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Using Metaheuristic OFA Algorithm for Service Placement in Fog Computing

computers, Materials & Continua 2023年第12期77卷 2881-2897页

作者： Riza Altunay Omer Faruk Bay Department of Information Systems Gazi UniversityAnkara06680Turkey Department of Computer Technologies Ondokuz Mayis UniversitySamsun55100Turkey Department of Electrical-Electronics Engineering Gazi UniversityAnkara06560Turkey

The use of fog computing in the Internet of Things(IoT)has emerged as a crucial solution,bringing cloud services closer to end users to process large amounts of data generated within the *** its advantages,the increasing task demands from IoT objects often overload fog devices with limited resources,resulting in system delays,high network usage,and increased energy *** of the major challenges in fog computing for IoT applications is the efficient deployment of services between fog *** address this challenge,we propose a novel Optimal Foraging Algorithm(OFA)for task placement on appropriate fog devices,taking into account the limited resources of each fog *** OFA algorithm optimizes task sharing between fog devices by evaluating incoming task requests based on their types and allocating the services to the most suitable fog *** our study,we compare the performance of the OFA algorithm with two other popular algorithms:Genetic Algorithm(GA)and Randomized Search Algorithm(RA).Through extensive simulation experiments,our findings demonstrate significant improvements achieved by the OFA ***,it leads to up to 39.06%reduction in energy consumption for the Elektroensefalografi(EEG)application,up to 25.86%decrease in CPU utilization for the Intelligent surveillance through distributed camera networks(DCNS)application,up to 57.94%reduction in network utilization,and up to 23.83%improvement in runtime,outperforming other *** a result,the proposed OFA algorithm enhances the system’s efficiency by effectively allocating incoming task requests to the appropriate fog devices,mitigating the challenges posed by resource limitations and contributing to a more optimized IoT ecosystem.

关键词： Internet of Things cloud computing fog computing

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Exploring the Impact of Robotics in STEM Education Activities and Competitive Challenges 2

Exploring the Impact of Robotics in STEM Education Activitie...

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2nd International Conference on Cybernetics and Innovations, ICCI 2023

作者： Farooq, Adil Zukhraf, Syeda Zillay Nain Maryam, Hafsa Attard, Conrad Kamal, Mohsin University of Cyprus Dept. of Electrical and Computer Engineering Cyprus National University of Computer and Emerging Sciences Dept. of Electrical Engineering Pakistan University of Malta Dept. of Computer Information Systems Msida Malta

ISBN: (纸本)9798350336573

As robotics and technology become increasingly integrated into education and daily life, it is important to recognize their essential role. Even young children have access to electronic toys and gadgets, which can facilitate their learning and interaction. This study highlights the significance of STEM (science, technology, engineering, and mathematics) education at the elementary school level, emphasizing how university students from a variety of disciplines can design and construct robots capable of carrying out challenging tasks for humanitarian purposes. The study also emphasizes the need to encourage women's participation in robotics to showcase their skills and innovative ideas. Furthermore, the study demonstrates how robotics can effectively be used in both STEM education and competitive challenges, showcasing its effectiveness in teaching and promoting technological advancement. By encouraging early engagement in STEM education and promoting diversity in the field of robotics, we can empower future generations to lead the way in creating innovative solutions to global challenges. © 2023 IEEE.

关键词： Intelligent systems

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FedDist-POIRec: Federated Distillation for Point-Of-Interest Recommendation in Human Mobility Prediction 4

FedDist-POIRec: Federated Distillation for Point-Of-Interest...

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4th IEEE International Conference on Human-Machine systems, ICHMS 2024

作者： Rezaei, Esmatollah Seyedmohammadi, S. Jamal Abouei, Jamshid Plataniotis, Konstantinos N. Yazd University Dept. Of Electrical Engineering Yazd Iran Concordia University Inst. Of Information Systems Engineering Montreal Canada University Of Toronto Edward S. Rogers Sr. Department Of Electrical And Computer Engineering Toronto Canada

ISBN: (纸本)9798350315790

Next Point-of-Interest (POI) recommendation plays a vital role in human mobility prediction within Location-based Social Networks (LSBN), assisting individuals to decide on their next destination. However, traditional centralized systems rely on large datasets of previous POI check-ins, which raises privacy and computation concerns. In this regard, Federated Learning (FL) has emerged as a promising solution, enabling distributed training of machine learning models while preserving user privacy. In this paper, we propose a novel FL-based POI recommendation system, FedDist-POIRec, that utilizes Federated Distillation (FD) in a semi-synchronous framework to promote privacy, recommendation efficiency, and cope with resource heterogeneity by considering heterogeneous model architectures among users. Additionally, to alleviate the sparsity of user checkin lists, we employ a innovative knowledge aggregation technique, where the knowledge of each user is aggregated with that of users who share similar preferences. Simulation results demonstrate that FedDist-POIRec outperforms the baseline methods in terms of recommendation efficiency and communication cost, while keeping the computation cost at an acceptable level. © 2024 IEEE.

关键词： Distillation

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Enhancing Wireless Sensor Network Data Collection through Aerial Unmanned Vehicles

Enhancing Wireless Sensor Network Data Collection through Ae...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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Accelerating the Use of Classical Infrastructure to Implement Quantum Networks

Accelerating the Use of Classical Infrastructure to Implemen...

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Quantum 2.0, QUANTUM 2022

作者： Broberg, Robert Bassett, Lee Smith, Jonathan University of Pennsylvania Departments of Electrical & Systems Engineering Computer and Information Science 3401 Walnut Street PhiladelphiaPA United States

For emerging quantum networks to coexist with the classical internet, they must conform to existing specifications. We examine the architectural model of the internet, especially physical layer requirements, and we pr...

ISBN: (纸本)9781957171111

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Performance of AI-Empowered Anti-Jamming Communications under Hardware Impairments

Performance of AI-Empowered Anti-Jamming Communications unde...

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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Arif, Muhammad Shahzad Muhaidat, Sami Argyriou, Antonios Sofotasios, Paschalis C. Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Khalifa University KU 6G Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering Ottawa Canada University of Thessaly Department of Electrical and Computer Engineering Volos Greece Khalifa University KU C2PS and 6G Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Tampere University Department of Electrical Engineering Tampere Finland

ISBN: (纸本)9798350376715

The present contribution quantifies the effect of Hardware Imperfections (HWI) on Artificial Intelligence (AI)empowered anti-jamming wireless communication scenarios. To that end, we consider an AI-empowered wireless link that employs reinforcement learning (RL) to counteract reactive jamming attacks. It is shown that contrary to the common belief that HWI always degrade the performance of wireless systems, our findings reveal that imperfections in the jammer's spectrum sensing capability can actually enhance its effectiveness against AI-empowered anti-jamming wireless communications. Extensive simulations demonstrate that a non-ideal reactive jammer with detection errors (e.g., false alarms and miss-detections) can perform significantly better than an ideal reactive jammer while using the same jamming resources. This improvement is attributed to the randomness induced in jamming patterns due to sensing errors, which distorts the learning process of the antijamming AI agent resulting in a considerable reduction in the anti-jamming capability of the AI-empowered wireless link. © 2024 IEEE.

关键词： Jamming

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An Intelligent Data Analytics Approach for Crisis Management in Healthcare systems

An Intelligent Data Analytics Approach for Crisis Management...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Madian, Fatma A. Ebrahim, Gamal A. Salah, Mostafa M. Abdelaal, Marwa A. Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Future University in Egypt Electrical Engineering Department Cairo Egypt Faculty of Computers and Information Sadat Academy for Management Sciences Computer Science Department Cairo Egypt

ISBN: (纸本)9798350367560

Crisis management is preparing for and managing possible crises that may impact organizations and individuals at different levels. It involves effective communication, quick decision-making, and strategic planning to minimize the negative impact of a crisis and ensure swift recovery. It plays a vital role in healthcare systems, especially in virus outbreaks. Its role is to monitor and manage the spread of viruses. Moreover, it can use Machine Learning (ML) techniques to achieve this goal. This paper discusses how to apply ML approaches to daily reports of infection cases. The adopted ML approaches are Auto Regressive Integrated Moving Average (ARIMA) model and the Long Short-Term Memory (LSTM) model. Furthermore, it uses Root Mean Square Error (RMSE) as a performance measure to evaluate the applied models. Simulation results show that ARIMA model performs better as compared to other models, which can provide a prediction accuracy of more than 99%. © 2024 IEEE.

关键词： Electronic health record

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TraiNDSim: A Simulation Framework for Comprehensive Performance Evaluation of Neuromorphic Devices for On-Chip Training 24

TraiNDSim: A Simulation Framework for Comprehensive Performa...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Heo, Donghyeok Bang, Hyeonsu Ko, Jong Hwan Department of Ai Systems Engineering Sungkyunkwan University Suwon Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798400706011

The advancement of neuromorphic devices (NDs) for processing deep neural networks has narrowed the accuracy gap with software-trained models. To accurately assess ND performance, reliable simulation frameworks for on-chip training are crucial. However, existing frameworks encounter difficulties accurately reflecting the characteristics of NDs in training simulations. Consequently, we introduce TraiNDSim, a novel framework that comprehensively evaluates the performance of NDs to address these difficulties. Specifically, we propose an advanced conductance normalization strategy called layer-wise normalization, which limits the weight range by taking the initial weight distribution into account. Additionally, our framework integrates three conductance models, notably refining one of the conventional models to depend solely on nonlinearity. Moreover, it features a bi-directional weight representation method with a unique conductance compensation technique. Our comprehensive analysis using TraiNDSim demonstrates its effectiveness in accurately reflecting the impact of ND parameters on training, promising more precise device performance evaluations. Our framework is available at https://***/donghyeokheo/TraiNDSim. © 2024 Copyright held by the owner/author(s).

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A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligence

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 3765-3779页

作者： Renkhoff, Justus Feng, Ke Meier-Doernberg, Marc Velasquez, Alvaro Song, Houbing Herbert Department of Information Systems BaltimoreMD21250 United States Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science Daytona BeachFL32114 United States University of Colorado Department of Computer Science BoulderCO80309 United States

Neurosymbolic artificial intelligence (AI) is an emerging branch of AI that combines the strengths of symbolic AI and subsymbolic AI. Symbolic AI is based on the idea that intelligence can be represented using semantically meaningful symbolic rules and representations, while deep learning (DL), or sometimes called subsymbolic AI, is based on the idea that intelligence emerges from the collective behavior of artificial neurons that are connected to each other. A major drawback of DL is that it acts as a 'black box,' meaning that predictions are difficult to explain, making the testing & evaluation (T&E) and validation & verification (V&V) processes of a system that uses subsymbolic AI a challenge. Since neurosymbolic AI combines the advantages of both symbolic and subsymbolic AI, this survey explores how neurosymbolic applications can ease the V&V process. This survey considers two taxonomies of neurosymbolic AI, evaluates them, and analyzes which algorithms are commonly used as the symbolic and subsymbolic components in current applications. Additionally, an overview of current techniques for the T&E and V&V processes of these components is provided. Furthermore, it is investigated how the symbolic part is used for T&E and V&V purposes in current neurosymbolic applications. Our research shows that neurosymbolic AI has great potential to ease the T&E and V&V processes of subsymbolic AI by leveraging the possibilities of symbolic AI. Additionally, the applicability of current T&E and V&V methods to neurosymbolic AI is assessed, and how different neurosymbolic architectures can impact these methods is explored. It is found that current T&E and V&V techniques are partly sufficient to test, evaluate, verify, or validate the symbolic and subsymbolic part of neurosymbolic applications independently, while some of them use approaches where current T&E and V&V methods are not applicable by default, and adjustments or even new approaches are needed. Our research shows that th

关键词： Neural networks

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An IoMT-Based Incremental Learning Framework with a Novel Feature Selection Algorithm for Intelligent Diagnosis in Smart Healthcare

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 370-383页

作者： Sai, Siva Bhandari, Kartikey Singh Nawal, Aditya Chamola, Vinay Sikdar, Biplab Department of Computer Science and Information Systems Pilani Campus Pilani333031 India Microsoft Campus Gachibowli Telangana Hyderabad500032 India Department of Electrical and Electronics Engineering Pilani Campus Pilani333031 India Pilani Campus Pilani333031 India National University of Singapore Department of Electrical and Computer Engineering Cluny Road Singapore117583 Singapore

Several recent research papers in the Internet of Medical Things (IoMT) domain employ machine learning techniques to detect data patterns and trends, identify anomalies, predict and prevent adverse events, and develop personalized patient treatment plans. Despite the potential of machine learning techniques in IoMT to revolutionize healthcare, several challenges *** conventional machine learning models in the IoMT domain are static in that they were trained on some datasets and are being used for real-time inferencing data. This approach does not consider the patient's recent health-related data. In the conventional machine learning models paradigm, the models must be re-trained again, even to incorporate a few sets of additional samples. Also, since the training of the conventional machine learning models generally happens on cloud platforms, there are also risks to security and privacy. Addressing these several issues, we propose an edge-based incremental learning framework with a novel feature selection algorithm for intelligent diagnosis of patients. The approach aims to improve the accuracy and efficiency of medical diagnosis by continuously learning from new patient data and adapting to patient conditions over time, along with reducing privacy and security issues. Addressing the issue of excessive features, which might increase the computational burden on incremental models, we propose a novel feature selection algorithm based on bijective soft sets, Shannon entropy, and TOPSIS(Technique for Order Preference by Similarity to Ideal Solution). We propose two incremental algorithms inspired by Aggregated Mondrian Forests and Half-Space Trees for classification and anomaly detection. The proposed model for classification gives an accuracy of 87.63%, which is better by 13.61% than the best-performing batch learning-based model. Similarly, the proposed model for anomaly detection gives an accuracy of 97.22%, which is better by 1.76% than the best-performing b

关键词： Patient treatment

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