Search Results (92)

Task decomposition is a fundamental challenge in multi-agent collaborative maintenance systems, where unstructured natural language instructions must be precisely translated into logically coherent, executable sub-task sequences. This paper formulates task decomposition as a constrained optimal path search problem on a heterogeneous knowledge graph that encodes coal mine equipment topology, fault causality, and maintenance procedures. We construct a composite cost function that systematically integrates semantic similarity from graph neural network embeddings, relation-type weights, and structural path length, transforming instruction parsing into a mathematically tractable combinatorial optimization. The cost function is derived from the principles of shortest-path reasoning in knowledge graphs: the relational weights capture domain-specific association strengths, the semantic similarity term promotes contextually coherent chains, and the path-length penalty prevents unnecessarily long derivations. A multi-hop reasoning algorithm coupling heterogeneous graph convolution with beam search is developed to solve this problem efficiently, achieving high-quality approximate solutions while ensuring computational tractability. The reasoning process is inherently interpretable, as the optimal path directly maps to a traceable atomic task sequence with explicit dependency relations. A formal complexity analysis shows the algorithm scales as $O$($b$·$K$·$d_{max}$), where b is the beam width and d_max is the maximum node degree. Several theoretical properties of the proposed framework are further derived: the cost function is non-negative and strictly monotonic, optimal paths satisfy the optimal substructure property, cycle-free optimal paths always exist, and beam search can yield globally optimal solutions given a sufficiently large beam width. These theoretical conclusions establish mathematical guarantees for the presented decomposition framework. Experiments on 200 composite maintenance instructions with gold-standard annotations (inter-annotator agreement Cohen’s $\kappa =0.88$) demonstrate that the proposed method achieves 94.3% task sequence accuracy (95% CI: 91.2–96.8%) and 96.4% dependency accuracy (95% CI: 93.5–98.1%), substantially outperforming both a rule-augmented baseline (58.6%, 62.1%) and a GPT-4o few-shot chain-of-thought baseline (73.2%, 70.5%); McNemar’s test yields p < 0.001 for both comparisons. The average inference time is 29.7 ms (SD 2.1 ms), meeting stringent industrial real-time constraints. Ablation studies quantify the contribution of each cost function component and confirm the robustness of the chosen beam width and hyperparameters. When integrated into a full multi-agent system, the framework delivers end-to-end response time within 3 s (P50: 1.87 s, P95: 2.83 s) and maintains an 86.7% task success rate even under dual agent failures, validating the robustness of the proposed mathematical formulation. This work establishes a rigorous graph-theoretic foundation for instruction decomposition in multi-agent systems, with direct applicability to safety-critical industrial environments. Full article

Underwater acoustic networks are critical for applications such as environmental monitoring, infrastructure inspection, and autonomous underwater operations. In these scenarios, autonomous underwater vehicles (AUVs) increasingly operate in cooperative swarms, requiring efficient route-discovery and multi-hop communication. However, acoustic underwater communication imposes extremely constrained conditions, including low data rates, long propagation delays, limited bandwidth, and unstable channels. These constraints make routing overhead a dominant factor limiting network performance. In this work, we propose a systematic redesign of the Ad hoc On-Demand Distance Vector Routing (AODV) protocol tailored for ultra-low-rate underwater acoustic networks. The proposed approach combines three complementary design strategies: (i) architectural simplification through a layer-2 AODV variant that eliminates layer-3 overhead, (ii) protocol-level compression using the Static Context Header Compression and Fragmentation (SCHC) standard to minimize packet size, and (iii) acoustic channel-aware parameter tuning to account for long propagation delays and limited channel capacity. The approaches are designed and implemented in ns-3.43 using the underwater acoustic network (UAN) model and evaluated under reachability and scalability scenarios against the default AODV protocol. The results show that the proposed protocols reduce the hello message loss percentage from over 10% to below 3%, decrease route creation time from around 35s to around 15s, and significantly improve the packet delivery ratio under increasing traffic load. In high-load scenarios, the proposed methods consistently outperform default AODV due to reduced channel occupancy and lower collision probability. Full article

Aiming at the problems of the traditional Statistical Priority-Based Multiple Access (SPMA) protocol in multi-hop Flying Ad Hoc Networks (FANETs), such as single-dimensional queueing only according to priority, unreasonable First-In-First-Out (FIFO) scheduling, high timeout dropping probability of multi-hop forwarding packets, and insufficient utilization of channel opportunities, this paper proposes a multi-hop adaptive SPMA protocol (MA-SPMA) suitable for dynamic multi-hop scenarios. The protocol adopts the Neighbor-Priority Two-Dimensional Queueing (NPTQ) mechanism to store packets jointly according to the next-hop neighbor and priority. A Priority-Utility Dual-round Decision (PUDD) mechanism is designed: in the first round, candidate queues that meet channel load conditions are selected in parallel; in the second round, a utility function constructed by normalized delay, priority, and the end-to-end transmission success rate is used to select the optimal packet for transmission. Theoretical analysis shows that the time and space complexity of MA-SPMA are linearly related to the number of neighbor nodes, with controllable overhead, which is suitable for resource-constrained Unmanned Aerial Vehicle (UAV) platforms. In the MATLAB simulation environment, the Reference Point Group Mobility (RPGM) model is used to construct a multi-hop topology, and comparisons are conducted with two typical improved protocols for multi-hop networks: DCLS-SPMA and BiLSTM-SPMA. The results show that the proposed protocol can significantly improve the end-to-end transmission success rate and network throughput, with more obvious advantages in scenarios with a high proportion of multi-hop services. This paper provides an effective solution for Medium Access Control (MAC) protocol design in FANETs. Full article

Secure communication is one of the foundational requirements for modern smart grid operations, where Distributed Network Protocol 3 (DNP3) remains a primary protocol for monitoring and control. Key stakeholders in the smart grid—such as transmission operators, distribution operators, utilities, and balancing authorities—are increasingly required to strengthen their cybersecurity protections. They must maintain the reliability of communications across complex, bandwidth-constrained wide-area networks (WANs). This paper presents a practical evaluation of four DNP3 deployment models: DNP3 in its native unsecured form, DNP3 with Secure Authentication (SA), DNP3 over Transport Layer Security (TLS 1.3), and DNP3 protected using IPsec encryption at the network layer. The paper evaluates how each security approach performs in a production-like smart grid environment. This paper primarily focuses on the widely deployed DNP3-SA model, which provides authentication and integrity but not confidentiality. Although newer Secure Authentication versions exist, including SAv6, they are still less common in operational utility environments. Experiments are conducted using a hardware-informed simulation testbed that emulates realistic utility WAN conditions, including multiple routing hops, MPLS as a transport for packets, and varied link capacities. Performance is assessed in terms of bandwidth efficiency, round-trip latency, and additional computational overhead on the field devices. Based on the findings, the paper presents a decision framework to help utilities select DNP3 security implementations that align with regulatory expectations and operational reliability. Full article

The Constrained Application Protocol (CoAP) is widely adopted to ensure end-to-end reliability in resource-constrained Artificial Intelligence of Things (AIoT) and Wireless Sensor Networks (WSNs). However, CoAP’s default retransmission timeout (RTO) mechanism lacks algorithmic responsiveness under volatile channel conditions, and state-of-the-art benchmarks like CoCoA+ and FASOR often suffer from over-conservative backoff states or destabilizing retransmission storms. To overcome these operational bottlenecks, this paper proposes a novel dual-adaptive Dynamic RTO algorithm specifically engineered for heterogeneous IoT deployment scales. The proposed framework dynamically adjusts its parameter inspection cycle (N) based on instantaneous round-trip time (RTT) variance while simultaneously scaling its tuning coefficient (α) in response to real-time packet loss indicators. To rigorously validate the algorithmic resilience, performance evaluations were conducted within a highly volatile network environment governed by the Gilbert–Elliott dynamic loss model across multi-hop linear (1 × 6) and grid (3 × 6, 5 × 6) topologies. Experimental results demonstrate that the proposed Dynamic RTO consistently optimizes the throughput–latency trade-off, achieving a total communication time of 25.92 s in complex grids—outperforming CoCoA+ and FASOR by 14.28% and 8.89%, respectively. Furthermore, the proposed mechanism significantly curtails transmission overhead, restricting the cumulative retransmission footprint to just 59 counts under severe localized impairments, thereby establishing a scalable, resource-efficient, and empirically robust transport-layer solution for next-generation edge-computing infrastructures. Full article

The rapid expansion of wireless connectivity has led to vast amounts of radio-frequency (RF) energy being continuously radiated into the environment, much of which is dissipated due to severe propagation losses. Recycling this otherwise wasted RF energy is, therefore, a critical enabler for energy-efficient and sustainable wireless systems. RF energy harvesting nodes and passive backscatter communication devices provide promising solutions by enabling battery-less or low-maintenance operation for future green networks. However, both paradigms suffer from fundamental limitations, including restricted communication range, near–far effects, and insufficient harvested energy at extended distances. This review examines how cooperative relays can address these challenges by harvesting ambient RF energy and assisting both information transfer and power delivery. From a communication perspective, we review cooperative backscatter communication and harvest-then-transmit (HTT) protocols, highlighting how multi-hop relaying significantly extends coverage and improves throughput for energy-constrained devices. Particular emphasis is placed on tag-to-tag (T2T) backscatter systems, relay-assisted architectures, decode-and-forward and amplify-and-forward protocols, and optimal multi-access time allocation strategies that mitigate the doubly near–far problem in passive networks. From an energy-transfer perspective, the review is structured around three pillars: wireless power transfer (WPT), multi-hop energy transfer (MET), and integrated charging-and-sensing frameworks. We discuss relay deployment and placement optimisation, UAV-enabled mobile energy relays, waveform and beam-forming design, and the transition from idealised linear harvesting models to practical nonlinear rectification models. Key practical constraints, such as regulatory limits, safety compliance, self-interference, protocol overhead, synchronisation, and imperfect channel knowledge, are systematically reviewed. The paper concludes by identifying the scalability limits of multi-hop cooperative systems, outlining how the joint optimisation of energy relaying and cooperative communication enables RF energy recycling for sustainable, low-carbon wireless networks and highlighting open challenges and future research directions. Full article

University admissions services must answer large volumes of applicant questions that differ substantially in complexity, ranging from repetitive FAQ-type requests to multi-step questions involving programs, entrance exams, admission rules, passing scores, and temporal comparisons. Ungrounded large language model responses are risky in this domain because answers must be factually correct, source-based, and consistent with official institutional data. This paper presents a three-tier hybrid architecture for an admissions dialogue assistant that combines deterministic FAQ matching, hybrid retrieval-augmented generation, and graph-grounded retrieval for complex queries. The first tier, Hash-FAQ, returns verified answers for frequent intents using normalized keys, hash-based lookup, near-duplicate fingerprinting, and semantic similarity checks. The second tier applies hybrid RAG based on BM25 retrieval, vector search, rank fusion, and optional cross-encoder reranking. The third tier uses GraphRAG to extract a constrained k-hop subgraph from a Neo4j knowledge graph built from relational admissions data and document-derived facts. All tiers are synchronized through a versioned indexing pipeline with shadow collections and atomic switching across lexical, vector, FAQ, relational, and graph stores. The system was evaluated using real admissions-campaign traffic and a labeled subset of applicant queries. Tier 1 resolved 68.7% of requests with low latency, while the GraphRAG branch improved factual accuracy with attribution on multi-step queries from 0.55 to 0.91 compared with the non-graph baseline. The main contribution of the study is a production-oriented, cost-aware retrieval-and-generation architecture that links tiered routing, synchronized knowledge publication, source attribution, and operational evaluation for applicant-facing institutional dialogue systems. Full article

Future networks should provide access to cloud-based content to subscribers in mountainous region. This research proposes a network architecture incorporating mountain data centers that provide content access via caching in a capital-constrained context. It also discusses the aspects of the power system supporting the network architecture. The use of content caching reduces content access latency. The research recognizes that mountains can host computing platforms while ensuring low to moderate operational costs. Using the proposed approach also reduces the number of network hops and associated power consumption by (26–37)% and (17–25)% on average, respectively. Full article

Unmanned aerial vehicle (UAV) swarms face significant performance degradation when operating on a single-frequency channel, as the Statistical Priority-based Multiple Access (SPMA) protocol suffers from intensified contention conflicts due to scarce frequency resources. To address this issue, this paper proposes a joint clustering and power optimization method for the SPMA protocol in frequency-constrained scenarios. First, a utility function centered on the end-to-end transmission success rate is constructed, and the optimal clustering scheme selection is formulated as a constrained combinatorial optimization problem. Second, a three-stage heuristic algorithm is designed; all iterations are executed virtually at network initialization. K-means is used to perform initial clustering and determine the minimum power required for intra-cluster services, GPSR is used to establish multi-hop routes for inter-cluster services, and the ant colony algorithm refines the transmission power of forwarding nodes, achieving joint optimization of cluster structure and power configuration. Simulation results show that, compared with the standalone SPMA protocol and the typical clustering algorithm ICW, the proposed algorithm reduces transmission power by 90.4% relative to SPMA (with slightly higher power than ICW) and achieves a comprehensive improvement over both benchmarks. Specifically, the success rate is improved by 63.5% compared with SPMA and 162.3% compared with ICW under high traffic loads, thus achieving a well-balanced compromise between power consumption and transmission reliability. This verifies the feasibility and effectiveness of the proposed optimization method in frequency-constrained scenarios. 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

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

Satellite communication protocols are increasingly optimized in software-defined, multiorbital networks that combine broadband satellite systems, non-terrestrial 5G components, and inter-satellite transport. This review examines intelligent optimization across the physical, medium-access, network, and transport layers, with emphasis on what can be measured, what can be controlled, and what can be safely deployed under standards and operational constraints. This paper first positions the literature across DVB/ETSI, 3GPP NTN, CCSDS/DTN, LEO routing, and recent AI and digital-twin research. It then links standards-defined control surfaces to layer-specific measurements, feedback delays, and safety constraints and compares optimization families using deployment-relevant criteria such as observability, runtime predictability, verification burden, and robustness. The review argues that the central challenge is not only a simulation-to-reality gap but an evidence gap between experimental gains and operational trust. To address this gap, this paper analyzes delayed observability, rare events, bounded onboard compute, action surface mismatch, certification, and security; formalizes a generic constrained optimization problem with delayed observations and standards-compliant actions; and proposes a digital-twin-assisted research methodology supported by a worked beam-hopping example. The main conclusion is that future progress is most likely to come from hybrid, standards-compliant, and twin-assisted optimization methods whose performance claims are tied to calibration, traceability, and explicit rollback logic. Full article

Water conservancy safety management faces several challenges. These include the integration of multi-source heterogeneous data and inefficient knowledge utilization. To address these issues, this study proposes a knowledge graph (KG) construction method that combines ontology modeling with large language models (LLMs). First, an ontology for water conservancy facility safety is constructed, encompassing four core elements: agencies and personnel, engineering equipment, risks and hidden dangers, and systems and processes. Subsequently, a KG-LLM-GraphRAG architecture is designed, which optimizes the knowledge extraction effectiveness of LLM through ontology-constrained prompt templates and utilizes the Neo4j graph database for knowledge storage and multi-hop reasoning. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches in entity-relationship extraction tasks. The resulting KG supports hazard identification, emergency decision-making, and knowledge reuse, offering an efficient tool for organizing and reasoning in water conservancy safety management, strongly propelling the digital transformation of the water conservancy industry. 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

In maritime non-terrestrial networks, long-distance links and resource heterogeneity give rise to significant system transmission delays. As an effective mechanism for alleviating link pressure and enhancing efficiency, caching has been widely employed in this scenario. However, existing studies have predominantly focused on the separate optimization of caching or beamforming, often overlooking the joint potential of the physical and network layers as well as the impact of dynamic channel variations. This paper develops a system model for cache-assisted maritime non-terrestrial networks that incorporates hit status, two-hop link characteristics, and a dynamic channel switching mechanism to characterize link instability. Accordingly, an optimization framework for the joint design of caching and beamforming is presented with the aim of minimizing average delay. The proposed algorithm decomposes the mixed non-convex problem into two sub-problems through a hierarchical alternating strategy, and integrates semidefinite relaxation, successive convex approximation, and a greedy mechanism to attain an efficient solution. Numerical results verify the rapid convergence of the joint design of caching and beamforming algorithm and demonstrate that the semidefinite relaxation relaxation achieves a rank-one recovery probability of over 92.8%, ensuring near-optimal beamforming design. Simulation results demonstrate that the joint design robustly outperforms various state-of-the-art benchmarks in delay performance and effectively circumvents physical-layer bottlenecks as network scales expand or satellite antenna resources become constrained. More importantly, the proactive caching design maintains a significant “robustness gap” against isolated optimization schemes, thereby offering solid theoretical support and a practical pathway for cross-layer collaborative optimization in integrated air-ground-space communication systems. Full article