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Using Geographic Location-Based Public Health Features In Survival Analysis, Navid Seidi, Ardhendu S. Tripathy, Sajal K. Das Jan 2023

Using Geographic Location-Based Public Health Features In Survival Analysis, Navid Seidi, Ardhendu S. Tripathy, Sajal K. Das

Computer Science Faculty Research & Creative Works

Time elapsed till an event of interest is often modeled using the survival analysis methodology, which estimates a survival score based on the input features. There is a resurgence of interest in developing more accurate prediction models for time-to-event prediction in personalized healthcare using modern tools such as neural networks. Higher quality features and more frequent observations improve the predictions for a patient, however, the impact of including a patient's geographic location-Based public health statistics on individual predictions has not been studied. This paper proposes a complementary improvement to survival analysis models by incorporating public health statistics in the input …

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Towards A Domain-Agnostic Knowledge Graph-As-A-Service Infrastructure For Active Cyber Defense With Intelligent Agents, Prasad Calyam, Mayank Kejriwal, Praveen Rao, Jianlin Cheng, Weichao Wang, Linquan Bai, V. Sriram Siddhardh Nadendla, Sanjay Kumar Madria, Sajal K. Das, Rohit Chadha, Khaza Anuarul Hoque, Kannappan Palaniappan, Kiran Neupane, Roshan Lal Neupane, Sankeerth Gandhari, Mukesh Singhal, Lotfi Othmane, Meng Yu Jan 2023

Towards A Domain-Agnostic Knowledge Graph-As-A-Service Infrastructure For Active Cyber Defense With Intelligent Agents, Prasad Calyam, Mayank Kejriwal, Praveen Rao, Jianlin Cheng, Weichao Wang, Linquan Bai, V. Sriram Siddhardh Nadendla, Sanjay Kumar Madria, Sajal K. Das, Rohit Chadha, Khaza Anuarul Hoque, Kannappan Palaniappan, Kiran Neupane, Roshan Lal Neupane, Sankeerth Gandhari, Mukesh Singhal, Lotfi Othmane, Meng Yu

Computer Science Faculty Research & Creative Works

Active cyber defense mechanisms are necessary to perform automated, and even autonomous operations using intelligent agents that defend against modern/sophisticated AI-inspired cyber threats (e.g., ransomware, cryptojacking, deep-fakes). These intelligent agents need to rely on deep learning using mature knowledge and should have the ability to apply this knowledge in a situational and timely manner for a given AI-inspired cyber threat. in this paper, we describe a 'domain-Agnostic knowledge graph-As-A-service' infrastructure that can support the ability to create/store domain-specific knowledge graphs for intelligent agent Apps to deploy active cyber defense solutions defending real-world applications impacted by AI-inspired cyber threats. Specifically, we …

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Internal Structure And Breakage Behavior Of Biogenic Carbonate Sand Grains, Elieh Mohtashami, C. Guney Olgun, Chenglin Wu, Tara Selly Jan 2023

Internal Structure And Breakage Behavior Of Biogenic Carbonate Sand Grains, Elieh Mohtashami, C. Guney Olgun, Chenglin Wu, Tara Selly

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This Study Investigates the Mechanical Behavior of Biogenic Carbonate Sands from Puerto Rico at Grain-Scale Level. Micro-Computed Tomography Has Also Been Used to Get Insights on the Internal Structure of These Particles Before and after Loading. the Crushing Strength of These Particles Are Smaller Comparing to the Values Reported for Silica Sands. It Has Also Been Shown that These Particles Have Complex Internal Structure Including a Network of Pores Connected with Channels. This Study Also Demonstrates the Effect of Intragrain Structure of Biogenic Carbonate Sands and Shows How Internal Grain Structure Plays a Role on Particle Fracture.

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A Deep Learning-Informed Design Scheme For Shear Friction At Concrete-To-Concrete Interface: Recommendations For Inclusion In Aashto Lrfd Guidelines, Tarutal Ghosh Mondal, Nikkolas Edgmond, Lesley Sneed, Genda Chen Jan 2023

A Deep Learning-Informed Design Scheme For Shear Friction At Concrete-To-Concrete Interface: Recommendations For Inclusion In Aashto Lrfd Guidelines, Tarutal Ghosh Mondal, Nikkolas Edgmond, Lesley Sneed, Genda Chen

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Recent advancements in construction technology have led to high-strength concrete and steel. However, these developments have depreciated the accuracy of the design equations in current provisions, which were based on normal-grade materials. To fill such a research gap, this study presents a novel deep learning-based computation scheme that can replace the current design provisions by virtue of its superior accuracy and reliability. The proposed approach exploits Neural Additive Models (NAMs) in which geometric and material properties associated with a normal weight concrete-to-concrete shear interface are inputted to individual neural network blocks. The outputs of the individual blocks are linearly combined …

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Optimizing Stochastic Task Migration In Vehicular Edge Computing, Ankur Nahar, Debasis Das, Sajal K. Das Jan 2023

Optimizing Stochastic Task Migration In Vehicular Edge Computing, Ankur Nahar, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

The performance of vehicular edge computing (VEC) depends on the effective optimization of task offloading. However, uneven distribution of vehicular traffic, rapidly changing network conditions, and stochastic nature of vehicular networks motivate us to innovate approaches to efficient resource management while maintaining system's stability. to address these challenges, we propose a novel queue length-Based stochastic task migration strategy that leverages model predictive control (MPC) and Lyapunov optimization techniques. Our approach employs the queue length at the edge node as the criterion for offloading decisions. the MPC controller dynamically allocates the processing power and bandwidth resources to vehicles based on their …

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Lightesd: Fully-Automated And Lightweight Anomaly Detection Framework For Edge Computing, Ronit Das, Tie (Tony) T. Luo Jan 2023

Lightesd: Fully-Automated And Lightweight Anomaly Detection Framework For Edge Computing, Ronit Das, Tie (Tony) T. Luo

Computer Science Faculty Research & Creative Works

Anomaly Detection is Widely Used in a Broad Range of Domains from Cybersecurity to Manufacturing, Finance, and So On. Deep Learning based Anomaly Detection Has Recently Drawn Much Attention Because of its Superior Capability of Recognizing Complex Data Patterns and Identifying Outliers Accurately. However, Deep Learning Models Are Typically Iteratively Optimized in a Central Server with Input Data Gathered from Edge Devices, and Such Data Transfer between Edge Devices and the Central Server Impose Substantial overhead on the Network and Incur Additional Latency and Energy Consumption. to overcome This Problem, We Propose a Fully Automated, Lightweight, Statistical Learning based Anomaly …

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Surviving Chatgpt In Healthcare, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Chao Cao, Haixing Dai, Ninghao Liu, Jun Liu, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu Jan 2023

Surviving Chatgpt In Healthcare, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Chao Cao, Haixing Dai, Ninghao Liu, Jun Liu, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu

Computer Science Faculty Research & Creative Works

At the dawn of Artificial General Intelligence (AGI), the emergence of large language models such as ChatGPT show promise in revolutionizing healthcare by improving patient care, expanding medical access, and optimizing clinical processes. However, their integration into healthcare systems requires careful consideration of potential risks, such as inaccurate medical advice, patient privacy violations, the creation of falsified documents or images, overreliance on AGI in medical education, and the perpetuation of biases. It is crucial to implement proper oversight and regulation to address these risks, ensuring the safe and effective incorporation of AGI technologies into healthcare systems. By acknowledging and mitigating …

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Inspire Newsletter Spring 2023, Missouri University Of Science And Technology. Inspire - University Transportation Center Jan 2023

Inspire Newsletter Spring 2023, Missouri University Of Science And Technology. Inspire - University Transportation Center

INSPIRE Newsletters

No abstract provided.

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Inspire Newsletter Fall 2023, Missouri University Of Science And Technology. Inspire - University Transportation Center Jan 2023

Inspire Newsletter Fall 2023, Missouri University Of Science And Technology. Inspire - University Transportation Center

INSPIRE Newsletters

No abstract provided.

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Optimizing Federated Learning In Leo Satellite Constellations Via Intra-Plane Model Propagation And Sink Satellite Scheduling, Mohamed Elmahallawy, Tie (Tony) T. Luo Jan 2023

Optimizing Federated Learning In Leo Satellite Constellations Via Intra-Plane Model Propagation And Sink Satellite Scheduling, Mohamed Elmahallawy, Tie (Tony) T. Luo

Computer Science Faculty Research & Creative Works

The advances in satellite technology developments have recently seen a large number of small satellites being launched into space on Low Earth orbit (LEO) to collect massive data such as Earth observational imagery. The traditional way which downloads such data to a ground station (GS) to train a machine learning (ML) model is not desirable due to the bandwidth limitation and intermittent connectivity between LEO satellites and the GS. Satellite edge computing (SEC), on the other hand, allows each satellite to train an ML model onboard and uploads only the model to the GS which appears to be a promising …

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Editorial: Nanoscale Insight Into The Hydration Products Of Cementitious Materials: From Theory To Engineering, Huiwen Sun, Muhan Wang, Hongyan Ma, Xiao Wang, Bo Pang Jan 2023

Editorial: Nanoscale Insight Into The Hydration Products Of Cementitious Materials: From Theory To Engineering, Huiwen Sun, Muhan Wang, Hongyan Ma, Xiao Wang, Bo Pang

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

No abstract provided.

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Fedfast: Selective Federated Learning Using Fittest Parameters Aggregation And Slotted Clients Training, Ferdinand Kahenga, Antoine Bagula, Sajal K. Das Jan 2023

Fedfast: Selective Federated Learning Using Fittest Parameters Aggregation And Slotted Clients Training, Ferdinand Kahenga, Antoine Bagula, Sajal K. Das

Computer Science Faculty Research & Creative Works

This paper proposes a novel selective federated learning (FL) algorithm, called fittest aggregation and slotted training (FedFaSt). It relies on a 'free-for-all' client training process to score clients' efficiency while applying the 'natural selection' principle to elect the fittest clients to be used in FL training and aggregation processes. While relying on a combined data quality and training performance metric for scoring clients, FedFaSt implements a slotted training model enabling teams of fittest clients to participate in the training and aggregation processes for a fixed number of successive rounds, called slots. Performance validation using X-ray datasets reveals that FedFaSt outperforms …

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Deep Meta Q-Learning Based Multi-Task Offloading In Edge-Cloud Systems, Nelson Sharma, Aswini Ghosh, Rajiv Misra, Sajal K. Das Jan 2023

Deep Meta Q-Learning Based Multi-Task Offloading In Edge-Cloud Systems, Nelson Sharma, Aswini Ghosh, Rajiv Misra, Sajal K. Das

Computer Science Faculty Research & Creative Works

Resource-Constrained Edge Devices Can Not Efficiently Handle the Explosive Growth of Mobile Data and the Increasing Computational Demand of Modern-Day User Applications. Task Offloading Allows the Migration of Complex Tasks from User Devices to the Remote Edge-Cloud Servers Thereby Reducing their Computational Burden and Energy Consumption While Also Improving the Efficiency of Task Processing. However, Obtaining the Optimal Offloading Strategy in a Multi-Task Offloading Decision-Making Process is an NP-Hard Problem. Existing Deep Learning Techniques with Slow Learning Rates and Weak Adaptability Are Not Suitable for Dynamic Multi-User Scenarios. in This Article, We Propose a Novel Deep Meta-Reinforcement Learning-Based Approach to …

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Second Order, Unconditionally Stable, Linear Ensemble Algorithms For The Magnetohydrodynamics Equations, John Carter, Daozhi Han, Nan Jiang Jan 2023

Second Order, Unconditionally Stable, Linear Ensemble Algorithms For The Magnetohydrodynamics Equations, John Carter, Daozhi Han, Nan Jiang

Mathematics and Statistics Faculty Research & Creative Works

We Propose Two Unconditionally Stable, Linear Ensemble Algorithms with Pre-Computable Shared Coefficient Matrices Across Different Realizations for the Magnetohydrodynamics Equations. the Viscous Terms Are Treated by a Standard Perturbative Discretization. the Nonlinear Terms Are Discretized Fully Explicitly within the Framework of the Generalized Positive Auxiliary Variable Approach (GPAV). Artificial Viscosity Stabilization that Modifies the Kinetic Energy is Introduced to Improve Accuracy of the GPAV Ensemble Methods. Numerical Results Are Presented to Demonstrate the Accuracy and Robustness of the Ensemble Algorithms.

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Unifying Threats Against Information Integrity In Participatory Crowd Sensing, Shameek Bhattacharjee, Sajal K. Das Jan 2023

Unifying Threats Against Information Integrity In Participatory Crowd Sensing, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

This article proposes a unified threat landscape for participatory crowd sensing (P-CS) systems. Specifically, it focuses on attacks from organized malicious actors that may use the knowledge of P-CS platform's operations and exploit algorithmic weaknesses in AI-based methods of event trust, user reputation, decision-making, or recommendation models deployed to preserve information integrity in P-CS. We emphasize on intent driven malicious behaviors by advanced adversaries and how attacks are crafted to achieve those attack impacts. Three directions of the threat model are introduced, such as attack goals, types, and strategies. We expand on how various strategies are linked with different attack …

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Preserving Privacy In Image Database Through Bit-Planes Obfuscation, Vishesh K. Tanwar, Ashish Gupta, Sanjay Kumar Madria, Sajal K. Das Jan 2023

Preserving Privacy In Image Database Through Bit-Planes Obfuscation, Vishesh K. Tanwar, Ashish Gupta, Sanjay Kumar Madria, Sajal K. Das

Computer Science Faculty Research & Creative Works

The recent surge in computer vision applications has caused visual privacy concerns to people who are either users or exposed to an underlying surveillance system. To preserve their privacy, image obfuscation lays out a strong road through which the usability of images can also be maintained without revealing any visual private information. However, prior solutions are susceptible to reconstruction attacks or produce non-trainable images even by leveraging the obfuscation ways. This paper proposes a novel bit-planes-based image obfuscation scheme, called Bimof, to protect the visual privacy of the user in the images that are input into a recognition-based system. By …

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Bits 2023 Welcome Message From General Chairs And Tpc Chairs, Sajal K. Das, Keiichi Yasumoto, Hayato Yamana, Shameek Bhattacharjee Jan 2023

Bits 2023 Welcome Message From General Chairs And Tpc Chairs, Sajal K. Das, Keiichi Yasumoto, Hayato Yamana, Shameek Bhattacharjee

Computer Science Faculty Research & Creative Works

No abstract provided.

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Lasa: Location-Aware Scheduling Algorithm In Industrial Iot Networks With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi Jan 2023

Lasa: Location-Aware Scheduling Algorithm In Industrial Iot Networks With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The Synchronized Single-hop Multiple Gateway (SHMG) is a framework recently proposed to support mobility into 6TiSCH, the standard network architecture defined for Industrial Internet of Things (IIoT) deployments. SHMG supports industrial applications with stringent requirements by adopting the Shared-Downstream Dedicated-Upstream (SD-DU) scheduling policy, which allocates to Mobile Nodes (MNs) a set of dedicated transmission opportunities for uplink data. Such allocation is performed on all the Border Routers (BRs) of the network without considering the location of MNs. Transmission opportunities are reserved also in BRs far from the current location of the MN, resulting in a waste of resources that limits …

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Cyber-Agricultural Systems For Crop Breeding And Sustainable Production, Soumik Sarkar, Baskar Ganapathysubramanian, Arti Singh, Fateme Fotouhi, Soumyashree Kar, Koushik Nagasubramanian, Girish Chowdhary, Sajal K. Das, George Kantor, Adarsh Krishnamurthy, Nirav Merchant, Asheesh K. Singh Jan 2023

Cyber-Agricultural Systems For Crop Breeding And Sustainable Production, Soumik Sarkar, Baskar Ganapathysubramanian, Arti Singh, Fateme Fotouhi, Soumyashree Kar, Koushik Nagasubramanian, Girish Chowdhary, Sajal K. Das, George Kantor, Adarsh Krishnamurthy, Nirav Merchant, Asheesh K. Singh

Computer Science Faculty Research & Creative Works

The Cyber-Agricultural System (CAS) Represents an overarching Framework of Agriculture that Leverages Recent Advances in Ubiquitous Sensing, Artificial Intelligence, Smart Actuators, and Scalable Cyberinfrastructure (CI) in Both Breeding and Production Agriculture. We Discuss the Recent Progress and Perspective of the Three Fundamental Components of CAS – Sensing, Modeling, and Actuation – and the Emerging Concept of Agricultural Digital Twins (DTs). We Also Discuss How Scalable CI is Becoming a Key Enabler of Smart Agriculture. in This Review We Shed Light on the Significance of CAS in Revolutionizing Crop Breeding and Production by Enhancing Efficiency, Productivity, Sustainability, and Resilience to Changing …

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A Distributed Algorithm For Identifying Strongly Connected Components On Incremental Graphs, S. Srinivasan, A. Khanda, S. Srinivasan, A. Pandey, S. (Sajal) K. Das, S. Bhowmick, B. Norris Jan 2023

A Distributed Algorithm For Identifying Strongly Connected Components On Incremental Graphs, S. Srinivasan, A. Khanda, S. Srinivasan, A. Pandey, S. (Sajal) K. Das, S. Bhowmick, B. Norris

Computer Science Faculty Research & Creative Works

Incremental graphs that change over time capture the changing relationships of different entities. Given that many real-world networks are extremely large, it is often necessary to partition the network over many distributed systems and solve a complex graph problem over the partitioned network. This paper presents a distributed algorithm for identifying strongly connected components (SCC) on incremental graphs. We propose a two-phase asynchronous algorithm that involves storing the intermediate results between each iteration of dynamic updates in a novel meta-graph storage format for efficient recomputation of the SCC for successive iterations. To the best of our knowledge, this is the …

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An Augmented Dataset For Vision-Based Unmanned Aerial Vehicles Detection And Tracking, Md Hasibur Rahman, Sanjay Madria Jan 2023

An Augmented Dataset For Vision-Based Unmanned Aerial Vehicles Detection And Tracking, Md Hasibur Rahman, Sanjay Madria

Computer Science Faculty Research & Creative Works

The rapid proliferation of Unmanned Aerial Vehicles (UAVs) or drones in military, disaster management, business, and entertainment applications has raised concerns about their potential airspace risks. Researchers are increasingly focused on developing methods for detecting and tracking UAVs with various data sources like radar, visual, acoustic, and radio-frequency data available. among these, visual data stands out as cost-effective and amenable to analysis using Computer Vision (CV) techniques. However, vision-Based tasks present challenges such as occlusions, shaky footage, and small UAVs at a distance, requiring timely and computationally efficient detection, especially given limited onboard computational power. to address these challenges, researchers …

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Small Modular Reactors, Joshua P. Schlegel, P. K. Bhowmik Jan 2023

Small Modular Reactors, Joshua P. Schlegel, P. K. Bhowmik

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

Small Modular Reactors (SMRs) have been a very promising development in nuclear power over the last two decades. SMRs are defined as nuclear reactors with a power output of less than 300MWe. This is in comparison to gigawatt-size reactors, which can have electrical output of 1000–1500MWe or more. This chapter will consist of two major sections. the first will be a detailed summary of the small modular reactor designs being proposed around the world. This section will focus on those that are the furthest along in their development but will also include some information about the wide variety of proposed …

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Q-Learning For Sum-Throughput Optimization In Wireless Visible-Light Uav Networks, Yuwei Long, Nan Cen Jan 2023

Q-Learning For Sum-Throughput Optimization In Wireless Visible-Light Uav Networks, Yuwei Long, Nan Cen

Computer Science Faculty Research & Creative Works

Unmanned Aerial Vehicles (UAVs) Have Been Adopted as Aerial Base Stations (ABSs) to Provide Wireless Connectivity to Ground Users in Events of Increased Network Demand, and Points-Of-Failure Infrastructure (Such as in Disasters). However, with the Existing Crowded Radio Frequency (RF) Spectrum, UAV ABSs Cannot Provide High-Data-Rate Communication Required in 5G and beyond. to Address This Challenge, Visible Light Communication (VLC) is Proposed to Be Equipped on UAVs to Take Advantage of the Flexible and On-Demand Deployment Feature of the UAV, and the High-Data-Rate Communication of the VLC. However, VLC Has Strong Alignment Requirements between Transceivers, Therefore, How to Determine the …

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Practical Imaging Applications Of Wettability Contact Angles On Kuwaiti Tight Carbonate Reservoir With Different Rock Types, Saleh Al-Sayegh, Ralph E. Flori, Waleed Al-Bazzaz, Sohaib Kholosy, Hasan Al-Saedi, Abdulaziz Abbas, Ali Qubian Jan 2023

Practical Imaging Applications Of Wettability Contact Angles On Kuwaiti Tight Carbonate Reservoir With Different Rock Types, Saleh Al-Sayegh, Ralph E. Flori, Waleed Al-Bazzaz, Sohaib Kholosy, Hasan Al-Saedi, Abdulaziz Abbas, Ali Qubian

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This study focuses on a tight carbonate reservoir which is located in Northern Kuwait and is classified as an unconventional reservoir. A practical imaging technique of wettability contact angle (θ°) presents "big data" as well as relative-permeability (Krw and Kro) measurements. Also, modeling, through rock image technology, the vast well-documented grain/pore boundary morphology available inside fresh rock fragments have achieved good results. Conventional laboratory relative-permeability experiments are expensive and time-consuming. This study introduces a novel method to measure/calculate relative permeability through fast, less expensive, non-destructive, and environmentally friendly techniques of imaging technology. One tight carbonate reservoir is selected, imaged, processed, …

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Chronic Environmental Circadian Disruption Increases Atherosclerosis And Dyslipidemia In Female, But Not Male, Apolipoproteine-Deficient Mice, Jeffrey M. Chalfant, Deborah A. Howatt, Victoria B. Johnson, Lisa R. Tannock, Alan Daugherty, Julie S. Pendergast Jan 2023

Chronic Environmental Circadian Disruption Increases Atherosclerosis And Dyslipidemia In Female, But Not Male, Apolipoproteine-Deficient Mice, Jeffrey M. Chalfant, Deborah A. Howatt, Victoria B. Johnson, Lisa R. Tannock, Alan Daugherty, Julie S. Pendergast

Teacher Education and Certification Faculty Research & Creative Works

Shift work chronically disrupts circadian rhythms and increases the risk of developing cardiovascular disease. However, the mechanisms linking shift work and cardiovascular disease are largely unknown. The goal of this study was to investigate the effects of chronically shifting the light-dark (LD) cycle, which models the disordered exposure to light that may occur during shift work, on atherosclerosis. Atherosclerosis is the progressive accumulation of lipid-filled lesions within the artery wall and is the leading cause of cardiovascular disease. We studied ApolipoproteinE-deficient (ApoE−/−) mice that are a well-established model of atherosclerosis. Male and female ApoE−/− mice were housed in control 12L:12D …

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Blockchain-Enabled Authenticated Key Agreement Scheme For Mobile Vehicles-Assisted Precision Agricultural Iot Networks, Anusha Vangala, Ashok Kumar Das, Ankush Mitra, Sajal K. Das, Youngho Park Jan 2023

Blockchain-Enabled Authenticated Key Agreement Scheme For Mobile Vehicles-Assisted Precision Agricultural Iot Networks, Anusha Vangala, Ashok Kumar Das, Ankush Mitra, Sajal K. Das, Youngho Park

Computer Science Faculty Research & Creative Works

Precision Farming Has a Positive Potential in the Agricultural Industry Regarding Water Conservation, Increased Productivity, Better Development of Rural Areas, and Increased Income. Blockchain Technology is a Better Alternative for Storing and Sharing Farm Data as It is Reliable, Transparent, Immutable, and Decentralized. Remote Monitoring of an Agricultural Field Requires Security Systems to Ensure that Any Sensitive Information is Exchanged Only among Authenticated Entities in the Network. to This End, We Design an Efficient Blockchain-Enabled Authenticated Key Agreement Scheme for Mobile Vehicles-Assisted Precision Agricultural Internet of Things (IoT) Networks Called AgroMobiBlock. the Limited Existing Work on Authentication in Agricultural Networks …

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Landmark Stereo Dataset For Landmark Recognition And Moving Node Localization In A Non-Gps Battlefield Environment, Ganesh Sapkota, Sanjay Madria Jan 2023

Landmark Stereo Dataset For Landmark Recognition And Moving Node Localization In A Non-Gps Battlefield Environment, Ganesh Sapkota, Sanjay Madria

Computer Science Faculty Research & Creative Works

In this paper, we have proposed a new strategy of using the landmark anchor node instead of a radio-Based anchor node to obtain the virtual coordinates (landmarkID, DISTANCE) of moving troops or defense forces that will help in tracking and maneuvering the troops along a safe path within a GPS-denied battlefield environment. the proposed strategy implements landmark recognition using the Yolov5 model and landmark distance estimation using an efficient Stereo Matching Algorithm. We consider that a moving node carrying a low-power mobile device facilitated with a calibrated stereo vision camera that can capture stereo images of a scene containing landmarks …

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Key Factors Affecting Labor Productivity In Offsite Construction Projects, Rayan H. Assaad, Islam H. El-Adaway, Makarand Hastak, Kim Lascola Needy Jan 2023

Key Factors Affecting Labor Productivity In Offsite Construction Projects, Rayan H. Assaad, Islam H. El-Adaway, Makarand Hastak, Kim Lascola Needy

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Offsite construction has been shown to possess many advantages and benefits in dealing with the construction industry's challenges, which include poor labor productivity. Nevertheless, all previous productivity-related research studies have focused mainly on factors impacting labor productivity in traditional stick-built construction methods. This paper filled this knowledge gap by following a multistep interrelated research methodology. First, a research team of 19 construction professionals and academics developed and distributed an industry survey to (1) quantify the likelihood of occurrence and relative impact of risk factors that adversely impact labor productivity in offsite construction projects; (2) identify and prioritize key risk factors …

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Smartlens: Robust Detection Of Rogue Device Via Frequency Domain Features In Lora-Enabled Iiot, Subir Halder, Amrita Ghosal, Thomas Newe, Sajal K. Das Jan 2023

Smartlens: Robust Detection Of Rogue Device Via Frequency Domain Features In Lora-Enabled Iiot, Subir Halder, Amrita Ghosal, Thomas Newe, Sajal K. Das

Computer Science Faculty Research & Creative Works

A challenging problem in Long Range (LoRa) communications enabled Industrial Internet of Things (IIoT) is the detection of rogue devices, which attempt to impersonate real devices by spoofing their authentic identifications in order to steal information and gain access to the system. Although machine learning (ML) offers a promising approach to detecting rogue devices, existing ML models rely on domain knowledge yet exhibit low detection accuracy and vulnerability against adversarial attacks. This paper proposes SmartLens, a novel real-time frequency domain feature based rogue device detection system, using a lightweight statistical ML algorithm and Mahalanobis distance to achieve high accuracy and …

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Detection Of False Data Injection In Smart Water Metering Infrastructure, Ayanfeoluwa Oluyomi, Shameek Bhattacharjee, Sajal K. Das Jan 2023

Detection Of False Data Injection In Smart Water Metering Infrastructure, Ayanfeoluwa Oluyomi, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart water metering (SWM) infrastructure collects real-Time water usage data that is useful for automated billing, leak detection, and forecasting of peak periods. Cyber/physical attacks can lead to data falsification on water usage data. This paper proposes a learning approach that converts smart water meter data into a Pythagorean mean-based invariant that is highly stable under normal conditions but deviates under attacks. We show how adversaries can launch deductive or camouflage attacks in the SWM infrastructure to gain benefits and impact the water distribution utility. Then, we apply a two-Tier approach of stateless and stateful detection, reducing false alarms without …

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