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International Conference on Intelligent Transportation

作者： Michael Yuhas Arvind Easwaran Energy Research Institute @ NTU Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

Learning enabled components (LECs), while critical for decision making in autonomous vehicles (AVs), are likely to make incorrect decisions when presented with samples outside of their training distributions. Out-of-distribution (OOD) detectors have been proposed to detect such samples, thereby acting as a safety monitor, however, both OOD detectors and LECs require heavy utilization of embedded hardware typically found in AVs. For both components, there is a tradeoff between non-functional and functional performance, and both impact a vehicle's safety. For instance, giving an OOD detector a longer response time can increase its accuracy at the expense of the LEC. We consider an LEC with binary output like an autonomous emergency braking system (AEBS) and use risk, the combination of severity and occurrence of a failure, to model the effect of both components' design parameters on each other's functional and non-functional performance, as well as their impact on system safety. We formulate a co-design methodology that uses this risk model to find the design parameters for an OOD detector and LEC that decrease risk below that of the baseline system and demonstrate it on a vision based AEBS. Using our methodology, we achieve a 42.3% risk reduction while maintaining equivalent resource utilization.

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A Deep Learning Approach for Real-Time Application-Level Anomaly Detection in IoT Data Streaming

A Deep Learning Approach for Real-Time Application-Level Ano...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Mahsa Raeiszadeh Ahsan Saleem Amin Ebrahimzadeh Roch H. Glitho Johan Eker Raquel A. F. Mini CIISE Concordia University Montréal QC Canada Computer Science Programme University of Western Cape Capetown South Africa Ericsson Research Sweden

The growth of streaming data originating from Internet of Things (IoT)-based Industry 4.0 opens doors to real-time analytics of time-sensitive services. However, this ever-increasing amount of data inevitably leads to anomalies, resulting in considerable risks for time-sensitive applications. Thus, real-time detection of anomalies is critical to prevent impending failures and resolve them in time. Given that the problem is to detect application-level anomalies in real time, we develop a deep learning-based technique, which integrates time-series data inference with a Long-Short Term Memory (LSTM)-based prediction model. Our proposed method relies on a novel metric called Sequence Inconsistency Distance (SID), which determines the abnormality likelihood of a target record in real time. Our trace-driven evaluations indicate that the proposed method achieves up to a 92.6% performance gain compared to the current state-of-the-art anomaly detection methods in terms of true positive and false positive rate while meeting the essential efficiency requirements.

关键词： Measurement Deep learning Predictive models Performance gain Real-time systems Data models Fourth Industrial Revolution

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Towards a Trustworthy Fog-based Mobile Crowdsensing in Internet-of-vehicles

Towards a Trustworthy Fog-based Mobile Crowdsensing in Inter...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Sunday Oyinlola Ogundoyin Ismaila Adeniyi Kamil Isaac Adewale Ojedokun John Oluwaseun Babalola Vincent Omollo Nyangaresi Electrical and Electronic Engineering Programme Bowen University Iwo Nigeria Department of Electrical and Electronic Engineering University of Ibadan Ibadan Nigeria Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and Technology Bando Kenya

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

To reduce communication latency and improve vehicular communication, it is crucial to integrate a fog-based mobile crowdsensing (FBMCS) system with the rapidly evolving Internet of Vehicles (IoV). A four-tier FBMCS architecture is proposed for IoV environments, partitioned into smart vehicles, vehicular fog, fog gateway, and cloud services. An extensive experimental simulation is conducted using iFogSim2 to evaluate the impact of trust, fog mobility, and fog-to-fog (F2F) collaboration on the system performance. The results show a reduction in communication latency and better network resource usage when vehicular fogs move at a maximum speed of 10m/s, with the trust management system contributing an insignificant increase in communication latency and network usage. Moreover, the fusion of these three mechanisms marginally increases energy consumption by approximately 0.33%, while the speed of vehicular fogs is limited to 10m/s. The findings demonstrate the potential of the proposed architecture to improve the overall performance of IoV services.

关键词： Energy consumption Crowdsensing System performance Collaboration Logic gates Delays Reliability Vehicle dynamics Trust management Internet of Vehicles

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Hybrid Competitive Swarm Optimization Algorithm Based Scheduling in the Cloud Computing Environment 5

Hybrid Competitive Swarm Optimization Algorithm Based Schedu...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Shrichandran, G.V. Narayana Tinnaluri, V.S. Senthil Murugan, J. Meeradevi, T. Dwivedi, Vijay Kumar Suma Christal Mary, S. B.Sc -CS Programme Srm Institute of Science and Technology Department of Computer Science & Applications Ramapuram Chennai89 India Koneru Lakshmaiah Education Foundation Department of Computer Science Vaddeswaram522025 India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Avadi Chennai India Kongu Engineering College Department of Ece Perundurai Erode India V.E.C. Ambikapur Dist Surguja497001 India Panimalar Engineering College Poonamalle Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350321425

Cloud computing (CC) platform not only offers resource sharing for users and also provide many on-demand services. Business procedures can be managed to utilize the workflow technology on the cloud which signifies most of the problems in employing the resources in an effectual approach because of the dependencies between the tasks. Task scheduling (TS) in a CC platform applies to the procedure of effectively allocating resources computing to tasks or jobs submitted to the CC environment. In a cloud platform, tasks are separated as smaller subtasks that are applied in parallel on distinct machines. There are many features to consider if the TS is in CC platform, comprising the kind of tasks like resources cost, resources required, and resources availability. Therefore, this study presents a Hybrid Competitive Swarm Optimization Algorithm based Task Scheduling (HCSOA-TS) technique in the CC platform. The presented HCSOA-TS technique schedules the tasks proficiently in such a way that maximum resource utilization and performance gets accomplished. In the design of HCSOA, Cauchy mutation operator is included in the CSO algorithm and thereby improves the overall performance. In addition, the derivation of the fitness function by the HCSOA-TS technique undergoes optimal scheduling process and decreases energy usage. The experimental result analysis of the HCSOA-TS technique is tested using a series of measures. The comprehensive comparison study highlighted the improvements of the HCSOA-TS technique over recent approaches. © 2023 IEEE.

关键词： Cloud computing

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Time-Domain Heart Sound Classification Using Neural Audio Codecs

Time-Domain Heart Sound Classification Using Neural Audio Co...

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International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Ansh Mishra Jia Qi Yip Eng Siong Chng Department of Physics IIT Delhi New Delhi India Interdisciplinary Graduate Programme Nanyang Technological University (NTU) Singapore School of Computer Science and Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798331520311

ISBN: (纸本)9798331520328

Heart auscultation remains a valuable approach for early detection of valvular heart disease, but its efficacy depends on physician skill. The rise of telehealth services necessitates consideration of heart sound transmission and classification through compressed formats. While neural audio codecs offer impressive compression rates, they may introduce distortions that could impact classification accuracy. We propose a novel approach to perform heart sound classification directly on the continuous representations of the codec, rather than on reconstructed audio. Our method demonstrates good performance on the heart sound classification task while leveraging the encoder of the audio codec, potentially improving robustness and efficiency in telehealth applications. Using the Descript Audio Codec encoder combined with a modified M5 convolutional neural network, our approach achieves a Classification Error Rate of 0.78 on the Yaseen Dataset, outperforming models trained on transmitted audio and comparing favorably with state-of-the-art methods. This performance, coupled with the method's inherent robustness to transmission artifacts, highlights its potential for improving remote cardiac diagnostics in telehealth settings.

关键词： Heart Codecs Error analysis Telemedicine Computational modeling Medical services Robustness Real-time systems Time-domain analysis Informatics

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Model Predictive Control of Five-Level Current Source Converter with Optimized Weighting Factors 3

Model Predictive Control of Five-Level Current Source Conver...

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3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

作者： Chen, Muyu Yuan, Zhige Faraji, Faramarz Ghias, Amer M. Y. M. Cha, Honnyong Nicholas, Vun Chan Hua School of Electrical and Electronic Engineering Nanyang Technological University Collab. Initiative Interdisciplinary Graduate Programme Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Energy Engineering Kyungpook National University Daegu Korea Republic of School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350324754

This paper presents the finite control set model predictive control (FCS-MPC) with improved control objectives using optimized weighting factors. The Pareto curve obtained from the Particle Swarm optimizes the weighting factors. The optimal weighting factors are then used in the FCS-MPC algorithm to enhance the control objective performance of a three-phase five-level current source converter (5L-CSC). In 5L-CSC, two identical three-level CSC (3L-CSC) modules are connected in parallel with one common current source supply to provide uniform five-level output current waveform. This CSC requires a current balance to operate the converter safely. Therefore, the control objective of the MPC is to control the output voltage and balance the internal currents. The weighting factors are optimized based on the converter's total harmonics distortion (THD) performance and inductor current ripple. The performance enhancement of 5L-CSC, utilizing optimal weighting factors, is confirmed through MATLAB simulations when compared to the model employing arbitrarily assigned weighting factors. The simulation outcomes additionally indicate that the integration of Particle Swarm Optimization (PSO) with FCS-MPC exhibits resilience across various condition settings. © 2023 IEEE.

关键词： Model predictive control

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A Sensor Network-Based Data Stream Clustering Algorithm for Pervasive Computing

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Chinese Journal of Electronics 2023年第2期18卷 255-258页

作者： Ning Ye Ruchuan Wang Institute of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Department of Information Science Nanjing College for Population Programme Management Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Pervasive computing is characterized by the integration with communication and digital media technology embedded to the people's living space. People can transparently access the digital service anywhere. Wireless sensor networks are a novel technology and have broad application prospects. With the maturity of the wireless sensor networks technology, pervasive computing is becoming a reality. It is become a new technology challenge to process the data streams of sensor networks for pervasive environment efficiently and to find useful knowledge in these data streams. A k-means data stream clustering algorithm based on sensor networks is presented. The main idea of this algorithm is to select the initial centroids according to the aggregation gain of the node, then to cluster the data stream using the average square error. The experimental results are showed that this algorithm is effective and efficient.

关键词： Pervasive computing Performance evaluation Knowledge engineering Wireless sensor networks Sociology Clustering algorithms Media

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On the Privacy Preservation and Secure Communication in Fog-Based Mobile Crowdsensing

SSRN

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SSRN 2024年

作者： Ogundoyin, Sunday Oyinlola Kamil, Ismaila Adeniyi Ojedokun, Isaac Adewale Babalola, John Oluwaseun Nyangaresi, Vincent Omollo Electrical and Electronic Engineering Programme Bowen University Iwo Nigeria Department of Electrical and Electronic Engineering University of Ibadan Ibadan Nigeria Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and Technology Bando 40601 Kenya

The rapid growth of mobile crowdsensing (MCS) presents significant opportunities for large-scale data collection through the collaborative use of smart devices. However, the increasing volume of sensitive data generated by MCS services poses critical challenges in terms of privacy and security, particularly latency-sensitive applications. While some solutions exist, many still lack the scalability, robustness, efficiency, and privacy preservation required by MCS services. This paper proposes a privacy-preserving and secure fog-based MCS (FB-MCS) scheme, comprising a four-tier fog computing architecture that incorporates dynamic lower-tier fog (LTF) and static upper-tier fog (UTF) systems to enable efficient data aggregation, privacy protection, and secure communication. The proposed scheme utilizes secret sharing and homomorphic MAC techniques for efficient and verifiable data aggregation over multi-dimensional data, allowing a data requester to verify the accuracy and integrity of the aggregated results. The scheme employs a lightweight elliptic curve cryptography (ECC) to ensure secure authentication without overburdening resource-constrained devices. An adaptive fog node selection strategy, based on trust and mobility, is proposed for reliable real-time task allocation. Extensive security analysis demonstrates that the scheme not only guarantees privacy preservation, integrity of aggregation results, strong anonymity, un-linkability, traceability, and resistance to well-known attacks but also achieves data confidentiality and unforgeability in the random oracle model under Type I and Type II adversaries, assuming the Computational Diffie-Hellman Problem (CDHP) and Discrete Logarithm Problem (DLP) are intractable. Moreover, performance assessments indicate that the proposed scheme surpasses previous advanced solutions, achieving a 48\% - 280\% improvement in efficiency. © 2024, The Authors. All rights reserved.

关键词： Fog computing

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CAUSAL DISCOVERY AND CLASSIFICATION USING LEMPEL-ZIV COMPLEXITY

arXiv

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arXiv 2024年

作者： Dhruthi Nagaraj, Nithin Harikrishnan, N.B. Department of Computer Science and Information Systems BITS Pilani K K Birla Goa Campus Goa 403726 India Complex Systems Programme National Institute of Advanced Studies Indian Institute of Science Campus Karnataka Bengaluru560012 India

Inferring causal relationships in the decision-making processes of machine learning algorithms is a crucial step toward achieving explainable Artificial Intelligence (AI). In this research, we introduce a novel causality measure and a distance metric derived from Lempel-Ziv (LZ) complexity. We explore how the proposed causality measure can be used in decision trees by enabling splits based on features that most strongly cause the outcome. We further evaluate the effectiveness of the causality-based decision tree and the distance-based decision tree in comparison to a traditional decision tree using Gini impurity. While the proposed methods demonstrate comparable classification performance overall, the causality-based decision tree significantly outperforms both the distance-based decision tree and the Gini-based decision tree on datasets generated from causal models. This result indicates that the proposed approach can capture insights beyond those of classical decision trees, especially in causally structured data. Based on the features used in the LZ causal measure based decision tree, we introduce a causal strength for each features in the dataset so as to infer the predominant causal variables for the occurrence of the outcome. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Joint Task Decomposition and Allocation in Tactile Internet FiWi Enhanced Mobile Networks

Joint Task Decomposition and Allocation in Tactile Internet ...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Amin Ebrahimzadeh Martin Maier Roch H. Glitho CIISE Concordia University Montreal Canada Optical Zeitgeist Laboratory INRS Montreal Canada Computer Science Programme University of Western Cape Capetown South Africa

In this paper, we shed light on how and to what extent formation of robotic teams may help reduce the average task completion time in a dynamic multi-robot task allocation scenario. Specifically, we develop an analytical framework to estimate the end-to-end packet delay as well as task completion time in a fiber-wireless (FiWi)-based Tactile Internet networking infrastructure. After formulating the problem of joint decompo-sition and allocation of physical tasks in a multi-robot scenario, we propose our water filling-based multi-robot allocation (WF-MRA) algorithm to coordinate robotic teams to minimize the task completion time as well as the waiting time for task execution to begin. We show that formation of robotic teams does not always help reduce the average task completion time, thus mandating the need for designing a proper coordination strategy. The results indicate that our proposed WF-MRA algorithm can achieve an up to 75.2 % reduction of the average task completion time compared to the benchmark nearest robot allocation (NRA) algorithm.

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