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Articles 421 - 450 of 919
Full-Text Articles in Computer Sciences
Nupt St-Data Miner: An Spatio-Temporal Data Analysis And Visualization System, Zhiqiang Zou, Junjie Xiong, Xuerong He, Haihong Dai
Nupt St-Data Miner: An Spatio-Temporal Data Analysis And Visualization System, Zhiqiang Zou, Junjie Xiong, Xuerong He, Haihong Dai
Computer Science Faculty Research & Creative Works
Given the increasing popularity and availability of location tracking devices, large quantities of Spatio-Temporal data (ST-data) are available from many different sources. For the ST-data, reflecting the mobile characteristic of the world, it is essential to build a functional system to perform quickly interactive analysis. In this paper, we present an analysis and visualization system, NUPT ST-data Miner, which facilitates users to visualize and analyze ST-data. It (1) provides a flexible and extensible framework based on cloud computing platform, (2) is able to quickly retrieve specified ST-data, (3) integrated multiple functions for the ST-data. To demonstrate its efficiency, we validate …
Multitask Allocation To Heterogeneous Participants In Mobile Crowd Sensing, Weiping Zhu, Wenzhong Guo, Zhiyong Yu, Haoyi Xiong
Multitask Allocation To Heterogeneous Participants In Mobile Crowd Sensing, Weiping Zhu, Wenzhong Guo, Zhiyong Yu, Haoyi Xiong
Computer Science Faculty Research & Creative Works
Task allocation is a key problem in Mobile Crowd Sensing (MCS). Prior works have mainly assumed that participants can complete tasks once they arrive at the location of tasks. However, this assumption may lead to poor reliability in sensing data because the heterogeneity among participants is disregarded. In this study, we investigate a multitask allocation problem that considers the heterogeneity of participants (i.e., different participants carry various devices and accomplish different tasks). A greedy discrete particle swarm optimization with genetic algorithm operation is proposed in this study to address the abovementioned problem. This study is aimed at maximizing the number …
Tensorviz: Visualizing The Training Of Convolutional Neural Network Using Paraview, Xinyu Chen, Qiang Guan, Xin Liang, Li-Ta Lo, Simon Su, Trilce Estrada, James Ahrens
Tensorviz: Visualizing The Training Of Convolutional Neural Network Using Paraview, Xinyu Chen, Qiang Guan, Xin Liang, Li-Ta Lo, Simon Su, Trilce Estrada, James Ahrens
Computer Science Faculty Research & Creative Works
Deep Convolutional Networks have been very successful in visual recognition tasks recently. Previous works visualize learned features at different layers o help people to understand how CNNs learn visual recognition tasks. However they do not help to accelerate the training process. We use Paraview to provides both qualitative and quantitative visualization that help understand the learning procedure, tune the learning parameters and direct merging and pruning of neural networks.
Tensorviz: Visualizing The Training Of Convolutional Neural Network Using Paraview (Poster), Xinyu Chen, Qiang Guan, Xin Liang, Li-Ta Lo, Simon Su, Trilce Estrada, James Ahrens
Tensorviz: Visualizing The Training Of Convolutional Neural Network Using Paraview (Poster), Xinyu Chen, Qiang Guan, Xin Liang, Li-Ta Lo, Simon Su, Trilce Estrada, James Ahrens
Computer Science Faculty Research & Creative Works
Deep Convolutional Networks have been very successful in visual recognition tasks recently. Lots of previous works aimed to help people to get senses of why those biology-inspired networks achieved such good performances. Deconvnet[1], Guided propagation[2] and a comprehensive visualization tool box[3] can help people to see features learned at different layers of the networks. These works in some extent provided understanding and support for the biology origin of how convolutional networks emulate visual recognition tasks. However, due to the complexity of searching in very high dimensional parameter space, the whole training remains in black-boxes. Normally a large network needs weeks …
Design The Capacity Of Onsite Generation System With Renewable Sources For Manufacturing Plant, Xiao Zhong, Md Monirul Islam, Haoyi Xiong, Zeyi Sun
Design The Capacity Of Onsite Generation System With Renewable Sources For Manufacturing Plant, Xiao Zhong, Md Monirul Islam, Haoyi Xiong, Zeyi Sun
Computer Science Faculty Research & Creative Works
The utilization of onsite generation system with renewable sources in manufacturing plants plays a critical role in improving the resilience, enhancing the sustainability, and bettering the cost effectiveness for manufacturers. When designing the capacity of onsite generation system, the manufacturing energy load needs to be met and the cost for building and operating such onsite system with renewable sources are two critical factors need to be carefully quantified. Due to the randomness of machine failures and the variation of local weather, it is challenging to determine the energy load and onsite generation supply at different time periods. In this paper, …
Realistic Models For Characterizing The Performance Of Unmanned Aerial Vehicles, Ken Goss, Riccardo Musmeci, Simone Silvestri
Realistic Models For Characterizing The Performance Of Unmanned Aerial Vehicles, Ken Goss, Riccardo Musmeci, Simone Silvestri
Computer Science Faculty Research & Creative Works
Unmanned Aerial Vehicles (UAVs) are increasingly being adopted for military and civilian applications. UAVs available on the market are well known to be resource constrained, especially in terms of available energy. As a result, it is very challenging to predict the critical performance characteristics of a UAV, such as flight time or the ability of a UAV to complete a mission, given the system parameters. Nevertheless, such predictions would have several benefits, such as improving the effectiveness of mission planners and optimization algorithms in general, as well as enabling researchers to perform more realistic simulations. The goal of this paper …
Minimal Coflow Routing And Scheduling In Openflow-Based Cloud Storage Area Networks, Chui Hui Chiu, Dipak Kumar Singh, Qingyang Wang, Kisung Lee, Seung Jong Park
Minimal Coflow Routing And Scheduling In Openflow-Based Cloud Storage Area Networks, Chui Hui Chiu, Dipak Kumar Singh, Qingyang Wang, Kisung Lee, Seung Jong Park
Computer Science Faculty Research & Creative Works
Researches affirm that coflow scheduling/routing substantially shortens the average application inner communication time in data center networks (DCNs). The commonly desirable critical features of existing coflow scheduling/routing framework includes (1) coflow scheduling, (2) coflow routing, and (3) per-flow rate-limiting. However, to provide the 3 features, existing frameworks require customized computing frameworks, customized operating systems, or specific external commercial monitoring frameworks on software-defined networking (SDN) switches. These requirements defer or even prohibit the deployment of coflow scheduling/routing in production DCNs. In this paper, we design a coflow scheduling and routing framework, MinCOF which has minimal requirements on hosts and switches for …
Augmenting Amdahl's Second Law: A Theoretical Model To Build Cost-Effective Balanced Hpc Infrastructure For Data-Driven Science, Arghya Kusum Das, Jaeki Hong, Sayan Goswami, Richard Platania, Kisung Lee, Wooseok Chang, Seung Jong Park, Ling Liu
Augmenting Amdahl's Second Law: A Theoretical Model To Build Cost-Effective Balanced Hpc Infrastructure For Data-Driven Science, Arghya Kusum Das, Jaeki Hong, Sayan Goswami, Richard Platania, Kisung Lee, Wooseok Chang, Seung Jong Park, Ling Liu
Computer Science Faculty Research & Creative Works
High-performance analysis of big data demands more computing resources, forcing similar growth in computation cost. So, the challenge to the HPC system designers is providing not only high performance but also high performance at lower cost. For high performance yet cost-effective cyberinfrastructure, we propose a new system model augmenting Amdahl's second law for balanced system to optimize price-performance-ratio. We express the optimal balance among CPU-speed, I/O-bandwidth and DRAM-size (i.e., Amdahl's I/O-and memory-number) in terms of application characteristics and hardware cost. Considering Xeon processor and recent hardware prices, we showed that a system needs almost 0.17GBPS I/O-bandwidth and 3GB DRAM per …
Coflourish: An Sdn-Assisted Coflow Scheduling Framework For Clouds, Chui Hui Chiu, Dipak Kumar Singh, Qingyang Wang, Seung Jong Park
Coflourish: An Sdn-Assisted Coflow Scheduling Framework For Clouds, Chui Hui Chiu, Dipak Kumar Singh, Qingyang Wang, Seung Jong Park
Computer Science Faculty Research & Creative Works
Existing coflow scheduling frameworks effectively shorten communication time and completion time of cluster applications. However, existing frameworks only consider available bandwidth on hosts and overlook congestion in the network when making scheduling decisions. Through extensive simulations using the realistic workload probability distribution from Facebook, we observe the performance degradation of the state-of-the-art coflow scheduling framework, Varys, in the cloud environment on a shared data center network (DCN) because of the lack of network congestion information. We propose Coflourish, the first coflow scheduling framework that exploits the congestion feedback assistances from the software-defined-networking (SDN)-enabled switches in the networks for available bandwidth …
Automated Breast Cancer Diagnosis Using Deep Learning And Region Of Interest Detection (Bc-Droid), Richard Platania, Jian Zhang, Shayan Shams, Kisung Lee, Seungwon Yang, Seung Jong Park
Automated Breast Cancer Diagnosis Using Deep Learning And Region Of Interest Detection (Bc-Droid), Richard Platania, Jian Zhang, Shayan Shams, Kisung Lee, Seungwon Yang, Seung Jong Park
Computer Science Faculty Research & Creative Works
Detection of suspicious regions in mammogram images and the subsequent diagnosis of these regions remains a challenging problem in the medical world. There still exists an alarming rate of misdiagnosis of breast cancer. This results in both over treatment through incorrect positive diagnosis of cancer and under treatment through overlooked cancerous masses. Convolutional neural networks have shown strong applicability to various image datasets, enabling detailed features to be learned from the data and, as a result, the ability to classify these images at extremely low error rates. In order to overcome the difficulty in diagnosing breast cancer from mammogram images, …
Gaslight: A Comprehensive Fuzzing Architecture For Memory Forensics Frameworks, Andrew Case, Arghya Kusum Das, Seung Jong Park, J. (Ram) Ramanujam, Golden G. Richard
Gaslight: A Comprehensive Fuzzing Architecture For Memory Forensics Frameworks, Andrew Case, Arghya Kusum Das, Seung Jong Park, J. (Ram) Ramanujam, Golden G. Richard
Computer Science Faculty Research & Creative Works
Memory forensics is now a standard component of digital forensic investigations and incident response handling, since memory forensic techniques are quite effective in uncovering artifacts that might be missed by traditional storage forensics or live analysis techniques. Because of the crucial role that memory forensics plays in investigations and because of the increasing use of automation of memory forensics techniques, it is imperative that these tools be resilient to memory smear and deliberate tampering. Without robust algorithms, malware may go undetected, frameworks may crash when attempting to process memory samples, and automation of memory forensics techniques is difficult. In this …
On Critical Service Recovery After Massive Network Failures, Novella Bartolini, Stefano Ciavarella, Thomas F. La Porta, Simone Silvestri
On Critical Service Recovery After Massive Network Failures, Novella Bartolini, Stefano Ciavarella, Thomas F. La Porta, Simone Silvestri
Computer Science Faculty Research & Creative Works
This paper addresses the problem of efficiently restoring sufficient resources in a communications network to support the demand of mission critical services after a large-scale disruption. We give a formulation of the problem as a mixed integer linear programming and show that it is NP-hard. We propose a polynomial time heuristic, called iterative split and prune (ISP) that decomposes the original problem recursively into smaller problems, until it determines the set of network components to be restored. ISP's decisions are guided by the use of a new notion of demand-based centrality of nodes. We performed extensive simulations by varying the …
Evaluation Of Deep Learning Frameworks Over Different Hpc Architectures, Shayan Shams, Richard Platania, Kisung Lee, Seung Jong Park
Evaluation Of Deep Learning Frameworks Over Different Hpc Architectures, Shayan Shams, Richard Platania, Kisung Lee, Seung Jong Park
Computer Science Faculty Research & Creative Works
Recent advances in deep learning have enabled researchers across many disciplines to uncover new insights about large datasets. Deep neural networks have shown applicability to image, time-series, textual, and other data, all of which are available in a plethora of research fields. However, their computational complexity and large memory overhead requires advanced software and hardware technologies to train neural networks in a reasonable amount of time. To make this possible, there has been an influx in development of deep learning software that aim to leverage advanced hardware resources. In order to better understand the performance implications of deep learning frameworks …
Cnn Based 3d Facial Expression Recognition Using Masking And Landmark Features, Huiyuan Yang, Lijun Yin
Cnn Based 3d Facial Expression Recognition Using Masking And Landmark Features, Huiyuan Yang, Lijun Yin
Computer Science Faculty Research & Creative Works
Automatically recognizing facial expression is an important part for human-machine interaction. In this paper, we first review the previous studies on both 2D and 3D facial expression recognition, and then summarize the key research questions to solve in the future. Finally, we propose a 3D facial expression recognition (FER) algorithm using convolutional neural networks (CNNs) and landmark features/masks, which is invariant to pose and illumination variations due to the solely use of 3D geometric facial models without any texture information. The proposed method has been tested on two public 3D facial expression databases: BU-4DFE and BU-3DFE. The results show that …
Automatic Privacy Prediction To Accelerate Social Image Sharing, Zhenzhong Kuang, Zongmin Li, Dan Lin, Jianping Fan
Automatic Privacy Prediction To Accelerate Social Image Sharing, Zhenzhong Kuang, Zongmin Li, Dan Lin, Jianping Fan
Computer Science Faculty Research & Creative Works
The manual process for privacy setting could be very time-consuming and challenging for common users. By assuming that there are hidden correlations between the visual properties of images (i.e., visual features) or object classes and the privacy settings for image sharing, an effective algorithm is developed in this paper to achieve automatic prediction of image privacy, so that the best-matching privacy setting can be recommended automatically for each single image being shared. Our algorithm for automatic image privacy prediction contains two approaches: (a) feature-based approach by learning more representative deep features and discriminative classifier for assigning each single image being …
Energy-Efficient Multi-Core Scheduling For Real-Time Dag Tasks, Zhishan Guo, Ashikahmed Bhuiyan, Abusayeed Saifullah, Nan Guan, Haoyi Xiong
Energy-Efficient Multi-Core Scheduling For Real-Time Dag Tasks, Zhishan Guo, Ashikahmed Bhuiyan, Abusayeed Saifullah, Nan Guan, Haoyi Xiong
Computer Science Faculty Research & Creative Works
In this work, we study energy-aware real-time scheduling of a set of sporadic Directed Acyclic Graph (DAG) tasks with implicit deadlines. While meeting all real-time constraints, we try to identify the best task allocation and execution pattern such that the average power consumption of the whole platform is minimized. To the best of our knowledge, this is the first work that addresses the power consumption issue in scheduling multiple DAG tasks on multi-cores and allows intra-task processor sharing. We first adapt the decomposition-based framework for federated scheduling and propose an energy-sub-optimal scheduler. Then we derive an approximation algorithm to identify …
Autonomous Mobile Sensor Placement In Complex Environments, Novella Bartolini, Tiziana Calamoneri, Stefano Ciavarella, Thomas La Porta, Simone Silvestri
Autonomous Mobile Sensor Placement In Complex Environments, Novella Bartolini, Tiziana Calamoneri, Stefano Ciavarella, Thomas La Porta, Simone Silvestri
Computer Science Faculty Research & Creative Works
In this article, we address the problem of autonomously deploying mobile sensors in an unknown complex environment. In such a scenario, mobile sensors may encounter obstacles or environmental sources of noise, so that movement and sensing capabilities can be significantly altered and become anisotropic. Any reduction of device capabilities cannot be known prior to their actual deployment, nor can it be predicted. We propose a new algorithm for autonomous sensor movements and positioning, called DOMINO (DeplOyment of Mobile Networks with Obstacles). Unlike traditional approaches, DOMINO explicitly addresses these issues by realizing a grid-based deployment throughout the Area of Interest (AoI) …
A Deep Learning Framework For Automated Vesicle Fusion Detection, Haohan Li, Zhaozheng Yin, Yingke Xu
A Deep Learning Framework For Automated Vesicle Fusion Detection, Haohan Li, Zhaozheng Yin, Yingke Xu
Computer Science Faculty Research & Creative Works
Quantitative analysis of vesicle-plasma membrane fusion events in the fluorescence microscopy, has been proven to be important in the vesicle exocytosis study. In this paper, we present a framework to automatically detect fusion events. First, an iterative searching algorithm is developed to extract image patch sequences containing potential events. Then, we propose an event image to integrate the critical image patches of a candidate event into a single-image joint representation as the input to Convolutional Neural Networks (CNNs). According to the duration of candidate events, we design three CNN architectures to automatically learn features for the fusion event classification. Compared …
Early Detection Of Diseases Using Electronic Health Records Data And Covariance-Regularized Linear Discriminant Analysis, Jiang Bian, Laura E. Barnes, Guanling Chen, Haoyi Xiong
Early Detection Of Diseases Using Electronic Health Records Data And Covariance-Regularized Linear Discriminant Analysis, Jiang Bian, Laura E. Barnes, Guanling Chen, Haoyi Xiong
Computer Science Faculty Research & Creative Works
The availability of Electronic Health Records (EHR) in health care settings provides terrific opportunities for early detection of patients' potential diseases. While many data mining tools have been adopted for EHR-based disease early detection, Linear Discriminant Analysis (LDA) is one of the most widely used statistical prediction methods. To improve the performance of LDA for early detection of diseases, we proposed to leverage CRDA - Covariance-Regularized LDA classifiers on top of diagnosis-frequency vector data representation. Specifically, CRDA employs a sparse precision matrix estimator derived based on graphical lasso to boost the accuracy of LDA classifiers. Algorithm analysis demonstrates that the …
Using Mobile Sensing To Test Clinical Models Of Depression, Social Anxiety, State Affect, And Social Isolation Among College Students, Philip I. Chow, Karl Fua, Yu Huang, Wesley Bonelli, Haoyi Xiong, Laura E. Barnes, Bethany A. Teachman
Using Mobile Sensing To Test Clinical Models Of Depression, Social Anxiety, State Affect, And Social Isolation Among College Students, Philip I. Chow, Karl Fua, Yu Huang, Wesley Bonelli, Haoyi Xiong, Laura E. Barnes, Bethany A. Teachman
Computer Science Faculty Research & Creative Works
Background: Research in psychology demonstrates a strong link between state affect (moment-to-moment experiences of positive or negative emotionality) and trait affect (e.g., relatively enduring depression and social anxiety symptoms), and a tendency to withdraw (e.g., spending time at home). However, existing work is based almost exclusively on static, self-reported descriptions of emotions and behavior that limit generalizability. Despite adoption of increasingly sophisticated research designs and technology (e.g., mobile sensing using a global positioning system [GPS]), little research has integrated these seemingly disparate forms of data to improve understanding of how emotional experiences in everyday life are associated with time spent …
Hadoop-Based Replica Exchange Over Heterogeneous Distributed Cyberinfrastructures, Richard Platania, Shayan Shams, Chui Hui Chiu, Nayong Kim, Joohyun Kim, Seung Jong Park
Hadoop-Based Replica Exchange Over Heterogeneous Distributed Cyberinfrastructures, Richard Platania, Shayan Shams, Chui Hui Chiu, Nayong Kim, Joohyun Kim, Seung Jong Park
Computer Science Faculty Research & Creative Works
We present Hadoop-based replica exchange (HaRE), a Hadoop-based implementation of the replica exchange scheme developed primarily for replica exchange statistical temperature molecular dynamics, an example of a large-scale, advanced sampling molecular dynamics simulation. By using Hadoop as a framework and the MapReduce model for driving replica exchange, an efficient task-level parallelism is introduced to replica exchange statistical temperature molecular dynamics simulations. In order to demonstrate this, we investigate the performance of our application over various distributed cyberinfrastructures (DCI), including several high-performance computing systems, our cyberinfrastructure for reconfigurable optical networks testbed, the global environment for network innovations testbed, and the CloudLab …
Daehr: A Discriminant Analysis Framework For Electronic Health Record Data And An Application To Early Detection Of Mental Health Disorders, Haoyi Xiong, Jinghe Zhang, Yu Huang, Kevin Leach, Laura E. Barnes
Daehr: A Discriminant Analysis Framework For Electronic Health Record Data And An Application To Early Detection Of Mental Health Disorders, Haoyi Xiong, Jinghe Zhang, Yu Huang, Kevin Leach, Laura E. Barnes
Computer Science Faculty Research & Creative Works
Electronic health records (EHR) provide a rich source of temporal data that present a unique opportunity to characterize disease patterns and risk of imminent disease. While many data-mining tools have been adopted for EHR-based disease early detection, linear discriminant analysis (LDA) is one of the most commonly used statistical methods. However, it is difficult to train an accurate LDA model for early disease diagnosis when too few patients are known to have the target disease. Furthermore, EHR data are heterogeneous with significant noise. In such cases, the covariance matrices used in LDA are usually singular and estimated with a large …
Network Connectivity Assessment And Improvement Through Relay Node Deployment, Maggie X. Cheng, Yi Ling, Brian M. Sadler
Network Connectivity Assessment And Improvement Through Relay Node Deployment, Maggie X. Cheng, Yi Ling, Brian M. Sadler
Computer Science Faculty Research & Creative Works
In wireless ad hoc networks, maintaining network connectivity is very important as high-level network functions all depend on it. However, how to measure network connectivity remains a fundamental challenge. For example, a network can have good overall k-connectivity and yet still have a communication bottleneck. In this paper, we address how to locate bottlenecks and relieve them. A new connectivity measure based on the Cheeger's Constant is used for bottleneck discovery, and a partition algorithm that divides the network at the bottleneck is developed. After the network is partitioned, we consider deploying a relay node to increase the conductance of …
Privacy Setting Recommendation For Image Sharing, Jun Yu, Zhenzhong Kuang, Zhou Yu, Dan Lin, Jianping Fan
Privacy Setting Recommendation For Image Sharing, Jun Yu, Zhenzhong Kuang, Zhou Yu, Dan Lin, Jianping Fan
Computer Science Faculty Research & Creative Works
This paper aims to simultaneously consider two inseparable issues for privacy setting recommendation: (1) sensitiveness of visual content of the images being shared; and (2) trustworthiness of users being granted. First, an object-based approach is developed for image content sensitiveness (privacy) representation. Secondly, the users on a social network are clustered into a set of representative social groups to generate a discriminative dictionary for user trustworthiness characterization. Finally, a tree classifier is trained hierarchically to recommend appropriate privacy settings for image sharing.
Ieee Access Special Section Editorial: Emergent Topics For Mobile And Ubiquitous Systems In Smartphone, Iot, And Cloud Computing Era, Takahiro Hara, Stephan Sigg, Lei Shu, Francesco De Pellegrini, Chiara Petrioli, Sanjay Kumar Madria
Ieee Access Special Section Editorial: Emergent Topics For Mobile And Ubiquitous Systems In Smartphone, Iot, And Cloud Computing Era, Takahiro Hara, Stephan Sigg, Lei Shu, Francesco De Pellegrini, Chiara Petrioli, Sanjay Kumar Madria
Computer Science Faculty Research & Creative Works
No abstract provided.
Multimodal Spontaneous Emotion Corpus For Human Behavior Analysis, Zheng Zhang, Jeffrey M. Girard, Yue Wu, Xing Zhang, Peng Liu, Umur Ciftci, Shaun Canavan, Michael Reale, Andrew Horowitz, Huiyuan Yang, Jeffrey F. Cohn, Qiang Ji, Lijun Yin
Multimodal Spontaneous Emotion Corpus For Human Behavior Analysis, Zheng Zhang, Jeffrey M. Girard, Yue Wu, Xing Zhang, Peng Liu, Umur Ciftci, Shaun Canavan, Michael Reale, Andrew Horowitz, Huiyuan Yang, Jeffrey F. Cohn, Qiang Ji, Lijun Yin
Computer Science Faculty Research & Creative Works
Emotion is expressed in multiple modalities, yet most research has considered at most one or two. This stems in part from the lack of large, diverse, well-annotated, multimodal databases with which to develop and test algorithms. We present a well-annotated, multimodal, multidimensional spontaneous emotion corpus of 140 participants. Emotion inductions were highly varied. Data were acquired from a variety of sensors of the face that included high-resolution 3D dynamic imaging, high-resolution 2D video, and thermal (infrared) sensing, and contact physiological sensors that included electrical conductivity of the skin, respiration, blood pressure, and heart rate. Facial expression was annotated for both …
On The Vulnerabilities Of Voronoi-Based Approaches To Mobile Sensor Deployment, Novella Bartolini, Stefano Ciavarella, Simone Silvestri, Thomas La Porta
On The Vulnerabilities Of Voronoi-Based Approaches To Mobile Sensor Deployment, Novella Bartolini, Stefano Ciavarella, Simone Silvestri, Thomas La Porta
Computer Science Faculty Research & Creative Works
Mobile sensor networks are the most promising solution to cover an Area of Interest (AoI) in safety critical scenarios. Mobile devices can coordinate with each other according to a distributed deployment algorithm, without resorting to human supervision for device positioning and network configuration. In this paper, we focus on the vulnerabilities of the deployment algorithms based on Voronoi diagrams to coordinate mobile sensors and guide their movements. We give a geometric characterization of possible attack configurations, proving that a simple attack consisting of a barrier of few compromised sensors can severely reduce network coverage. On the basis of the above …
Cloud-Enhanced Robotic System For Smart City Crowd Control, Akhlaqur Rahman, Jiong Jin, Antonio Cricenti, Ashfaqur Rahman, Marimuthu Palaniswami, Tony Tie Luo
Cloud-Enhanced Robotic System For Smart City Crowd Control, Akhlaqur Rahman, Jiong Jin, Antonio Cricenti, Ashfaqur Rahman, Marimuthu Palaniswami, Tony Tie Luo
Computer Science Faculty Research & Creative Works
Cloud robotics in smart cities is an emerging paradigm that enables autonomous robotic agents to communicate and collaborate with a cloud computing infrastructure. It complements the Internet of Things (IoT) by creating an expanded network where robots offload data-intensive computation to the ubiquitous cloud to ensure quality of service (QoS). However, offloading for robots is significantly complex due to their unique characteristics of mobility, skill-learning, data collection, and decision-making capabilities. In this paper, a generic cloud robotics framework is proposed to realize smart city vision while taking into consideration its various complexities. Specifically, we present an integrated framework for a …
A Hypothesis Testing Approach For Topology Error Detection In Power Grids, Wei Biao Wu, Maggie X. Cheng, Bei Gou
A Hypothesis Testing Approach For Topology Error Detection In Power Grids, Wei Biao Wu, Maggie X. Cheng, Bei Gou
Computer Science Faculty Research & Creative Works
When the grid topology is changed due to incidents and the state estimator is not updated with the topological change, it is considered a topology error. In this paper, we develop a new method for detecting topology errors in power grids. The proposed method considers the measurement data as a nonstationary Gaussian process, explores the dependence structure of the underlying process. It detects errors by testing the hypothesis of whether the mean vector of a nonstationary Gaussian process is zero and does not rely on the convergence of the standard weighted least-squares (WLS) state estimation algorithm. It is very effective …
Fine-Grained Multitask Allocation For Participatory Sensing With A Shared Budget, Jiangtao Wang, Yasha Wang, Daqing Zhang, Leye Wang, Haoyi Xiong, Abdelsalam Helal, Yuanduo He, Feng Wang
Fine-Grained Multitask Allocation For Participatory Sensing With A Shared Budget, Jiangtao Wang, Yasha Wang, Daqing Zhang, Leye Wang, Haoyi Xiong, Abdelsalam Helal, Yuanduo He, Feng Wang
Computer Science Faculty Research & Creative Works
For participatory sensing, task allocation is a crucial research problem that embodies a tradeoff between sensing quality and cost. An organizer usually publishes and manages multiple tasks utilizing one shared budget. Allocating multiple tasks to participants, with the objective of maximizing the overall data quality under the shared budget constraint, is an emerging and important research problem. We propose a fine-grained multitask allocation framework (MTPS), which assigns a subset of tasks to each participant in each cycle. Specifically, considering the user burden of switching among varying sensing tasks, MTPS operates on an attention-compensated incentive model where, in addition to the …