33 citations · 54 across the 12 of their papers we have counts for
13 papers
An Adaptive Indoor Localization Approach Using WiFi RSSI Fingerprinting with SLAM-Enabled Robotic Platform and Deep Neural Networks
Seyed Alireza Rahimi Azghadi, Atah Nuh Mih, Asfia Kawnine +3
Indoor localization plays a vital role in the era of the IoT and robotics, with WiFi technology being a prominent choice due to its ubiquity. We present a method for creating WiFi…
Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment
Atah Nuh Mih, Alireza Rahimi, Asfia Kawnine +4
This paper proposes an optimization of an existing Deep Neural Network (DNN) that improves its hardware utilization and facilitates on-device training for resource-constrained edge…
Developing a Resource-Constraint EdgeAI model for Surface Defect Detection
Atah Nuh Mih, Hung Cao, Asfia Kawnine +1
Resource constraints have restricted several EdgeAI applications to machine learning inference approaches, where models are trained on the cloud and deployed to the edge device. Th…
Evaluating Multi-Global Server Architecture for Federated Learning
Asfia Kawnine, Hung Cao, Atah Nuh Mih +1
Federated learning (FL) with a single global server framework is currently a popular approach for training machine learning models on decentralized environment, such as mobile devi…
ECAvg: An Edge-Cloud Collaborative Learning Approach using Averaged Weights
Atah Nuh Mih, Hung Cao, Asfia Kawnine +1
The use of edge devices together with cloud provides a collaborative relationship between both classes of devices where one complements the shortcomings of the other. Resource-cons…
Fostering new Vertical and Horizontal IoT Applications with Intelligence Everywhere
Hung Cao, Monica Wachowicz, Rene Richard +1
Intelligence Everywhere is predicated on the seamless integration of IoT networks transporting a vast amount of data streams through many computing resources across an edge-to-clou…
TransferD2: Automated Defect Detection Approach in Smart Manufacturing using Transfer Learning Techniques
Atah Nuh Mih, Hung Cao, Joshua Pickard +2
Quality assurance is crucial in the smart manufacturing industry as it identifies the presence of defects in finished products before they are shipped out. Modern machine learning…
An edge-fog-cloud platform for anticipatory learning process designed for Internet of Mobile Things
Hung Cao, Monica Wachowicz, Chiara Renso +1
This paper presents a novel architecture for data analytics targeting an anticipatory learning process in the context of the Internet of Mobile Things. The architecture is geo-dist…
Implementing an Edge-Fog-Cloud architecture for stream data management
Lilian Hernandez, Hung Cao, Monica Wachowicz
The Internet of Moving Things (IoMT) requires support for a data life cycle process ranging from sorting, cleaning and monitoring data streams to more complex tasks such as queryin…
What is the next innovation after the internet of things?
Hung Cao
The world had witnessed several generations of the Internet. Starting with the Fixed Internet, then the Mobile Internet, scientists now focus on many types of research related to t…
Combining edge and cloud computing for mobility analytics
Ikechukwu Maduako, Hung Cao, Lilian Hernandez +1
Mobility analytics using data generated from the Internet of Mobile Things (IoMT) is facing many challenges which range from the ingestion of data streams coming from a vast number…
The design of a streaming analytical workflow for processing massive transit feeds
Hung Cao, Monica Wachowicz
Retrieving and analyzing transit feeds relies on working with analytical workflows that can handle the massive volume of data streams that are relevant to understand the dynamics o…
Developing an edge computing platform for real-time descriptive analytics
Hung Cao, Monica Wachowicz, Sangwhan Cha
The Internet of Mobile Things encompasses stream data being generated by sensors, network communications that pull and push these data streams, as well as running processing and an…