Search Results (173)

The rapid growth of maritime communication networks introduces significant challenges to routing optimization, arising from large-scale network topologies, highly dynamic node mobility, and stringent real-time communication requirements. Conventional routing algorithms often exhibit limited scalability and poor adaptability when facing frequent topology variations. The routing problem is modeled as a multi-agent distributed decision-making process, where each node acts as an autonomous agent. In this paper, we propose a graph-partitioned hierarchical graph representation learning framework (GP-HGRL) for scalable and continual routing optimization in dynamic maritime networks. By explicitly modeling the network as a time-evolving graph, GP-HGRL first partitions the global topology into topology-aware subgraphs, enabling distributed learning and inference with reduced computational complexity. A hierarchical graph neural network architecture is then developed to jointly capture intra-subgraph local structures and inter-subgraph global dependencies, producing topology-aware embeddings for routing decision-making. Based on the learned representations, a deep reinforcement learning policy is employed to perform distributed next-hop routing decisions. To effectively handle topology dynamics induced by node mobility and link variations, we further introduce a continual graph learning mechanism that selectively updates representations and routing policies only within affected subgraphs, thereby avoiding costly global retraining and preserving routing stability. Extensive simulations demonstrate that GP-HGRL consistently outperforms shortest-path routing and existing reinforcement learning-based approaches in terms of packet delivery ratio, retransmission rate, packet loss, and training efficiency under various network loads and dynamic conditions. Full article

In Low Earth Orbit Beam-Hopping Satellite Systems (L-BHSS), co-channel interference among beams severely degrades communication quality. To address the inter-beam co-channel interference avoidance problem, this paper proposes a Parameter-Sharing Multi-Agent Deep Deterministic Policy Gradient-Based Beam Geometry Multi-Parameter Optimization Algorithm (PS-MADDPG-BGMPOA) for the joint optimization of satellite beam geometric parameters. The effects of free-space path loss, atmospheric impairments, and Rician fading are comprehensively considered, and a beam geometric multi-parameter optimization model is formulated with the objective of maximizing the long-term Signal-to-Interference-plus-Noise Ratio (SINR), incorporating beamwidth, beam center offset from the satellite nadir direction angle, inter-beam separation angle, and beam activation states. To tackle the resulting high-dimensional mixed action space, the proposed algorithm employs parameter sharing and grouped decision-making, which alleviates the dimensionality explosion problem and decouples the network scale from the number of beams, enabling efficient cooperative optimization with reduced training complexity. Simulation results show that, under various channel conditions and beam configurations, the proposed method effectively enhances communication quality and spectral efficiency while exhibiting superior real-time performance and stability. Full article

Time-Sensitive Networking (TSN) with Cyclic Queuing and Forwarding (CQF) is a critical mechanism for ensuring deterministic forwarding. However, existing incremental schedulers typically rely on single-dimensional heuristics, which fail to address the coupled impact of traffic characteristics and spatiotemporal resource distribution. This limitation leads to suboptimal scheduling success, especially under complex topologies and high network loads. To address this, we propose TMCOA–MFS, a joint incremental scheduling framework that integrates the Triple-Mode Cooperative Optimization Algorithm (TMCOA) with a Multi-Feature Scheduling (MFS) strategy. The logic of our approach is twofold: First, to balance spatial resource distribution, we introduce the TMCOA—inspired by table-tennis offensive–defensive behaviors—to optimize path selection by minimizing port-load variance and escaping local optima through a three-mode population partition. Second, building upon the optimized spatial paths, the MFS strategy is employed to resolve temporal scheduling conflicts. By computing a composite priority score that accounts for path hops, offset configuration difficulty, and flow size, MFS enables a robust incremental offset search with integrated feasibility checking. Extensive simulations on benchmark functions and diverse TSN scenarios demonstrate that the TMCOA offers superior convergence and stability. More importantly, the integrated TMCOA–MFS framework significantly enhances scheduling success rates and load balancing, effectively overcoming the bottlenecks of high-load and topologically complex environments. Full article

For large-scale embedded sensor-actuator networks, such as robotic swarms deployed over vast areas and other embedded intelligent devices, end-to-end message exchange is often impossible due to their limited communication range, power constraints, and device mobility. Devices, thus, rely on multi-hop relaying, exposing them to Man-in-the-Middle (MitM) attacks where compromised relays tamper with, forge, or inject false messages. The existing countermeasures, including end-to-end encryption or Byzantine consensus, involve high overhead while requiring global coordination and, thus, renders them impractical for time-sensitive message exchange in embedded intelligence. Security management on communication among embodied devices is highly desired. To address this challenge, we propose Reputation-Guided Dynamic Relay Selection (RDRS), a lightweight, distributed countermeasure against MitM attacks that leverages interactive feedback to evaluate reputation of embedded devices. Specifically, each device maintains reputation scores updated via recent interaction success rates with decay factors to counter dynamic adversaries. During exchanging messages, embedded devices select next-hop neighbors weighted by reputation scores, effectively bypassing malicious devices without explicit detection or in-path verification. Comprehensive simulations in embedded sensor-actuator networks demonstrate that RDRS reduces tampering success rate (TSR) by 80–95% compared to the baselines, martians request satisfaction rate (RSR) above 79% even at 40% malicious nodes, and achieves lower delay 64% with comparable overhead. Full article

LoRa mesh networks enable long-range, low-power connectivity but are constrained by very low bitrate, spreading-factor-specific SNR thresholds, and regional duty-cycle limits. This article presents a snapshot routing framework that separates feasibility from optimality. Feasibility is enforced as hard constraints-only radio options that satisfy SNR thresholds (with safety margin) and fit within remaining duty windows, which are admitted using an Okumura–Hata backbone with a model-agnostic specialization for link geometry. Optimality is achieved on a spreading-factor-expanded directed graph, where each feasible SF is represented as a distinct edge, and a composite, dimensionless hop metric balances airtime-driven energy expenditure, current and incremental duty usage, and optional quality penalties. The method yields per-hop SF selection via shortest-path computation and supports rapid re-planning without event-level simulation. Snapshot-based evaluation indicates improved control of airtime, duty exposure, and energy, providing a practical basis for multi-criteria routing in LoRa mesh networks with applicability to airborne and infrastructure-sparse deployments. Full article

Dense retrievers rely heavily on high-quality training triplets, yet existing data construction strategies remain inadequate for reasoning-intensive retrieval tasks involving multi-hop reasoning, entity relation tracing, and implicit evidence composition. Positive samples are often based on shallow semantic relevance and fail to capture explicit reasoning chains, while negative samples are typically sampled from lexical overlap or random candidates and therefore provide limited supervision for learning clear decision boundaries. To address these issues, we propose S-Gens, a structure-aware synthetic data generation framework for enhancing reasoning-intensive dense retrieval. S-Gens uses relation paths in an external knowledge graph to synthesize queries and structurally consistent positive samples, and further constructs semantically similar but structurally inconsistent hard negatives. To improve data reliability, we introduce a Siamese graph neural network-based consistency filtering mechanism. Because S-Gens operates entirely during offline supervision construction, it remains model-agnostic, preserves the original inference architecture, and is complementary to graph-guided retrieval or RAG pipelines that inject structure online. Experiments on five benchmark datasets show that S-Gens consistently improves multiple trainable retrievers, with the largest gains on multi-hop reasoning tasks such as WebQSP and HotpotQA. These results indicate that structure-aware synthetic supervision can effectively improve dense retrieval in reasoning-intensive settings. Full article

Vehicular Ad hoc Networks (VANETs) feature high node mobility and volatile topologies, rendering the conventional Greedy Perimeter Stateless Routing (GPSR) protocol prone to weak link stability and inefficient route discovery due to its lack of direction awareness. Existing direction-aware improvements typically rely on multi-criteria weighting or clustering, introducing heavy parameter fusion and computational overhead that conflict with the resource-constrained nature of onboard units. To overcome these limitations, this paper presents VANET-GPSR+, a lightweight enhanced routing protocol. Its key novelty is that it discards multi-parameter fusion and relies solely on movement direction, supported by a synergistic framework of three lightweight mechanisms: direction-aware neighbor classification to prioritize nodes with consistent trajectories, adaptive greedy forwarding region expansion in sparse and dynamic networks, and path deviation angle-based next-hop selection. This work builds a probabilistic link lifetime model that theoretically quantifies the stability gains of direction awareness—a novel theoretical foundation. Comprehensive urban and highway simulations show that VANET-GPSR+ improves the packet delivery ratio by 16.3% and reduces end-to-end delay by 27.5% compared with standard GPSR, and it outperforms both OP-GPSR and AK-GPSR. It introduces negligible CPU and memory overhead, with CPU usage over 50% lower than the two benchmark protocols at 80 vehicles/km, and demonstrates strong robustness against varying beacon intervals and communication radii. Retaining GPSR’s stateless and distributed traits, VANET-GPSR+ delivers substantial performance gains with minimal overhead, serving as an efficient routing solution for highly dynamic VANETs. Full article

Retrieval-Augmented Generation (RAG) has become an important paradigm for knowledge-intensive natural language processing, as it enables Large Language Models (LLMs) to access external evidence beyond their parametric memory. However, existing RAG pipelines often rely on static user queries and predominantly semantic matching, which makes them less effective in data-intensive scenarios that require structured knowledge and multi-hop evidence aggregation. To address these limitations, we propose DMAR, a dynamic multi-anchor retrieval framework for retrieval refinement in knowledge-augmented generation. DMAR first identifies high-confidence anchor documents from an initial candidate pool through a dual-path evaluator that combines semantic relevance with knowledge-graph-based structural association. The selected anchors are then used to guide generative query reformulation, producing an enriched query for second-stage retrieval, followed by fidelity-controlled reranking to preserve alignment with the user’s original intent. We further model structural relevance using Subgraph Shapley Values and a learnable Siamese GNN-based similarity module. Experiments on five knowledge-intensive benchmarks, covering open-domain question answering, multi-hop reasoning, and fact verification, show that DMAR consistently improves retrieval and downstream answer quality over strong baselines. For example, DMAR achieves an F1 score of 62.5% on HotpotQA and 79.0% on TriviaQA. These results demonstrate that dynamically integrating semantic retrieval, structural knowledge, and query reformulation is an effective approach for robust knowledge-augmented NLP systems. Full article

The knowledge graph plays an important role in the construction of artificial intelligence applications. However, the incompleteness of the knowledge graph seriously affects the performance of downstream applications. The problem has fueled a lot of researches on knowledge graph completion (also known as the tasks of link prediction). Reinforcement learning-based multi-hop reasoning that formulates link prediction as a sequential decision problem has also become an interesting and promising approach. Nevertheless, in an incomplete knowledge graph environment, the policy-based agent might travel a large number of low-quality or spurious search trajectories, which inhibits the model performance. Therefore, in this paper, we propose a path and structural features-enhanced reinforcement learning model (referred as PGATRL). First, we leverage the path constraint resource allocation algorithm to mine high-quality inference paths, which can be employed to pre-train the LSTM path encoder module in the reinforcement learning architecture, and thus play a role in guiding the agent’s action decision-making. Second, we exploit the adapted graph attention networks to encode the local structural features of an entity, which can provide more evidence for the agent to find a more suitable reasoning path. With extensive experiments on several benchmark datasets, our proposed approach gains significant improvements compared with the state-of-the-art baselines. Full article

Retrieval-augmented generation (RAG) has shown strong potential for knowledge-intensive tasks, yet its performance degrades sharply when applied to structured long-context documents in power systems, where dense entity–relation dependencies, cross-document references, and strict traceability requirements exist. To address this Structured Long-Context RAG (SLCRAG) challenge, this paper proposes a hierarchical graph-enhanced RAG (HG-RAG) framework tailored for power system question answering. HG-RAG constructs a globally consistent knowledge graph via sliding-window entity–relation extraction to mitigate semantic fragmentation, and employs multi-granularity structured indexing for precise entity/relation retrieval. A hierarchical structured retrieval mechanism with multi-hop expansion and semantic distillation maximizes recall while minimizing redundancy. Furthermore, a regex-enhanced retrieval module records authoritative file_path provenance and constrains downstream retrieval to the same source documents, effectively eliminating cross-document interference—especially in cases where different documents contain similar entities and relations. Combined with version control and deduplication-merging, HG-RAG supports incremental knowledge updates with minimal forgetting and negligible token overhead. Experimental results on a domain-authentic power system QA dataset demonstrate that HG-RAG outperforms LightRAG and GraphRAG, achieving up to 85.47% accuracy in short-answer tasks with significantly lower token consumption. Ablation studies confirm that semantic distillation primarily improves precision and efficiency, while regex-enhanced retrieval safeguards recall in edge cases. Full article

Multi-hop question answering (MHQA) requires models to retrieve and reason over evidence distributed across multiple documents, which remains challenging for conventional retrieval-augmented generation (RAG) approaches. Although RAG improves factual grounding by incorporating external knowledge, flat retrieval strategies often struggle to maintain coherent reasoning chains when implicit dependencies among entities and documents are involved. This paper presents a query-driven dual-layer graph retrieval framework for MHQA. The framework operates on a unified heterogeneous graph integrating entities, relations, and supporting texts, and dynamically constructs candidate subgraphs through joint retrieval over entities and relations, complemented by lexical retrieval signals. Reasoning paths are refined by combining structural strength modeling with contrastive learning-based path scoring, and an adaptive pruning strategy is employed to regulate evidence scale according to query complexity and path score distributions. Experiments on HotpotQA and 2WikiMultihopQA show that the proposed framework achieves higher EM and F1 scores than existing RAG and graph-based retrieval methods, particularly in complex multi-hop scenarios. These results indicate the importance of structured and query-adaptive evidence organization for multi-hop reasoning. Full article

Knowledge graph completion via link prediction is critical for intelligent equipment maintenance systems to support accurate fault diagnosis and maintenance decision making. However, existing approaches struggle to simultaneously capture local structural dependencies and perform effective multi-hop reasoning due to limited receptive fields or inefficient path exploration mechanisms. Traditional path-based methods implicitly assume path symmetry, treating all reasoning chains equally without considering their task-specific relevance. To address this issue, we propose a Graph Attention Network (GAT)-guided semantic path reasoning framework that breaks this symmetry through attention-driven asymmetric weighting, integrating local structural encoding with global multi-hop inference. The key innovation lies in a target-guided biased path sampling strategy, which transforms GAT attention weights into probabilistic transition biases, enabling adaptive exploration of high-quality semantic paths relevant to specific prediction targets. GATs learn importance-aware local representations, which guide biased random walks to efficiently sample task-relevant reasoning paths. The sampled paths are encoded and aggregated to form global semantic context representations, which are then fused with local embeddings through a gating mechanism for final link prediction. Experimental evaluations on FB15k-237, WN18RR, and a real-world equipment maintenance knowledge graph demonstrate that the proposed method consistently outperforms state-of-the-art baselines, achieving an MRR of 0.614 on the maintenance dataset and 0.485 on WN18RR. Further analysis shows that the learned path attention weights provide interpretable asymmetric reasoning evidence, enhancing transparency for safety-critical maintenance applications. Full article

Recent GraphRAG methods based on knowledge graphs (KGs) primarily rely on either under-reasoning or a structural path-level retriever, which prevents them from jointly capturing fine-grained semantic relevance and explicit multi-hop reasoning paths. This separation often results in semantic mismatch—where logical links are missing—or structural over-constraint in reasoning— where rigid dependencies limit flexible reasoning—thereby degrading both answer accuracy and the reliability of evidence in complex KGQA tasks. To address these issues, we propose HybRAG, a hybrid retrieval framework that synergistically integrates a semantic node-level retriever and structural path-level retriever. HybRAG constructs a hybrid subgraph that jointly reflects the semantic proximity of entities and the relational structures encoded in the KG. Furthermore, we incorporate retrieval-augmented fine-tuning, which enables the model to internalize advanced reasoning strategies for interpreting disparate semantic and structural signals, rather than merely memorizing domain facts. Through extensive experiments on the WebQSP and CWQ benchmarks, we demonstrate that HybRAG effectively bridges the gap between LLM-centric semantic approaches and GNN-centric structural approaches, outperforming single-retriever baselines. Our findings, including detailed sensitivity and ablation analyses, provide empirical evidence that the systematic alignment of semantic and structural signals is essential for ensuring the reasoning reliability and scalability of next-generation GraphRAG systems. Full article

Graph-enhanced Retrieval-Augmented Generation (RAG) frameworks, such as GraphRAG, improve large language model (LLM)-based question answering (QA) by constructing and leveraging structured, knowledge-condensed graph information. However, they still face challenges in complex multi-hop reasoning tasks and often incur substantial time and resource costs, resulting in low efficiency. To address these limitations, we propose DualGraphRAG, a dual-view graph-enhanced RAG framework designed to achieve both high QA performance and computational efficiency for complex reasoning over open-domain corpora. Specifically, DualGraphRAG constructs a knowledge graph (KG) by automatically extracting triples from unstructured text using LLMs, and embeds KG nodes with unified text embeddings. For each query, multiple types of KG nodes are generated through a dedicated query enhancement module. Based on these nodes, DualGraphRAG employs a dual-view retrieval strategy to retrieve both one-hop triples that capture local context and shortest paths that compress global connectivity information, thereby facilitating answer generation. Experimental results show that, compared with NaiveRAG, GraphRAG, and LightRAG, DualGraphRAG achieves the best or competitive performance on benchmark datasets and significantly improves efficiency. Overall, DualGraphRAG organizes and exploits KG information in a dual-view manner, leveraging triples and shortest paths to offer a reliable and efficient framework for open-domain QA with complex multi-hop reasoning. Full article

Cross-medium acoustic–radio collaborative networks enable integrated communication among underwater, surface, and aerial nodes for marine observation and detection. However, heterogeneous propagation characteristics of acoustic and radio channels significantly degrade the performance of conventional single-medium routing protocols, resulting in excessive control overhead, a low packet delivery ratio (PDR), and high latency. To address these challenges, this paper proposes an optimized AODV protocol for Cross-medium Acoustic–Radio Collaborative Networks (CACN-OAODV). The proposed protocol incorporates a medium-aware routing initiation mechanism to reduce unnecessary broadcasts, a link stability factor that jointly considers hop count and channel quality for reliable path selection, and a lightweight control optimization scheme to limit routing overhead in acoustic environments. Extensive simulations conducted in NS-3 with realistic multi-channel propagation models demonstrate that CACN-OAODV significantly outperforms the standard AODV protocol, achieving improved PDR, higher throughput, and reduced end-to-end delay. These results indicate that CACN-OAODV provides an effective routing solution for heterogeneous cross-medium marine communication networks. Full article