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23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Ju, Keyi Zhong, Hui Zhang, Xinyue Qin, Xiaoqi Pan, Miao Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Kennesaw State University Department of Computer Science MariettaGA30060 United States

Quantum computing has revolutionized the approach to solving complex problems and handling vast datasets. However, data leakage in quantum computing may present privacy risks. While differential privacy (DP) has been a classical solution to protect privacy by injecting artificial noises, the implementation of DP within the quantum domain remains an under-explored area. We observe that there is a potential to leverage the inherent noises generated by Noisy Intermediate-Scale Quantum (NISQ) devices during quantum operations for achieving DP in quantum computing. Given that these inherent noises are uncontrollable, we take the lead to utilize quantum error mitigation (QEM) techniques to manage quantum differential privacy (QDP) protection levels. In our approach, we offer a tangible description of the "closeness"between neighboring quantum datasets and introduce a novel QDP definition based on observables of interest. Our simulations reveal that factors like the distance between neighboring quantum states and the number of circuit executions influence the privacy budget. Notably, by controlling the QEM samples while keeping the number of circuit executions fixed, QEM allows for substantial noise control to meet a desired privacy budget. Furthermore, we extend analysis from single-qubit to multiple qubits and discuss the impact of observables of interest on the privacy budget. © 2024 IEEE.

关键词： Qubits

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IoT-Enabled Black Box for Driver Behavior Analysis Using Cloud Computing

IoT-Enabled Black Box for Driver Behavior Analysis Using Clo...

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Advances in Data engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： J. Jegan M. Raja Suguna M. Shobana H. Azath S. Murugan M. Rajmohan Department of Computer Science and Engineering School of Technology The Apollo University Chittoor Andhra Pradesh India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Networking and Communication SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Bhopal India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The Internet of Things (IoT)-powered black box for advanced driver behavior analysis and vehicle safety is a revolutionary strategy for improving driver behavior and road safety via the use of black boxes powered by the Internet of Things. This project captures realtime data on driving behavior, encompassing characteristics such as speed, acceleration, braking, and adherence to lanes via the seamless integration of IoT technologies into the vehicle's black box. It does this by providing in-depth insights into drivers' behaviors, which encourages adopting safe driving practices thanks to the advanced data analytics it employs. In addition to its behavior monitoring capabilities, the IoT-driven black box enhances the vehicle's safety by including collision detection technologies. If an accident occurs, the system immediately sends an alert, communicating the severity of the collision and the exact GPS locations to the emergency services, which speeds up the process of responding to the incident. This attempt combines cutting-edge Internet of Things capabilities with vehicle safety to develop responsible driving behaviors and, eventually, a safer highway environment for all parties involved. This invention can modify driving norms thanks to its comprehensive approach, highlighting the potential of technology-driven solutions in promoting safer roadways.

关键词： Technological innovation Data analysis Vehicle safety Emergency services Road safety Internet of Things Collision avoidance

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Improving Data Encoding for Enhanced AI Performance in Complex Datasets via Quantum Feature Space Mapping: Harnessing Quantum Algorithms

Improving Data Encoding for Enhanced AI Performance in Compl...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Gurpreet Singh Walia Sathiya Priya S KV Karthikeya D. Suresh P Sudheer V. Syambabu Lead Enterprise Security and Networking Architect NetApp Inc. Durham NC Department of ECE Panimalar Engineering College Chennai AT&T Sattva Knowledge City Hyderabad Department of ECE St. Joseph's Institute of Technology Chennai Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The purpose of this study is to investigate the potential for quantum feature space mapping to be utilized in the enhancement of data encoding approaches for artificial intelligence (AI) systems that are capable of managing complicated datasets. Traditional artificial intelligence models frequently face challenges when confronted with the high-dimensionality and non-linearity of such datasets. This can hinder their performance in areas such as pattern recognition, optimization, and predictive modeling. Quantum computing, which is able to make use of quantum superposition and entanglement, provides a method that is revolutionary in nature by means of quantum feature space mapping. By encoding data into quantum states, this technology enables the construction of feature spaces that are tenfold bigger than those that would be possible in classical conditions. This makes it much simpler to differentiate between intricate patterns that would otherwise be indistinguishable. The purpose of this research is to investigate the application of quantum algorithms, such as Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC), with the intention of enhancing the capacity of artificial intelligence to learn from complex and high-dimensional data. We demonstrate that quantum feature space mapping leads to more efficient and accurate data classification, regression, and clustering tasks by comparing the performance of quantum-enhanced artificial intelligence models to that of their classical counterparts on difficult datasets. Additionally, the research addresses important difficulties, such as the constraints of the quantum hardware that is now available and the requirement for quantum circuits that are scalable. Quantum computing has the potential to improve the performance of artificial intelligence in the management of complicated, high-dimensional data, which may ultimately pave the way for advances in domains such as genetics, finance, and cryptogra

关键词： Support vector machines Quantum computing Quantum algorithm Quantum entanglement Computational modeling Space mapping Quantum state Encoding Hardware Quantum circuit

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NextGen Robot: An Efficient and Effortless Wall Painting Robot Design Based on Artificial Intelligence (AI) Principles

NextGen Robot: An Efficient and Effortless Wall Painting Rob...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S Jeya Mary Sajithra S G Divya Lakshmi. N Pallavi Giri K. Al-Qawasmi Department of ECE Einstein College of Engineering Tirunelveli Department of Computer Science and Engineering R.M.K. Engineering College Chennai Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TamilNadu India Department of Master of Computer Applications S.A. Engineering College Chennai Department of Civil engineering Laxminarayan Innovation Technological University Nagpur India Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

A fully autonomous wall-painting robot is the focus of this paper's development, expansion, and implementation efforts. Research efforts have not communicated much about interior wall painting. Painters’ eyes and respiratory systems are particularly vulnerable to the toxic chemicals used in the industry. Additionally, the monotony, time commitment, and physical exertion inherent in the painting process are hard to resist. The entire construction process may be enhanced and time and labor saved by using robots and human workers appropriately into building jobs. The Dynamic Smart Painting Robo Model (DSPRM) is a novel robot that this study introduces for intense wall painting. To test its efficacy, it is crossvalidated with the usual technique, the usual Painting Bot (CPB). Furthermore, it would provide an opportunity to lessen or eliminate human exposure to challenging and dangerous conditions, which would address most of the issues, provide protection, and make it happen more often. An autonomous robotic painting system is being developed as a result of the wall painting robot.

关键词： Service robots Buildings Toxic chemicals Resists Chatbots Artificial intelligence Respiratory system Protection System analysis and design Painting

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Plant Leaf Disease Detection Using IoT and Deep Learning Approach

Plant Leaf Disease Detection Using IoT and Deep Learning App...

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Cognitive Computing in engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： N. Senthamarai R. Srinivasan M. Kavitha R. Kavitha Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Modern detecting innovations, with a specific spotlight on manageability related issues, are impacting the fate of farming as the globe turns out to be more interconnected. The majority of emerging countries' economies is based on agriculture. The agricultural industry is one of the most vital industries, and crops are a necessity for providing food to people all over the world. The agricultural sector depends heavily on early disease detection and recognition. Crop fields are susceptible to the outbreak of plant diseases, either as a result of spreading viruses and fungus or unfavorable weather conditions, due to the lack of trained labor and enough supporting infrastructure. Owners of community gardens that want to use affordable strategies for sustainable agriculture through a sustainable. This model's suggestion offers a practical means of identifying various ailments in a range of plant species. Using the recently created dataset, an improved variant of the best deep learning (DL) model, has been suggested. In this unique circumstance, think about the possible utilization of the Social Internet of Things for detecting and interchanges, deep learning for the identification of plant sicknesses, and crowd sourcing for the collection and grouping of pictures, drawing in ranchers and local area garden proprietors and specialists. Across data fusion and deep learning, the created structure can take utility of the information gathered and estimate with a certain level of accuracy when a plant would (or would not) contract a disease. The user application must be able to interface with the virtualization layer and must be able to assess the health of the plants using images, data from garden sensors, and Internet access. Take some photos of the plant to assess its health and to provide feedback on any photos of the plant that haven't yet been tagged for the classifier data set. In addition to the user's smart phone's photo, the diagnosis of a crop disease necessitates c

关键词： Deep learning Plant diseases Technological innovation Accuracy Crops Agriculture Social Internet of Things Virtualization Intelligent sensors Tuning

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement Learning

Multi-Agent Systems for Autonomous IoT Network Management Us...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services Throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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Capacity Analysis and Rate Maximization Design in RIS-Aided Uplink Multi-User MIMO

Capacity Analysis and Rate Maximization Design in RIS-Aided ...

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IEEE Conference on Wireless Communications and networking

作者： Wei Jiang Hans D. Schotten Intelligent Networking Research Group German Research Center for Artificial Intelligence (DFKI) Germany Department of Electrical and Computer Engineering Technische Universität (TU) Kaiserslautern Germany

Reconfigurable intelligent surface (RIS) has recently drawn intensive attention due to its potential of simultaneously realizing high spectral and energy efficiency in a sustainable way. This paper focuses on the design of efficient transmission methods to maximize the uplink sum throughput in a RIS-aided multi-user multi-input multi-output (MU-MIMO) system. To provide an insightful basis, the channel capacity of RIS-aided MU-MIMO is theoretically analyzed. Then, the conventional transmission schemes based on orthogonal multiple access are presented as the baseline. From the information-theoretic perspective, we propose two novel schemes, i.e., joint transmission based on the semidefinite relaxation of quadratic optimization problems and opportunistic transmission relying on the best user selection. The superiority of the proposed schemes over the conventional ones in terms of achievable rates is justified through simulation results.

关键词： Monte Carlo methods Channel capacity Simulation Throughput Energy efficiency Complexity theory Uplink

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Social Sensing for Personalized Orienteering Mediating the Need for Sociality and the Risk of COVID-19

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 323-332页

作者： Pilato, Giovanni Persia, Fabio Ge, Mouzhi D'Auria, Daniela Institute for High Performance Computing and Networking National Research Council of Italy ICAR-CNR Palermo90146 Italy University of l'Aquila Computer Science and Mathematics Department of Information Engineering L'Aquila67100 Italy Deggendorf Institute of Technology Faculty of European Campus Rottal-Inn Deggendorf94469 Germany Free University of Bozen-Bolzano Faculty of Computer Science Bolzano39100 Italy

Orienteering or itinerary planning applications aim to optimize travel routes exploiting user preference and other constraints, such as time budget or traffic conditions. For these algorithms, it is essential to explore the user preference to predict potential Points-Of-Interest (POI) or touristic routes. However, user preference has been significantly affected by the COVID-19, since health concern plays a key trade-off role now. For example, people may try to avoid crowdedness, even if there is a strong social desire. However, most orienteering applications just focus on user preferences, thus paying less attention to the variety of the data inputs, which has become an essential factor for the utility of the application in the COVID-19 era. Therefore, this paper proposes a social sensing system that considers the trade-off between user preference and various factors, such as crowdedness, fear of being infected, knowledge of the COVID-19, POI features, and desire for socialization. The experiments are conducted on profiling user interests with Doc2Vec and FastText based on the Yelp dataset. Furthermore, the proposed system is modular and can be efficiently adapted to different applications for COVID-aware itinerary planning. © 2020 IEEE.

关键词： COVID-19

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Task Offloading to Edge Cloud Balancing Utility and Cost for Energy Harvesting Internet of Things

SSRN

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SSRN 2022年

作者： Nandi, Pranjal Kumar Reaj, Md. Rejaul Islam Sarker, Sujan Razzaque, Md. Abdur Mamun-Or-Rashid, Md. Roy, Palash Green Networking Research Group Department of Computer Science and Engineering University of Dhaka Bangladesh Department of Robotics and Mechatronics Engineering University of Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh

Task offloading from many Internet of Things (IoT) devices to a mobile edge computing (MEC) system, consisting of multiple collaborative edge servers (CES), results in reduced task execution delay as well as energy consumption. However, exploitation of computation resources of CESs incurs additional cost. Existing works in the literature either focused on minimizing execution latency or energy. In this paper, we have developed a task offloading policy that aims at making a trade-off between device utility and execution cost. The utility is defined as a function of task execution latency and energy consumption of energy harvesting IoT devices. The task offloading problem is formulated as a subset selection problem that makes the desired trade-off. The offloading problem is proven to be NP-hard and thus we develop a meta-heuristic approach, namely SCOPE, inspired by Social Cognitive Optimization (SCO) to obtain the desired Polynomial time Execution. The results show its potency compared to the state-of-the-art methods in terms of task execution latency, energy consumption, utility per unit cost and task drop rate. © 2022, The Authors. All rights reserved.

关键词： Energy harvesting

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