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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Upadhyay, Abhay Gupta, Mridul Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Deemed University Centre for Internet of Things Madhav Institute of Technology and Science Gwalior India

ISBN: (纸本)9798350392319

The dynamic resource allocation and security strategies for cooperative relaying systems proposed here provide significant performance improvements along with robust security features over traditional methods. The method improves throughput, energy efficiency and secrecy capacity by dynamically provisioning resources according to real-time network conditions in conjunction with its interference-suppression techniques such adaptive beamforming and artificial noise. The system is quite good at preventing eavesdropping, and it has such high signal-to-noise ratios that there is a very low error rate. The use case results show that the integrated framework optimizes resource consumption while mitigating security threats, thereby offering a trade-off between existing solutions thus solving some of their shortcomings. Our work demonstrates the opportunity for efficient and secure communication in dynamic environments. © 2024 IEEE.

关键词： Energy efficiency

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Underwater Acoustic Point-cloud Filtering via Adaptive Unsharp Masking 22

Underwater Acoustic Point-cloud Filtering via Adaptive Unsha...

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5th International Conference on computer science and Software engineering, CSSE 2022

作者： Wang, Jisong Zhang, Xuewu Xu, Xiaolong Song, Ke College of Internet of Things Engineering Hohai University China College of Computer Science and Technology Huaiyin Normal University China

ISBN: (纸本)9781450397780

Owing to the complex water environment, the acoustic point-cloud model formed by the detection method based on acoustic reflection mechanism is inevitably disturbed by the noise, which seriously affects the reconstruction effect of the underwater targets. Distinguishing between geometric features and noise is of paramount importance for the underwater point-cloud model filtering. Inspired by the classic image detail enhancement method of unsharp masking, we take the geometric coordinate information of the point as the research object and design a geometric feature-preserving adaptive unsharp masking filtering for the underwater point-cloud model. First, the proposed method directly performed a low-pass filtering using the neighborhood information to obtain the main structure of the input point-cloud model. Second, the detail layer was yielded by the difference between the input point-cloud model and the base layer. Third, the different scaling factors measuring the importance of the points with respect to the whole base layer were used to adaptively enhance the detail layer. Experimental results show that the proposed algorithm can effectively remove noise while maintaining the geometric characteristics of the model, which is obviously better than other comparison methods. © 2022 ACM.

关键词： Adaptive filtering

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Utilization of Non-Orthogonal Multiple Access (NOMA) for Spectral Efficiency in Mixed Fading Environments 2

Utilization of Non-Orthogonal Multiple Access (NOMA) for Spe...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Sharma, Rashmi Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Symbiosis Institute of Technology Department of CSE Nagpur Campus India Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798350366570

We explore in this paper a potential application of Non-Orthogonal Multiple Access (NOMA) to improve spectral efficiency in multi-fading environments that are ubiquitous in wireless communication systems. This is enabled by NOMA, which distributes the transmit power over different users according to their channel conditions and effectively utilizes the spectrum resources. The performance of NOMA is evaluated by extensive simulations and analytical results are provided to analyze the gains of NOMA over Orthogonal Multiple Access (OMA) in various fading models such as Rayleigh, Rician, and Nakagami-m. The following key metrics including Spectral Efficiency, Bit Error Rate (BER), Capacity, Outage Probability, Throughput and Cumulative Distribution Function (CDF) of User Rates are analyzed to measure the performance gains provided by NOMA for system throughput improvement, reduced errors, support for additional users as well as a fair resource allocation. Simulation results show the superiority of NOMA technique in comparison to OMA, especially for high density users and different channel models, making NOMA an important candidate as a future radio access scheme. © 2024 IEEE.

关键词： Distribution functions

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Fault Detection On Heat Pump Operational Data Using Machine Learning Algorithms 11

Fault Detection On Heat Pump Operational Data Using Machine ...

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11th International Conference on internet of things: Systems, Management and Security, IOTSMS 2024

作者： Rahman, Md Mahbubur Malekian, Reza Akerstroem, Vilhelm Malmö University Department of Computer Science and Media Technology Malmö20506 Sweden Malmö University Internet of Things and People Research Centre Malmö20506 Sweden Engineering Software Robert Bosch GmbH Lund Sweden

ISBN: (纸本)9798350366501

Heat pumps, being complex systems, are susceptible to various malfunctions. By harnessing contemporary IoT technologies, these devices continuously transmit data which enables monitoring, maintenance, and efficiency. This study focuses on identifying compressor short duration cycles as faults through supervised machine learning algorithms such as XGBoost, Random Forest, SVM, and k-NN. Data preprocessing and labeling were conducted using extensive logged data from heat pump systems, addressing issues like high dimensionality, data sparsity, and temporal dependencies. The methodology included feature engineering, interpolation of missing data, and downsampling for compressor short duration cycles. Supervised machine learning models were applied to classify these short duration cycles. Among the models, XGBoost achieved the highest accuracy and F1-scores, effectively distinguishing between normal and fault conditions. The findings highlight the potential of machine learning to enhance predictive maintenance and operational efficiency in heat pumps. © 2024 IEEE.

关键词： Predictive maintenance

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Optimal denial-of-service attack scheduling for remote state estimation via sequential observations 43

Optimal denial-of-service attack scheduling for remote state...

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43rd Chinese Control Conference, CCC 2024

作者： Yang, Xinxin Ni, Yuqing Chen, Xiaomeng Chen, Shan Liu, Fei Jiangnan University The Key Laboratory of Advanced Process Control for Light Industry Ministry of Education School of Internet of Things Engineering Wuxi214122 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong999077 Hong Kong

ISBN: (纸本)9789887581581

This study investigates the optimal allocation problem of attack energy in multi-system remote state estimation for Cyber-Physical Systems (CPSs). We consider a scenario where there are M independent systems, each equipped with a remote sensor monitoring system. The local estimates from these systems are sent to a remote estimator through a packet loss channel. The objective of the attacker is to degrade the communication channel between the sensors and the remote estimator. Due to capacity limitations, the attacker is restricted to attacking at most one out of M channels. We incorporate interference from other sensors or interference sources into the communication model based on the SINR (Signal-to-Interference-plus-Noise Ratio) model. It should be noted that the same attack power can lead to different packet loss rates in different network environments. From the attacker's perspective, the goal is to find the optimal attack strategy that maximizes the average estimation error of the remote estimator. Given the uncertainties of wireless network environments, we employ the sequentially-observed Markov decision process (SOMDP) framework to solve the problem. By observing the wireless channels, we gather additional information and incorporate it into the decision-making policy, resulting in better performance than using a standard Markov decision process (MDP). The simulation results support our theoretical findings. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Markov processes

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Investigation of attention mechanism for speech command recognition

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Multimedia Tools and Applications 2024年 1-16页

作者： Xie, Jie Zhu, Mingying Hu, Kai Zhang, Jinglan Guo, Ya School of Computer and Electronic Information and School of Artificial Intelligence Nanjing Normal University Nanjing China School of Economics Nanjing University 22 Hankou RoadJiangsu Nanjing210093 China Johns Hopkins University - Hopkins-Nanjing Center for Chinese and American Studies Nanjing University 162 Shanghai RoadJiangsu Nanjing210093 China School of Internet of Things Engineering Jiangnan University Wuxi China School of Computer Science Faculty of Science Queensland University of Technology Brisbane Australia

As an application area of speech command recognition, the smart home has provided people with a convenient way to communicate with various digital devices. Deep learning has demonstrated its effectiveness in speech command recognition. However, few studies have conducted extensive research on leveraging attention mechanisms to enhance its performance. In this study, we aim to investigate the deep learning architectures for improved speaker-independent speech command recognition. Specifically, we first compare the log-Mel-spectrogram and log-Gammatone spectrogram using VGG style and VGG-skip style networks. Next, the best-performing model is selected and investigated using different attention mechanisms including channel-time attention, channel-frequency attention, and channel-time-frequency attention. Finally, a dual CNN with cross-attention is used for speech command classification. A self-made dataset including 40 participants with 12 classes is used for the experiment which are all recorded in Mandarin Chinese, utilizing a variety of smartphone devices across diverse settings. Experimental results indicate that using log-Gammatone spectrogram and VGG-skip style networks with cross attention can achieve the best performance, where the accuracy, precision, recall and F1-score are 94.59%, 95.84%, 94.64%, and 94.57%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Spectrographs

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Partial Weighted K-nearest Neighbor Classification of Incomplete Data 4

Partial Weighted K-nearest Neighbor Classification of Incomp...

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4th IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2022

作者： Gao, Shengtao Yuan, Xinying Tang, Jinlin Jiangnan University School of Internet of Things Engineering Wuxi China Qingdao University School of Computer Science and Technology Qingdao China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

Data classification is a hot topic in machine learning research. Most classification algorithms need complete data sets, but missing data widely exists in our life. It is necessary to classify the data sets with missing data. Generally speaking, the existence of missing data will affect the classification effect, so the processing method of missing data largely determines the quality of the classification algorithm. In this paper, K-nearest Neighbor (KNN) algorithm is improved with the help of Patial Distance Strategy(PDS)to be applied to incomplete data sets. The processed data set is visualized. The classification effect is compared with the mean imputation and 0-value imputation, and the different features of the data set are weighted in various ways. The results show that our method has high accuracy on incomplete data sets. © 2022 IEEE.

关键词： Classification (of information)

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A Denial of Service Attack Defense Method for Roadside Units 12

A Denial of Service Attack Defense Method for Roadside Units

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Wang, Huanbin He, Gaofeng Zhu, Haiting Xu, Bingfeng Zhang, Lu Guo, Naixuan School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China College of Information Science and Technology Nanjing Forestry University Nanjing China School of Computer Science Nanjing Audit University Nanjing China School of Information Engineering Yancheng Institute of Technology Yancheng China

ISBN: (纸本)9798331511074

Roadside Units (RSUs) constitute a vital component of Vehicular Ad Hoc Network (VANET) due to their primary role in gathering vehicle information. However, as indicated in the literature, they are vulnerable to Denial of Service (DoS) attacks. Existing research has proposed several methods for detecting DoS attacks in RSUs, while subsequent steps such as mitigation and elimination remain unexplored. In this work, we propose a novel DoS Attack defense method for roadside units. We employ re-authentication to identify Sybil vehicles and discard the corresponding packets, thereby mitigating the attacks. Subsequently, we calculate the location of the actual malicious vehicle using a triangulation algorithm, in cooperation with traffic cameras and a cloud platform. This allows us to confirm the attacker and eliminate the DoS attacks. Simulation results demonstrate that our proposed method effectively defends against DoS attacks and successfully traces the attackers. Our method is a supplement to existing detection techniques. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Wireless Federated Learning With Hybrid Local and Centralized Training: A Latency Minimization Design

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IEEE Journal on Selected Topics in Signal Processing 2023年第1期17卷 248-263页

作者： Huang, Ning Dai, Minghui Wu, Yuan Quek, Tony Q. S. Shen, Xuemin State Key Lab of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519031 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore National Cheng Kung University Taiwan University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed alg

关键词： Information management

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Machine Learning-Driven QoS Prediction in 6G Networks Under α-η-μ Fading with Selection Combining and Co-Channel Interference Mitigation 1

Machine Learning-Driven QoS Prediction in 6G Networks Under ...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Upadhyay, Deepak Rallapalli, Sreekanth Hussain, Zakir Sharma, Rashmi Venu, Nookala Ranswal, Aakash Singh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Nitte Meenakshi Institute of Technology Department of MCA Bengaluru India Department of CSE Nagpur Campus Pune India Centre for Internet Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798331518578

In this paper, we study machine learning for resource utilization and QoS performance improvement in 6G networks under α-η-μ fading and co-channel interference (CCI). We conduct extensive experiments in simulation comparing our proposal with previous work and showing substantial improvements for both throughput, latency and resource efficiency over traditional as well as deep reinforcement learning based approaches. Their adaptive nature enables real-time optimization, and they run well in both dynamic and complex network conditions. Our experimental results illustrate that machine learned models significantly outperform state-of-the-art baselines on the following dimensions prediction accuracy, resource management and QoS metrics. This work underscores the importance of machine learning as a core enabler for sustainable 6G networks and providing better performance in future wireless communication systems. © 2025 IEEE.

关键词： Cochannel interference

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