Trust based energy efficient data collection with unmanned aerial vehicle in edge network
B Jiang, G Huang, T Wang, J Gui… - Transactions on …, 2022 - Wiley Online Library
Large‐scale sensing devices spread over a wide area and compose the supervisory control
and data acquisition (SCADA) system to remotely control and monitor a specific process …
and data acquisition (SCADA) system to remotely control and monitor a specific process …
Toward energy-aware caching for intelligent connected vehicles
With the widespread application of infotainment services in intelligent connected vehicles
(ICVs), network traffic has grown exponentially, bringing huge burden and energy …
(ICVs), network traffic has grown exponentially, bringing huge burden and energy …
Tsagen: synthetic time series generation for kpi anomaly detection
A key performance indicator (KPI) consists of critical time series data that reflect the runtime
states of network systems (eg, response time and available bandwidth). Despite the …
states of network systems (eg, response time and available bandwidth). Despite the …
Adversarial training of LSTM-ED based anomaly detection for complex time-series in cyber-physical-social systems
With the development and maturity of smart cities, more and more Cyber-Physical-Social
Systems (CPSSs) need to monitor a variety of time-series data from sensors and network …
Systems (CPSSs) need to monitor a variety of time-series data from sensors and network …
Unsupervised anomaly detection via nonlinear manifold learning
A Yousefpour, M Shishehbor… - Journal of …, 2024 - asmedigitalcollection.asme.org
Anomalies are samples that significantly deviate from the rest of the data and their detection
plays a major role in building machine learning models that can be reliably used in …
plays a major role in building machine learning models that can be reliably used in …
A survey of graph-based deep learning for anomaly detection in distributed systems
Anomaly detection is a crucial task in complex distributed systems. A thorough
understanding of the requirements and challenges of anomaly detection is pivotal to the …
understanding of the requirements and challenges of anomaly detection is pivotal to the …
Robust KPI anomaly detection for large-scale software services with partial labels
To ensure the reliability of software services, operators collect and monitor a large number of
KPI (Key Performance Indicator) streams constantly. KPI anomaly detection is vitally …
KPI (Key Performance Indicator) streams constantly. KPI anomaly detection is vitally …
A Survey of Time Series Anomaly Detection Methods in the AIOps Domain
Internet-based services have seen remarkable success, generating vast amounts of
monitored key performance indicators (KPIs) as univariate or multivariate time series …
monitored key performance indicators (KPIs) as univariate or multivariate time series …
A joint matrix factorization and clustering scheme for irregular time series data
Abstract Key Performance Indicator (KPI) clustering plays an important role in Artificial
Intelligence for IT Operations (AIOps) when the number of KPIs is large. This approach can …
Intelligence for IT Operations (AIOps) when the number of KPIs is large. This approach can …
Explainable machine learning for performance anomaly detection and classification in mobile networks
JM Ramírez, F Díez, P Rojo, V Mancuso… - Computer …, 2023 - Elsevier
Mobile communication providers continuously collect many parameters, statistics, and key
performance indicators (KPIs) with the goal of identifying operation scenarios that can affect …
performance indicators (KPIs) with the goal of identifying operation scenarios that can affect …