MAPS‐PBFT is proposed, a mobility‐aware orchestration layer around unmodified PBFT, a mobility‐aware orchestration layer around unmodified PBFT that discriminates between viable and non‐viable committees.
Abstract
PBFT provides deterministic finality under Byzantine faults but assumes stable committee membership, an assumption that breaks in vehicular networks where mobility‐driven churn can render committee members unreachable mid‐protocol. Existing IoV adaptations filter participants by trust or reputation, or modify the protocol's internal structure, yet none assess whether a selected committee will survive the full consensus execution window. We propose MAPS‐PBFT, a mobility‐aware orchestration layer around unmodified PBFT. The framework explicitly partitions the fault budget into a Byzantine slice be$$ {b}_e $$ and a mobility‐loss slice he$$ {h}_e $$ , treating mobility‐induced unreachability as crash‐equivalent failure. Committee selection is formulated as a survivability‐constrained problem over a logical multi‐hop committee graph: candidates are scored by beacon freshness, multi‐hop communication delay, predicted displacement, and inter‐vehicle proximity, then assembled via greedy incremental construction that rewards intra‐committee reachability. A model‐based launch score, derived from a joint formulation of mobility and Byzantine‐membership risk, gates each consensus instance through a user‐configurable threshold. To make the scope of our claims explicit, we distinguish components that are theoretically developed (the joint Monte Carlo launch score, the mobility‐loss budget, and the view‐change–inclusive execution horizon), fully implemented (mobility‐aware committee selection and epoch orchestration around unmodified PBFT in a full IEEE 802.11p co‐simulation), and approximated in the present experiments (a deterministic proxy for the candidate score, with the launch gate evaluated post hoc and view changes disabled). A 756‐run co‐simulation using OMNeT++, INET, Veins, and SUMO across three topologies and four vehicle densities shows that MAPS outperforms six baselines, improving average success rate by +30.7% over random, +8.8% over nearest, and +10.2% over connectivity‐only selection, with advantages that widen as committee size grows. Post hoc threshold analysis confirms that the launch score discriminates between viable and non‐viable committees, raising conditional success from 76.0% to 92.8% at a moderate gate threshold.
This work introduced an integrated cross‐layer framework featuring three innovative algorithms: mobility‐aware black hole clustering (M‐BHC), energy‐aware piranha optimization algorithm (EPOA), and cross‐layer multi‐attribute blockchain routing with congestion control (CL‐MABRC).
K. Satheshkumar, S. Ramalingam, A. Suresh Babu et al.· International Journal of Com...· 0 citations
Results show that topology‐aware cooperative learning can provide a scalable and practical solution for reliable UAV communication in future intelligent aerial and 6G‐enabled networks, particularly in dense deployments.
V. Nam, A. Chehri, Weiwei Jiang et al.· Expert Syst. J. Knowl. Eng.· 0 citations
Route decisions in mobile ad hoc networks must consider preserving battery energy, resisting forwarding manipulation as well as being tolerant of mobility. Here we propose BlockLLM-E2MR, a blockchain-verified large-language-model-guided multipath routing framework where the language model cannot directly implant a route. Paths violating residual-energy and trust constraints first turn infeasible due to a deterministic feasibility gate, then the surviving paths are ranked with respect to a frozen LLM preference vector, followed by only storing the route digest and trust update as determined by a permissioned committee. Routing objective includes transmission energy, expected delay, posterior misbehavior risk, mobility exposure, residual-energy imbalance distance between any 2 nodes in same cluster and also non-linear interaction penalties. A 1000 m × 1000 m region with 60 nodes configured for mobility between 1–10 m/s, 4096-bit packets, and will execute over independent runs (N = 24) where a fraction of the nodes have been corrupted (ft= [0%,30%]). In an environment with 30% malicious nodes, BlockLLM-E2MR achieves a packet-delivery ratio of 46.27% and consumes 66.270 mJ per offered packet; compared to the stated model, this is a delivery improvement of 8.41 percentage points combined with an energy reduction of 1.35%. Both delivery (p=0.00108) and energy (p=8.07e-08) are impactful hence, have their pairwise comparisons done against the blockchain-based trust routing still significant in favour of delivery and energy respectively. The study can be independently reproduced by a worked example of route selection, complete parameter table, confidence intervals and ablation logic are explained here along with complexity bounds, ledger break-even inequality.
Unknown authors· International Journal of App...· 0 citations
Rogue Base Stations (RBS) remain a persistent security threat to fifth-generation (5G) and emerging sixth-generation (6G) cellular systems by impersonating legitimate infrastructure and exploiting vulnerabilities in pre-authentication signaling and mobility procedures. The risk is particularly critical in vehicular and Vehicle-to-Everything (V2X) environments, where high mobility and millisecond-scale handover operations tightly couple communication reliability with safety-critical control functions. Although prior surveys examine LTE identity catchers and general cellular security threats, they rarely evaluate RBS detection under vehicular mobility dynamics or within the latency and reliability constraints of Ultra-Reliable Low-Latency Communication (URLLC) services. In addition, the limited availability of realistic measurement report (MR) datasets have hindered reproducible benchmarking of data-driven detection methods. This article presents a vehicular-oriented survey of RBS detection in 5G and beyond networks, explicitly addressing mobility-constrained detection, handover-security interactions, and V2X safety requirements that are not systematically addressed in prior surveys, which primarily focus on pre-5G threat models, IMSI-catcher attacks, or general cellular security. We introduce a method-centric taxonomy that organizes existing approaches into five families based on their primary evidence sources and inference mechanisms: signal anomaly detection, protocol and traffic analytics, RF fingerprinting, network-level frameworks, and machine-learning-based detection. Using a PRISMA-compatible structured literature review across 102 included studies and a structured comparative evaluation framework, each family is analyzed across detection latency, computational overhead, robustness to mobility, false alarm susceptibility, and feasibility within quantified pre-handover decision windows. Direct cross-study quantitative comparison is precluded by heterogeneous reporting conventions across the surveyed literature; the framework, therefore, provides structured qualitative synthesis and indicative performance ranges rather than pooled empirical estimates. The analysis reveals that no individual technique satisfies vehicular URLLC constraints in isolation, motivating layered architectures combining lightweight UE-side detection with edge-assisted and operator-level analytics. A scenario-driven safety analysis links detection error rates to operational consequences across five V2X use cases under varying URLLC severity levels. The survey formalizes evaluation criteria for MR-driven detection and highlights realistic MR generation as a foundation for reproducible evaluation and cross-study comparison in next-generation vehicular communication systems.
Roland Lamptey, M. Saedi, V. Stankovic et al.· IEEE Open Journal of the Com...· 0 citations
Maritime emergency networks require routing policies that provide timely and secure packet delivery under sparse deployment, mobility-induced topology variation, heterogeneous node ownership, and potential adversarial interference. Existing approaches usually optimize communication efficiency or trust evaluation separately, offering limited support for mission-priority traffic under constrained link resources. This article proposes a layered trust-aware routing framework that couples task-hierarchical service control with security-aware deep reinforcement learning. The method first applies global pre-screening to remove infeasible or low-value relay candidates according to policy constraints, resource quotas, link load, energy, mobility, and geometric progress. It then uses a dynamic weighted fusion DQN to select the next hop from the screened candidates based on behavioral reputation, jurisdictional/policy attributes, geo-situational awareness, and Network-Operational Safety. Priority-aware scheduling, retransmission control, and task-aware reward shaping are further embedded into the learning loop. In the mobility-controlled performance evaluation at a node speed of 20 m/s, the proposed method improves throughput by 18.3% and reduces end-to-end delay by 22.7% compared with classical routing baselines. In the separate security-oriented evaluation, the method maintains a packet delivery ratio above 92.6% under a representative adversarial setting with 15% malicious nodes.
Bo Lin, Lianyou Lai· Journal of Internet Technolo...· 0 citations
Vehicular Ad Hoc Networks (VANETs) face resource constraints, high node mobility, and stringent latency requirements, especially in safety-critical applications such as collision avoidance, path planning, and emergency braking. Task offloading to nearby vehicles or Roadside Units (RSUs) mitigates local computational limits, but dynamic conditions, unreliable nodes, and rapid topology changes complicate dependable node selection. This paper proposes a Tiny Machine Learning (TinyML)-enhanced, credibility-based task offloading framework for real-time decision-making in vehicular networks. RSUs evaluate vehicle reliability through a three-component Credibility Assessment Module: a Task Assignment Component that distributes lightweight test tasks and filters unreliable nodes via TinyML inference; a Verification Component that applies TinyML anomaly detection to validate execution with minimal cloud dependence; and a Scoring Component that updates credibility using an exponentially weighted delay-penalty model. A hybrid approach then integrates Random Forest (RF) for credibility-aware node selection with TinyLSTM for link stability prediction, while vehicles pre-screen candidates locally by mobility and resource availability before querying RSUs, reducing signalling overhead. Simulations in OMNeT++, Veins, and SUMO using a Luxembourg City mobility trace show a 24% higher task success rate, 9% lower completion time, 21% lower packet drop ratio, and 65% fewer disconnection-induced failures over heuristic, MLP, and deep-RL baselines, at only 9–18% RSU-side CPU overhead. The RF (380 KB) and TinyLSTM (48 KB post-quantization) models fit automotive-grade microcontroller budgets, confirming practical deployability for next-generation Intelligent Transportation Systems (ITS).
Muhammad Ali, Tariq Qayyum, A. Tariq et al.· IEEE Open Journal of the Com...· 0 citations
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