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International Journal of Interactive Mobile Technologies 2024年第11期18卷 80-91页

作者： Abdullah, Rijal Yaacob, Noorulsadiqin Azbiya Salameh, Anas A. Zaki, Nur Amalina Mohamad Bahardin, Nur Fadhilah Faculty of Engineering Universitas Negeri Padang Padang Indonesia School of Technology Management and Logistics Universiti Utara Malaysia Kedah Malaysia Department of Management Information Systems College of Business Administration Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Faculty of Business Economics and Social Development Universiti Malaysia Terengganu Terengganu Malaysia Department of Built Environment and Technology College of Built Environment UiTM Perak Branch Perak Malaysia

Mobile edge computing (MEC) has emerged as a pivotal technology to address the computational demands of resource-constrained mobile devices by offloading tasks to nearby edge servers. However, ensuring the security and efficiency of computation offloading in multi-access MEC networks remains a critical challenge. This paper proposes a novel approach that leverages deep reinforcement learning (DRL) for secure computation offloading in multi-access MEC networks. The proposed framework utilizes DRL agents to dynamically make offloading decisions based on the current network conditions, resource availability, and security requirements. The agents learn optimal offloading policies through interactions with the environment, aiming to maximize task completion efficiency while minimizing security risks. To enhance security, the framework integrates encryption techniques and access control mechanisms to protect sensitive data during offloading. The proposed approach undergoes comprehensive simulations to assess its performance in terms of security, efficiency, and scal-ability. The results demonstrate that the DRL-based approach effectively balances the trade-offs between security and efficiency, achieving robust and adaptive computation offloading in multi-access MEC networks. This study contributes to advancing the state-of-the-art in secure and efficient mobile edge computing systems, fostering the development of intelligent and resilient MEC solutions for future mobile networks. © 2024 by the authors of this article. Published under CC-BY.

关键词： Reinforcement learning

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Smart Aero Generator Monitoring System using IoT 2

Smart Aero Generator Monitoring System using IoT

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Sathyamoorthy, Malathy Vanitha, C.N. Dhanaraj, Rajesh Kumar Mon, Feslin Anish Rai, Priti Kpr Institute of Engineering and Technology Department of Information Technology Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India Pune India University of Technology and Applied Sciences Faculty in Department of Information Technology Ibri Oman Gl Bajaj Institute of Management Greater Noida India

ISBN: (纸本)9798350325799

Wind turbine plays a major role in energy production. From small wind turbines to ocean wind turbines, wind turbine industry introduces the latest version with bigger flexible blades, high towers, good efficiency and low-cost repairs. Small wind turbines malfunctioning will lead to a reduction in total energy. Faults like increasing in temperature may lead to fire accidents. The excessive vibration of the turbines may cause the turbine to sustain damage. The current shock will be passed to the people from the turbine if they try to contact it. IoT sensors can help us to overcome these obstacles. The proposed system will monitor the temperature, measures the speed, smoke and motion near the windmills. If the temperature exceeds the threshold then an alert will be generated and also water will be splashed in order to overcome the fire accident. If the speed of the wind is more than the threshold then the windmill be turned off to avoid damage in the turbines. Also, if an unnecessary movement is detected after a particular distance then the gate will be closed in the proposed system. It also measures the smoke for the gases released and generates an alert to avoid further damage to human lives and the windmill. The experimental results prove that the proposed system will improve the security of the windmill and also reduces the damage that occurs in the environment. The alert will be generated within 15s of an incident which proves that this is an efficient system when compared to the existing ones. © 2023 IEEE.

关键词： Smoke

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Design and Optimization of PID Controller based on Metaheuristic algorithms for Hybrid Robots 20

Design and Optimization of PID Controller based on Metaheuri...

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20th International Learning and technology Conference, L and T 2023

作者： Aly, Rabab Hamed M. Hussien Rahouma, Kamel Hussein, Aziza I. The Higher Institute for Management and Information Technology Minya61768 Egypt Minya University Faculty of Engineering Minya Egypt Effat University College of Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350300307

Metaheuristics optimization techniques are significant to search methods that are used to solve challenging Artificial intelligence (AI) problems. In hybrid robot control systems, Meta-heuristic optimization methods are widely applied. The major goal of this paper is to develop optimized PID control parameters to improve the performance of the hybrid robot control system. For that purpose, two optimization techniques followed by fine-tuning are proposed and simulated to get the optimized PID parameters. The first proposed optimization method applies the Satin Bowerbird (SB) optimization technique to optimize the PID parameters. The Crow Search Optimization (CSO) technique is applied to the SB results to improve the algorithm's performance and the PID parameters. The second proposed method applies the Emperor Penguin Optimization (EPO) technique for the optimization of the PID parameters. The results of both methods are fine-tuned. Moreover, a Kalman filter is used to improve the outcomes after and before tuning the PID parameters. Simulation results show that the proposed first method is more effective for the optimization methods of the PID controller, and its results outperform the results given by previously published research. © 2023 IEEE.

关键词： Optimization

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Hospital Out Patient Visit Forecasting using Gated Recurrent Unit 6

Hospital Out Patient Visit Forecasting using Gated Recurrent...

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6th International Conference on information Systems and Computer Networks, ISCON 2023

作者： Thapa, Krishna Timalsina, Arun K. Pokhara University Faculty of Management Nepal College of Information Technology Kathmandu Nepal Pulchowk Campus Institute of Engineering Tribhuvan University Department of Electronics and Computer Engineering Nepal

ISBN: (纸本)9798350346961

Recently, people have become more interested in their healthcare and well-being, resulting in a significant increase in patient flow for healthcare providers. To provide quality care, hospitals must manage patient flow and resources effectively. Hospitals need to forecast daily patient flow in the Out Patient department to ensure they have enough resources to provide care to all patients, manage resources efficiently, reduce wait times, and improve patient satisfaction. This study is about optimizing patient visit forecasting using deep learning techniques on univariate time series data. The LSTM-based deep learning approach is a reliable module for forecasting such data, however, an enhanced version of LSTM called GRU produced even more effective results. The experimental data from a private hospital's daily outpatient visits over the last 5 years verified that the GRU model achieved a higher accuracy of 92.42% before 1 minute 29 seconds compared to the LSTM model whose accuracy of 87.15% on an average time of 13minute 8 seconds.. © 2023 IEEE.

关键词： Forecasting

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Creating Mathematical Models to Help Formulate Company Strategies for Knowledge management System Implementation 9

Creating Mathematical Models to Help Formulate Company Strat...

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9th International Conference on information management and technology, ICIMTech 2024

作者： Sardjono, Wahyu Perdana, Widhilaga Gia Pranata, Dewi Sagita BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia Post Graduate Program of Management Science State University of Jakarta Jakarta13220 Indonesia King's College London Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences Department of Informatics Strands West MinsterWC2R 2LS United Kingdom

ISBN: (纸本)9798350390025

This research aims to build a mathematical model to formulate the problems of implementing knowledge management systems in companies that often face obstacles in achieving the desired objectives and goals. With increasing competition in the business sector, organizations or organizations realize the importance of utilizing the knowledge assets that reside in each individual and organization. If this knowledge can be managed optimally, it can become a competitive advantage for the company due to the emergence of innovative ideas by the concepts of knowledge management theory. By implementing optimal knowledge management (KM), companies can design innovative solutions to improve business operations and increase overall revenue. The factor analysis method will be used to find the determining factors for the success of the implementation of knowledge management systems (KMS) and the regression analysis method will also be used to form a mathematical model of several new factors which are formed as independent variables with the current level of community understanding of KMS as the dependent variable. The research results provide insight into strategies to improve success factors to successfully facilitate KMS implementation. This study contributes to existing knowledge management by providing insight into dynamics that go beyond the technical aspects of KMS, ultimately developing strategies for more effective and efficient utilization of knowledge and organizational growth and supporting competitive advantage for companies. © 2024 IEEE.

关键词： Knowledge organization system (KOS)

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Optimal Placement and Sizing for Distributed Generation based on an Improved Differential Evolution Optimization 1

Optimal Placement and Sizing for Distributed Generation base...

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1st IEEE International Conference on Advanced Power engineering and Energy, APEE 2024

作者： Sobli, Mohd Ashraf Mohd Sapari, Norazliani Md Hussin, Siti Maherah Yusoff, Khairul Huda Rasid, Madihah Md Said, Dalila Mat Nasir, Syed Norazizul Syed NM7 Services Sdn Bhd Kuala Lumpur Wilayah Persekutuan Malaysia University Technology Malaysia UTM Faculty of Electrical Engineering Johor Bharu Johor Malaysia Management and Science University Faculty of Information Science and Engineering Selangor Malaysia

ISBN: (纸本)9798350360936

Various methods have been developed to optimize Distributed Generation (DG) placement and sizing in the power system network. While better advanced algorithms for DG optimization are being continuously developed, simpler general purpose metaheuristic algorithms that had been developed earlier in the past decade, particularly Differential Evolution (DE), should be reevaluated in term of its application for DG optimization. Despite DE simplicity and significant advancement, DE application in DG optimization study is still minimal. In this paper, an improved differential evolution algorithm for DG optimization for power loss minimization in IEEE-69 radial network has been proposed. And improve the population diversity significantly increase its capability of escaping local optima. The results show the proposed method had 84% success rate, a significant improvement from the other selected DE variants with success rate ranging from 16% to 54%. This finding concludes that the proposed method has high potential of escaping the local optima and consequently can minimize the number of the required runs to find the optimal solution. © 2024 IEEE.

关键词： Optimization algorithms

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Offsets Reconstruction Method for Clock Skew Measurement Over High-Jitter Communication

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SN Computer Science 2025年第5期6卷 1-14页

作者： Sastra, Nyoman Putra Saputra, Komang Oka Teng, Wei-Chung Study Program of Electrical Engineering Faculty of Engineering Udayana University Jalan Raya Kampus Bukit Jimbaran Bali Badung 80361 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Taipei 106 China

Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the clock skew sought. When some outliers separate from the thick line due to higher delay jitter, current advanced approaches use the lower bound or the majority of offsets to derive accurate clock skew. However, outliers in offsets that shape parallel dotted lines, reduce the availability of offsets in the lower region, and scatter widely the offsets from their majority zone. These conditions drive the current skew estimation approaches to lose their accuracy. This paper then developed a new method, which reconstructs back the outliers into their origin positions, in order to provide ideal offsets for clock skew measurement. The experimental evaluations have demonstrated that the clock skew accuracy of dotted-lines offsets tampered by outliers was improved from 2.1 to 0.9 ppm by the proposed reconstruction method. For a broader purpose, the reconstruction method has been adjusted to be implemented also on non-dotted-lines offsets. The robustness of the proposed method for this case has also been exhibited by the very ideal reconstructed offsets that shape like a thick line. © The Author(s) 2025.

关键词： Clock skew Delay jitter Dotted lines Hough transform

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Behind the Scenes: An Explainable Artificial Intelligence (XAI) on the Service Classification of the 5G/B5G Network 3

Behind the Scenes: An Explainable Artificial Intelligence (X...

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3rd International Conference on Digital Transformation and Applications, ICDXA 2024

作者： Allias, Noormadinah Kadir, Diyana Ab Abdullahi, Akibu Mahmoud Ismail, Shahrinaz Tunku Abdul Rahman University of Management and Technology Faculty of Computing and Information Technology Kuala Lumpur Malaysia Computer Engineering Technology Universiti Kuala Lumpur- Malaysia Institute of Information Technology Kuala Lumpur Malaysia School of Computing and Informatics Albukhary International University Kedah Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350373424

Fifth generation and beyond (5G/B5G) is part of the next generation of mobile technology, which is expected to offer more capacity and faster speeds than the previous generation Long-Term Evolution (LTE) network. These features enable 5G/B5G to offer users a wide range of services, including smart cities, entertainment and multimedia, healthcare and mission-critical applications that benefit the entire economy and communities. However, due to growing demand, telecom service providers need to provide better Quality of service (QoS) on their networks to meet user expectations. Therefore, it is necessary to implement service classification that enables service providers to select the right network slices for each service to improve network QoS. Previous studies have predicted the classification of 5G/B5G services using traditional machine learning algorithms such as random forest and decision tree, achieving good accuracy. However, prediction based on black-box machine learning models was not transparent enough, so mobile service providers could not understand the reliability and interpretation of the results. Therefore, this article aims to model the system performance of 5G Key Performance Indicators (KPI) and extend the experiment to Explainable Artificial Intelligence (XAI) by using the Explainable AI to look behind the scenes of the chosen black-box models for service classification of 5G/B5G networks, as this is crucial for telecom service providers to make critical investment decisions around customer and employee experience. The results from the experiments show that the random forest has the highest confidence towards class Label 3 with the probability of 35 percent. © 2024 IEEE.

关键词： Decision trees

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Agent-Based Modeling for Forest Fire Simulation in Chiang Mai, Thailand 23

Agent-Based Modeling for Forest Fire Simulation in Chiang Ma...

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23rd International Symposium on Communications and information Technologies, ISCIT 2024

作者： Tappanom, Teerapat Leelawat, Natt Saengtabtim, Kumpol Tang, Jing Risk and Disaster Management Program Graduate School Chulalongkorn University Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Industrial Engineering Bangkok Thailand Graduate School of System Design and Management Keio University Yokohama Japan Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9798350353952

Forest fires have a significant impact on ecosystems and human life, especially in northern Thailand, where Chiang Mai is severely affected. Traditional methods for detecting forest fires have limitations, so there is a need for advanced simulation models. This research uses Agent-Based Modeling (ABM) to develop a forest fire simulation for Chiang Mai. The methodology involves collecting historical data, performing Multiple Regression Analysis, designing the simulation, and testing it. Data from 1998 to 2021, including temperature, relative humidity, wind speed/direction, burn area, and slope, were collected from various sources. Multiple regression analysis identified wind speed as the most significant factor affecting burn area. The forest fire simulation, designed using ZF Wang's spread model and tested with AnyLogic, showed that wind direction and speed are crucial in fire spread. The simulation accurately predicted high-risk areas, helping in proactive planning and response. This study confirms that wind is a critical factor in forest fire spread, providing a valuable tool for fire districts to improve preparedness and management. Future research should focus on refining the model with localized data and integrating real-time detection to improve its accuracy and applicability. © 2024 IEEE.

关键词： Forest ecology

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A Flexible Water Monitoring System for Pond Aquaculture 1

A Flexible Water Monitoring System for Pond Aquaculture

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1st International Conference on Digital Government technology and Innovation, DGTi-Con 2022

作者： Wannee, Noppadol Samanchuen, Taweesak Mahidol University Technology of Information System Management Division Faculty of Engineering Nakhon Pathom Thailand

ISBN: (数字)9781665495554

ISBN: (纸本)9786168001226

Water quality is the most important factor for pond aquaculture. Farmers must be able to control a water quality to be within the appropriate range for each kind of aquatic animal, which needs both skill and knowledge. This work proposes a flexible water quality monitoring system for pond aquaculture, which can be configured to fit with a particular aquatic animal. Three types of sensors are applied to measure the water quality including pH, temperature, and dissolved oxygen sensors with 2 types of wireless connection, i.e., WiFi and NB-IoT. A microcontroller is used to process these sensing signals and send data to a cloud server. The system monitors the water quality all the time. If the water quality is not within the controlled range, the alert message will be sent to the farmers. The dashboard is also provided for real-time system monitoring. Systems can be selected as three kinds of aquatic animals including white shrimp, white sea bass, and tilapia. The controlled range will be changed according to the type of aquatic animal that the user selected. As the result, we found that the proposed system can perform as designed and the accuracies of the prototype are 98.5%, 97.6%, and 95.1% for pH, temperature, and dissolved oxygen, respectively, when compared with the standard tools. The proposed system can support both connections, i.e., WiFi and NB-IoT. © 2022 IEEE.

关键词： Water quality

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