Blockchain technology provides a promising solution to data security and trust challenges in UAV ad hoc networks. However, conventional consensus mechanisms suffer from notable inefficiencies in resource-constrained environments, characterized by high computational and communication overhead, and prolonged consensus latency. To address these limitations, this paper proposes a blockchain consensus mechanism based on Transaction Collector and Address Aggregation (TCAA) for UAV ad hoc networks. We introduce a block proposal algorithm using a transaction collector, which dynamically assigns block proposal rights through a difficulty threshold. This threshold is defined by transaction types and quantities, thereby eliminating dependence on hash competition or voting verification. The block proposal algorithm operates in parallel with the gossip protocol, ensuring randomness in mempool transactions and unpredictability in proposer selection. Moreover, TCAA incorporates an encrypted address aggregation and recognition scheme to accelerate block synchronization. This scheme allows the block sender to proactively discern the state of the receiver’s mempool, which substantially reduces communication rounds and message redundancy across the network. We model the message synchronization process using a two-dimensional Markov chain and derive closed-form expressions for the end-to-end latency, bandwidth consumption, consensus latency, and transaction collection rounds. Experimental results demonstrate that for a block containing 1000 transactions and a RTT of 50 ms, TCAA reduces the end-to-end latency by 29.7%, 25.3%, and 22.9% compared to Compact, Graphene, and XThin, respectively, while achieving a communication complexity of 2n.
Optimizing congestion control algorithms (CCAs) has the potential to enhance video quality of experience (QoE). The goal of this work is to devise a congestion control framework that (i) ensures that individual users enjoy high video QoE, while (ii) minimizing variance, such that QoE is fairly distributed across all users, especially in fluctuating network, such as cellular network. We present Themis, a video-centric congestion control framework. Themis first uses a distributed approach to allocate a fair target QoE for each client. Based on this fair QoE, Themis then selects congestion control actions to optimize for video QoE (rather than throughput) based on application-layer signals provided by the client. Thus, rather than trying to maximize a flow’s (fair) share of bandwidth, Themis optimizes a flow’s share of the QoE budget. We evaluate Themis in both emulated and production networks. We show that in cellular network Themis achieves a 12.4% QoE improvement compared with BBR, and 37.1% QoE standard deviation decrease compared with the state-of-the-art, Minerva.
Federated Learning (FL) has emerged as a key enabler for privacy-preserving, decentralized sensing systems, giving rise to Federated Mobile Crowdsensing (F-MCS). A well-known bottleneck in such systems is the inefficiency of synchronous training, which stalls for all participants and is susceptible to stragglers in heterogeneous environments. Although asynchronous FL methods have been explored to alleviate this, they often introduce the critical issue of stale updates, which can degrade model convergence and accuracy. To simultaneously address the challenges of efficiency, staleness, and robustness, this paper proposes a novel Dropout-Tolerant Privacy Aggregation (DTPA) scheme for FL that operates without a trusted third party (TTP). Our solution leverages the distributed decryption feature of the lifted EC-ElGamal cryptosystem to enable secure, decentralized model aggregation. We further introduce an efficient worker selection algorithm to systematically reduce waiting time. Moreover, a dedicated dropout-tolerant mechanism is developed to maintain protocol execution even under a high rate of client failures, thereby enhancing robustness. Security analysis confirms that our scheme fulfills essential privacy and security requirements. Extensive simulations demonstrate that the proposed DTPA scheme significantly improves training efficiency and convergence stability compared to state-of-the-art methods, while remaining practical for deployment on resource-constrained mobile devices.
Open-source testbeds for intrusion detection and mitigation enable benchmarking the efficacy of machine-learningbased cyber-defensive systems under increasingly realistic, heterogeneous network scenarios. In this context, the open-world nature of network intrusion detection requires defences to use continual learning strategies to adapt pattern-matching to new attack classes. The cost of periodically fine-tuning pre-trained detectors is not only computational but also encompasses the broader Cyber Threat Intelligence (CTI) life-cycle, which involves collecting, analyzing, and processing raw data into actionable insights. For ML-driven defensive systems, such actionable CTI ultimately takes the form of curated, labelled traffic traces of novel attacks. However, the concurrent optimisation of these intelligence-gathering costs and defence effectiveness has received little attention from the research community. In this respect, this work presents TIGER, an open-source Threat Intelligence Game Environment for Reinforcement learning-based agents to be trained and evaluated toward the optimisation of the costs-benefit trade-off associated with realistic ML-driven cyberdefence life-cycles. TIGER uses realistic network simulation software to model an active-learning game in which an agent learns to timely purchase CTI—abstracted in our environment as labelled samples of Zero-day attacks— to retrain its intrusion detection machinery on new attack patterns, while considering a constrained resource availability scenario.
Sketches have been widely used in network measurement thanks to their low resource overheads. Network-wide sketch deployment is essential for measuring flows across the entire network to enable comprehensive monitoring and decision-making. Most frameworks for network-wide sketch deployment formulate it as a mixed integer linear programming (MILP) problem and utilize commercial solvers such as Gurobi to produce the optimal nodes deployed with sketches. However, the network topology changes frequently. When the topology changes, it is necessary to reconstruct the MILP and re-solve it. Due to the NP hardness, the solvers have to handle a substantial number of variables and constraints, and iteratively converge to the optimal nodes, which is too time-consuming to adapt to frequent topology changes. To this end, a framework for fast network-wide sketch deployment via graph convolution network called FSD-GCN is proposed. Unlike the solvers that gradually converge to the optimal nodes, FSD-GCN transforms the MILP derived from the network-wide sketch deployment problem into a graph-structured representation, and utilizes a graph convolution network to directly obtain the probability of deploying sketches at each node. Meanwhile, an integer linear programming model called NCR is proposed to be used in FSD-GCN, which can achieve maximum flow cover rate with minimum redundant measurement while requiring the fewest deployed nodes. The experimental results show that NCR solved by FSD-GCN can reduce the number of deployed nodes and redundant measurement, while achieving the highest flow cover rate. Meanwhile, compared with the state-of-the-art frameworks using Gurobi, NCR solved by FSD-GCN reduces solving time more than 90%.
