Sofie: Smart Operating System For Internet Of Everything

The proliferation of Internet of Things and the success of rich cloud services have pushed the horizon of a new computing paradigm, Edge computing, which calls for processing the data at the edge of the network. Applications such as cloud offloading, smart home, and smart city are idea area for Edge computing to achieve better performance than cloud computing. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. However, there are still some challenges for applying Edge computing in our daily life. The missing of the specialized operating system for Edge computing is holding back the flourish of Edge computing applications. Service management, device management, component selection as well as data privacy and security is also not well supported yet in the current computing structure. To address the challenges for Edge computing systems and applications in these aspects, we have planned a series of empirical and theoretical research. We propose SOFIE: Smart Operating System For Internet Of Everything. SOFIE is the operating system specialized for Edge computing running on the Edge gateway. SOFIE could establish and maintain a reliable connection between cloud and Edge device to handle the data transportation between gateway and Edge devices; to provide service management and data management for Edge applications; to protect data privacy and security for Edge users; to guarantee the wellness of the Edge devices. Moreover, SOFIE also provide a naming mechanism to connect Edge device more efficiently. To solve the component selection problem in Edge computing paradigm, SOFIE also include our previous work, SURF, as a model to optimize the performance of the system. Finally, we deployed the design of SOFIE on an IoT/M2M system and support semantics with access control

Big Data and Large-scale Data Analytics: Efficiency of Sustainable Scalability and Security of Centralized Clouds and Edge Deployment Architectures

One of the significant shifts of the next-generation computing technologies will certainly be in the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD landmark, evolved as a widely deployed BD operating system. Its new features include federation structure and many associated frameworks, which provide Hadoop 3.x with the maturity to serve different markets. This dissertation addresses two leading issues involved in exploiting BD and large-scale data analytics realm using the Hadoop platform. Namely, (i)Scalability that directly affects the system performance and overall throughput using portable Docker containers. (ii) Security that spread the adoption of data protection practices among practitioners using access controls. An Enhanced Mapreduce Environment (EME), OPportunistic and Elastic Resource Allocation (OPERA) scheduler, BD Federation Access Broker (BDFAB), and a Secure Intelligent Transportation System (SITS) of multi-tiers architecture for data streaming to the cloud computing are the main contribution of this thesis study

ICE-MILK: Intelligent Crowd Engineering using Machine-based Internet of Things Learning and Knowledge Building

Title from PDF of title page viewed June 1, 2022Dissertation advisor: Sejun SongVitaIncludes bibliographical references (pages 136-159)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2022The lack of proper crowd safety control and management often leads to spreading human casualties and infectious diseases (e.g., COVID-19). Many Machine Learning (ML) technologies inspired by computer vision and video surveillance systems have been developed for crowd counting and density estimation to prevent potential personal injuries and deaths at densely crowded political, entertaining, and religious events. However, existing crowd safety management systems have significant challenges and limitations on their accuracy, scalability, and capacity to identify crowd characterization among people in crowds in real-time, such as a group characterization, impact of occlusions, mobility and contact tracing, and distancing. In this dissertation, we propose an Intelligent Crowd Engineering platform using Machine-based Internet of Things Learning, and Knowledge Building approaches (ICE-MILK) to enhance the accuracy, scalability, and crowd safety management capacity in real-time. Specifically, we design an ICE-MILK structure with three critical layers: IoT-based mobility characterization, ML-based video surveillance, and semantic information-based application layers. We built an IoT-based mobility characterization system by predicting and preventing potential disasters through real-time Radio Frequency (RF) data characterization and analytics. We tackle object group identification, speed, direction detection, and density for the mobile group among the many crowd mobility characteristics. Also, we tackled an ML-based video surveillance approach for effective dense crowd counting by characterizing scattered occlusions, named CSONet. CSONet recognizes the implications of event-induced, scene-embedded, and multitudinous obstacles such as umbrellas and picket signs to achieve an accurate crowd analysis result. Finally, we developed a couple of group semantics to track and prevent crowd-caused infectious diseases. We introduce a novel COVID-19 tracing application named Crowd-based Alert and Tracing Services (CATS) and a novel face masking and social distancing monitoring system for Modeling Safety Index in Crowd (MOSAIC). CATS and MOSAIC apply privacy-aware contact tracing, social distancing, and calculate spatiotemporal Safety Index (SI) values for the individual community to provide higher privacy protection, efficient penetration of technology, greater accuracy, and effective practical policy assistance.Introduction -- Literature review -- IoT-based mobility characterization -- ML-based video/image surveillance -- Semantic knowledge information-based tracing application -- Conclusions and future directions -- Appendi

End-to-end anomaly detection in stream data

Nowadays, huge volumes of data are generated with increasing velocity through various systems, applications, and activities. This increases the demand for stream and time series analysis to react to changing conditions in real-time for enhanced efficiency and quality of service delivery as well as upgraded safety and security in private and public sectors. Despite its very rich history, time series anomaly detection is still one of the vital topics in machine learning research and is receiving increasing attention. Identifying hidden patterns and selecting an appropriate model that fits the observed data well and also carries over to unobserved data is not a trivial task. Due to the increasing diversity of data sources and associated stochastic processes, this pivotal data analysis topic is loaded with various challenges like complex latent patterns, concept drift, and overfitting that may mislead the model and cause a high false alarm rate. Handling these challenges leads the advanced anomaly detection methods to develop sophisticated decision logic, which turns them into mysterious and inexplicable black-boxes. Contrary to this trend, end-users expect transparency and verifiability to trust a model and the outcomes it produces. Also, pointing the users to the most anomalous/malicious areas of time series and causal features could save them time, energy, and money. For the mentioned reasons, this thesis is addressing the crucial challenges in an end-to-end pipeline of stream-based anomaly detection through the three essential phases of behavior prediction, inference, and interpretation. The first step is focused on devising a time series model that leads to high average accuracy as well as small error deviation. On this basis, we propose higher-quality anomaly detection and scoring techniques that utilize the related contexts to reclassify the observations and post-pruning the unjustified events. Last but not least, we make the predictive process transparent and verifiable by providing meaningful reasoning behind its generated results based on the understandable concepts by a human. The provided insight can pinpoint the anomalous regions of time series and explain why the current status of a system has been flagged as anomalous. Stream-based anomaly detection research is a principal area of innovation to support our economy, security, and even the safety and health of societies worldwide. We believe our proposed analysis techniques can contribute to building a situational awareness platform and open new perspectives in a variety of domains like cybersecurity, and health