96 papers
This paper proposes PreHO, a predictive handover mechanism for Low-Earth Orbit Satellite Networks that leverages the stable and predictable channel states of fast-moving satellites to proactively plan optimal handover strategies, thereby significantly reducing signaling overhead and latency compared to traditional reactive schemes.
This paper proposes an energy-efficient online scheduling framework for Wireless Powered Mobile Edge Computing networks that utilizes Lyapunov optimization and a relax-then-adjust approach to solve the joint wireless power transfer and computation offloading problem, achieving a fundamental trade-off between latency and energy consumption while ensuring theoretical performance guarantees.
This paper introduces Structured Gossip DNS, a partition-resilient name resolution system for large-scale dynamic networks that leverages DHT finger tables and passive stabilization to achieve eventual consistency with reduced message complexity and without requiring global coordination.
This paper introduces StageFinder, a temporal graph learning framework that fuses host and network provenance data with graph neural networks and LSTMs to accurately and stably estimate Advanced Persistent Threat (APT) attack stages, achieving a macro F1-score of 0.96 on DARPA datasets.
This paper presents the first physical testbed evaluation of the Internet of Mirrors (IoM), demonstrating that while offloading computation to higher-tier nodes reduces local latency and resource load, the optimal task placement strategy depends on a dynamic trade-off between network conditions, payload size, and concurrent user load.
This paper introduces Spectra, a three-step scheduling algorithm that efficiently manages time-varying AI traffic across parallel optical circuit switches by decomposing traffic matrices, assigning permutations load-awarely, and balancing loads through controlled splitting, thereby significantly reducing makespan compared to state-of-the-art baselines.
Uber's Failover Architecture (UFA) replaces its costly uniform 2x capacity model with a differentiated, criticality-based approach that opportunistically shares resources and preempts non-critical services during peak failovers, thereby reducing steady-state provisioning from 2x to 1.3x and eliminating over one million CPU cores while maintaining 99.97% availability.
This paper proposes a lightweight, digital-twin-based framework for edge-assisted vehicle tracking and collision prediction that achieves approximately 88% accuracy by relying on object detection and K-D tree-indexed path maps instead of computationally intensive trajectory prediction models.
This empirical study demonstrates that maintaining end-to-end determinism in 5G-TSN networks for IIoT applications requires carefully tuning Time-Aware Shaper (TAS) transmission window offsets based on high-order percentile bounds of 5G downlink delay and jitter to prevent excessive latency or scheduling failures.
The paper presents pqRPKI, a practical post-quantum RPKI framework that utilizes a multi-layer Merkle Tree Ladder and a redesigned manifest structure to significantly reduce bandwidth and validation costs compared to naive post-quantum signature swaps, while enabling efficient dual-stack deployment with minimal overhead.
This paper introduces SIL-GPO, a reinforcement learning framework that combines graph attention networks with self-imitation learning to optimize the joint deployment and routing of heterogeneous edge AI and microservices, significantly reducing end-to-end latency and improving resource utilization compared to existing methods.
This paper presents SDN-SYN PoW, an adaptive defense architecture that leverages a Software-Defined Networking controller to dynamically adjust non-interactive Proof-of-Work difficulty based on real-time traffic analysis, effectively mitigating multi-domain SYN floods while maintaining negligible overhead for legitimate clients.
This paper presents the first demonstration of a low-latency, always-on fully-connected mesh network using four RFSoC-based software-defined radios that achieves an aggregate throughput of 1.2 Gbps to simultaneously stream twelve real-time, uncompressed 4K video feeds over 2x2 MIMO links.
This paper proposes a Digital Twin-enabled framework (DAPR) that integrates asynchronous federated learning, a GRU-VAE prediction model, and deep reinforcement learning to optimize client selection and content request prediction, thereby significantly improving cache hit ratios and reducing transmission latency in vehicular edge computing systems.
This paper proposes a stateful, hierarchical multi-agent framework for 5G/6G Intent-Based Networking orchestration that leverages both Large and Small Language Models, demonstrating that while both achieve similar translation accuracy, Small Language Models improve the overall lifecycle completion speed by 20%.
This paper proposes an improved Contact Graph Routing algorithm for Delay Tolerant Networks that incorporates contact splitting and edge pruning to proactively enforce capacity and buffer constraints during route search, thereby ensuring optimal delivery times and reducing collisions compared to traditional reactive methods.
This paper proposes the combinatorial data augmentation-guided NLoS detection (CDA-ND) algorithm, which generates NLoS evidence vectors from multilateration-based location clusters to enable both hard and soft NLoS detection modes, significantly improving 6G positioning accuracy in indoor factory environments by reducing mean absolute error by up to 66%.
This paper proposes a process mining-based method that analyzes online gaming network traffic to unsupervisedly characterize device states as interpretable Petri nets and accurately classify different video games, demonstrating its effectiveness through the UPSIDE case study involving Clash Royale and Rocket League.
This paper proposes a Networked Mixture-of-Experts (NMoE) system and a hybrid federated learning framework that enable collaborative inference and efficient, privacy-preserving training of large AI models on resource-constrained mobile edge devices by leveraging neighbor expertise and balancing personalization with generalization.
This paper proposes and validates a hardware-efficient, explainable Convolutional Tsetlin Machine (CTM) for real-time 5G jamming detection that achieves comparable accuracy to convolutional neural networks while significantly reducing training time, memory usage, and enabling deterministic FPGA deployment.