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2nd International Conference of Modern Applications on Information and Communication Technology, ICMAICT 2020

作者： Al-Dosary, Duaa Ali Alheeti, Khattab M. Al-Rawi, Salah Sleibi College of Computer and Information Technology Computer Sciences Department University of Anbar Anbar Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq College of Computer and Information Technology Information Systems Dept. University of Anbar Anbar Iraq

The interesting with embedded systems is related to the matters of providing privacy and security. There is a need to ensure the integrity and authenticity of the system. The methods such as passwords can be stolen or forgotten therefore the security of any data kept on the system can be exposed by any unauthorised access. In this paper, a new proposal is introduced that exploits Integrated Circuit Metrics (ICMetric) technology to provide identification and improve system security. ICMetric generates device identification based on the features of a device. In this paper, device identification can be generated by using the MEMS sensors, which are gyroscope and magnetometer embedded in many devices. The experiment is based on sensors embedded in intelligent wheelchair. Readings are obtained from the sensors and analysed mathematically and statistically to generate ICMetric number that used to provide identification of the device. ICMetric number is integrated to the dataset extracted from the trace file generated by network simulator version two (ns-2). If the system is attacked, there will be no theft because the ICMetric is not stored on the system and can be regenerated when necessary. The proposed system shows that the using of MEMS sensors to generate ICMetric number increase authentication and security of the system. The simulation results have been compared and studied for high levels of performance metrics and detection capability. © Published under licence by IOP Publishing Ltd.

关键词： Embedded systems

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Energy Conservation Strategies in Wireless Sensor Networks: A Review

Energy Conservation Strategies in Wireless Sensor Networks: ...

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2021 International Conference of Modern Trends in Information and Communication Technology Industry, MTICTI 2021

作者： Inad, Sarah Amjad Ali Alheeti, Khattab M. Al-Rawi, Salah Sleibi University of Anbar College of Computer and Information Technology Computer Science Department Ramadi Iraq University of Anbar College of Computer Sciences and Information Technology Computer Networking Systems Department Ramadi Iraq University of Anbar College of Computer and Information Technology Information Systems Dept. Ramadi Iraq

ISBN: (纸本)9781665414449

Wireless Sensor Networks (WSNs) consist of small devices that sense environmental and physical phenomena. WSNs have proven their high capabilities in various fields, but there are some challenges facing sensor nodes, such as limited battery power and their communication and storage capabilities. This review of recent literature on energy improvement strategies is compressed in this review paper. The concepts of WSNs, power sources, WSNs node component, Energy Saving Techniques, power consumption were extracted and clarified. Finally, the results of previous studies regarding energy improvement were extracted, discussed, and compared at the end of the paper. © 2021 IEEE.

关键词： Electric power utilization

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Heterogeneous Statistical QoS Provisioning for Scalable Software-Defined 6G Mobile Networks

Heterogeneous Statistical QoS Provisioning for Scalable Soft...

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Annual Conference on Information Sciences and systems (CISS)

作者： Xi Zhang Qixuan Zhu H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University TX USA Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

Due to the explosively increasing number of mobile users and the new types of data demands in the fifth generation (5G) mobile wireless network, research in wireless networks has shifted toward the development of the sixth-generation (6G) wireless network. Although the research for software-defined network (SDN) architectures in 5G mainly focuses on the dynamic programming for the internet backbone, these software programming techniques can be also applied at the network edge to support the exponentially increasing demands from mobile users under constrained wireless resources. In order to study the interference problem resulted by massive mobile users, the scaling law is a powerful tool to show how fast the levels of network imperfections can be tolerated as the number of mobile users increases. In this paper, we investigate the scaling behavior of software-defined architectures over 6G wireless networks. We consider the wireless channel in three scenarios: single-input-single-output (SISO), multiple-input-single-output (MISO), and multiple-input-multiple-output (MIMO), where we derive the corresponding scaling law for each scenario, respectively. Our derived scaling law shows how the network performance scales with the number of mobile users in a wireless network. Then, we propose a software-defined network slicing scheme to select the optimal mobile users and derive their optimal resource allocations, according to our derived scaling law, under SISO, MISO, and MIMO wireless channel, respectively. Finally, we validate and evaluate the derived scaling behavior of the software-defined architecture over 6G wireless networks through numerical analyses.

关键词： 6G mobile communication Wireless networks Microprocessors computer architecture Quality of service MISO communication Behavioral sciences

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FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method

FAD-SAR: A Novel Fishing Activity Detection System via Synth...

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2024 International Symposium on Intelligent Signal Processing and Communication systems, ISPACS 2024

作者： Bai, Yanbing Li, Siao Ju, Rui-Yang Yang, Zihao Yu, Jinze Chiang, Jen-Shiun Center for Applied Statistics School of Statistics Renmin University of China Beijing100872 China Graduate Institute of Networking and Multimedia National Taiwan University Taipei City106335 Taiwan Department of Mechanical Systems Engineering Tokyo University of Agriculture and Technology Tokyo183-8538 Japan Department of Electrical and Computer Engineering Tamkang University New Taipei City251301 Taiwan

ISBN: (纸本)9798350389210

Illegal, unreported, and unregulated (IUU) fishing activities seriously affect various aspects of human life. However, traditional methods for detecting and monitoring IUU fishing activities at sea have limitations. Although synthetic aperture radar (SAR) can complement existing vessel detection systems, extracting useful information from SAR images using traditional methods remains a challenge, especially in IUU fishing. This paper proposes a deep learning based fishing activity detection system, which is implemented on the xView3 dataset using six classical object detection models: SSD, RetinaNet, FSAF, FCOS, Faster R-CNN, and Cascade R-CNN. In addition, this work employs different enhancement techniques to improve the performance of the Faster R-CNN model. The experimental results demonstrate that training the Faster R-CNN model using the Online Hard Example Mining (OHEM) strategy increases the Avg-F1 value from 0.212 to 0.216. © 2024 IEEE.

关键词： Deep learning

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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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Quality of Experience Aware Task Offloading in Digital Twinning Vehicular Edge Computing

Quality of Experience Aware Task Offloading in Digital Twinn...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Mostakim Jihad Mashraba Tasnim Rodshi Abdullah Al Fahad Palash Roy Md. Abdur Razzaque Mohammad Mehedi Hassan Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Green Networking Research Group University of Dhaka Bangladesh Information Systems Dept. College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Adopting Digital Twin (DT) technology in vehicular edge computing (VEC) enables efficient capture of real-time state information of applications, thereby addressing complex task scheduling problems. Existing literature studies considered only minimizing service latency for task offloading; however, there is room for exploring strategies to enhance user Quality of Experience (QoE) in timeliness and reliability domains. In this paper, we have developed an optimization framework using Mixed Integer Linear Programming (MILP), namely QuETOD, which minimizes service latency by allocating task execution responsibility to highly reliable and reputed vehicles in a DT-enabled VEC environment. The developed QuETOD framework clusters the vehicles based on the demand-supply theory of economics by considering computing resources and utilizing the multi-weighted subjective logic for getting the proper reputation update of the vehicles. The experimental results of the developed QuETOD system depict significant performance improvement in terms of QoE and reliability compared to the state-of-the-art works as high as 15% and 25%, respectively.

关键词： Processor scheduling Computational modeling Reliability theory Real-time systems Polynomials Quality of experience Servers

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Dual-timescales Optimization for Resource Slicing and Task Scheduling in Satellite Edge Computing Networks

Dual-timescales Optimization for Resource Slicing and Task S...

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IEEE International Conference on Communications (ICC)

作者： Zeru Fang Qinqin Tang Renchao Xie Tao Huang Tianjiao Chen F. Richard Yu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China China Mobile Research Institute Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

This paper establishes a dual-timescale framework for joint resource slicing and task scheduling in satellite edge computing (SEC) networks. Specifically, to capture network dynamics and task stochasticity at small timescales, we formulate the task scheduling problem as a Markov decision process (MDP) to minimize task delay, network energy consumption, and packet loss. We design a deep reinforcement learning-assisted task scheduling (DRTS) algorithm inspired by the soft actor-critic (SAC) algorithm to learn the scheduling policy. Task processing performance is affected by communication and computing re-sources allocated to respective resource slices. Thus, considering that frequent resource slicing has a significant management over-head, we further optimize resource slices on a larger timescale. To obtain a policy with low complexity, we propose a greedy-based heuristic algorithm. A hierarchical solution is constructed to find the optimal solution due to the correlation between the two timescale problems. Finally, to validate the effectiveness and superiority of the proposed scheme, extensive simulations are performed.

关键词： Energy consumption Satellites Processor scheduling Heuristic algorithms Packet loss Dynamic scheduling Delays

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Alzheimer’s Disease Classification via Pre-trained Convolutional Neural Network Variants Utilizing Multi-level Thresholding based on Kapur’s Entropy

Alzheimer’s Disease Classification via Pre-trained Convolut...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Nivedhitha Mahendran P M Durai Raj Vincent Department of Networking and Communications School of Computing Technologies SRM Institute of Science and Technology Chennai Kattankulathur India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Neurological diseases are the disorders of brain and the nerves that run throughout the human body. There is a range of symptoms that arise as a result of chemical, structural, or electrical modifications in the regions of brain. One such neurological disease is the Alzheimer’s Disease (AD), is considered as a slow and progressive disease that majorly affects the cognitive abilities of the individual. It is most common forms of dementia without any proper cure. However, early diagnosis and treatment might slow down the symptoms and improve the lifestyle of the individual. The most common techniques to identify the neurological disorders are imaging of the brain regions, namely, CT, MRI, PET, etc. The learning approaches, such as Deep Learning and Machine Learning are identified to be effective in analyzing these images to provide better accuracies in detecting the AD. In this study, four versions of Convolutional Neural Networks (CNN) are used to classify AD and Control cases. Further, the Kapur’s thresholding technique is also implemented to segment the images for improving the classification accuracy of AD. From the results shown in the study, the inference shows that the VGG16 of CNN provides considerable improvement than the other deep learning models before and after the implementation of thresholding.

关键词： Deep learning Neurological diseases Image segmentation Accuracy Thresholding (Imaging) Magnetic resonance imaging Computed tomography Convolutional neural networks Alzheimer's disease Information technology

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An IoT Network Identifier Using Machine Learning-Assisted Feature Selection and SMOTE

An IoT Network Identifier Using Machine Learning-Assisted Fe...

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Communication and Information Technologies (IICCIT), Iraqi International Conference on

作者： Abdualrahman Ahmed Khattab M Ali Alheeti Computer Sciences Department College of Computer and Information Technology University of Anbar Anbar Iraq Computer Networking Systems Department College of Computer and In Technology University of Anbar Iraq

ISBN: (纸本)9781665472210

The Internet of Things (IoT) is a network of interconnected physical devices and things that gather, process, and distribute data using sensors, actuators, computers, and network nodes. With the proliferation of the Internet of Things (IoT) devices in recent years, businesses have opened themselves up to a more significant number of potential security flaws and ***, institutions must identify how many and what kinds of devices are connected to their network and whether or not they constitute a security concern. Feature information is first retrieved by intercepting device data and then exploited to offer device classification. This method has gained favour in recent years for identifying devices via networks using supervised learning. Existing efforts predominantly suffer from a lack of scalability due to their reliance on pattern matching to identify devices; any newly introduced device kinds are automatically labelled as anomalous. Each year, millions of new Internet of Things devices are created in the real world. In this research, a supervised learning approach for automatically classifying devices that have not been manually tagged is offered. The device proposed in this paper incorporates a complete classification of many ensemble balance classification strategies, which may be used to categorize devices as either authorized or unauthorized.

关键词： Fault diagnosis Scalability Supervised learning Feature extraction Sensors Information and communication technology Internet of Things

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Modeling Statistical Delay, Error-Rate, and Joint-Delay/Error-Rate QoS-Exponents Over M-MIMO Mobile Wireless Networks Using FBC

Modeling Statistical Delay, Error-Rate, and Joint-Delay/Erro...

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IEEE International Symposium on Information Theory

作者： Xi Zhang Jingqing Wang H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

To support increasing demands for real-time multimedia wireless data traffic, there have been considerable efforts toward guaranteeing stringent quality-of-service (QoS) when designing massive multiple-input and multiple-output (m-MIMO) mobile wireless network architectures for massive ultra-reliable and low-latency communications (mURLLC). One of the major design issues raised by mURLLC is how to characterize QoS metrics for upper-bounding both delay and error-rate when implementing short-packet data communications, such as finite blocklength coding (FBC), over highly time-varying m-MIMO based wireless fading channels. To efficiently accommodate statistical QoS for mURLLC traffic, it is crucial to model and investigate m-MIMO based wireless fading channels’ stochastic-characteristics by defining and identifying new statistical QoS metrics and their analytical relationships, such as delay-bound-violating probability, effective capacity, decoding error probability, etc., in the finite blocklength regime. However, how to rigorously and efficiently characterize the stochastic dynamics of m-MIMO mobile wireless networks in terms of statistically upper-bounding FBC-based both delay and error-rate QoS metrics has been neither fundamentally understood nor thoroughly studied before. To overcome these challenges, in this paper we develop analytical modeling techniques and frameworks for statistical delay and error-rate bounded QoS in the finite blocklength regime. First, we establish system models using FBC. Second, we develop a set of new statistical delay and error-rate bounded QoS metrics including delay, error-rate, and joint-delay/error-rate QoS-exponents, and the corresponding ϵ-effective capacities. Finally, our simulations validate and evaluate our developed modeling schemes for statistical QoS to support 6G mURLLC.

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