects. Based on 4D HDR NeRF prior, we reconstruct temporally coherent HDR scenes from multi-exposure inputs. A sigmoid-based tone mapping function then projects the HDR scenes to LDR domain, enabling SAM2 to generate precise object masks within the reconstructed scenes. Subsequently, manual segmentation prompts in single-view input are fed into SAM2 to generate sequential 2D segmentation masks, which guide the learning of a mask deformation field network. This network models 4D geometric transformations in voxel space, while a lightweight decoder predicts masks at arbitrary timestamps. To enhance geometric consistency, we introduce an annealing jitter noise mechanism that injects controlled perturbations during training, mitigating alignment errors between NeRF-rendered geometries and SAM2-generated 2D masks. Our method establishes an effective segmentation framework for 4D HDR scenes with neural radiance fields, bridging the semantic gap between 2D visual foundation models and 4D HDR scenes while achieving state-of-the-art accuracy on HDR datasets.

To address the challenge of data acquisition in unknown disaster environments — such as earthquake zones, flood-affected regions, or industrial accident sites — this paper proposes a Hybrid Centralized - Distributed Collaborative Coverage (HCC) strategy for UAV swarms. The HCC framework integrates centralized trajectory planning with distributed obstacle avoidance to achieve rapid, efficient, and safe coverage of Points of Interest (PoIs). Specifically, a Centralized Collaborative Optimization (CCO) strategy is designed to compute secure cooperative coverage schemes on an edge server, while a Distributed Obstacle-avoidance Coverage Strategy (DOCS) enables each UAV to perform safety-aware navigation based on local sensing. Unlike sequential exploration, the proposed method supports parallel and synchronized coverage execution, ensuring that data acquisition across the entire region occurs concurrently. Simulation results demonstrate that the proposed method outperforms benchmark algorithms in terms of coverage rate, safety, energy efficiency, and network lifetime.

Blockchain is increasingly integrated in Multiaccess Edge Computing (MEC) to coordinate secure and lowlatency resource provisioning and service orchestration among resource-constrained embodied AI devices. However, conventional blockchains perform costly transaction verification during propagation, which can be exploited by spam transaction attacks and overload resource-limited edge devices. To mitigate the substantial overhead of verification, we propose a two-stage BLS multi-signature backed transaction propagation mechanism for blockchain-enabled MEC: a small-scope random-walk phase with deep verification and signing, followed by a large-scope propagation phase with probabilistic verification. In the first stage, nodes conduct deep verification and sign valid transactions using the BLS multi-signature, then forward the signed transaction to a small and randomly sampled subset of neighbors to rapidly accumulate valid signatures. In the second stage, transactions whose aggregated signature count exceeds a threshold will be broadcast throughout the entire blockchain network and undergo deep verification with a specific probability, relieving edge nodes' verification burden. Moreover, verifiers record signers associated with failed deep verifications. Signers whose failures exceed a system threshold are quarantined to restrain the spread of spam transactions. Experimental results demonstrate that the proposed mechanism reduces energy consumption by at least 60% and 18.6% compared with the original and benchmark mechanisms respectively, while maintaining nearly identical transmission performance and ensuring that the proportion of invalid transactions propagated to honest nodes does not exceed 14%.

Due to the destruction of ground communication facilities, emergency communication networks are crucial for emergency rescue missions. UAV-Assisted Communication Network (UACN) is becoming a promising approach to establish emergency communication networks. However, the complexity and dynamics of UACN in disaster areas impose strict requirements on routing algorithms. To this end, this paper proposes an efficient routing scheme based on a double-layer UAV-assisted communication architecture to optimize the network performance of UACN and help emergency rescue missions to be completed efficiently. Specifically, we propose a double-layer UAV-assisted communication architecture based on the high coverage requirements of emergency scenarios and the delay-sensitive requirements of emergency rescue. Then, we propose an Attention mechanism-based Message passing Deep Reinforcement Learning (AMDRL) to optimize the routing problem in view of the fact that existing schemes rarely make full use of the information of the network environment. AMDRL uses the characteristics of graph neural network (GNN) to interact with the network topology environment and extract available information through the information passing process between topological links. Finally, we conduct simulation experiments in Knowledge-Defined Networking (KDN). The results show that the network performance of AMDRL is better than that of the baseline algorithm.

Recommendation systems have primarily been limited to research on a single dataset compared to natural language processing and computer vision, which have seen tremendous growth in transferable tasks. Existing approaches for recommendation systems need to be more scalable to arbitrary tasks, given that previous research efforts on transferable recommendations have only yielded brief explorations and neglected systematic studies of sequential tasks. In this regard, we propose the interaction subgraph sequential topology-aware network (ISTN), which overcomes this limitation, enabling transferable sequence recommendations. ISTN performs subgraph sampling and node labeling of user interactions, captures the topological features of the user interaction sequences with the sequential topology auto-encoder, and employs the sequential preference decoupling module to decouple user interaction sequences for transferable adaptive granularity modeling of user preferences. ISTN requires no fine-tuning, and its knowledge transfer capability from the training dataset to the new dataset delivers accurate, individualized recommendation results. ISTN outperforms state-of-the-art performance in transferable contexts with only minor performance degradation compared to the traditional baseline, as shown in Yelp, MovieLens, and Foursquare experiments.

Recently, computation offloading methods have greatly improved the Quality of Experience (QoE) in Multi-access Edge Computing (MEC) by offloading tasks to the edge servers. Since well-coordinated actions of Terminal Devices (TDs) are critical to improving the performance of the entire individual system, many practical MEC-based applications, i.e., firefighting robots and unmanned aerial vehicles, require great teamwork among TDs. However, real-world scenarios are usually bound by resource conditions. For instance, network connectivity may weaken or experience interruptions during emergency situations. In cases where the communication medium is utilized by multiple TDs, achieving effective coordination poses a significant challenge. In this paper, we propose a computation offloading scheme based on Scheduled Multi-agent Deep Reinforcement Learning (SMDRL) to make the most efficient decision in a resource-constrained scenario. First, we design a virtual energy queue based on the MEC system and maximize the QoE (related to service delay and energy consumption) in a real-time manner. Subsequently, we propose a scheduled multi-agent deep reinforcement learning algorithm to support each TD in learning how to encode messages, select actions, and schedule itself based on the received messages. Furthermore, a TopK mechanism is introduced. This mechanism chooses the most crucial TDs to broadcast their messages, and then the computation offloading problem in a communication-constrained MEC environment can be solved in a low-communication manner. Also, we prove that even under limited communication conditions, our proposed methods can still lead to the close-to-optimal performance. The final performance analysis shows that the developed scheme has significant advantages over other representative schemes.

As a core component of the Internet of Vehicles, reasoning about the trajectory of pedestrians or vehicles in complex road conditions plays a critical role in autonomous driving and socially aware robotic navigation. Most existing methods do not adequately consider the effects of heterogeneous traffic agents. Toward this end, we propose the traffic trajectory prediction algorithm based on the convolutional attention network (TraGCAN) to predict the trajectories of heterogeneous traffic agents in dense traffic. The algorithm of the proposed method examines the behavior of different traffic agents in terms of both time and space dimensions to identify their movement patterns and interactions. We construct the spatial relationship of traffic agents as a graph structure and introduce a graph convolutional network to extract spatial interactions. In addition, we design a spatial attention mechanism to adaptively calculate weights for all spatial interactions to capture different influences from neighboring agents. To improve the accuracy of trajectory prediction, the algorithm considers the influence of the heterogeneous characteristics of traffic agents on their motion behaviors. We evaluated the performance of the proposed TraGCAN on heterogeneous traffic data sets, and the results demonstrate that the error of TraGCAN is reduced by 15% compared to existing methods.

With the increasing popularity of cab services such as Didi and Uber, cities are faced with the challenge of high carbon emissions and traffic congestion. Ride-sharing services, as a novel green mode of transportation, have emerged as a key technology in smart transportation for addressing these problems. The implementation of ride-sharing is predicated on an accurate ridership demand forecasting model, which can effectively prevent vehicle resource waste, alleviate traffic congestion, and reduce carbon emissions. In this paper, a periodic attentional graph convolutional spatio-temporal network model (PAG-TSN) is proposed to predict regional ridership demand. Specifically, the model is trained using a large amount of GPS data and user demand data collected by the travel service provider. PAG-TSN consists of two parts: the bicomponent attention graph convolution model (BAT-GCN) and the periodic attentional gated recurrent unit model (PA-GRU). The former uses GCN to extract spatial features from pointwise and edgewise graphs; the latter uses the spatial feature vectors extracted from the former with external information as input, and uses GRU to extract temporal features from feature data of different periods, and finally uses attention mechanism and POI requirement correlation to integrate the extracted spatio-temporal information to derive prediction results. Extensive experiments and evaluations on the CD2Date and XA2Date datasets show that PAG-TSN outperforms other baseline models in accurately predicting regional ridership demand, with MAPE and RMSE values of 0.1147 and 5.56, respectively.

Numerous state-of-the-art recommendation frameworks employ deep neural networks in Collaborative Filtering (CF). In this paper, we propose a cross feature fusion neural network (CFFNN) for the enhancement of CF. Existing studies overlook either user preferences for various item features or the relationship between item features and user features. To solve this problem, we construct a cross feature fusion network to enable the fusion of user features and item features as well as a self-attention network to determine users’ preferences for items. Specifically, we design a feature extraction layer with multiple MLP (Multilayer Perceptrons) modules to extract both user features and item features. Then, we introduce a cross feature fusion mechanism for an accurate determination of the relationship between different user-item interactions. The features of users and items are crossly embedded and then fed into a prediction network. The attention mechanism enables the model to focus on more effective features. The effectiveness of CFFNN model is demonstrated through extensive experiments on four real-world datasets. The experimental results indicate that CFFNN significantly outperforms the existing state-of-the-art models, with a relative improvement of 3.0 to 12.1 percent on hit ratio (HR) and normalized discounted cumulative gain (NDCG) compared with the baselines.

In Industrial Wireless Networks (IWNs), the communication through Machine‐to‐Machine (M2M) is often affected by the noise in the industrial environment, which leads to the decline of communication reliability. In this paper, we investigate how to improve route stability through M2M in an industrial environment. We first compare different link quality estimations, such as Signal‐Noise Ratio (SNR), Received Signal Strength Indicator (RSSI), Link Quality Indicator (LQI), Packet Reception Ratio (PRR), and Expected Transmission Count (ETX). We then propose a link quality estimation combining LQI and PRR. Finally, we propose a Hybrid Link Quality Estimation‐Based Reliable Routing (HLQEBRR) algorithm for IWNs, with the object of maximizing link stability. In addition, HLQEBRR provides a recovery mechanism to detect node failure, which improves the speed and accuracy of node recovery. OMNeT++‐based simulation results demonstrate that our HLQEBRR algorithm significantly outperforms the Collection Tree Protocol (CTP) algorithm in terms of end‐to‐end transmission delay and packet loss ratio, and the HLQEBRR algorithm achieves higher reliability at a small additional cost.

Point-of-Interest (POI) demand modeling in urban regions is critical for building smart cities with various applications, e.g., business location selection and urban planning. However, existing work does not fully utilize human mobility data and ignores the interactive-aware information. In this work, we design a refined POI demand modeling framework, named RePiDeM, to identify region POI demands based on multi-source data, including cellular data, POI data, satellite image, geographic data, etc. Specifically, we introduce a Cellular Data (CD) based visit inference algorithm to estimate the POI visit probability based on human mobility and POI data. Further, to address the data sparsity issue, we design a multi-source attention neural collaborative filtering (MANCF) model to output region POI demands considering various aspect attention. We conduct extensive experiments on real-world data collected in the Chinese city Shenyang, which show that RePiDeM is effective for modeling region POI demands.

With the drastic increase in the number and capabilities of mobile devices, user demands for bandwidth-hungry applications such as video streaming and multimedia file sharing are pushing the limits of current cellular systems. Offloading resources sharing services from the cellular infrastructure to device-to-device (D2D) networks can improve spectrum efficiency and expand system capacity. This paper designs and implements new D2D networks formation mechanism based on commercial off-the-shelf smartphones, and data transmission mechanism among these devices using Wi-Fi Direct(WFD). In this system, we realize the single-hop and multi-hop real-time video streaming transmissions, and compare the performance between the multicast and the unicast transmission mechanisms. To demonstrate and evaluate the system, we implement a prototype system using various brand handsets with Andorid OS and conduct experiments. The results show that the system can transmit real-time videos at the bitrate of 2200kbps and frame rate (fps) of 25 for 4-5 receivers simultaneously within 75 meters in case of single-hop transmission. As for performing multi-hop transmission, the real-time videos can be transmitted to 1 recevier at the bitrate of 5000kbps and fps of 25.

Device-to-Device (D2D) communication is considered as an efficient approach to improve current wireless networks. However, for inter-group communication, since devices are using the same IP address segment, IP conflicts can be induced into D2D networks. To address this problem, we propose an Information-Centric resource sharing system for D2D communication network. In the system, both P2P and WLAN network adaptor interfaces are adopted simultaneously for establishing a D2D network. A socket reusing mechanism is proposed for inter-group communication. In addition, to improve content distribution and retrieval, we base our system on Information-Centric networking (ICN) paradigm. We design ICN flow tables on network nodes to realize request forwarding and data backtracking. To demonstrate the feasibility and effectiveness, we have implemented the system on commercial Android devices using Wi-Fi Direct technology. Experimental results show that the proposed Information-Centric resource sharing system is feasible for D2D network in terms of data transmission and energy consumption. The average latency for transmitting 1MB data is 257.23ms; i.e., the throughput is up to 31.1 Mbps. The average energy consumption for transmitting 100MB data is only about 0.66 mAh.

Mobile crowdsensing (MCS) is a new data collection paradigm profiting from the human-centric cyber social computing. However, due to the humans' uncontrollable mobility, it raises severe concerns of data redundancy and poor data quality. In this paper, we propose a novel data gathering architecture based on mobile edge computing (MEC), which distributes computing resources [edge nodes (ENs)] in the sensing scenarios close to the mobile users, and thus enables significant improvements to handle users' frequent location changes and reduce the specified quantity of sensing tasks. Based on the MEC-enhanced architecture, we design a quality-aware sparse data collection (QSDC) algorithm in the MCS systems. In the ENs' part, QSDC exploits the implicit correlation (IC) among the sensing data to reduce the data redundancy and selects the appropriate users' group to ensure the spatiotemporal coverage of sensing grids. In the cloud server part, QSDC leverages the compressive sensing to recover the sensing data in the whole sensing area with high data quality. Extensive experiments verify the performance of QSDC based on real data sets under different experiment settings and demonstrate the effectiveness and availability of QSDC.

with the strong impact of OTT (Over The Top) business in the mobile Internet era, operators urgently need to discover the user value information from massive data to help them provide personalized accurate services and expand business customer services. The construction of social user groups based on mobile communication data can help operators to accurately analyze customer social structures, thus promoting quality service and improving marketing quality. In this paper, we design a set of social group construction algorithm based on user behavior characteristics excavated from massive user data in mobile communication network. Due to the huge volume of mobile communication data sets, a parallel design based on MapReduce is exploited. The experimental results show that the ADBLINKw algorithm performs well on the efficiency and community detection quality.

With the integration of industrialization and informatization, there are a large number of embedded industrial wireless physical devices that are susceptible to malicious intrusion by malicious nodes within the industrial wireless networks, which leads to perform unique internal attacks, such as DOS attacks, selfish attacks, Sinkhole attacks and black hole attacks. In order to detect and resist external attacks, the SDN technology is introduced into the existing industrial wireless network and the network equipment is managed by a centralized controller. At the same time, the controller shields the differences between network devices in the network bottom layer. Open control enables network users to define their own network routing and forwarding strategies, making the network more flexible and intelligent. Moreover, SDN controller can make global routing strategy according to the global state of the network and the underutilized resources in the network we introduce SDN paradigm into the industrial wireless network and propose a secure routing mechanism for industrial wireless networks based on SDN. The internal malicious nodes are detected by computing the node comprehensive trust value of node. We design a biologic heuristic algorithm based on the foraging principle of physarum as a network security routing algorithm to calculate the network security transmission path. The performance of packet delivery rate and average end to end delay are compared and analyzed. The result shows that the routing mechanism depicted in the thesis has better performance, and the security of data transmission is greatly improved.