Steve Drew

The University of Calgary | HBI · Department of Electrical and Software Engineering

Blood transfusion is a commonly used, life-saving medical thera-peutics worldwide. A significant challenge is the high variability of supply and demand in blood products, making it difficult to maintain a balance between preventing shortages of blood products and preventing wastage. Recent studies used data-driven methods on demand forecasting for...

Deep Neural Networks (DNNs) possess powerful prediction capability thanks to their over-parameterization design, although the large model complexity makes it suffer from noisy supervision. Recent approaches seek to eliminate impacts from noisy labels by excluding data points with large loss values and showing promising performance. However, these a...

Federated learning provides a promising privacy-preserving way for utilizing large-scale private edge data from massive Internet-of-Things (IoT) devices. While existing research extensively studied optimizing the learning process, computing efficiency, and communication overhead, one important and often overlooked aspect is that participants contri...

Federated learning (FL) is a distributed and privacy-preserving learning framework for predictive modeling with massive data generated at the edge by Internet of Things (IoT) devices. One major challenge preventing the wide adoption of FL in IoT is the pervasive power supply constraints of IoT devices due to the intensive energy consumption of batt...

The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of scams of \$102m to financial institutions in 2022. Developing a mechanism for battling financial crimes is an impending task that requires in-depth collaboration from multiple institutions, and yet such collaboration im...

The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge. With its simple yet effective approach, federated learning (FL) is proved to be a natural solution for massive user-owned devices in edge computing with distributed and private training data. Mos...

Home exercise and self-served gyms allow a larger population to exercise regularly without the cost of hiring private coaches. In absence of professional guidance, however, exercisers can suffer from injuries to muscles and joints. High-precision, affordable arm tracking with commercial, off-the-shelf (COTS) wearable devices has become an urgent ne...

Author name disambiguation (AND) is a fundamental task in knowledge alignment for building a knowledge graph network or an online academic search system. Existing AND algorithms tend to cause over-splitting and over-merging problems of papers, severely jeopardizing the performance of downstream tasks. In this paper, we demonstrate the problem of pa...

Recently, a new distributed learning scheme called Federated Learning (FL) has been introduced. FL is designed so that server never collects user-owned data meaning it is great at preserving privacy. FL's process starts with the server sending a model to clients, then the clients train that model using their data and send the updated model back to...

The rise of Federated Learning (FL) is bringing machine learning to edge computing by utilizing data scattered across edge devices. However, the heterogeneity of edge network topologies and the uncertainty of wireless transmission are two major obstructions of FL's wide application in edge computing, leading to prohibitive convergence time and high...

The analytical research has recently indicated that the computational resources of Connected Autonomous Vehicles (CAVs) have been wasted since almost all vehicles spend over 95% of their time in parking lots. This paper presents a collaborative computing framework to efficiently offload online computational tasks to parked vehicles (PVs) during pea...

Recent analytical research has pointed out that almost all vehicles spend over 95\% of their time in parking lots where their powerful computing resources are wasted. In this paper, we propose a novel collaborative computing paradigm that efficiently offloads online heterogeneous computation tasks to parked vehicles (PVs) during peak hours. A cont...

As most vehicles spend over 95% of their time in the parking lots, the powerful computing resources of parked vehicles (PVs) are underutilized, that can be considered as available computing nodes to run tasks as well as an extension of the existing infrastructure. In this paper, we propose EdgePV, a collaborative computing paradigm to efficiently i...

With the proliferation of blockchain projects and applications, cryptocurrency exchanges, which provides exchange services among different types of cryptocurrencies, become pivotal platforms that allow customers to trade digital assets on different blockchains. Because of the anonymity and trustlessness nature of cryptocurrency, one major challenge...

With the proliferation of blockchain projects and applications, cryptocurrency exchanges, which provides exchange services among different types of cryptocurrencies, become pivotal platforms that allow customers to trade digital assets on different blockchains. Because of the anonymity and trustlessness nature of cryptocurrency, one major challenge...

Recent advances in blockchain technologies have provided exciting opportunities for decentralized applications. Specifically, blockchain-based smart contracts enable credible transactions without authorized third parties. The attractive properties of smart contracts facilitate distributed data vending, allowing for proprietary data to be securely e...

Recent advances in blockchain technologies have provided exciting opportunities for decentralized applications. Specifically, blockchain-based smart contracts enable credible transactions without authorized third parties. The attractive properties of smart contracts facilitate distributed data vending, allowing for proprietary data to be securely e...

For embracing the ubiquitous Internet-of-Things (IoT) devices, edge computing and Network Function Virtualization (NFV) have been enabled in branch offices and homes in the form of virtual Customer-Premises Equipment (vCPE). A Service Provider (SP) deploys vCPE instances as Virtual Network Functions (VNFs) on top of generic physical Customer-Premis...

The state-of-the-art mobile edge applications are generating intense traffic and posing rigorous latency requirements to service providers. While resource sharing across multiple service providers can be a way to maximize the utilization of limited resources at the network edge, it requires a centralized repository maintained by all parties for ser...

Methods for authenticating a security device at a local network location for providing a secure access from the local network location to a remote network location are provided. A security device is registered by installing private security Software on the security device that generates an asymmetrical encryption key pair including an encryption ke...

The increasing complexity and explosive growth of smartphone applications, together with the more prevalent use of cloud computing, have inspired strong motivation for offloading computation of mobile apps to the cloud. However, there exist vulnerabilities in apps if they are offloaded in public cloud environment. In this paper, we argue that keepi...

Nowadays, the harmful blue-green algae blooms on lakes threaten the daily life of millions of people in China. We designed and developed a cyber physical networking system on Lake Tai for the monitoring and cleanup of the water blooms which is at work in Wuxi City, Jiangsu Province. We designed the sensor device and algorithm to monitor the order o...

Deployment is an important issue in wireless sensor networks (WSNs). Most of recent researches focus on the full-coverage deployment with the minimum number of sensors. In practice, there are finite number of nodes in WSNs applications, and many regions are too vast to be monitored by the limited number of sensors; On the other hand, the informatio...

Nowadays, the harmful blue-green algae blooms on lakes or streams threaten the daily life of millions of people in China. In this paper, we demonstrate the sensor network system we built on Lake Tai for the surveillance and cleanup of the algae blooms which is at work in Wuxi City, Jiangsu Province. We designed the sensor device and algorithm to mo...

The energy consumption rates of nodes vary in different positions in sensor networks, leading to the early depletion of the nodes and the disconnection of a network. In this paper, we present a distributed mobile sink guiding scheme (DLING) based on the center of gravity theory in physics. DLING regards the sensor network as an object composed of p...

Traditional simulators have deficiencies of scalability, thus they are not so efficient in running simulations with large-scale networks. In this paper, we present a new simulator, namely EasiSim, specifically for evaluating sensor networks on a large scale. EasiSim is featured by organizing its core components, including nodes, topology and scenar...

Traditional simulators cannot meet the requirement of modeling large scale networks due to their deficiency in scalability. In this paper, we present a new simulator, namely EasiSim, for sensor networks on a large scale. EasiSim is featured by a structure-based modeling method and a hierarchical organization of the relevant functional components, i...