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Articles 1951 - 1980 of 33676
Full-Text Articles in Entire DC Network
Exploring The Influences On Construction Bidding Decisions: Insights From Literature And Industry Experts, Muaz O. Ahmed, Islam H. El-Adaway
Exploring The Influences On Construction Bidding Decisions: Insights From Literature And Industry Experts, Muaz O. Ahmed, Islam H. El-Adaway
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Decision-Making In Construction Bidding Is One Of The Complicated Processes. In Determining Related Decisions, Contractors Weigh Various Bidding Factors To Assess The Expected Benefits Of A Construction Project. Various Studies Have Identified Factors That Impact Construction Bidding-Related Decisions. However, There Is A Lack Of Research That Examines The Alignment Between Industry And Literature As Related To The Perception Of The Importance Of Bidding Factors On Bidding Decisions. This Paper Fills This Area Of Research Need. First, The Authors Performed A Content Analysis Of 124 Construction Bidding-Related Journal Papers, And Accordingly, 43 Bidding Factors Were Identified And Mapped With Analyzed Journal …
Studying Contribution Of Associated Stakeholders In Risk Control Of Modularized Construction Under Different Project Delivery Methods: A Graph-Restricted Cooperative Games Approach, Mohamad Abdul Nabi, Islam H. El-Adaway
Studying Contribution Of Associated Stakeholders In Risk Control Of Modularized Construction Under Different Project Delivery Methods: A Graph-Restricted Cooperative Games Approach, Mohamad Abdul Nabi, Islam H. El-Adaway
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Modularization is associated with various project benefits including cost savings, schedule reduction, improved quality, among others. However, successful implementation of modular projects requires early collaboration and proactive management of various associated risks. Both project communication structures and early engagement of all project stakeholders have a great impact on risk mitigation efficiency in modular construction. The goal of this paper is to study the stakeholders' contribution in risk control of modular construction projects under different delivery methods. To this end, the authors followed a multistep methodology including (1) literature analysis to assign 17 modular risks among the various project stakeholders; (2) …
Improving Aggregate Abrasion Resistance Prediction Via Micro-Deval Test Using Ensemble Machine Learning Techniques, Alireza Roshan, Magdy Abdelrahman
Improving Aggregate Abrasion Resistance Prediction Via Micro-Deval Test Using Ensemble Machine Learning Techniques, Alireza Roshan, Magdy Abdelrahman
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Aggregate is the most extracted material from the world's mines and widely used in civil and construction projects. The Micro-Deval abrasion test (MD) is one of the most important tests that provides characteristics of crushed aggregates that show their resistance against mechanical abrasive factors such as repeated impact loading. The impact of various factors on abrasive resistance properties of aggregates has led researchers to seek correlations, often focusing on limited data samples, leading to reduced accuracy. This study employs machine learning (ML) methods to predict MD abrasion values, considering diverse aggregate properties. Various ensemble ML methods were applied, revealing the …
Effect Of Concrete Mix Design Factors On Static Yield Stress Changes Due To Vibration, Ahmed Abd El Fattah, Dimitri Feys, Kyle Riding, Syed Imran
Effect Of Concrete Mix Design Factors On Static Yield Stress Changes Due To Vibration, Ahmed Abd El Fattah, Dimitri Feys, Kyle Riding, Syed Imran
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Digital fabrication of concrete structures has gained substantial research traction over the last decade, enabling efficient material use and adding more architectural freedom. Current research focuses on chemical and mineral admixtures, as well as manipulating the cement hydration reaction to control the yield stress evolution with time in the cement paste. Instead of providing yield stress through the concrete fluid properties, a high yield stress can be provided by interparticle friction from the use of high aggregate volumes and large nominal maximum aggregate size, with flow enhanced for material extrusion by vibration. Granular physics was applied to concrete mixture design …
An Accurate-Pricing Estimate Game-Theoretic Model For Determining Price Escalations In Construction Projects During Economic Uncertainties, Yasser Jezzini, Rayan H. Assaad, Islam H. El-Adaway, Mohamad Abdul Nabi
An Accurate-Pricing Estimate Game-Theoretic Model For Determining Price Escalations In Construction Projects During Economic Uncertainties, Yasser Jezzini, Rayan H. Assaad, Islam H. El-Adaway, Mohamad Abdul Nabi
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Economic Market Uncertainties, Such As Those Experienced During The COVID-19 Pandemic, Can Make Determining Accurate Prices Estimate For Construction Materials A Challenging Task. While Previous Research Focused On The Contractual Aspect Of This Issue By Studying Price Escalation Clauses, There Is Still A Gap In The Literature When It Comes To Proposing An Accurate Pricing Model. Thus, This Study Develops An Accurate Pricing-Estimate Game-Theoretical Model That Can Efficiently And Competitively Account For Escalations In Construction Materials Prices During Uncertain Market Conditions. First, Data On Past Producer Price Indexes (PPIs) Of Different Construction Materials Were Collected. Second, The Percentage Changes In …
A Hypergraph Approach To Deep Learning Based Routing In Software-Defined Vehicular Networks, Ankur Nahar, Nishit Bhardwaj, Debasis Das, Sajal K. Das
A Hypergraph Approach To Deep Learning Based Routing In Software-Defined Vehicular Networks, Ankur Nahar, Nishit Bhardwaj, Debasis Das, Sajal K. Das
Computer Science Faculty Research & Creative Works
Software-Defined Vehicular Networks (SDVNs) revolutionize modern transportation by enabling dynamic and adaptable communication infrastructures. However, accurately capturing the dynamic communication patterns in vehicular networks, characterized by intricate spatio-temporal dynamics, remains a challenge with traditional graph-Based models. Hypergraphs, due to their ability to represent multi-way relationships, provide a more nuanced representation of these dynamics. Building on this hypergraph foundation, we introduce a novel hypergraph-Based routing algorithm. We jointly train a model that incorporates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) using a Deep Deterministic Policy Gradient (DDPG) approach. This model carefully extracts spatial and temporal traffic matrices, capturing elements …
Collect Spatiotemporally Correlated Data In Iot Networks With An Energy-Constrained Uav, Wenzheng Xu, Heng Shao, Qunli Shen, Jian Peng, Wen Huang, Weifa Liang, Tang Liu, Xin Wei Yao, Tao Lin, Sajal K. Das
Collect Spatiotemporally Correlated Data In Iot Networks With An Energy-Constrained Uav, Wenzheng Xu, Heng Shao, Qunli Shen, Jian Peng, Wen Huang, Weifa Liang, Tang Liu, Xin Wei Yao, Tao Lin, Sajal K. Das
Computer Science Faculty Research & Creative Works
UAVs (Unmanned Aerial Vehicles) Are Promising Tools For Efficient Data Collections Of Sensors In IoT Networks. Existing Studies Exploited Both Spatial And Temporal Data Correlations To Reduce The Amount Of Collected Redundant Data, In Which Sensors Are First Partitioned Into Different Clusters, A Master Sensor In Each Cluster Then Collects Raw Data From Other Sensors And Compresses The Received Data. An Energy-Constrained UAV Finally Collects The Maximum Amount Of Compressed Data From Different Master Sensors. We However Notice That The Compressed Data From Only A Portion Of Clusters Are Collected By The UAV In The Existing Studies, While The Data …
Mobility Management In Tsch-Based Industrial Wireless Networks, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Mobility Management In Tsch-Based Industrial Wireless Networks, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Computer Science Faculty Research & Creative Works
Wireless Sensor and Actuator Networks (WSANs) are an effective technology for improving the efficiency and productivity in many industrial domains and are also the building blocks for the Industrial Internet of Things (IIoT). To support this trend, the IEEE has defined the 802.5.4 Time-Slotted Channel Hopping (TSCH) protocol. Unfortunately, TSCH does not provide any mechanism to manage node mobility, while many current industrial applications involve Mobile Nodes (MNs), e.g., mobile robots or wearable devices carried by workers. In this article, we present a framework to efficiently manage mobility in TSCH networks, by proposing an enhanced version of the Synchronized Single-hop …
Posca: Path Optimization For Solar Cover Amelioration In Urban Air Mobility, Debjyoti Sengupta, Anurag Satpathy, Sajal K. Das
Posca: Path Optimization For Solar Cover Amelioration In Urban Air Mobility, Debjyoti Sengupta, Anurag Satpathy, Sajal K. Das
Computer Science Faculty Research & Creative Works
Urban Air Mobility (UAM) encompasses both piloted and autonomous aerial vehicles, spanning from small unmanned aerial vehicles (UAVs) like drones to passenger-carrying personal air vehicles (PAVs), to revolutionize smart transportation in congested urban areas. This emerging paradigm is anticipated to offer disruptive solutions to the mobility challenges in congested cities. In this context, a pivotal concern centers on the sustainability of transitioning to this mode of transportation, especially with the focus on incorporating clean technology into developing innovative solutions from the ground up. Recent studies highlight that a significant portion of the total energy consumption in UAM can be attributed …
Uncovering The Causes Of Emotions In Software Developer Communication Using Zero-Shot Llms, Mia Mohammad Imran, Preetha Chatterjee, Kostadin Damevski
Uncovering The Causes Of Emotions In Software Developer Communication Using Zero-Shot Llms, Mia Mohammad Imran, Preetha Chatterjee, Kostadin Damevski
Computer Science Faculty Research & Creative Works
Understanding and identifying the causes behind developers' emotions (e.g., Frustration caused by 'delays in merging pull requests') can be crucial towards finding solutions to problems and fostering collaboration in open-source communities. Effectively identifying such information in the high volume of communications across the different project channels, such as chats, emails, and issue comments, requires automated recognition of emotions and their causes. To enable this automation, large-scale software engineering-specific datasets that can be used to train accurate machine learning models are required. However, such datasets are expensive to create with the variety and informal nature of software projects' communication channels. In …
Mobilytics: Mobility Analytics Framework For Transferring Semantic Knowledge, Shreya Ghosh, Soumya K. Ghosh, Sajal K. Das, Prasenjit Mitra
Mobilytics: Mobility Analytics Framework For Transferring Semantic Knowledge, Shreya Ghosh, Soumya K. Ghosh, Sajal K. Das, Prasenjit Mitra
Computer Science Faculty Research & Creative Works
The proliferation of sensor-equipped smartphones has led to the generation of vast amounts of GPS data, such as timestamped location points, enabling a range of location-based services. However, deciphering the spatio-temporal dynamics of mobility to understand the underlying motivations behind travel patterns presents a significant challenge. his paper focuses on how individuals' GPS traces (latitude, longitude, timestamp) interpret the connection and correlations among different entities such as people, locations or point-of-interests (POIs), and semantic contexts (trip-purpose). We introduce a mobility analytics framework, named Mobilytics designed to identify trip purposes from individual GPS traces by leveraging a “mobility knowledge graph” (MKG) …
Log Sequence Anomaly Detection Based On Template And Parameter Parsing Via Bert, Xiaolin Chai, Hang Zhang, Jue Zhang, Yan Sun, Sajal K. Das
Log Sequence Anomaly Detection Based On Template And Parameter Parsing Via Bert, Xiaolin Chai, Hang Zhang, Jue Zhang, Yan Sun, Sajal K. Das
Computer Science Faculty Research & Creative Works
Logs record various operations and events during system running in text format, which is an essential basis for detecting and identifying potential security threats or system failures and is widely used in system management to ensure security and reliability. Existing log sequence anomaly detection is limited by log parsing and does not consider all key features of logs, which may cause false or missed detection. In this paper, we propose a fast and accurate log parsing method and feed the entire log content into the deep learning network for analysis. To avoid semantic loss during parsing, we replace some variables …
Secure Location-Based Authenticated Key Establishment Scheme For Maritime Communication, Saurabh Agrawal, Anusha Vangala, Ashok Kumar Das, Neeraj Kumar, Sachin Shetty, Sajal K. Das
Secure Location-Based Authenticated Key Establishment Scheme For Maritime Communication, Saurabh Agrawal, Anusha Vangala, Ashok Kumar Das, Neeraj Kumar, Sachin Shetty, Sajal K. Das
Computer Science Faculty Research & Creative Works
Maritime communication helps vessels and ports plan their movements, exchange environmental information, and communicate among themselves. The vessels' movement and changing location are critical to keep them secure from data interception and data tampering by unauthorized parties during transmission. To secure maritime communication, we propose a novel lightweight authentication scheme sensitive to the current ship location. We assess the effectiveness of the proposed protocol in defending against a range of security threats while keeping communication and computation costs low and meeting the desired security and functional requirements of anonymity and untraceability. The detailed security analysis using the widely accepted Scyther …
Analyzing Real-Time Insect Detection In Smart Connected Farms, Ashish Gupta, Vishesh Kumar Tanwar, Amit Nath Jha, Sajal K. Das
Analyzing Real-Time Insect Detection In Smart Connected Farms, Ashish Gupta, Vishesh Kumar Tanwar, Amit Nath Jha, Sajal K. Das
Computer Science Faculty Research & Creative Works
With a vision of smart connected farms, this research proposes an insect detection framework (InsDet) to identify the most harmful corn crop insect, known as corn rootworm beetle. InsDet employs an object detection model with varying sizes to localize the insects in sticky-trap images.
Freyr⁺: Harvesting Idle Resources In Serverless Computing Via Deep Reinforcement Learning, Hanfei Yu, Hao Wang, Jian Li, Xu Yuan, Seung Jong Park
Freyr⁺: Harvesting Idle Resources In Serverless Computing Via Deep Reinforcement Learning, Hanfei Yu, Hao Wang, Jian Li, Xu Yuan, Seung Jong Park
Computer Science Faculty Research & Creative Works
Serverless computing has revolutionized online service development and deployment with ease-to-use operations, auto-scaling, fine-grained resource allocation, and pay-as-you-go pricing. However, a gap remains in configuring serverless functions - the actual resource consumption may vary due to function types, dependencies, and input data sizes, thus mismatching the static resource configuration by users. Dynamic resource consumption against static configuration may lead to either poor function execution performance or low utilization. This paper proposes Freyr+, a novel resource manager (RM) that dynamically harvests idle resources from over-provisioned functions to accelerate under-provisioned functions for serverless platforms. Freyr+ monitors each function's resource utilization in real-time …
Approximation Algorithm And Applications For Connected Submodular Function Maximization Problems, Ziming Wang, Jing Li, He Xue, Wenzheng Xu, Weifa Liang, Zichuan Xu, Jian Peng, Pan Zhou, Xiaohua Jia, Sajal K. Das
Approximation Algorithm And Applications For Connected Submodular Function Maximization Problems, Ziming Wang, Jing Li, He Xue, Wenzheng Xu, Weifa Liang, Zichuan Xu, Jian Peng, Pan Zhou, Xiaohua Jia, Sajal K. Das
Computer Science Faculty Research & Creative Works
In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget $K$ , the problem is to find a subset $S$ with $K$ nodes from a graph $G$ , so that a given submodular function $f(S)$ on $S$ is maximized and the induced subgraph $G[S]$ by the nodes in $S$ is connected, where the submodular function $f$ can be used to model many practical application problems, such as the number of users within different service …
Protecting Activity Sensing Data Privacy Using Hierarchical Information Dissociation, Guangjing Wang, Hanqing Guo, Yuanda Wang, Bocheng Chen, Ce Zhou, Qiben Yan
Protecting Activity Sensing Data Privacy Using Hierarchical Information Dissociation, Guangjing Wang, Hanqing Guo, Yuanda Wang, Bocheng Chen, Ce Zhou, Qiben Yan
Computer Science Faculty Research & Creative Works
Smartphones and wearable devices have been integrated into our daily lives, offering personalized services. However, many apps become overprivileged as their collected sensing data contains unnecessary sensitive information. For example, mobile sensing data could reveal private attributes (e.g., gender and age) and unintended sensitive features (e.g., hand gestures when entering passwords). To prevent sensitive information leakage, existing methods must obtain private labels and users need to specify privacy policies. However, they only achieve limited control over information disclosure. In this work, we present Hippo to dissociate hierarchical information including private metadata and multi-grained activity information from the sensing data. Hippo …
A Comprehensive Survey On Pretrained Foundation Models: A History From Bert To Chatgpt, Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, Jianxin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun
A Comprehensive Survey On Pretrained Foundation Models: A History From Bert To Chatgpt, Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, Jianxin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun
Computer Science Faculty Research & Creative Works
Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks across different data modalities. A PFM (e.g., BERT, ChatGPT, GPT-4) is trained on large-scale data, providing a solid parameter initialization for a wide range of downstream applications. In contrast to earlier methods that use convolution and recurrent modules for feature extraction, BERT learns bidirectional encoder representations from Transformers, trained on large datasets as contextual language models. Similarly, the Generative Pretrained Transformer (GPT) method employs Transformers as feature extractors and is trained on large datasets using an autoregressive paradigm. Recently, ChatGPT has demonstrated significant success in large language …
Smart Connected Farms And Networked Farmers To Improve Crop Production, Sustainability And Profitability, Asheesh K. Singh, Behzad J. Balabaygloo, Barituka Bekee, Samuel W. Blair, Suzanne Fey, Fateme Fotouhi, Ashish Gupta, Amit Jha, Jorge C. Martinez-Palomares, Kevin Menke, Aaron Prestholt, Vishesh K. Tanwar, Xu Tao, Anusha Vangala, Matthew E. Carroll, Sajal K. Das, Guilherme Depaula
Smart Connected Farms And Networked Farmers To Improve Crop Production, Sustainability And Profitability, Asheesh K. Singh, Behzad J. Balabaygloo, Barituka Bekee, Samuel W. Blair, Suzanne Fey, Fateme Fotouhi, Ashish Gupta, Amit Jha, Jorge C. Martinez-Palomares, Kevin Menke, Aaron Prestholt, Vishesh K. Tanwar, Xu Tao, Anusha Vangala, Matthew E. Carroll, Sajal K. Das, Guilherme Depaula
Computer Science Faculty Research & Creative Works
To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture experts including farmers are needed. Rapid advances in information and communication technology, precision agriculture and data analytics, are creating a perfect opportunity for the creation of smart connected farms (SCFs) and networked farmers. a network and coordinated farmer network provide unique advantages to farmers to enhance farm production and profitability, while tackling adverse climate events. the aim of this article is to provide a comprehensive overview of the state of the art in SCF including the …
Inspire Newsletter Fall 2024, Missouri University Of Science And Technolgy Inspire - University Transportation Center
Inspire Newsletter Fall 2024, Missouri University Of Science And Technolgy Inspire - University Transportation Center
INSPIRE Newsletters
No abstract provided.
Federated Graph Anomaly Detection Via Contrastive Self-Supervised Learning, Xiangjie Kong, Wenyi Zhang, Hui Wang, Mingliang Hou, Xin Chen, Xiaoran Yan, Sajal K. Das
Federated Graph Anomaly Detection Via Contrastive Self-Supervised Learning, Xiangjie Kong, Wenyi Zhang, Hui Wang, Mingliang Hou, Xin Chen, Xiaoran Yan, Sajal K. Das
Computer Science Faculty Research & Creative Works
Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain sensitive situations. Although federated graph learning offers a promising solution by enabling collaborative model training in distributed systems while preserving data privacy, a practical challenge arises as each client typically possesses a limited amount of graph data. Consequently, naively applying federated graph learning directly to …
Disseminating Over-The-Air Updates Via Intelligent Labeling In Multi-Tier Networks, Atefeh Asayesh, Asad Waqar Malik, Sajal K. Das
Disseminating Over-The-Air Updates Via Intelligent Labeling In Multi-Tier Networks, Atefeh Asayesh, Asad Waqar Malik, Sajal K. Das
Computer Science Faculty Research & Creative Works
Connected Vehicles Rely on Sophisticated Software Systems for Diverse Features, Including Navigation, Entertainment, Communication, and Safety Functions. as Technology Continues to Advance, the Reliance on Software in Connected Vehicles Becomes Increasingly Integral to their overall Performance and the Delivery of Innovative Features. Therefore, in the Domain of Software-Enabled Automobiles, the Implementation of over-The-Air (OTA) Software Updates is Deemed Essential for the Dissemination of Software and Fixes in Connected Vehicles. the Conventional Method of Addressing This Matter Entailed Manufacturers Undertaking the Task of Recalling Outdated Vehicles; However, the Central Issue Lies in the Considerable Challenge of Effectively Notifying Owners through Recall …
Message From The Phd Dissertation Showcase Chairs, Panos K. Chrysanthis, Sanjay Madria
Message From The Phd Dissertation Showcase Chairs, Panos K. Chrysanthis, Sanjay Madria
Computer Science Faculty Research & Creative Works
No abstract provided.
Personalized Federated Graph Learning On Non-Iid Electronic Health Records, Tao Tang, Zhuoyang Han, Zhen Cai, Shuo Yu, Xiaokang Zhou, Taiwo Oseni, Sajal K. Das
Personalized Federated Graph Learning On Non-Iid Electronic Health Records, Tao Tang, Zhuoyang Han, Zhen Cai, Shuo Yu, Xiaokang Zhou, Taiwo Oseni, Sajal K. Das
Computer Science Faculty Research & Creative Works
Understanding The Latent Disease Patterns Embedded In Electronic Health Records (EHRs) Is Crucial For Making Precise And Proactive Healthcare Decisions. Federated Graph Learning-Based Methods Are Commonly Employed To Extract Complex Disease Patterns From The Distributed EHRs Without Sharing The Client-Side Raw Data. However, The Intrinsic Characteristics Of The Distributed EHRs Are Typically Non-Independent And Identically Distributed (Non-IID), Significantly Bringing Challenges Related To Data Imbalance And Leading To A Notable Decrease In The Effectiveness Of Making Healthcare Decisions Derived From The Global Model. To Address These Challenges, We Introduce A Novel Personalized Federated Learning Framework Named PEARL, Which Is Designed For …
Achieving Efficient And Privacy-Preserving Reverse Skyline Query Over Single Cloud, Yubo Peng, Xiong Li, Ke Gu, Jinjun Chen, Sajal K. Das, Xiaosong Zhang
Achieving Efficient And Privacy-Preserving Reverse Skyline Query Over Single Cloud, Yubo Peng, Xiong Li, Ke Gu, Jinjun Chen, Sajal K. Das, Xiaosong Zhang
Computer Science Faculty Research & Creative Works
Reverse skyline query (RSQ) has been widely used in practice since it can pick out the data of interest to the query vector. To save storage resources and facilitate service provision, data owners usually outsource data to the cloud for RSQ services, which poses huge challenges to data security and privacy protection. Existing privacy-preserving RSQ schemes are either based on a two-cloud model or cannot fully protect privacy. To this end, we propose an efficient privacy-preserving reverse skyline query scheme over a single cloud (ePRSQ). Specifically, we first design a privacy-preserving inner product's sign determination scheme (PIPSD), which can determine …
Stitching Satellites To The Edge: Pervasive And Efficient Federated Leo Satellite Learning, Mohamed Elmahallawy, Tony Tie Luo
Stitching Satellites To The Edge: Pervasive And Efficient Federated Leo Satellite Learning, Mohamed Elmahallawy, Tony Tie Luo
Computer Science Faculty Research & Creative Works
In the Ambitious Realm of Space AI, the Integration of Federated Learning (FL) with Low Earth Orbit (LEO) Satellite Constellations Holds Immense Promise. However, Many Challenges Persist in Terms of Feasibility, Learning Efficiency, and Convergence. These Hurdles Stem from the Bottleneck in Communication, Characterized by Sporadic and Irregular Connectivity between LEO Satellites and Ground Stations, Coupled with the Limited Computation Capability of Satellite Edge Computing (SEC). This Paper Proposes a Novel FL-SEC Framework that Empowers LEO Satellites to Execute Large-Scale Machine Learning (ML) Tasks Onboard Efficiently. its Key Components Include I) Personalized Learning Via Divide-And-Conquer, Which Identifies and Eliminates Redundant …
L3geocast: Enabling P4-Based Customizable Network-Layer Geocast At The Network Edge, Xindi Hou, Shuai Gao, Ningchun Liu, Fangtao Yao, Hongke Zhang, Sajal K. Das
L3geocast: Enabling P4-Based Customizable Network-Layer Geocast At The Network Edge, Xindi Hou, Shuai Gao, Ningchun Liu, Fangtao Yao, Hongke Zhang, Sajal K. Das
Computer Science Faculty Research & Creative Works
Geocast is a one-to-many communication paradigm that enables the transmission of data packets to a designated area rather than an IP address. The most common geocast solutions rely on the application-layer Geolocation-to-IP database. But these IP-based approaches cannot cope with the challenges of flexibility and mobility in a granularity-customizable geocast scenario. While some non-IP network-layer (L3) attempts have resulted in low addressing accuracy and poor routing scalability. Besides, the clean-slate design is incompatible with the existing network. To address these issues, this article proposes an innovative network-layer geographic addressing scheme that leverages P4-based Software Defined Networks (SDN) to enable flexible …
Unsafe Events Detection In Smart Water Meter Infrastructure Via Noise-Resilient Learning, Ayanfeoluwa Oluyomi, Sahar Abedzadeh, Shameek Bhattacharjee, Sajal K. Das
Unsafe Events Detection In Smart Water Meter Infrastructure Via Noise-Resilient Learning, Ayanfeoluwa Oluyomi, Sahar Abedzadeh, Shameek Bhattacharjee, Sajal K. Das
Computer Science Faculty Research & Creative Works
Residential smart water meters (SWMs) collect real-time water consumption data, enabling automated billing and peak period forecasting. The presence of unsafe events is typically detected via deviations from the benign profile of water usage. However, profiling the benign behavior is non-trivial for large-scale SWM networks because once deployed, the collected data already contain those events, biasing the benign profile. To address this challenge, we propose a real-time data-driven unsafe event detection framework for city-scale SWM networks that automatically learns the profile of benign behavior of water usage. Specifically, we first propose an optimal clustering of SWMs based on the recognition …
Adversarial Hidden Link Threats In Meta Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu
Adversarial Hidden Link Threats In Meta Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu
Computer Science Faculty Research & Creative Works
In the emerging field of Meta Computing, where data collection and integration are essential components, the threat of adversary hidden link attacks poses a significant challenge to web crawlers. In this paper, we investigate the impact of these attacks on data collection by web crawlers, emphasizing their evasion of traditional detection methods. Through empirical evaluation, we uncover vulnerabilities in existing crawler mechanisms, particularly in code inspection, and propose enhancements to mitigate these weaknesses. Our assessment of real-world web pages reveals the prevalence and impact of adversary hidden link attacks, emphasizing the necessity for robust countermeasures. Furthermore, we introduce a mitigation …
The Perils Of Wi-Fi Spoofing Attack Via Geolocation Api And Its Defense, Xiao Han, Junjie Xiong, Wenbo Shen, Mingkui Wei, Shangqing Zhao, Zhuo Lu, Yao Liu
The Perils Of Wi-Fi Spoofing Attack Via Geolocation Api And Its Defense, Xiao Han, Junjie Xiong, Wenbo Shen, Mingkui Wei, Shangqing Zhao, Zhuo Lu, Yao Liu
Computer Science Faculty Research & Creative Works
Location spoofing attack deceiving a Wi-Fi positioning system has been studied for over a decade. However, it has been challenging to construct a practical spoofing attack in urban areas with dense coverage of legitimate Wi-Fi APs. This paper identifies the vulnerability of the Google Geolocation API, which returns the location of a mobile device based on the information of the Wi-Fi access points that the device can detect. We show that this vulnerability can be exploited by the attacker to reveal the black-box localization algorithms adopted by the Google Wi-Fi positioning system and easily launch the location spoofing attack in …