114 citations · 356 across the 19 of their papers we have counts for
20 papers
Efficient Co-simulator Integration with Application to Smart Grids
Talha Ibn Aziz, Jihoon Og, Ioanis Nikolaidis +1
The evolution of the electric power grid towards a ``smart grid'' is an example of an emerging large-scale cyber-physical system. To study the potential of cyberattacks or other so…
Ray-Traced Augmentation for Signal Strength Based Localization
Jihoon Og, Ningze Sun, Ioanis Nikolaidis +1
Indoor localization based on Wi-Fi typically relies on extensive collection of real-world received signal strength (RSS) fingerprints, making deployment costly and time-consuming.…
Age Performance Analysis in Resource-Constrained Adversarial IoT Systems
Aresh Dadlani, Muthukrishnan Senthil Kumar, Omid Ardakanian +1
Timely updates are critical for real-time monitoring and control applications powered by the Internet of Things (IoT). As these systems scale, they become increasingly vulnerable t…
CLOAK: Contrastive Guidance for Latent Diffusion-Based Data Obfuscation
Xin Yang, Omid Ardakanian
Data obfuscation is a promising technique for mitigating attribute inference attacks by semi-trusted parties with access to time-series data emitted by sensors. Recent advances lev…
Budgeted Indirect Adversarial Attack on Graph-Based Anomaly Detection in Sensor Networks
Sanju Xaviar, Omid Ardakanian
Graph Neural Networks (GNNs) have emerged as powerful models for anomaly detection in sensor networks, particularly when analyzing multivariate time series. In this work, we introd…
Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption
Abdullah Al Omar, Xin Yang, Euijin Choo +1
Federated Learning (FL) is susceptible to privacy attacks, such as data reconstruction attacks, in which a semi-honest server or a malicious client infers information about other c…
Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis
Amirhossein Sohrabbeig, Omid Ardakanian, Petr Musilek
Long-term forecasting of multivariate urban data poses a significant challenge due to the complex spatiotemporal dependencies inherent in such datasets. This paper presents DST, a…
PrivDiffuser: Privacy-Guided Diffusion Model for Data Obfuscation in Sensor Networks
Xin Yang, Omid Ardakanian
Sensor data collected by Internet of Things (IoT) devices can reveal sensitive personal information about individuals, raising significant privacy concerns when shared with semi-tr…
Grey-box Bayesian Optimization for Sensor Placement in Assisted Living Environments
Shadan Golestan, Omid Ardakanian, Pierre Boulanger
Optimizing the configuration and placement of sensors is crucial for reliable fall detection, indoor localization, and activity recognition in assisted living spaces. We propose a…
Robust Multimodal Fusion for Human Activity Recognition
Sanju Xaviar, Xin Yang, Omid Ardakanian
The proliferation of IoT and mobile devices equipped with heterogeneous sensors has enabled new applications that rely on the fusion of time-series data generated by multiple senso…
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning
Xin Yang, Omid Ardakanian
This paper proposes a sensor data anonymization model that is trained on decentralized data and strikes a desirable trade-off between data utility and privacy, even in heterogeneou…
False Data Injection Attack on Electric Vehicle-Assisted Voltage Regulation
Yuan Liu, Omid Ardakanian, Ioanis Nikolaidis +1
With the large scale penetration of electric vehicles (EVs) and the advent of bidirectional chargers, EV aggregators will become a major player in the voltage regulation market. Th…
A Data-Efficient Approach to Behind-the-Meter Solar Generation Disaggregation
Xinlei Chen, Moosa Moghimi Haji, Omid Ardakanian
With the emergence of cost effective battery storage and the decline in the solar photovoltaic (PV) levelized cost of energy (LCOE), the number of behind-the-meter solar PV systems…
Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach
Omid Hajihassani, Omid Ardakanian, Hamzeh Khazaei
The abundance of data collected by sensors in Internet of Things (IoT) devices, and the success of deep neural networks in uncovering hidden patterns in time series data have led t…
Flexible, Decentralized Access Control for Smart Buildings with Smart Contracts
Leepakshi Bindra, Kalvin Eng, Omid Ardakanian +1
Large commercial buildings are complex cyber-physical systems containing expensive and critical equipment that ensure the safety and comfort of their numerous occupants. Yet occupa…
Identifying Grey-box Thermal Models with Bayesian Neural Networks
Md Monir Hossain, Tianyu Zhang, Omid Ardakanian
Smart thermostats are one of the most prevalent home automation products. They learn occupant preferences and schedules, and utilize an accurate thermal model to reduce the energy…
In (Stochastic) Search of a Fairer Alife
Dmitriy Volinskiy, Lana Cuthbertson, Omid Ardakanian
Economies and societal structures in general are complex stochastic systems which may not lend themselves well to algebraic analysis. An addition of subjective value criteria to th…
On Identification of Distribution Grids
Omid Ardakanian, Vincent W. S. Wong, Roel Dobbe +4
Large-scale integration of distributed energy resources into residential distribution feeders necessitates careful control of their operation through power flow analysis. While the…
Event Detection and Localization in Distribution Grids with Phasor Measurement Units
Omid Ardakanian, Ye Yuan, Roel Dobbe +3
The recent introduction of synchrophasor technology into power distribution systems has given impetus to various monitoring, diagnostic, and control applications, such as system id…
Inverse Power Flow Problem
Ye Yuan, Steven Low, Omid Ardakanian +1
This paper formulates an inverse power flow problem which is to infer a nodal admittance matrix (hence the network structure of a power system) from voltage and current phasors mea…