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2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Al-Taie, Ali Hameed Luaibi Aljumaily, Mustafa S. Ayal, A.M. Al-Khaleefa, Ahmed Dakhil, Adel Manaa Alsalamy, Ali Shahid Chamran University of Ahvaz Faculty of Engineering Computer Engineering Department Ahvaz Iran University of Tennessee Electrical Engineering and Computer Science Department KnoxvilleTN United States University of Misan Maysan Iraq University of Misan Faculty of Education Department of Physics Maysan Iraq University of Misan College of Engineering Department of Electrical Engineering Maysan Iraq Imam Ja'afar Al-Sadiq University College of Information Technology Department of Computer Technical Engineering Al-Muthanna Iraq

ISBN: (纸本)9798350341881

The number of sensors is constantly increasing as a result of the urgent need for smart homes, factories, hospitals, military facilities, etc. Consequently, the energy consumption of these mostly battery-driven devices has increased, and due to their continuous work without stopping, it became necessary to think about and study to reduce energy consumption. In this paper, we are suggesting reducing energy consumption by establishing a schedule to turn the IoT sensors on and off according to the user demand and under their control. This research proposed a different On/Off time scenario when sensors are distributed as groups, each connected to gateways (Microcontrollers), which in turn are connected to the main server via a wireless connection, the Microcontrollers continuously check the readings sent by these sensors. The results showed the proposed algorithm increased the residual energy of the network by assigning sleeping intervals depending on the covered area and the number of neighboring nodes. © 2023 IEEE.

关键词： Energy efficiency

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Over-the-Air FEEL with Integrated Sensing: Joint Scheduling and Beamforming Design

arXiv

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arXiv 2025年

作者： Asaad, Saba Wang, Ping Tabassum, Hina Department of Electrical Engineering and Computer Science York University Toronto Canada

Employing wireless systems with dual sensing and communications functionalities is becoming critical in next generation of wireless networks. In this paper, we propose a robust design for over-the-air federated edge learning (OTA-FEEL) that leverages sensing capabilities at the parameter server (PS) to mitigate the impact of target echoes on the analog model aggregation. We first derive novel expressions for the Cramér-Rao bound of the target response and mean squared error (MSE) of the estimated global model to measure radar sensing and model aggregation quality, respectively. Then, we develop a joint scheduling and beamforming framework that optimizes the OTA-FEEL performance while keeping the sensing and communication quality, determined respectively in terms of Cramér-Rao bound and achievable downlink rate, in a desired range. The resulting scheduling problem reduces to a combinatorial mixed-integer nonlinear programming problem (MINLP). We develop a low-complexity hierarchical method based on the matching pursuit algorithm used widely for sparse recovery in the literature of compressed sensing. The proposed algorithm uses a step-wise strategy to omit the least effective devices in each iteration based on a metric that captures both the aggregation and sensing quality of the system. It further invokes alternating optimization scheme to iteratively update the downlink beamforming and uplink post-processing by marginally optimizing them in each iteration. Convergence and complexity analysis of the proposed algorithm is presented. Numerical evaluations on MNIST and CIFAR-10 datasets demonstrate the effectiveness of our proposed algorithm. The results show that by leveraging accurate sensing, the target echoes on the uplink signal can be effectively suppressed, ensuring the quality of model aggregation to remain intact despite the interference. © 2025, CC BY.

关键词： Mean square error

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Cyberattacks on Large Language Models - Attack Detection and Architecture Adaptability

Cyberattacks on Large Language Models - Attack Detection and...

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IEEE Southeastcon

作者： Srikar Alla Ali Shiri Sichani Electrical Engineering and Computer Science Department University of Missouri Columbia MO

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Large Language Models (LLMs) like GPT and $PaLM$ have transformed natural language processing, enabling advancements in text generation, language translation, and conversational AI. However, their increasing adoption has exposed critical vulnerabilities, making them susceptible to cyberattacks such as prompt injection, data poisoning, and Distributed Denial of Service (DDoS) attacks. These threats compromise the security, reliability, and integrity of LLM applications in real-world scenarios. To mitigate these challenges, advanced defense mechanisms such as attack detectors and attack libraries play a crucial role. Attack detectors analyze input patterns and monitor model responses to identify anomalies and potential security breaches. These systems rely on attack libraries that act as structured repositories of predefined attack patterns, known exploits, and evolving threats. The attack library functions as a dynamic lookup table, enabling real-time vulnerability detection and rapid response to existing and novel threats. By integrating systematic vulnerability detection techniques and structured attack analysis, these mechanisms strengthen LLM security. Attack detectors and libraries provide a comprehensive approach to identifying, classifying, and mitigating security risks. This framework ensures the safe deployment of LLMs in various industries, strengthening their resilience and minimizing exposure to emerging cyber threats in sensitive applications.

关键词： Translation Systematics Large language models Detectors Libraries Real-time systems Security Reliability computer crime Resilience

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An Analysis of Anonymous MQTT for Publish-Subscribe-Based IoT Networks

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IEEE Transactions on Network science and engineering 2025年

作者： Fukushima, Yudai Tsunamoto, Hotaru Sakai, Kazuya Sun, Min-Te Ku, Wei-Shinn Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo191-0065 Japan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan Auburn University Department of Computer Science and Software Engineering AuburnAL36849 United States

Message queuing telemetry transport (MQTT) is widely used as a communication primitive in publish-subscribe-based IoT applications. However, the current MQTT standard does not support the privacy of IoT devices and users. Therefore, in this paper, we address the anonymous communications for MQTT-based IoT networks by tailored onion routing. To this end, we first introduce the concept of distributed IoT broker networks, in which a collection of IoT brokers are interconnected with each other. These brokers serve as onion routers. Then, we propose a generic anonymous messaging protocol (A-MQTT) for publish-subscribe-based IoT systems. Unlike typical anonymous communications, there are multiple destination IoT devices, called subscribers, in MQTT contexts. Hence, the privacy notions, such as traceable rate, device anonymity, and path anonymity, are redesigned. To illuminate the fundamental privacy issues, closed-form approximations for these privacy metrics are modeled. In addition, simulation results demonstrate that our A-MQTT significantly improves the security performance and that our proposed models closely approximate the various privacy performance. Furthermore, a proof-of-concept protocol is implemented on a Raspberry Pi 4 in order to show that the proposed A-MQTT can be deployed in an IoT environment. © 2013 IEEE.

关键词： Anonymity

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Wireless Health Monitoring for Calculating Vitals Device by Using IoT 2

Wireless Health Monitoring for Calculating Vitals Device by ...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Pradeep Ghantasala, G.S. Mahaveer, S. Ghosh, Debopriya Ananthakrishnan, P.S. Masali, Abhishek Basavaraj Singh, Susheel Kumar Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Alliance University Department of Electrical and Electronics Engineering Bengaluru India Heera Lal Yadav Balika Degree College UP Lucknow India

ISBN: (纸本)9798350364699

The fast evolution of the Internet of Things (IoT) has paved the way for innovative healthcare solutions. This paper affords the format and implementation of a wireless health monitoring device the use of IoT technology. The tool continuously tracks critical signs and signs along with coronary heart rate, blood pressure, and oxygen saturation, transmitting real-time facts to healthcare providers. The proposed solution enhances affected man or woman care with the useful resource of the usage of allowing early detection of anomalies and properly timed medical intervention. The paper discusses the architecture, hardware components, software program software integration, and functionality challenges, offering insights into future trends in IoT-enabled healthcare. © 2024 IEEE.

关键词： Blood pressure monitoring Healthcare Heart rate monitoring IoT Patient care Wireless Health Monitoring

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Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model

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computer Systems science & engineering 2023年第5期45卷 1679-1694页

作者： Mashael M Asiri Heba G.Mohamed Mohamed K Nour Mesfer Al Duhayyim Amira Sayed A.Aziz Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer Science College of Science&Art atMahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application ***,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication *** traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find *** study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)*** major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT *** the preliminary stage,data pre-processing is carried out to transform the input data into useful *** addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection ***,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of ***,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization *** experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several *** simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.

关键词： Cyberattacks security deep learning internet of things feature selection data classification

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Understanding and Modeling the Effects of Task and Context on Drivers’ Gaze Allocation

Understanding and Modeling the Effects of Task and Context o...

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IEEE Symposium on Intelligent Vehicle

作者： Iuliia Kotseruba John K. Tsotsos Department of Computer Science and Electrical Engineering York University Canada

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

To further advance driver monitoring and assistance systems, it is important to understand how drivers allocate their attention, in other words, where do they tend to look and why. Traditionally, factors affecting human visual attention have been divided into bottom-up (involuntary attraction to salient regions) and top-down (driven by the demands of the task being performed). Although both play a role in directing drivers’ gaze, most of the existing models for drivers’ gaze prediction apply techniques developed for bottom-up saliency and do not consider influences of the drivers’ actions explicitly. Likewise, common driving attention benchmarks lack relevant annotations for drivers’ actions and the context in which they are performed. Therefore, to enable analysis and modeling of these factors for drivers’ gaze prediction, we propose the following: 1) we correct the data processing pipeline used in DR(eye)VE to reduce noise in the recorded gaze data; 2) we then add per-frame labels for driving task and context; 3) we benchmark a number of baseline and SOTA models for saliency and driver gaze prediction and use new annotations to analyze how their performance changes in scenarios involving different tasks; and, lastly, 4) we develop a novel model that modulates drivers’ gaze prediction with explicit action and context information. While reducing noise in the DR(eye)VE gaze data improves results of all models, we show that using task information in our proposed model boosts performance even further compared to bottom-up models on the cleaned up data, both overall (by 24% KLD and 89% NSS) and on scenarios that involve performing safety-critical maneuvers and crossing intersections (by up to 10–30% KLD). Extended annotations and code are available at https://***/ykotseruba/SCOUT.

关键词： Analytical models Visualization Annotations Noise Predictive models Benchmark testing Feature extraction

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Learning Skill-based Industrial Robot Tasks with User Priors 18

Learning Skill-based Industrial Robot Tasks with User Priors

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18th IEEE International Conference on Automation science and engineering, CASE 2022

作者： Mayr, Matthias Hvarfner, Carl Chatzilygeroudis, Konstantinos Nardi, Luigi Krueger, Volker Department of Computer Science LundSE 221 00 Sweden Greece Stanford University Department of Computer Science and Electrical Engineering CA94305 United States

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill parameters. One strategy is to learn these parameters by allowing the robot system to learn directly on the task. For a learning problem, a robot operator can typically specify the type and range of values of the parameters. Nevertheless, given their prior experience, robot operators should be able to help the learning process further by providing educated guesses about where in the parameter space potential optimal solutions could be found. Interestingly, such prior knowledge is not exploited in current robot learning frameworks. We introduce an approach that combines user priors and Bayesian optimization to allow fast optimization of robot industrial tasks at robot deployment time. We evaluate our method on three tasks that are learned in simulation as well as on two tasks that are learned directly on a real robot system. Additionally, we transfer knowledge from the corresponding simulation tasks by automatically constructing priors from well-performing configurations for learning on the real system. To handle potentially contradicting task objectives, the tasks are modeled as multi-objective problems. Our results show that operator priors, both user-specified and transferred, vastly accelerate the discovery of rich Pareto fronts, and typically produce final performance far superior to proposed baselines. © 2022 IEEE.

关键词： Learning systems

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Real-Time Anomaly Detection in Large-Scale Sensor Networks using Isolation Forests

Real-Time Anomaly Detection in Large-Scale Sensor Networks u...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Rawat, Ruchira Al-Attab, Kassem Dixit, Krishna Kant Deepak, A. Pushkarna, Gaurav Harikrishna, M. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Electrical Engineering Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This research delves into real-time anomaly detection in enormous-scope sensor networks, employing Isolation Forest, One-Class SVM, Local Outlier Factor, and Recursive Partitioning algorithms. The review features Isolation Forest as a champion entertainer, accomplishing an accuracy of 0.92 and a review of 0.88. One-Class SVM and Local Outlier Factor display cutthroat abilities with accuracy upsides of 0.87 and 0.89 , and review upsides of 0.82 and 0.85 , individually. Recursive Partitioning, stressing interpretability, keeps a good accuracy of 0.82 and a review of 0.76. The examination stretches out to related work, investigating imaginative methodologies, for example, Subspace Occasional Grouping, profound exchange learning for modern shortcoming findings, and anomaly detection in unambiguous spaces like lodgings and microservicebased frameworks. Multimodal information combination, AI for 5G-based IoT network security, and novel structures like TCFTrans add to the rich scene of anomaly detection. © 2024 IEEE.

关键词： Anomaly detection

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Joint Design of Binary Probing Sequences and Mismatched Receive Filters for PMCW Radar 20

Joint Design of Binary Probing Sequences and Mismatched Rece...

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20th European Radar Conference, EuRAD 2023

作者： Lin, Ronghao Chen, Yutao Li, Jian University of Science and Technology of China Department of EEIS Anhui Hefei China University of Florida Department of Electrical and Computer Engineering GainesvilleFL United States

ISBN: (纸本)9782874870743

Automotive radar is becoming increasingly popular for enhanced driving safety and autonomous driving. However, the increasingly severe mutual interference problems can cause the commercially available linear frequency-modulated continuous-wave (LFMCW) automotive radar to become the victim of its success. Phase-modulated continuous-wave (PMCW) automotive radar systems can be used to mitigate the mutual interference problems. To attain this goal, long binary periodic probing sequences with low correlation sidelobes, arbitrary period lengths, and ample diversity are needed by PMCW radar systems. To further reduce the sidelobe levels, at the slight loss of the signal-to-noise ratio (SNR), mismatch receive filters are also viable options for PMCW radar. We introduce herein the joint design of probing sequences and mismatched receive filters, under the constraint of maximum allowable SNR loss, for PMCW radar. © 2023 European Microwave Association (EuMA).

关键词： Binary sequences

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