Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Drone (5)
- IoT (4)
- Anomaly Detection (3)
- Edge computing (3)
- Federated learning (3)
-
- Internet of Things (3)
- Lossy compression (3)
- Routing (3)
- Behavioral sciences (2)
- Energy efficiency (2)
- Feature extraction (2)
- Privacy-preserving (2)
- Scientific data (2)
- Simulation (2)
- Smart Transportation (2)
- Smart grids (2)
- 5G (1)
- 6TiSCH (1)
- Access control (1)
- Active Learning (1)
- Age of information (1)
- Air traffic (1)
- Anomaly (attack) detection (1)
- Anomaly detection (1)
- Approximation algorithms (1)
- Artificial intelligence (1)
- Attacker (1)
- Attackers (1)
- Attribute based Encryption (1)
- Attribute-based encryption (1)
Articles 1 - 30 of 50
Full-Text Articles in Physical Sciences and Mathematics
Transcriptional Regulatory Network Topology With Applications To Bio-Inspired Networking: A Survey, Satyaki Roy, Preetam Ghosh, Nirnay Ghosh, Sajal K. Das
Transcriptional Regulatory Network Topology With Applications To Bio-Inspired Networking: A Survey, Satyaki Roy, Preetam Ghosh, Nirnay Ghosh, Sajal K. Das
Computer Science Faculty Research & Creative Works
The advent of the edge computing network paradigm places the computational and storage resources away from the data centers and closer to the edge of the network largely comprising the heterogeneous IoT devices collecting huge volumes of data. This paradigm has led to considerable improvement in network latency and bandwidth usage over the traditional cloud-centric paradigm. However, the next generation networks continue to be stymied by their inability to achieve adaptive, energy-efficient, timely data transfer in a dynamic and failure-prone environment - the very optimization challenges that are dealt with by biological networks as a consequence of millions of years …
Stable Matching Based Resource Allocation For Service Provider's Revenue Maximization In 5g Networks, Ajay Pratap, Sajal K. Das
Stable Matching Based Resource Allocation For Service Provider's Revenue Maximization In 5g Networks, Ajay Pratap, Sajal K. Das
Computer Science Faculty Research & Creative Works
5G technology is foreseen to have a heterogeneous architecture with the various computational capability, and radio-enabled service providers (SPs) and service requesters (SRs), working altogether in a cellular model. However, the coexistence of heterogeneous network model spawns several research challenges such as diverse SRs with uneven service deadlines, interference management, and revenue maximization of non-uniform computational capacities enabled SPs. Thus, we propose a coexistence of heterogeneous SPs and SRs enabled cellular 5G network and formulate the SPs' revenue maximization via resource allocation, considering different kinds of interference, data rate, and latency altogether as an optimization problem and further propose a …
Dtc: A Dynamic Transaction Chopping Technique For Geo-Replicated Storage Services, Ning Huang, Lihui Wu, Weigang Wu, Sajal K. Das
Dtc: A Dynamic Transaction Chopping Technique For Geo-Replicated Storage Services, Ning Huang, Lihui Wu, Weigang Wu, Sajal K. Das
Computer Science Faculty Research & Creative Works
Replicating data across geo-distributed datacenters is usually necessary for large scale cloud services to achieve high locality, durability and availability. One of the major challenges in such geo-replicated data services lies in consistency maintenance, which usually suffers from long latency due to costly coordination across datacenters. Among others, transaction chopping is an effective and efficient approach to address this challenge. However, existing chopping is conducted statically during programming, which is stubborn and complex for developers. In this article, we propose Dynamic Transaction Chopping (DTC), a novel technique that does transaction chopping and determines piecewise execution in a dynamic and automatic …
An Energy Efficient Smart Metering System Using Edge Computing In Lora Network, Preti Kumari, Rahul Mishra, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das
An Energy Efficient Smart Metering System Using Edge Computing In Lora Network, Preti Kumari, Rahul Mishra, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das
Computer Science Faculty Research & Creative Works
An important research issue in smart metering is to correctly transfer the smart meter readings from consumers to the operator within the given time period by consuming minimum energy. In this paper, we propose an energy efficient smart metering system using Edge computing in Long Range (LoRa). We assume that all appliances in a house are connected to a smart meter that is affixed with Edge device and LoRa node for processing and transferring the processed smart meter readings, respectively. The energy consumption of the appliances can be represented as an energy multivariate time series. The system first proposes a …
Efficient Data Collection In Iot Networks Using Trajectory Encoded With Geometric Shapes, Xiaofei Cao, Sanjay Kumar Madria
Efficient Data Collection In Iot Networks Using Trajectory Encoded With Geometric Shapes, Xiaofei Cao, Sanjay Kumar Madria
Computer Science Faculty Research & Creative Works
The mobile edge computing (MEC) paradigm changes the role of edge devices from data producers and requesters to data consumers and processors. MEC mitigates the bandwidth limitation between the edge server and the remote cloud by directly processing the large amount of data locally generated by the network of the internet of things (IoT) at the edge. An efficient data-gathering scheme is crucial for providing quality of service (QoS) within MEC. To reduce redundant data transmission, this paper proposes a data collection scheme that only gathers the necessary data from IoT devices (like wireless sensors) along a trajectory. Instead of …
A Survey On Mobile Charging Techniques In Wireless Rechargeable Sensor Networks, Amar Kaswan, Prasanta K. Jana, Sajal K. Das
A Survey On Mobile Charging Techniques In Wireless Rechargeable Sensor Networks, Amar Kaswan, Prasanta K. Jana, Sajal K. Das
Computer Science Faculty Research & Creative Works
The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of studies have been carried out over the last decade in this regard. However, no comprehensive survey exists to compile the state-of-the-art literature and provide insight into future research directions. To fill this gap, we put forward a detailed survey on mobile charging techniques (MCTs) in WRSNs. In particular, we first describe the network model, various WPT techniques with empirical models, system design issues and performance metrics concerning the …
Guest Editorial: Special Section On Distributed Intelligence Over Internet Of Things, Honglong Chen, Joel Rodrigues, Feng Xia, Sajal K. Das
Guest Editorial: Special Section On Distributed Intelligence Over Internet Of Things, Honglong Chen, Joel Rodrigues, Feng Xia, Sajal K. Das
Computer Science Faculty Research & Creative Works
No abstract provided.
Compressed Sensing Based Low-Power Multi-View Video Coding And Transmission In Wireless Multi-Path Multi-Hop Networks, Nan Cen, Zhangyu Guan, Tommaso Melodia
Compressed Sensing Based Low-Power Multi-View Video Coding And Transmission In Wireless Multi-Path Multi-Hop Networks, Nan Cen, Zhangyu Guan, Tommaso Melodia
Computer Science Faculty Research & Creative Works
Wireless Multimedia Sensor Network (WMSN) is increasingly being deployed for surveillance, monitoring and Internet-of-Things (IoT) sensing applications where a set of cameras capture and compress local images and then transmit the data to a remote controller. Such captured local images may also be compressed in a multi-view fashion to reduce the redundancy among overlapping views. In this paper, we present a novel paradigm for compressed-sensing-enabled multi-view coding and streaming in WMSN. We first propose a new encoding and decoding architecture for multi-view video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and …
Dynamic Path Planning For Unmanned Aerial Vehicles Under Deadline And Sector Capacity Constraints, Sudharsan Vaidhun, Zhishan Guo, Jiang Bian, Haoyi Xiong, Sajal K. Das
Dynamic Path Planning For Unmanned Aerial Vehicles Under Deadline And Sector Capacity Constraints, Sudharsan Vaidhun, Zhishan Guo, Jiang Bian, Haoyi Xiong, Sajal K. Das
Computer Science Faculty Research & Creative Works
The US National Airspace System is currently operating at a level close to its maximum potential. The limitation comes from the workload demand on the air traffic controllers. Currently, the air traffic flow management is based on the flight path requests by the airline operators, whereas the minimum separation assurance between flights is handled strategically by air traffic control personnel. In this paper, we propose a scalable framework that allows path planning for a large number of unmanned aerial vehicles (UAVs) taking into account the deadline and weather constraints. Our proposed solution has a polynomial-time computational complexity that is also …
Drone-Truck Cooperated Delivery Under Time Varying Dynamics, Arindam Khanda, Federico Corò, Sajal K. Das
Drone-Truck Cooperated Delivery Under Time Varying Dynamics, Arindam Khanda, Federico Corò, Sajal K. Das
Computer Science Faculty Research & Creative Works
Rapid technological developments in autonomous unmanned aerial vehicles (or drones) could soon lead to their large-scale implementation in the last-mile delivery of products. However, drones have a number of problems such as limited energy budget, limited carrying capacity, etc. On the other hand, trucks have a larger carrying capacity, but they cannot reach all the places easily. Intriguingly, last-mile delivery cooperation between drones and trucks can synergistically improve delivery efficiency. In this paper, we present a drone-truck co-operated delivery framework under time-varying dynamics. Our framework minimizes the total delivery time while considering low energy consumption as the secondary objective. The …
Region-Adaptive, Error-Controlled Scientific Data Compression Using Multilevel Decomposition, Qian Gong, Ben Whitney, Chengzhu Zhang, Xin Liang, Anand Rangarajan, Jieyang Chen, Lipeng Wan, Paul Ullrich, Qing Liu, Robert Jacob, Sanjay Ranka, Scott Klasky
Region-Adaptive, Error-Controlled Scientific Data Compression Using Multilevel Decomposition, Qian Gong, Ben Whitney, Chengzhu Zhang, Xin Liang, Anand Rangarajan, Jieyang Chen, Lipeng Wan, Paul Ullrich, Qing Liu, Robert Jacob, Sanjay Ranka, Scott Klasky
Computer Science Faculty Research & Creative Works
The increase of computer processing speed is significantly outpacing improvements in network and storage bandwidth, leading to the big data challenge in modern science, where scientific applications can quickly generate much more data than that can be transferred and stored. As a result, big scientific data must be reduced by a few orders of magnitude while the accuracy of the reduced data needs to be guaranteed for further scientific explorations. Moreover, scientists are often interested in some specific spatial/temporal regions in their data, where higher accuracy is required. The locations of the regions requiring high accuracy can sometimes be prescribed …
Mgard+: Optimizing Multilevel Methods For Error-Bounded Scientific Data Reduction, Xin Liang, Ben Whitney, Jieyang Chen, Lipeng Wan, Qing Liu, Dingwen Tao, James Kress, David Pugmire, Matthew Wolf, Norbert Podhorszki, Scott Klasky
Mgard+: Optimizing Multilevel Methods For Error-Bounded Scientific Data Reduction, Xin Liang, Ben Whitney, Jieyang Chen, Lipeng Wan, Qing Liu, Dingwen Tao, James Kress, David Pugmire, Matthew Wolf, Norbert Podhorszki, Scott Klasky
Computer Science Faculty Research & Creative Works
Nowadays, data reduction is becoming increasingly important in dealing with the large amounts of scientific data. Existing multilevel compression algorithms offer a promising way to manage scientific data at scale but may suffer from relatively low performance and reduction quality. In this paper, we propose MGARD+, a multilevel data reduction and refactoring framework drawing on previous multilevel methods, to achieve high-performance data decomposition and high-quality error-bounded lossy compression. Our contributions are four-fold: 1) We propose to leverage a level-wise coefficient quantization method, which uses different error tolerances to quantize the multilevel coefficients. 2) We propose an adaptive decomposition method which …
Ultrafast Error-Bounded Lossy Compression For Scientific Datasets, Xiaodong Yu, Sheng Di, Kai Zhao, Jiannan Tian, Dingwen Tao, Xin Liang, Franck Cappello
Ultrafast Error-Bounded Lossy Compression For Scientific Datasets, Xiaodong Yu, Sheng Di, Kai Zhao, Jiannan Tian, Dingwen Tao, Xin Liang, Franck Cappello
Computer Science Faculty Research & Creative Works
Today's scientific high-performance computing applications and advanced instruments are producing vast volumes of data across a wide range of domains, which impose a serious burden on data transfer and storage. Error-bounded lossy compression has been developed and widely used in the scientific community because it not only can significantly reduce the data volumes but also can strictly control the data distortion based on the user-specified error bound. Existing lossy compressors, however, cannot offer ultrafast compression speed, which is highly demanded by numerous applications or use cases (such as in-memory compression and online instrument data compression). In this paper, we propose …
Accelerating Serverless Computing By Harvesting Idle Resources, Hanfei Yu, Hao Wang, Jian Li, Xu Yuan, Seung Jong Park
Accelerating Serverless Computing By Harvesting Idle Resources, Hanfei Yu, Hao Wang, Jian Li, Xu Yuan, Seung Jong Park
Computer Science Faculty Research & Creative Works
Serverless computing automates fine-grained resource scaling and simplifies the development and deployment of online services with stateless functions. However, it is still non-trivial for users to allocate appropriate resources due to various function types, dependencies, and input sizes. Misconfiguration of resource allocations leaves functions either under-provisioned or over-provisioned and leads to continuous low resource utilization. This paper presents Freyr, a new resource manager (RM) for serverless platforms that maximizes resource efficiency by dynamically harvesting idle resources from over-provisioned functions to under-provisioned functions. Freyr monitors each function's resource utilization in real-time, detects over-provisioning and under-provisioning, and learns to harvest idle resources …
A Parallel Algorithm Template For Updating Single-Source Shortest Paths In Large-Scale Dynamic Networks, Arindam Khanda, Sriram Srinivasan, Sanjukta Bhowmick, Boyana Norris, Sajal K. Das
A Parallel Algorithm Template For Updating Single-Source Shortest Paths In Large-Scale Dynamic Networks, Arindam Khanda, Sriram Srinivasan, Sanjukta Bhowmick, Boyana Norris, Sajal K. Das
Computer Science Faculty Research & Creative Works
The Single Source Shortest Path (SSSP) problem is a classic graph theory problem that arises frequently in various practical scenarios; hence, many parallel algorithms have been developed to solve it. However, these algorithms operate on static graphs, whereas many real-world problems are best modeled as dynamic networks, where the structure of the network changes with time. This gap between the dynamic graph modeling and the assumed static graph model in the conventional SSSP algorithms motivates this work. We present a novel parallel algorithmic framework for updating the SSSP in large-scale dynamic networks and implement it on the shared-memory and GPU …
Minimizing The Deployment Cost Of Uavs For Delay-Sensitive Data Collection In Iot Networks, Wenzheng Xu, Tao Xiao, Junqi Zhang, Weifa Liang, Zichuan Xu, Xuxun Liu, Xiaohua Jia, Sajal K. Das
Minimizing The Deployment Cost Of Uavs For Delay-Sensitive Data Collection In Iot Networks, Wenzheng Xu, Tao Xiao, Junqi Zhang, Weifa Liang, Zichuan Xu, Xuxun Liu, Xiaohua Jia, Sajal K. Das
Computer Science Faculty Research & Creative Works
In this paper, we study the deployment of Unmanned Aerial Vehicles (UAVs) to collect data from IoT devices, by finding a data collection tour for each UAV. To ensure the 'freshness' of the collected data, the total time spent in the tour of each UAV that consists of the UAV flying time and data collection time must be no greater than a given delay B, e.g., 20 minutes. In this paper, we consider a problem of deploying the minimum number of UAVs and finding their data collection tours, subject to the constraint that the total time spent in each tour …
Improving I/O Performance For Exascale Applications Through Online Data Layout Reorganization, Lipeng Wan, Axel Huebl, Junmin Gu, Franz Poeschel, Ana Gainaru, Ruonan Wang, Jieyang Chen, Xin Liang, Dmitry Ganyushin, Todd Munson, Ian Foster, Jean Luc Vay, Norbert Podhorszki, Kesheng Wu
Improving I/O Performance For Exascale Applications Through Online Data Layout Reorganization, Lipeng Wan, Axel Huebl, Junmin Gu, Franz Poeschel, Ana Gainaru, Ruonan Wang, Jieyang Chen, Xin Liang, Dmitry Ganyushin, Todd Munson, Ian Foster, Jean Luc Vay, Norbert Podhorszki, Kesheng Wu
Computer Science Faculty Research & Creative Works
The applications being developed within the U.S. Exascale Computing Project (ECP) to run on imminent Exascale computers will generate scientific results with unprecedented fidelity and record turn-around time. Many of these codes are based on particle-mesh methods and use advanced algorithms, especially dynamic load-balancing and mesh-refinement, to achieve high performance on Exascale machines. Yet, as such algorithms improve parallel application efficiency, they raise new challenges for I/O logic due to their irregular and dynamic data distributions. Thus, while the enormous data rates of Exascale simulations already challenge existing file system write strategies, the need for efficient read and processing of …
Measurement Errors In Range-Based Localization Algorithms For Uavs: Analysis And Experimentation, Francesco Betti Sorbelli, Cristina M. Pinotti, Simone Silvestri, Sajal K. Das
Measurement Errors In Range-Based Localization Algorithms For Uavs: Analysis And Experimentation, Francesco Betti Sorbelli, Cristina M. Pinotti, Simone Silvestri, Sajal K. Das
Computer Science Faculty Research & Creative Works
Localizing Ground Devices (GDs) is an Important Requirement for a Wide Variety of Applications, Such as Infrastructure Monitoring, Precision Agriculture, Search and Rescue Operations, to Name a Few. to This End, Unmanned Aerial Vehicles (UAVs) or Drones Offer a Promising Technology Due to their Flexibility. However, the Distance Measurements Performed using a Drone, an Integral Part of a Localization Procedure, Incur Several Errors that Affect the Localization Accuracy. in This Paper, We Provide Analytical Expressions for the Impact of Different Kinds of Measurement Errors on the Ground Distance between the UAV and GDs. We Review Three Range-Based and Three Range-Free …
Netchain: A Blockchain-Enabled Privacy-Preserving Multi-Domain Network Slice Orchestration Architecture, Guobiao He, Wei Su, Shuai Gao, Ningchun Liu, Sajal K. Das
Netchain: A Blockchain-Enabled Privacy-Preserving Multi-Domain Network Slice Orchestration Architecture, Guobiao He, Wei Su, Shuai Gao, Ningchun Liu, Sajal K. Das
Computer Science Faculty Research & Creative Works
Multi-domain networking slice orchestration is an essential technology for the programmable and cloud-native 5G network. However, existing research solutions are either based on the impractical assumption that operators will reveal all the private network information or time-consuming secure multi-party computation which is only applicable to limited computation scenarios. To provide agile and privacy-preserving end-to-end network slice orchestration services, this paper proposes NetChain, a multi-domain network slice orchestration architecture based on blockchain and trusted execution environment. Correspondingly, we design a novel consensus algorithm CoNet to ensure the strong security, scalability, and information consistency of NetChain. In addition, a bilateral evaluation mechanism …
Fedvcp: A Federated-Learning-Based Cooperative Positioning Scheme For Social Internet Of Vehicles, Xiangjie Kong, Haoran Gao, Guojiang Shen, Gaohui Duan, Sajal K. Das
Fedvcp: A Federated-Learning-Based Cooperative Positioning Scheme For Social Internet Of Vehicles, Xiangjie Kong, Haoran Gao, Guojiang Shen, Gaohui Duan, Sajal K. Das
Computer Science Faculty Research & Creative Works
Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes …
Speeding Up Routing Schedules On Aisle Graphs With Single Access, Francesco Betti Sorbelli, Stefano Carpin, Federico Coro, Sajal K. Das, Alfredo Navarra, Cristina M. Pinotti
Speeding Up Routing Schedules On Aisle Graphs With Single Access, Francesco Betti Sorbelli, Stefano Carpin, Federico Coro, Sajal K. Das, Alfredo Navarra, Cristina M. Pinotti
Computer Science Faculty Research & Creative Works
In this article, we study the orienteering aisle-graph single-access problem (OASP), a variant of the orienteering problem for a robot moving in a so-called single-access aisle graph, i.e., a graph consisting of a set of rows that can be accessed from one side only. Aisle graphs model, among others, vineyards or warehouses. Each aisle-graph vertex is associated with a reward that a robot obtains when it visits the vertex itself. As the energy of the robot is limited, only a subset of vertices can be visited with a fully charged battery. The objective is to maximize the total reward collected …
Greedy Algorithms For Scheduling Package Delivery With Multiple Drones, Francesco Betti Sorbelli, Federico Corò, Sajal K. Das, Lorenzo Palazzetti, Cristina M. Pinotti
Greedy Algorithms For Scheduling Package Delivery With Multiple Drones, Francesco Betti Sorbelli, Federico Corò, Sajal K. Das, Lorenzo Palazzetti, Cristina M. Pinotti
Computer Science Faculty Research & Creative Works
Unmanned Aerial Vehicles (or drones) can be used for a myriad of civil applications, such as search and rescue, precision agriculture, or last-mile package delivery. Interestingly, the cooperation between drones and ground vehicles (trucks) can even enhance the quality of service. In this paper, we investigate the symbiosis among a truck and multiple drones in a last-mile package delivery scenario, introducing the Multiple Drone-Delivery Scheduling Problem (MDSP). From the main depot, a truck takes care of transporting a team of drones that will be used to deliver packages to customers. Each delivery is associated with a drone's energy cost, a …
Distributed Matrix Tiling Using A Hypergraph Labeling Formulation, Avah Banerjee, Maxwell Reeser, Guoli Ding
Distributed Matrix Tiling Using A Hypergraph Labeling Formulation, Avah Banerjee, Maxwell Reeser, Guoli Ding
Computer Science Faculty Research & Creative Works
Partitioning large matrices is an important problem in distributed linear algebra computing, used in ML among others. Briefly, our goal is to perform a sequence of matrix algebra operations in a distributed manner on these large matrices. However, not all partitioning schemes work well with different matrix algebra operations and their implementations (algorithms). This is a type of data tiling problem. In this paper we consider a data tiling problem using hypergraphs. We prove some hardness results and give a theoretical characterization of its complexity on random instances. Additionally, we develop a greedy algorithm and experimentally show its efficacy.
Toward Feature-Preserving Vector Field Compression, Xin Liang, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, Tom Peterka, Hanqi Guo
Toward Feature-Preserving Vector Field Compression, Xin Liang, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, Tom Peterka, Hanqi Guo
Computer Science Faculty Research & Creative Works
The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear and bilinear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress …
More To Less (M2l): Enhanced Health Recognition In The Wild With Reduced Modality Of Wearable Sensors, Huiyuan Yang, Han Yu, Kusha Sridhar, Thomas Vaessen, Inez Myin-Germeys, Akane Sano
More To Less (M2l): Enhanced Health Recognition In The Wild With Reduced Modality Of Wearable Sensors, Huiyuan Yang, Han Yu, Kusha Sridhar, Thomas Vaessen, Inez Myin-Germeys, Akane Sano
Computer Science Faculty Research & Creative Works
Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effectively fuse information from multiple sensors. Fusing multiple sensors is a common choice in many applications but may not always be feasible in real-world scenarios. For example, although combining bio signals from multiple sensors (i.e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context. To solve the challenges, we propose an effective more to less (M2L) learning framework …
Extensive Thiol Profiling For Assessment Of Intracellular Redox Status In Cultured Cells By Hplc-Ms/Ms, Jiandong Wu, Anna Chernatynskaya, Annalise Pfaff, Huari Kou, Nan Cen, Nuran Ercal, Honglan Shi
Extensive Thiol Profiling For Assessment Of Intracellular Redox Status In Cultured Cells By Hplc-Ms/Ms, Jiandong Wu, Anna Chernatynskaya, Annalise Pfaff, Huari Kou, Nan Cen, Nuran Ercal, Honglan Shi
Computer Science Faculty Research & Creative Works
Oxidative stress may contribute to the pathology of many diseases, and endogenous thiols, especially glutathione (GSH) and its metabolites, play essential roles in the maintenance of normal redox status. Understanding how these metabolites change in response to oxidative insult can provide key insights into potential methods of prevention and treatment. Most existing methodologies focus only on the GSH/GSH disulfide (GSSG) redox couple, but GSH regulation is highly complex and depends on several pathways with multiple redox-active sulfur-containing species. In order to more fully characterize thiol redox status in response to oxidative insult, a high-performance liquid chromatography with tandem mass spectrometry …
A Drone-Based Application For Scouting Halyomorpha Halys Bugs In Orchards With Multifunctional Nets, Francesco Betti Sorbelli, Federico Coro, Sajal K. Das, Emanuele Di Bella, Lara Maistrello, Lorenzo Palazzetti, Cristina M. Pinotti
A Drone-Based Application For Scouting Halyomorpha Halys Bugs In Orchards With Multifunctional Nets, Francesco Betti Sorbelli, Federico Coro, Sajal K. Das, Emanuele Di Bella, Lara Maistrello, Lorenzo Palazzetti, Cristina M. Pinotti
Computer Science Faculty Research & Creative Works
In this work, we consider the problem of using a drone to collect information within orchards in order to scout insect pests, i.e., the stink bug Halyomorpha halys. An orchard can be modeled as an aisle-graph, which is a regular and constrained data structure formed by consecutive aisles where trees are arranged in a straight line. For monitoring the presence of bugs, a drone flies close to the trees and takes videos and/or pictures that will be analyzed offline. As the drone's energy is limited, only a subset of locations in the orchard can be visited with a fully charged …
Federated Secure Data Sharing By Edge-Cloud Computing Model*, Arijit Karati, Sajal K. Das
Federated Secure Data Sharing By Edge-Cloud Computing Model*, Arijit Karati, Sajal K. Das
Computer Science Faculty Research & Creative Works
Data sharing by cloud computing enjoys benefits in management, access control, and scalability. However, it suffers from certain drawbacks, such as high latency of downloading data, non-unified data access control management, and no user data privacy. Edge computing provides the feasibility to overcome the drawbacks mentioned above. Therefore, providing a security framework for edge computing becomes a prime focus for researchers. This work introduces a new key-aggregate cryptosystem for edge-cloud-based data sharing integrating cloud storage services. The proposed protocol secures data and provides anonymous authentication across multiple cloud platforms, key management flexibility for user data privacy, and revocability. Performance assessment …
Volunteer Selection In Collaborative Crowdsourcing With Adaptive Common Working Time Slots, Riya Samanta, Vaibhav Saxena, Soumya K. Ghosh, Sajal K. Das
Volunteer Selection In Collaborative Crowdsourcing With Adaptive Common Working Time Slots, Riya Samanta, Vaibhav Saxena, Soumya K. Ghosh, Sajal K. Das
Computer Science Faculty Research & Creative Works
Skill-based volunteering is an expanding branch of crowdsourcing where one may acquire sustainable services, solutions, and ideas from the crowd by connecting with them online. The optimal mapping between volunteers and tasks with collaboration becomes challenging for complex tasks demanding greater skills and cognitive ability. Unlike traditional crowdsourcing, volunteers like to work on their own schedule and locations. To address this problem, we propose a novel two-phase framework consisting of Initial Volunteer-Task Mapping (i-VTM) and Adaptive Common Slot Finding (a-CSF) algorithms. The i-VTM algorithm assigns volunteers to the tasks based on their skills and spatial proximity, whereas the a-CSF algorithm …
Noise Resilient Learning For Attack Detection In Smart Grid Pmu Infrastructure, Prithwiraj Roy, Shameek Bhattacharjee, Sahar Abedzadeh, Sajal K. Das
Noise Resilient Learning For Attack Detection In Smart Grid Pmu Infrastructure, Prithwiraj Roy, Shameek Bhattacharjee, Sahar Abedzadeh, Sajal K. Das
Computer Science Faculty Research & Creative Works
Falsified data from compromised Phasor Measurement Units (PMUs) in a smart grid induce Energy Management Systems (EMS) to have an inaccurate estimation of the state of the grid, disrupting various operations of the power grid. Moreover, the PMUs deployed at the distribution layer of a smart grid show dynamic fluctuations in their data streams, which make it extremely challenging to design effective learning frameworks for anomaly-based attack detection. In this paper, we propose a noise resilient learning framework for anomaly-based attack detection specifically for distribution layer PMU infrastructure, that show real time indicators of data falsifications attacks while offsetting the …