40 research outputs found
Scaling Science for Performance: Implementing a Cost-Effective "Big Data" Environment for Research Analysis
These slides support the oral presentations of Gordon Springer and Prasad Calyam delivered at Cyberinfrastructure Day 2013 on October 10, 2013 on the University of Missouri campus
Hyperprofile-based Computation Offloading for Mobile Edge Networks
In recent studies, researchers have developed various computation offloading
frameworks for bringing cloud services closer to the user via edge networks.
Specifically, an edge device needs to offload computationally intensive tasks
because of energy and processing constraints. These constraints present the
challenge of identifying which edge nodes should receive tasks to reduce
overall resource consumption. We propose a unique solution to this problem
which incorporates elements from Knowledge-Defined Networking (KDN) to make
intelligent predictions about offloading costs based on historical data. Each
server instance can be represented in a multidimensional feature space where
each dimension corresponds to a predicted metric. We compute features for a
"hyperprofile" and position nodes based on the predicted costs of offloading a
particular task. We then perform a k-Nearest Neighbor (kNN) query within the
hyperprofile to select nodes for offloading computation. This paper formalizes
our hyperprofile-based solution and explores the viability of using machine
learning (ML) techniques to predict metrics useful for computation offloading.
We also investigate the effects of using different distance metrics for the
queries. Our results show various network metrics can be modeled accurately
with regression, and there are circumstances where kNN queries using Euclidean
distance as opposed to rectilinear distance is more favorable.Comment: 5 pages, NSF REU Site publicatio
The Role of Vidura Chatbot in the Diffusion of KnowCOVID-19 Gateway
The COVID-19 pandemic is an unprecedented global emergency. Clinicians and medical researchers are suddenly thrown into a situation where they need to keep up with the latest and best evidence for decision-making at work in order to save lives and develop solutions for COVID-19 treatments and preventions. However, a challenge is the overwhelming numbers of online publications with a wide range of quality. We explain a science gateway platform designed to help users to filter the overwhelming amount of literature efficiently (with speed) and effectively (with quality), to find answers to their scientific questions. It is equipped with a chatbot to assist users to overcome infodemic, low usability, and high learning curve. We argue that human-machine communication via a chatbot play a critical role in enabling the diffusion of innovations
Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications
Social Virtual Reality based Learning Environments (VRLEs) such as vSocial
render instructional content in a three-dimensional immersive computer
experience for training youth with learning impediments. There are limited
prior works that explored attack vulnerability in VR technology, and hence
there is a need for systematic frameworks to quantify risks corresponding to
security, privacy, and safety (SPS) threats. The SPS threats can adversely
impact the educational user experience and hinder delivery of VRLE content. In
this paper, we propose a novel risk assessment framework that utilizes attack
trees to calculate a risk score for varied VRLE threats with rate and duration
of threats as inputs. We compare the impact of a well-constructed attack tree
with an adhoc attack tree to study the trade-offs between overheads in managing
attack trees, and the cost of risk mitigation when vulnerabilities are
identified. We use a vSocial VRLE testbed in a case study to showcase the
effectiveness of our framework and demonstrate how a suitable attack tree
formalism can result in a more safer, privacy-preserving and secure VRLE
system.Comment: Tp appear in the CCNC 2019 Conferenc
Environmentally-Aware and Energy-Efficient Multi-Drone Coordination and Networking for Disaster Response
In a Disaster Response Management (DRM) Scenario, Communication and Coordination Are Limited, and Absence of Related Infrastructure Hinders Situational Awareness. Unmanned Aerial Vehicles (UAVs) or Drones Provide New Capabilities for DRM to Address These Barriers. However, There is a Dearth of Works that Address Multiple Heterogeneous Drones Collaboratively Working Together to Form a Flying Ad-Hoc Network (FANET) with Air-To-Air and Air-To-Ground Links that Are Impacted By: (I) Environmental Obstacles, (Ii) Wind, and (Iii) Limited Battery Capacities. in This Paper, We Present a Novel Environmentally-Aware and Energy-Efficient Multi-Drone Coordination and Networking Scheme that Features a Reinforcement Learning (RL) based Location Prediction Algorithm Coupled with a Packet Forwarding Algorithm for Drone-To-Ground Network Establishment. We Specifically Present Two Novel Drone Location-Based Solutions (I.e., Heuristic Greedy, and Learning-Based) in Our Packet Forwarding Approach to Support Application Requirements. These Requirements Involve Improving Connectivity (I.e., Optimize Packet Delivery Ratio and End-To-End Delay) Despite Environmental Obstacles, and Improving Efficiency (I.e., by Lower Energy Use and Time Consumption) Despite Energy Constraints. We Evaluate Our Scheme with State-Of-The-Art Networking Algorithms in a Trace-Based DRM FANET Simulation Testbed Featuring Rural and Metropolitan Areas. Results Show that Our Strategy overcomes Obstacles and Can Achieve 81-To-90% of Network Connectivity Performance Observed under No Obstacle Conditions. in the Presence of Obstacles, Our Scheme Improves the Network Connectivity Performance by 14-To-38% While Also Providing 23-To-54% of Energy Savings in Rural Areas; the Same in Metropolitan Areas Achieved an Average of 25% Gain When Compared with Baseline Obstacle Awareness Approaches with 15-To-76% of Energy Savings