Hung Cao

University of New Brunswick · Faculty of Computer Science

Society has a very ambitious vision of building smart interconnected cities through theInternet of Things (IoT). Billions of data streams will be generated by devices using differentnetworking infrastructures of smart cities, enabling the automation of how the data thatare being collected can be analysed for. However, significant scientific and tec...

The Internet of Things is expected to generate an unprecedented number of unbounded data streams that will produce a paradigm shift when it comes to data analytics. We are moving away from performing analytics in a public or private cloud to performing analytics locally at the fog and edge resources. In this paper, we propose a network of tasks uti...

Exploring Internet of Things (IoT) data streams generated by smart cities means not only transforming data into better business decisions in a timely way but also generating long-term location intelligence for developing new forms of urban governance and organization policies. This paper proposes a new architecture based on the edge-fog-cloud conti...

Despite many efforts on developing protocols, architectures, and physical infrastructures for the Internet of Things (IoT), previous research has failed to fully provide automated analytical capabilities for exploring IoT data streams in a timely way. Mobility and co-location, coupled with unprecedented volumes of data streams generated by geo-dist...

The proliferation of Internet of Things (IoT) systems has received much attention from the research community, and it has brought many innovations to smart cities, particularly through the Internet of Moving Things (IoMT). The dynamic geographic distribution of IoMT devices enables the devices to sense themselves and their surroundings on multiple...

This paper presents a Multimodal Ambient Context-enriched Intelligence Platform (MACeIP) for Smart Cities, a comprehensive system designed to enhance urban management and citizen engagement. Our platform integrates advanced technologies, including Internet of Things (IoT) sensors, edge and cloud computing, and Multimodal AI, to create a responsive...

LangXAI is a framework that integrates Explainable Artificial Intelligence (XAI) with advanced vision models to generate textual explanations for visual recognition tasks. Despite XAI advancements, an understanding gap persists for end-users with limited domain knowledge in artificial intelligence and computer vision. LangXAI addresses this by furn...

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 fingerprinting datasets to enhance indoor localization systems and address the gap in WiFi fingerprinting dataset creation. We used the Simultaneous Localization And Mapp...

This paper proposes an optimization of an existing Deep Neural Network (DNN) that improves its hardware utilization and facilitates on-device training for resourceconstrained edge environments. We implement efficient parameter reduction strategies on Xception that shrink the model size without sacrificing accuracy, thus decreasing memory utilizatio...

Recent advancements in deep learning have significantly improved visual quality inspection and predictive maintenance within industrial settings. However, deploying these technologies on low-resource edge devices poses substantial challenges due to their high computational demands and the inherent complexity of Explainable AI (XAI) methods. This pa...

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. This poses challenges such as bandwidth, latency, and privacy associated with storing data off-site for model building. Training on the edge device can overcome these chall...

Intelligence Everywhere is predicated on the seamless integration of INTERNET of Things (IoT) networks transporting a vast amount of data streams through many computing resources across an edge-to-cloud continuum, relying on the orchestration of distributed machine learning models. The result is an interconnected and collective intelligent ecosyste...

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 techniques can be leveraged to provide rapid and accurate detection of these imperfections. We, therefore, propose a transfer learning approach, namely TransferD2, to cor...

Decoupling vehicles from the immediate consumption of fossil fuels introduces new opportunities in supporting sustainable mobility. Fostering a shift from vehicles with internal combustion engines to Electric Vehicles (EV) often involves using publicly funded subsidies. Given early EV adoption challenges, some charging stations may be under-utilize...

Transport electrification introduces new opportunities in supporting sustainable mobility. Fostering Electric Vehicle (EV) adoption integrates vehicle range and infrastructure deployment concerns. An understanding of EV charging patterns is crucial for optimizing charging infrastructure placement and managing costs. Clustering EV charging events ha...

Transport electrification introduces new opportunities in supporting sustainable mobility. Fostering Electric Vehicle (EV) adoption integrates vehicle range and infrastructure deployment concerns. An understanding of EV charging patterns is crucial for optimizing charging infrastructure placement and managing costs. Clustering EV charging events ha...

Supplementary material for the article "Discovering self-quantified patterns using multi-time window models"

Purpose A new research domain known as the Quantified Self has recently emerged and is described as gaining self-knowledge through using wearable technology to acquire information on self-monitoring activities and physical health related problems. However, very little is known about the impact of time window models on discovering self-quantified pa...

IIoT sensors are usually deployed on a massive scale with stringent scalability, modularity, and interoperability requirements. It is indisputable that they produce a large amount of high-speed and heterogeneous data streams that pose many challenges to perform management, processing, and analytical tasks. This paper proposes an integrated edge-clo...

The Internet of Things is a multi-sensor technology with the unique advantage of supporting non-intrusive and non-device occupancy detection, while also allowing us to explore new forecasting occupancy models. However, forecasting occupancy presence is not a trivial task, since it is still unknown the main criteria in selecting a forecasting modell...

IIoT sensors are usually deployed on a massive scale with stringent scalability, modularity, and interoperability requirements. It is indisputable that they produce a large amount of high-speed and heterogeneous data streams that pose many challenges to perform management, processing, and analytical tasks. This paper proposes an integrated edge-clo...

Electric vehicles (EVs) are part of the solution towards cleaner transport and cities. Clustering EV charging events has been useful for ensuring service consistency and increasing EV adoption. However, clustering presents challenges for practitioners when first selecting the appropriate hyperparameter combination for an algorithm and later when as...

The vision for smart cities is to provide a core infrastructure that enables a good quality of life for their citizens and the sustainable management of natural resources. Towards this vision, supporting the adoption of Electric Vehicles (EV) contributes to improved air quality, sustainable mobility, and utility distribution. Fostering EV adoption...

The vision for smart cities is to provide a core infrastructure that enables a good quality of life for their citizens and the sustainable management of natural resources. Towards this vision, supporting the adoption of Electric Vehicles (EV) contributes to improved air quality, sustainable mobility, and utility distribution. Fostering EV adoption...

The proliferation of IoT systems has received much attention from the research community, and it has brought many innovations to smart cities, particularly through the Internet of Moving Things (IoMT). The dynamic geographic distribution of IoMT devices enables the devices to sense themselves and their surroundings at multiple spatio-temporal scale...

Despite many efforts on developing protocols, architectures, and physical infrastructures for the Internet of Things (IoT), previous research has failed to fully provide automated analytical capabilities for exploring IoT data streams in a timely way. Mobility and co-location, coupled with unprecedented volumes of data streams generated by geo-dist...

The unbounded data streams generated by IoT sensors/devices are posing many technical challenges and requires a one-size-fits-all solution to cope with the massive amount and the high speed of the incoming IoT data arriving simultaneously. In this study, we try to integrate batch and stream processing in a unique system as a premise to handle Volum...

Exploring new insights from IoT data means not only providing higher-level intelligence in a timely way but also generating long-term predictions and decisions from historical IoT data. This paper aims to explore the synergy of various data rates, message passing, and processing algorithms to support streaming analytics at the edge, fog, and cloud...

Current research in indoor sensor networks has pointed out an emerging interest in occupancy detection for Building Information Management (BIM) because buildings use 68% of Canada's energy in operation and contribute 17% of greenhouse gas (GHG) emissions. This research paper aims at developing a non-intrusive sensing method for predicting occupanc...

The unbounded data streams generated by IoT sensors/devices are posing many technical challenges and requires a one-size-fits-all solution to cope with the massive amount and the high speed of the incoming IoT data arriving simultaneously. In this study, we try to integrate batch and stream processing in a unique system as a premise to handle Volum...

Exploring new insights from IoT data means not only providing higher-level intelligence in a timely way but also generating long-term predictions and decisions from historical IoT data. This paper aims to explore the synergy of various data rates, message passing, and processing algorithms to support streaming analytics at the edge, fog, and cloud...

Current research in indoor sensor networks has pointed out an emerging interest in occupancy detection for Building Information Management (BIM) because buildings use 68% of Canadas energy in operation and contribute 17% of greenhouse gas (GHG) emissions. This research paper aims at developing a non-intrusive sensing method for predicting occupancy...

Exploring new insights from IoT data means not only providing higher-level intelligence in a timely way but also generating long-term predictions and decisions from historical IoT data. This paper aims to explore the synergy of various data rates, message passing, and processing algorithms to support streaming analytics at the edge, fog, and cloud...

The unbounded data streams generated by IoT sensors/devices are posing many technical challenges and requires a one-size-fits-all solution to cope with the massive amount and the high speed of the incoming IoT data arriving simultaneously. In this study, we try to integrate batch and stream processing in a unique system as a premise to handle Volum...

Current research in indoor sensor networks has pointed out an emerging interest in occupancy detection for Building Information Management (BIM) because buildings use 68% of Canadas energy in operation and contribute 17% of greenhouse gas (GHG) emissions. This research paper aims at developing a non-intrusive sensing method for predicting occupancy...

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 analytics that can effectively leverage actionable information for transportation planning, management, and business advantage. Edge computing emerges as a new paradigm tha...

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-distributed and composed by edge, fog, and cloud resources that operate collectively to support such an anticipatory learning process. We designed the architecture to manage...

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 querying, aggregation, and analytics. Current solutions for stream data management in IoMT have been focused on partial aspects of a data life cycle process, with special emphas...

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 of fog nodes and IoMT devices to avoiding overflowing the cloud with useless massive data streams that can trigger bottlenecks [1]. Managing data flow is becoming an imp...

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 the "Thing" Internet (or Internet of Things). The question is "what is the next Internet generation after the Thing Internet?" This paper envisions about the Tactile Inter...

The Internet of Mobile Things (IoMT) requires support for a data lifecycle process ranging from sorting, cleaning and monitoring data streams to more complex tasks such as querying, aggregation, and analytics. Current solutions for stream data management in IoMT have been focused on partial aspects of a data lifecycle process, with special emphasis...

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 of fog nodes and IoMT devices to avoiding overflowing the cloud with useless massive data streams that can trigger bottlenecks [1]. Managing data flow is becoming an imp...

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 of transit networks which are entirely deterministic in the geographical space in which they takes place. In this paper, we consider the fundamental issues in developing a...

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 analytics that can effectively leverage actionable information for planning, management, and business advantage. Edge computing emerges as a new paradigm that decentralizes...

Laser scanning (also known as Light Detection And Ranging) has been widely applied in various application. As part of that, aerial laser scanning (ALS) has been used to collect topographic data points for a large area, which triggers to million points to be acquired. Furthermore, today, with integrating full wareform (FWF) technology during ALS dat...

Abstract: The metropolitan area of Greater Moncton is the fastest growing census metropolitan area in eastern Canada. We are working with CODIAC transit to promote efficient transportation services and reduce private car dependency. CODIAC Transpo currently operates 30 regular routes Monday to Saturday, some of which provide additional evening and...

The metropolitan area of Greater Moncton is the fastest growing census metropolitan area in eastern Canada. We are working with CODIAC transit to promote efficient transportation services and reduce private car dependency. CODIAC Transpo currently operates 30 regular routes Monday to Saturday, some of which provide additional evening and Sunday ser...

The metropolitan area of Greater Moncton is the fastest growing census metropolitan area in eastern Canada. We are working with CODIAC transit to promote efficient transportation services and reduce private car dependency. CODIAC Transpo currently operates 30 regular routes Monday to Saturday, some of which provide additional evening and Sunday ser...

This research project has developed a graph database model to provide easy-to-access information on transit options, incentivize the use of transit, and explore different transport capacities. Graph databases are unique and novel because they provide different stakeholders with a new set of policy education, exploration, and management options.

This research project has developed a graph database model to provide easy-to-access information on transit options, incentivize the use of transit, and explore different transport capacities. Graph databases are unique and novel because they provide different stakeholders with a new set of policy education, exploration, and management options. The...

Recent years, Light Detection And Ranging (LiDAR) is continually developed and applied in many fields. The improvement of hardware and methodology of LiDAR data acquisition makes data significantly increased both in volume and complexity. This poses a great challenge to effectively handle a vast amount and complexity of LiDAR data. Even though sign...

— Laser scanning (also known as Light Detection And Ranging) has been widely applied in various application. As part of that, aerial laser scanning (ALS) has been used to collect topographic data points for a large area, which triggers to million points to be acquired. Furthermore, today, with integrating full wareform (FWF) technology during ALS d...

This paper presents a novel approach to optimize data in Vietnameses speech synthesis using Unit Selection method. First, we conduct analysis of Vietnamese tone using Fujisaki model to find out the parameters of fundamental frequency contours (F0 contours) influencing on Vietnamese vowels while speech is expressed. Next, analysis, testing, and eval...