Research
On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

Trend · papers per month

count% of corpus
0111 · Jun 201819922001200920172026
6 results for “AoI”
Aging Bandits: Regret Analysis and Order-Optimal Learning Algorithm for Wireless Networks with Stochastic Arrivalseess.SY

New algorithm reduces age of information in wireless networks with unknown channel reliability.

problem Learning optimal source-channel pairs to minimize age of information in wireless networks.
method Introduces AoI regret, novel learning algorithm with bounded AoI regret.
result Developed a learning algorithm with O(1)O(1) AoI regret, improving upon Θ(log⁡T)Θ(\log T).
AoIWireless NetworksStochastic ArrivalsBanditsLearning Algorithms
2020-12-16abs ↗pdf ↗
Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approachcs.NI

An active learning approach reduces AoI violation in vehicular networks.

problem Dynamic nature of vehicular networks makes resource allocation challenging.
method Gaussian process regression (GPR) for online decentralized active learning.
result Significant improvement in AoI violation probability with at least 50% reduction.
vehicular communicationactive learningage of informationresource allocationGaussian process regression
2019-11-27abs ↗pdf ↗
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenancecs.LG

AOI algorithm improved predictive maintenance accuracy.

problem Improving predictive maintenance accuracy in industrial assets.
method Extended AOI algorithm with LSTM for anomaly detection and RUL calculation.
result Methodology accurately detected anomalies and calculated RUL with high precision.
Predictive MaintenanceAttribute Oriented InductionLSTMAnomaly DetectionRemaining Useful Life
2019-12-02abs ↗pdf ↗
Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approacheess.SP

Paper optimizes UAV navigation for IoT data freshness and energy efficiency.

problem Improving data freshness and connectivity for IoT devices with UAVs.
method Deep reinforcement learning model with experience replay for energy-efficient UAV trajectory optimization.
result The proposed approach is 3.6% and 3.13% more energy efficient than greedy and baseline methods.
UAV navigationIoT data freshnessDeep reinforcement learningEnergy efficiencyTrajectory optimization
2020-02-21abs ↗pdf ↗