Ralph Deters

Bio: Ralph Deters is an academic researcher from University of Saskatchewan. The author has contributed to research in topics: Mobile computing & Mobile device. The author has an hindex of 28, co-authored 199 publications receiving 3252 citations. Previous affiliations of Ralph Deters include Bundeswehr University Munich & Pennsylvania State University.

Blockchain as a Service for IoT

Abstract: A blockchain is a distributed and decentralized ledger that contains connected blocks of transactions. Unlike other ledger approaches, blockchain guarantees tamper proof storage of approved transactions. Due to its distributed and decentralized organization, blockchain is beeing used within IoT e.g. to manage device configuration, store sensor data and enable micro-payments. This paper presents the idea of using blockchain as a service for IoT and evaluates the performance of a cloud and edge hosted blockchain implementation.

Security, Performance, and Applications of Smart Contracts: A Systematic Survey

Abstract: Blockchain is the promising technology of recent years, which has attracted remarkable attention in both academic studies and practical industrial applications. The smart contract is a programmable transaction that can perform a sophisticated task, execute automatically, and store on the blockchain. The smart contract is the key component of the blockchain, which has made blockchain a technology beyond the scope of the cryptocurrencies and applicable for a variety of applications such as healthcare, IoT, supply chain, digital identity, business process management, and more. Although in recent years the progress toward improving blockchain technology with the focus on the smart contract has been impressive, there is a lack of reviewing the smart contract topic. This paper systematically reviews the key concepts and proposes the direction of recent studies and developments regarding the smart contract. The research studies are presented in three main categories: 1) security methods and tools; 2) performance improvement approaches; and 3) decentralized applications based on smart contracts.

Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach

Abstract: The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia with critical complications, including septic shock, pulmonary edema, acute respiratory distress syndrome and multi-organ failure. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates. In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification. To obtain the most accurate feature, which is an essential component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep convolutional neural networks. The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control. This approach avoided task-specific data pre-processing methods to support a better generalization ability for unseen data. The performance of the proposed method was validated on a publicly available COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature extractor with Bagging tree classifier achieved the best performance with 99% classification accuracy. The second-best learner was a hybrid of the a ResNet50 feature extractor trained by LightGBM with an accuracy of 98%.

Maximizing Cloud Providers' Revenues via Energy Aware Allocation Policies

Abstract: Cloud providers, like Amazon, offer their data centers' computational and storage capacities for lease to paying customers. High electricity consumption, associated with running a data center, not only reflects on its carbon footprint, but also increases the costs of running the data center itself. This paper addresses the problem of maximizing the revenues of Cloud providers by trimming down their electricity costs. As a solution allocation policies which are based on the dynamic powering servers on and off are introduced and evaluated. The policies aim at satisfying the conflicting goals of maximizing the users' experience while minimizing the amount of consumed electricity. The results of numerical experiments and simulations are described, showing that the proposed scheme performs well under different traffic conditions.

Maximizing Cloud Providers Revenues via Energy Aware Allocation Policies

Abstract: Cloud providers, like Amazon, offer their data centers' computational and storage capacities for lease to paying customers. High electricity consumption, associated with running a data center, not only reflects on its carbon footprint, but also increases the costs of running the data center itself. This paper addresses the problem of maximizing the revenues of Cloud providers by trimming down their electricity costs. As a solution allocation policies which are based on the dynamic powering servers on and off are introduced and evaluated. The policies aim at satisfying the conflicting goals of maximizing the users' experience while minimizing the amount of consumed electricity. The results of numerical experiments and simulations are described, showing that the proposed scheme performs well under different traffic conditions.