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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 71-92页

作者： Singh, Raghubir Gill, Sukhpal Singh Department of Computer Science University of Bath Bath United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

Artificial Intelligence (AI) at the edge is the utilization of AI in real-world devices. Edge AI refers to the practice of doing AI computations near the users at the network's edge, instead of centralised location like a cloud service provider's data centre. With the latest innovations in AI efficiency, the proliferation of Internet of Things (IoT) devices, and the rise of edge computing, the potential of edge AI has now been unlocked. This study provides a thorough analysis of AI approaches and capabilities as they pertain to edge computing, or Edge AI. Further, a detailed survey of edge computing and its paradigms including transition to Edge AI is presented to explore the background of each variant proposed for implementing Edge Computing. Furthermore, we discussed the Edge AI approach to deploying AI algorithms and models on edge devices, which are typically resource-constrained devices located at the edge of the network. We also presented the technology used in various modern IoT applications, including autonomous vehicles, smart homes, industrial automation, healthcare, and surveillance. Moreover, the discussion of leveraging machine learning algorithms optimized for resource-constrained environments is presented. Finally, important open challenges and potential research directions in the field of edge computing and edge AI have been identified and investigated. We hope that this article will serve as a common goal for a future blueprint that will unite important stakeholders and facilitates to accelerate development in the field of Edge AI. © 2023 The Authors

关键词： Machine learning

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Optimizing Waste Management: Integrated Pollution Detection and Systematic Reporting for Sustainable Disposal 2

Optimizing Waste Management: Integrated Pollution Detection ...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Samuel Prabakaran, M. Sophia, S. Sophas Samuel, S. Karunya Institute of Technology and Sciences Department of Computer Engineering Coimbatore India Karunya Institute of Technology and Science Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

Effective waste management and pollution control are paramount for sustainable environmental stewardship. This study presents a comprehensive approach leveraging cutting-edge technologies such as YOLO object recognition, Faster R-CNN, and SVM-based AQI computation. The system integrates YOLOv7 for real-time rubbish classification, facilitating swift and precise trash sorting and disposal decisions. Additionally, it employs Faster R-CNN for fog density analysis, enabling thorough estimations of air pollution levels. To calculate the Air Quality Index (AQI), support vector machine (SVM) algorithms combine various air quality indicators, ensuring thorough and accurate assessments. Enhanced trash classification capabilities enable more accurate evaluation of the environmental impacts of waste management systems. By integrating fog density monitoring with AQI calculation, the approach offers comprehensive trend analysis of pollution, aiding in the formulation of data-driven environmental policies and effective pollution management measures. Furthermore, the system evaluates soil quality, complementing its waste management and pollution control functions. Despite its advancements, the system faces challenges in real-world implementation, including data acquisition, model training, and system integration. Addressing these challenges is critical for realizing the full potential of integrated waste management and pollution control systems. © 2024 IEEE.

关键词： Data acquisition

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Enhancing the Stability of Organic Photovoltaics with Multifunctional Composite Coatings 52

Enhancing the Stability of Organic Photovoltaics with Multif...

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52nd IEEE Photovoltaic Specialist Conference, PVSC 2024

作者： Seralin, Aidar Zinedenova, Aruzhan Ibragim, Mukhammadali Aidarkhanov, Damir Ng, Annie Nazarbayev University Department of Electrical and Computer Engineering Astana010000 Kazakhstan

ISBN: (纸本)9781665464260

Organic photovoltaics (OPVs) show promise as a sustainable energy solution but lack long-term stability under real-world operating conditions. Exposure to UV light and moisture can cause performance degradation over time. Protective coatings that isolate devices from these factors without compromising OPV advantages like flexibility and light weight are helpful to enhance the device lifetime. This work develops down-converting fluorophore-embedded composite coatings to address the device stability. The coatings are designed to convert harmful UV photons into visible light that can be harvested by OPVs. The coating formulation was optimized to maximize downconversion while preventing fluorescence quenching. When the developed coatings applied to PM6:Y6 solar cells, the operational lifetime of devices are significantly extended. The tested device can retain 95% of its initial efficiency more than 600 hours under the maximum power point condition. © 2024 IEEE.

关键词： Fluorescence quenching

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PairingNet: A Multi-Frame Based Vehicle Trajectory Prediction Deep Learning Network

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IEEE Access 2023年 11卷 29566-29575页

作者： Chen, Guan-Wen Sun, Min-Te Ik, Tsi-Ui National Yang Ming Chiao Tung University College of Computer Science Department of Computer Science Hsinchu30010 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan320317 Taiwan

The traffic infrastructure of a city requires evaluation and improvement through a large amount of data analysis. The construction and laborious work of traditional methods make computer vision flourish in traffic analysis. Among different computer vision technologies for intelligent transportation system, one of the most important algorithms is multiple object tracking (MOT). At present, MOT used in traffic analysis has several shortcomings, such as lack of the output of vehicle speed and movement direction, and the consideration of single factor in trajectory tracking. These limitations have affected the results of traffic analysis. This research proposes an end-to-end deep learning network, called PairingNet. In addition to retaining the function and accuracy of the original detection network, PairingNet integrates the calculation of vehicle trajectory into the network through the feature fusion of consecutive images, which is introduced to predict the movement direction and speed of the vehicle. These additional features can be used to better track the trajectories of vehicles. In addition, a pipeline is designed to reduce the loading latency incurred by using the consecutive frames as the input for PairingNet. The experiment results indicate that the vehicle identification of PairingNet reaches 96% accuracy, surpassing the original YOLOv3 as the backbone structure, and reaches a near 100% accuracy rate in the predicted vehicle position. Moreover, with the pipeline process, the inference speed of PairingNet is very close to the original YOLOv3. In MOT results, the MOTA of PairingNet also has a high performance of 91%. © 2013 IEEE.

关键词： Trajectories

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A GMM-CGABC Based Energy Replenishment Scheme for On-Demand Wireless Rechargeable Sensor Networks 1

A GMM-CGABC Based Energy Replenishment Scheme for On-Demand ...

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1st International Symposium on Parallel Computing and Distributed Systems, PCDS 2024

作者： Mouli, S.Sudhamsu Veeraiah, T. Mahesh, K. Singh, M.P. Ecole Centrale School of Engineering Mahindra University Department of Computer Science and Engineering Hyderabad India National Institute of Technology Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350349658

Wireless Sensor Networks (WSNs) are constrained by the limited energy capacity of Sensor Nodes (SNs), which hinders their perpetual operation. The advent of Wireless Energy Transfer (WET) technology has emerged as a promising solution to this challenge, enabling the creation of Wireless Rechargeable Sensor Networks (WRSNs). Recent research has shown growing interest in integrating Wireless Mobile Chargers (WMCs) into WRSNs to replenish the energy reserves of critical SNs through WET. A key aspect of WET involves using one or more WMCs to recharge SNs, with optimizing the charging schedule (i.e., the sequence in which SNs are charged) crucial in on-demand WRSNs. In addition, spatiotemporal constraints imposed by the requesting SNs must be considered when devising the WMC's charging schedule. To address these challenges, this work proposes the integration of a Gaussian Mixture Model with a Chaotic Gaussian Artificial Bee Colony (GMM-CGABC) to develop an efficient recharge schedule that revitalizes SNs. This model is designed to optimize SN deployment, maximize network coverage, minimize energy consumption, and extend network longevity. The proposed method determines the optimal charging path for WMCs within each cluster to minimize Average Charging Latency (ACL) while incorporating a novel strategy for fitness function evaluation. Extensive simulations demonstrate that the proposed system outperforms state-of-the-art algorithms, including First Come, First Served (FCFS), Nearest Job Next with Preemption (NJNP), and the Gravitational Search Algorithm (GSA), in terms of charging latency. © 2024 IEEE.

关键词： Inductive power transmission

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Food ingredient recognition model via image and textual feature extraction and hybrid classification strategy

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International Journal of Reasoning-based Intelligent Systems 2024年第1期16卷 74-90页

作者： Madival, Sharanabasappa A. Jawaligi, Shivkumar S. Department of Computer Science and Engineering Faculty of Engineering and Technology Sharnbasva University India Department of Electronic and Communication Engineering Faculty of Engineering and Technology Sharnbasva University Karanataka Kalburgi India

This research work focuses on food recognition, especially, the identification of the ingredients from food images. Here, the developed model includes two stages namely: 1) feature extraction;2) classification. Initially, the image features and textual features will be extracted, where image features like SIFT and improved CNN-based deep features, textural features are extracted. Then, the hybrid classifier is used for the identification of food ingredients that combines the models like neural network (NN) as well as long short-term memory (LSTM). In order to make the accurate results, the weights of NN and LSTM are fine-tuned via the Chebyshev map evaluated teamwork optimisation (CME-TWO) algorithm. At the final stage, the primacy of the offered scheme is proven concerning varied metrics. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Feature extraction

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A Proactive Crowdsourcing Framework for Fake Base Station Detection and Avoidance 15

A Proactive Crowdsourcing Framework for Fake Base Station De...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Saad, Omar A. Etman, Ahmed A. Abdel-Malek, Mai A. Azab, Mohamed Alexandria University Faculty of Engineering Department of Computer and Communications Engineering Alexandria Egypt The University of Arizona Department of Electrical and Computer Engineering AZ United States Virginia Military Institute Department of Computer and Information Sciences VA United States

ISBN: (纸本)9798331507695

With next-generation communication technologies becoming more integrated into critical infrastructure, securing these networks against sophisticated threats has become paramount. One of the most persistent threats to this communication is the growing presence of fake base stations (FBS). These FBSs are malicious entities that pose as legitimate base stations to intercept, manipulate, or exploit mobile communications, leading to serious privacy breaches. While some existing literature addresses the detection of such malicious entities, detecting FBSs has traditionally been the responsibility of network operators, who rely on tools like signal strength analysis and radio frequency (RF) monitoring to identify unusual patterns or rogue signals. However, these centralized methods often struggle to detect FBSs in complex environments characterized by high mobility, dense urban areas, and sophisticated spoofing techniques. This paper proposes a scalable and pervasive blockchain-based crowd-sourcing mechanism to enable extensive device participation in identifying FBSs. In a fully automated and distributed manner, the proposed platform uses vetted and verified information recorded on the blockchain to identify and report suspicious FBSs in the area. This allows other users to dissociate from these malicious entities automatically. A reputation management system is also employed to ensure the reliability and accountability of participating users. Using qualitative and quantitative evaluations, the proposed approach demonstrated effectiveness and efficiency in achieving all targeted goals. © 2025 IEEE.

关键词： Crowdsourcing

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Punny_Punctuators@DravidianLangTech-EACL2024: Transformer-based Approach for Detection and Classification of Fake News in Malayalam Social Media Text 4

Punny_Punctuators@DravidianLangTech-EACL2024: Transformer-ba...

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4th Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, DravidianLangTech 2024

作者： Tabassum, Nafisa Aodhora, Sumaiya Rahman Akter, Rowshon Hossain, Jawad Ahsan, Shawly Hoque, Mohammed Moshiul Department of Computer Science and Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (纸本)9798891760783

The alarming rise of fake news on social media poses a significant threat to public discourse and decision-making. While automatic detection of fake news offers a promising solution, research in low-resource languages like Malayalam often falls behind due to limited data and tools. This paper presents the participation of team Punny_Punctuators in the Fake News Detection in Dravidian Languages shared task at DravidianLangTech@EACL 2024, addressing this gap. The shared task focuses on two subtasks: 1. classifying social media texts as original or fake, and 2. categorizing fake news into 5 categories. We experimented with various machine learning (ML), deep learning (DL) and transformer-based models as well as processing techniques such as transliteration. Malayalam-BERT achieved the best performance on both sub-tasks, which obtained us 2nd place with a macro f1-score of 0.87 for the subtask-1 and 11th place with a macro f1-score of 0.17 for the subtask-2. Our results highlight the potential of transformer models for low-resource languages in fake news detection and pave the way for further research in this crucial area. © 2024 Association for Computational Linguistics.

关键词： Decision making

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Paillier Cryptography Based Message Authentication Code for IoMT Security

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computer Systems science & engineering 2023年第3期44卷 2209-2223页

作者： S.Siamala Devi Chandrakala Kuruba Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science and Engineering Vignan Nirula Institute of Technology and ScienceGuntur522009India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Health care visualization through Internet of Things(IoT)over wireless sensor network(WSN)becomes a current research attention due to medical sensor evolution of *** digital technology-based communication system is widely used in all *** of medical thing(IoMT)assisted health-care application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human *** smart medical devices may consume with limited resources and also the data generated by these devices are large in *** IoMT based applications suffer from the issues such as security,anonymity,privacy,and *** overcome these issues,data aggregation methods are the solution that can concatenate the data generated by the sensors and forward it into the base station through fog node with efficient encryption and *** article proposed a well-organized data aggregation and secured transmission *** data generated by the sensor are collected and *** nodes(AN)received the compressed data and concatenate *** concatenated and encrypted data is forward to fog node using the enhanced Paillier cryptogra-phy-based encryption with Message Authentication code(MAC).Fog node extracts the forwarded data from AN using Fog message extractor method(FME)with *** proposed system ensures data integrity,security and also protects from security *** proposed model is simulated in Net-work Simulator 2.35 and the evaluated simulation results proves that the aggregation with MAC code will ensures the security,privacy and also reduces the communication *** node usages in between Aggregator and base station,will reduce the cloud server/base station computational overhead and storage *** proposed ideology is compared with existing data aggregation schemes in terms of computational cost,storage cost,communication cost and energy *** of communica

关键词： Fog IoMT wireless sensor network cloud aggregation encryption decryption energy

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Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

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computer Systems science & engineering 2023年第5期45卷 1113-1128页

作者： Kuldeep Chouhan Mukesh Yadav Ranjeet Kumar Rout Kshira Sagar Sahoo NZ Jhanjhi Mehedi Masud Sultan Aljahdali Computer Science and Engineering I.T.S Engineering CollegeGreater Noida201310India Computer Science and Engineering DPG Institute of Technology and ManagementGurgaon122004India Computer Science and Engineering National Institute of Technology SrinagarJammu and KashmirIndia Department of Computer Science and Engineering SRM UniversityAmaravatiAndhra Pradesh522240India School of Computer Science SCS Taylor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social *** researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder *** sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning *** assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,*** addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral *** this work,we obligated Twitter Application Programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account *** distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.

关键词： Machine learning Naive Bayes natural language processing sentiment analysis social media analytics support vector machine Twitter application programming interface

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