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Missouri University of Science and Technology

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Articles 241 - 270 of 1938

Full-Text Articles in Computer Sciences

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 Jan 2024

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 …

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Protecting Activity Sensing Data Privacy Using Hierarchical Information Dissociation, Guangjing Wang, Hanqing Guo, Yuanda Wang, Bocheng Chen, Ce Zhou, Qiben Yan Jan 2024

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 …

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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 Jan 2024

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 …

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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 Jan 2024

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 …

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Federated Graph Anomaly Detection Via Contrastive Self-Supervised Learning, Xiangjie Kong, Wenyi Zhang, Hui Wang, Mingliang Hou, Xin Chen, Xiaoran Yan, Sajal K. Das Jan 2024

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 …

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Disseminating Over-The-Air Updates Via Intelligent Labeling In Multi-Tier Networks, Atefeh Asayesh, Asad Waqar Malik, Sajal K. Das Jan 2024

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 …

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Message From The Phd Dissertation Showcase Chairs, Panos K. Chrysanthis, Sanjay Madria Jan 2024

Message From The Phd Dissertation Showcase Chairs, Panos K. Chrysanthis, Sanjay Madria

Computer Science Faculty Research & Creative Works

No abstract provided.

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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 Jan 2024

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 …

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Achieving Efficient And Privacy-Preserving Reverse Skyline Query Over Single Cloud, Yubo Peng, Xiong Li, Ke Gu, Jinjun Chen, Sajal K. Das, Xiaosong Zhang Jan 2024

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 …

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Stitching Satellites To The Edge: Pervasive And Efficient Federated Leo Satellite Learning, Mohamed Elmahallawy, Tony Tie Luo Jan 2024

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 …

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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 Jan 2024

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 …

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Unsafe Events Detection In Smart Water Meter Infrastructure Via Noise-Resilient Learning, Ayanfeoluwa Oluyomi, Sahar Abedzadeh, Shameek Bhattacharjee, Sajal K. Das Jan 2024

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 …

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Adversarial Hidden Link Threats In Meta Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu Jan 2024

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 …

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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 Jan 2024

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 …

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Communication-Efficient Federated Learning For Leo Constellations Integrated With Haps Using Hybrid Noma-Ofdm, Mohamed Elmahallawy, Tony T. Luo, Khaled Ramadan Jan 2024

Communication-Efficient Federated Learning For Leo Constellations Integrated With Haps Using Hybrid Noma-Ofdm, Mohamed Elmahallawy, Tony T. Luo, Khaled Ramadan

Computer Science Faculty Research & Creative Works

Space AI has become increasingly important and sometimes even necessary for government, businesses, and society. An active research topic under this mission is integrating federated learning (FL) with satellite communications (SatCom) so that numerous low Earth orbit (LEO) satellites can collaboratively train a machine learning model. However, the special communication environment of SatCom leads to a very slow FL training process up to days and weeks. This paper proposes NomaFedHAP, a novel FL-SatCom approach tailored to LEO satellites, that (1) utilizes high-altitude platforms (HAPs) as distributed parameter servers (PSs) to enhance satellite visibility, and (2) introduces non-orthogonal multiple access (NOMA) …

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Lease: Leveraging Energy-Awareness In Serverless Edge For Latency-Sensitive Iot Services, Aastik Verma, Anurag Satpathy, Sajal K. Das, Sourav Kanti Addya Jan 2024

Lease: Leveraging Energy-Awareness In Serverless Edge For Latency-Sensitive Iot Services, Aastik Verma, Anurag Satpathy, Sajal K. Das, Sourav Kanti Addya

Computer Science Faculty Research & Creative Works

Resource Scheduling Catering to Real-Time IoT Services in a Serverless-Enabled Edge Network is Particularly Challenging Owing to the Workload Variability, Strict Constraints on Tolerable Latency, and Unpredictability in the Energy Sources Powering the Edge Devices. This Paper Proposes a Framework LEASE that Dynamically Schedules Resources in Serverless Functions Catering to Different Microservices and Adhering to their Deadline Constraint. to Assist the Scheduler in Making Effective Scheduling Decisions, We Introduce a Priority-Based Approach that Offloads Functions from over-Provisioned Edge Nodes to Under-Provisioned Peer Nodes, Considering the Expended Energy in the Process Without Compromising the Completion Time of Microservices. for Real-World Implementations, …

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Landmark-Based Localization Using Stereo Vision And Deep Learning In Gps-Denied Battlefield Environment, Ganesh Sapkota, Sanjay Madria Jan 2024

Landmark-Based Localization Using Stereo Vision And Deep Learning In Gps-Denied Battlefield Environment, Ganesh Sapkota, Sanjay Madria

Computer Science Faculty Research & Creative Works

Localization in a battlefield environment is increasingly challenging as GPS connectivity is often denied or unreliable, and physical deployment of anchor nodes across wireless networks for localization can be difficult in hostile battlefield terrain. This paper proposes a novel framework for the localization of moving objects in non-GPS battlefield environments using stereo vision and a deep learning model by recognizing naturally existing or artificial landmarks as anchors. The proposed method utilizes a custom-calibrated stereo vision camera for distance estimation and the YOLOv8s model, which is trained and fine-tuned with our real-world dataset for landmark anchor recognition. The depth images are …

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Warmonger Attack: A Novel Attack Vector In Serverless Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu Jan 2024

Warmonger Attack: A Novel Attack Vector In Serverless Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu

Computer Science Faculty Research & Creative Works

We debut the Warmonger attack, a novel attack vector that can cause denial-of-service between a serverless computing platform and an external content server. The Warmonger attack exploits the fact that a serverless computing platform shares the same set of egress IPs among all serverless functions, which belong to different users, to access an external content server. As a result, a malicious user on this platform can purposefully misbehave and cause these egress IPs to be blocked by the content server, resulting in a platform-wide denial of service. To validate the effectiveness of the Warmonger attack, we conducted extensive experiments over …

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Mild Cognitive Impairment Classification Using A Novel Finer-Scale Brain Connectome, Yanjun Lyu, Lu Zhang, Xiaowei Yu, Chao Cao, Tianming Liu, Dajiang Zhu Jan 2024

Mild Cognitive Impairment Classification Using A Novel Finer-Scale Brain Connectome, Yanjun Lyu, Lu Zhang, Xiaowei Yu, Chao Cao, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Mild cognitive impairment (MCI) is recognized as a precursor to Alzheimer's disease (AD), a progressive and irreversible neurodegenerative disorder of the brain. The neurodegeneration of brain connectivity networks plays a pivotal role in the development and progression of MCI. Traditionally, brain networks are generated using coarse-grained brain regions, where the regions serve as nodes and their functional or structural connections are used as edges. Recently, a novel finer scale brain folding patterns named 3hinge gyrus (3HG) was identified, which is defined as the conjunctions coming from three directions on gyral crests. 3HGs have been shown playing an important role in …

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Cav-Ad: A Robust Framework For Detection Of Anomalous Data And Malicious Sensors In Cav Networks, Md Sazedur Rahman, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong Jan 2024

Cav-Ad: A Robust Framework For Detection Of Anomalous Data And Malicious Sensors In Cav Networks, Md Sazedur Rahman, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

The adoption of connected and automated vehicles (CAVs) has sparked considerable interest across diverse industries, including public transportation, underground mining, and agriculture sectors. However, CAVs' reliance on sensor readings makes them vulnerable to significant threats. Manipulating these readings can compromise CAV network security, posing serious risks for malicious activities. Although several anomaly detection (AD) approaches for CAV networks are proposed, they often fail to: i) detect multiple anomalies in specific sensor(s) with high accuracy or F1 score, and ii) identify the specific sensor being attacked. In response, this paper proposes a novel framework tailored to CAV networks, called CAV-AD, for …

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Persistent Monitoring Of Insect-Pests On Sticky Traps Through Hierarchical Transfer Learning And Slicing-Aided Hyper Inference, Fateme Fotouhi, Kevin Menke, Aaron Prestholt, Ashish Gupta, Matthew E. Carroll, Hsin Jung Yang, Edwin J. Skidmore, Matthew O'Neal, Nirav Merchant, Sajal K. Das, Peter Kyveryga, Baskar Ganapathysubramanian, Asheesh K. Singh, Arti Singh, Soumik Sarkar Jan 2024

Persistent Monitoring Of Insect-Pests On Sticky Traps Through Hierarchical Transfer Learning And Slicing-Aided Hyper Inference, Fateme Fotouhi, Kevin Menke, Aaron Prestholt, Ashish Gupta, Matthew E. Carroll, Hsin Jung Yang, Edwin J. Skidmore, Matthew O'Neal, Nirav Merchant, Sajal K. Das, Peter Kyveryga, Baskar Ganapathysubramanian, Asheesh K. Singh, Arti Singh, Soumik Sarkar

Computer Science Faculty Research & Creative Works

Introduction: Effective monitoring of insect-pests is vital for safeguarding agricultural yields and ensuring food security. Recent advances in computer vision and machine learning have opened up significant possibilities of automated persistent monitoring of insect-pests through reliable detection and counting of insects in setups such as yellow sticky traps. However, this task is fraught with complexities, encompassing challenges such as, laborious dataset annotation, recognizing small insect-pests in low-resolution or distant images, and the intricate variations across insect-pests life stages and species classes. Methods: to tackle these obstacles, this work investigates combining two solutions, Hierarchical Transfer Learning (HTL) and Slicing-Aided Hyper Inference …

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Prediction Of Carbonation Capacity Of Scms Using Ensemble Learning Method, Kangyi Cai, Jian Liu, Edward Mwanza, Mahelet G. Fikru, Hongyan Ma, Donald C. Wunsch Jan 2024

Prediction Of Carbonation Capacity Of Scms Using Ensemble Learning Method, Kangyi Cai, Jian Liu, Edward Mwanza, Mahelet G. Fikru, Hongyan Ma, Donald C. Wunsch

Economics Faculty Research & Creative Works

The utilization of supplementary cementitious materials (SCMs) subjected to carbonation processing represents a viable strategy to mitigate anthropogenic CO2 emissions associated with concrete production, potentially contributing to the achievement of carbon neutrality. However, existing studies have limitations in effectively predicting the varying carbonation capacities of different SCMs, a gap that this research aims to address. Recent research efforts focused on the carbonation of waste-material-sourced SCMs are reviewed, along with a comparative discussion on diverse carbonation methods. A detailed data set encapsulating the properties of SCMs, and carbonation configurations was compiled. At the same time, six ensemble learning models were …

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Energy Consumption Optimization Of Uav-Assisted Traffic Monitoring Scheme With Tiny Reinforcement Learning, Xiangjie Kong, Chenhao Ni, Gaohui Duan, Guojiang Shen, Yao Yang, Sajal K. Das Jan 2024

Energy Consumption Optimization Of Uav-Assisted Traffic Monitoring Scheme With Tiny Reinforcement Learning, Xiangjie Kong, Chenhao Ni, Gaohui Duan, Guojiang Shen, Yao Yang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Unmanned Aerial Vehicles (UAVs) can capture pictures of road conditions in all directions and from different angles by carrying high-definition cameras, which helps gather relevant road data more effectively. However, due to their limited energy capacity, drones face challenges in performing related tasks for an extended period. Therefore, a crucial concern is how to plan the path of UAVs and minimize energy consumption. To address this problem, we propose a multi-agent deep deterministic policy gradient based (MADDPG) algorithm for UAV path planning (MAUP). Considering the energy consumption and memory usage of MAUP, we have conducted optimizations to reduce consumption on …

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Towards Fine-Gained Services: Nfv-Assisted Tracking And Positioning Using Micro-Services For Multi-Robot Cooperation, Bo Yi, Lin Qiu, Jianhui Lv, Yingpu Nian, Xingwei Wang, Sajal K. Das Jan 2024

Towards Fine-Gained Services: Nfv-Assisted Tracking And Positioning Using Micro-Services For Multi-Robot Cooperation, Bo Yi, Lin Qiu, Jianhui Lv, Yingpu Nian, Xingwei Wang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Robotics as a Service (RaaS) emerges as a new paradigm to motivate diversified potential of the "remote-controlled economy" for flexible and efficient service provision with the help of cloud computing. The multi-robot cooperation (MRC) technology has been widely used in various intelligent logistics scenarios, such as warehouses, factories, airports and subway stations, benefiting from the advantages of high operational efficiency and low labor cost. While promising, the corresponding challenge is that the service functions deployed on logistics robots (LRs) are more prone to failures such as resource exhaustion and error configuration in the multi-robot system (MRS). In this way, it …

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Undeniable Authentication Of Digital Twin-Managed Smart Microfactory, Anusha Vangala, Ashok Kumar Das, Sajal K. Das Jan 2024

Undeniable Authentication Of Digital Twin-Managed Smart Microfactory, Anusha Vangala, Ashok Kumar Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart Microfactories Use Additive Manufacturing to Create Products with Mixed Materials and Variable Sizes. Digital Twin Technology Enhances Control of the Additive Manufacturing Equipment in These Factories, Increasing Productivity and Minimizing Errors. the Digital Twins Communicate with the Machines to Furnish Sensitive Data and Instructions, Which Must Be Protected from Tampering. Authentication Rescues the Digital and Physical Twins from Menacing Attacks Such as Privileged Insider, Impersonation, Ephemeral Secret Leakage (ESL) and Man-In-The-Middle (MiTM) Attacks. to This End, We Propose Lightweight Authentication among the Digital and Physical Twins with the Undeniability of Issued Commands and Deniable Key Agreement. It Achieves Perfect …

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Optimizing Uav-Assisted Data Collection In Iot Sensor Networks Using Dual Cluster Head Strategy, Keiwan Soltani, Federico Coro, Sajal K. Das Jan 2024

Optimizing Uav-Assisted Data Collection In Iot Sensor Networks Using Dual Cluster Head Strategy, Keiwan Soltani, Federico Coro, Sajal K. Das

Computer Science Faculty Research & Creative Works

The proliferation of the Internet of Things (IoT) has significantly impacted the integration of digital and physical realms, with Wireless Sensor Networks (WSN s) playing a crucial role. However, these sensor nodes often face challenges related to battery constraints and deployment in inaccessible terrains. The advent of Unmanned Aerial Vehicles (UAVs) presents a transformative solution, particularly for data collection from remote IoT devices. This work explores the application of UAV s to improve data collection in dense IoT sensor networks. We propose a novel approach called optimizing UAV-assisted data collection in IoT sensor networks using Dual Cluster Head (UAVDCH) that …

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Minerrouter : Effective Message Routing Using Contact-Graphs And Location Prediction In Underground Mine, Abhay Goyal, Sanjay Madria, Samuel Frimpong Jan 2024

Minerrouter : Effective Message Routing Using Contact-Graphs And Location Prediction In Underground Mine, Abhay Goyal, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Location-based distributed communication in underground mines has been a hard problem to solve due to unreliable centralized architecture such as leaky feeder systems, high attenuation, and the unavailability of GPS signals. Delay Tolerant Networks (DTN) enable decentralized message routing using the store-carry-forward method that can help in creating situational awareness needed to handle emergency and disaster scenarios. The ability to predict where the DTN nodes (miner) might have been at/are headed to (with respect to the mine regions and pillars) at different times, combined with contact-based routing and intelligent handling of buffer, can be used for better delivery of messages. …

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Message From Bits 2024 Co-Chairs And Technical Program Co-Chairs; Smartcomp 2024, Sajal K. Das, Hayato Yamana, Keiichi Yasumoto, Shameek Bhattacharjee Jan 2024

Message From Bits 2024 Co-Chairs And Technical Program Co-Chairs; Smartcomp 2024, Sajal K. Das, Hayato Yamana, Keiichi Yasumoto, Shameek Bhattacharjee

Computer Science Faculty Research & Creative Works

No abstract provided.

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Traffic Prediction-Based Vnf Auto-Scaling And Deployment Mechanism For Flexible And Elastic Service Provision, Bo Yi, Jiacheng Wang, Qiang He, Xingwei Wang, Min Huang, Sajal K. Das, Keqin Li Jan 2024

Traffic Prediction-Based Vnf Auto-Scaling And Deployment Mechanism For Flexible And Elastic Service Provision, Bo Yi, Jiacheng Wang, Qiang He, Xingwei Wang, Min Huang, Sajal K. Das, Keqin Li

Computer Science Faculty Research & Creative Works

Network Function Virtualization (NFV) provides a flexible way to provision new services by decoupling network functions from hardware and implementing them as Virtual Network Functions (VNFs). However, the rapid development of technologies greatly promotes the explosion of diverse services, which directly results in the exponential increase of heterogeneous traffic. In addition, such a tremendous amount of heterogeneous traffic will generate bursts in a more dynamic and unexpected manner, so it becomes extremely hard to satisfy the customer demands. Aiming at addressing these challenges, this work proposes a positive and elastic VNF deployment mechanism for service provisioning, which introduces three novelties: …

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Move: Matching Game For Partial Offloading In Vehicular Edge Computing, Mahmuda Akter, Debjyoti Sengupta, Anurag Satpathy, Sajal Das Jan 2024

Move: Matching Game For Partial Offloading In Vehicular Edge Computing, Mahmuda Akter, Debjyoti Sengupta, Anurag Satpathy, Sajal Das

Computer Science Faculty Research & Creative Works

Autonomous Vehicles (AV s) require substantial computational resources to perform operations that safely navigate vehicles in urban road networks. Resource-intensive operations are offloaded to roadside units (RSUs), acting as edge servers, to improve the responsiveness and reduce the energy consumed in execution. In this context, a cooperative execution involving the vehicular on-board units (OBUs) and the RSUs can act as a game changer. However, partial offloading is non-trivial and demands addressing the following research challenges. Firstly, the RSU's resources are limited, necessitating regulated resource assignments. Secondly, capturing distinctive vehicle parameters using a unified ranking scheme is imperative. Thirdly, an efficient …

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