arXiv subjects

Tu Dac Ho

Publications and source records attributed to Tu Dac Ho.

3 recordsLinked to original sources

arXiv2026

Blockchain and AI: Securing Intelligent Networks for the Future

Blockchain and artificial intelligence (AI) are increasingly proposed together for securing intelligent networks, but the literature remains fragmented across ledger design, AI-driven detection, cyber-physical applications, and emerging agentic workflows. This paper synthesizes the area through three reusable contributions: (i) a taxonomy of blockchain-AI security for intelligent networks, (ii) integration patterns for verifiable and adaptive security workflows, and (iii) the Blockchain-AI Security Evaluation Blueprint (BASE), a reporting checklist spanning AI quality, ledger behavior, end-to-end service levels, privacy, energy, and reproducibility. The paper also maps the evidence landscape across IoT, critical infrastructure, smart grids, transportation, and healthcare, showing that the conceptual fit is strong but real-world evidence remains uneven and often prototype-heavy. The synthesis clarifies where blockchain contributes provenance, trust, and auditability, where AI contributes detection, adaptation, and orchestration, and where future work should focus on interoperable interfaces, privacy-preserving analytics, bounded agentic automation, and open cross-domain benchmarks. The paper is intended as a reference for researchers and practitioners designing secure, transparent, and resilient intelligent networks.

cs.CR↗

arXiv2025

UAV-Assisted Resilience in 6G and Beyond Network Energy Saving: A Multi-Agent DRL Approach

This paper investigates the unmanned aerial vehicle (UAV)-assisted resilience perspective in the 6G network energy saving (NES) scenario. More specifically, we consider multiple ground base stations (GBSs) and each GBS has three different sectors/cells in the terrestrial networks, and multiple cells may become inactive due to unexpected events such as power outages, disasters, hardware failures, or erroneous energy-saving decisions made by external network management systems. During the time required to reactivate these cells, UAVs are deployed to temporarily restore user service. To address this, we propose a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to enable UAV-assisted communication by jointly optimizing UAV trajectories, transmission power, and user-UAV association under a sleeping ground base station (GBS) strategy. This framework aims to ensure the resilience of active users in the network and the long-term operability of UAVs. Specifically, it maximizes service coverage for users during power outages or NES zones, while minimizing the energy consumption of UAVs. Simulation results demonstrate that the proposed MADDPG policy consistently achieves high coverage ratio across different testing episodes, outperforming other baselines. Moreover, the MADDPG framework attains the lowest total energy consumption, while maintaining a comparable user service rate. These results confirm the effectiveness of the proposed approach in achieving a superior trade-off between energy efficiency and service performance, supporting the development of sustainable and resilient UAV-assisted cellular networks.

cs.NI↗

arXiv2025

Event Driven CBBA with Reduced Communication

In various scenarios such as multi-drone surveillance and search-and-rescue operations, deploying multiple robots is essential to accomplish multiple tasks at once. Due to the limited communication range of these vehicles, a decentralised task allocation algorithm is crucial for effective task distribution among robots. The consensus-based bundle algorithm (CBBA) has been promising for multi-robot operation, offering theoretical guarantees. However, CBBA demands continuous communication, leading to potential congestion and packet loss that can hinder performance. In this study, we introduce an event-driven communication mechanism designed to address these communication challenges while maintaining the convergence and performance bounds of CBBA. We demonstrate theoretically that the solution quality matches that of CBBA and validate the approach with Monte-Carlo simulations across varying targets, agents, and bundles. Results indicate that the proposed algorithm (ED-CBBA) can reduce message transmissions by up to 52%.

cs.RO↗