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Semantic communications, semantic edge computing, and semantic caching with applications to the Metaverse and 6G mobile networks 43

Semantic communications, semantic edge computing, and semant...

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43rd ieee international conference on Distributed computing systems (ICDCS)

作者： Yu, Wenhan Zhao, Jun Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350339864

The increasing popularity of applications like the Metaverse has led to the exploration of new, more effective ways of communication. Semantic communication, which focuses on the meaning behind transmitted information, represents a departure from traditional communication paradigms. As mobile devices become increasingly prevalent, it is important to explore the potential of edge computing to aid the semantic encoding/decoding process, which requires significant computing power and storage capabilities. However, establishing knowledge bases (KBs) for domain-oriented communication can be time-consuming. To address this challenge, this paper proposes a semantic caching model in edge computing system that caches domain-specialized general models and user-specific individual models. This approach has the potential to reduce the time and resources required to establish individual KBs while accurately capturing the semantics behind users' messages, ultimately leading to more efficient and accessible semantic communication.

关键词： Semantic communication Caching Edge computing

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Predictive Analytics for Digital Twins: The Concept and systems applications 7

Predictive Analytics for Digital Twins: The Concept and Syst...

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7th international conference on Information and Computer Technologies, ICICT 2024

作者： Mohamed, Nader Al-Jaroodi, Jameela Pennsylvania Western University Department Of Computing And Engineering Technology CaliforniaPA United States Robert Morris University Department Of Engineering PittsburghPA United States

ISBN: (纸本)9798350385625

Digital twins were introduced to offer digitalized models of real-world systems. These systems can contain systems of systems and processes to perform important tasks in the system. The digital twin technology offers a virtual representation (an operational digital replica) of the real system that can be employed for performing assessments and continuous improvements of these systems and their operations and processes. This paper introduces the concept of integrating predictive analytics with digital twins. A predictive analytics process can be applied on a series of historical digitalized models created from time stamped digital twins to construct future digitalized models that emulate the expected future behaviors and performance of their corresponding physical systems. These generated future digital models can have many potential applications in the industry and business fields and in this paper, we discuss some of these potential applications. © 2024 ieee.

关键词： Predictive analytics

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An Efficient and Fast Filter Pruning Method for Object Detection in embedded systems 6

An Efficient and Fast Filter Pruning Method for Object Detec...

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6th international conference on AI Circuits and systems (AICAS)

作者： Ko, Hyunjun Kang, Jin-Ku Kim, Yongwoo Inha Univ Dept Elect & Comp Engn Incheon 22212 South Korea Korea Natl Univ Educ Dept Technol Educ Cheongju 28173 South Korea

ISBN: (纸本)9798350383638;9798350383645

Recently, CNN-based networks have exhibited high performance in computer vision. On the other hand, due to the networks becoming deeper and wider, it is hard to implement the model in real-time embedded environments. To overcome the drawback, filter pruning has been widely studied for neural network compression. Filter pruning does not need any special hardware or software because it removes filters of CNN and accelerates inference without any special software or hardware. In this paper, we proposed efficient and fast filter pruning (EFFP), which focuses on reducing the training computation resources and searching optimal pruned networks. The success stems from two significant improvements upon other pruning methods. (1) Short training time: In the pruning stage, we make redundant filters to zero to make the output feature map the same as a lightweight model, and (2) adjust the change of redundancy using regrowing: It is difficult to get an optimal pruned model by pruning redundant filters at once. Therefore, we use the pruning/regrowing method to gradually remove unimportant filters to avoid permanently pruning important filters to get an optimal lightweight model. Experimental results indicate that EFFP can reduce the FLOPs and parameters more efficiently and faster than other pruning methods on the object detection model. The inference time is measured on NVIDIA Jetson Xavier NX. As a result, we improve mAP and inference time by a maximum of 45 % compared to other pruning methods.

关键词： CNN Network compression Filter pruning Object detection Inference time

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Intrusion Detection at the Edge computing: A Deep Learning Approach Using the UNSW-NB15 Dataset 14

Intrusion Detection at the Edge Computing: A Deep Learning A...

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14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Kumar, Vipin Kumar, Vivek Bhadauria, Abhay Pratap Singh Dixit, Jay Kumar, Amit Haridwar India Department of Computer Engineering & Applications GLA University UP Mathura India

ISBN: (纸本)9798331531935

Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. The findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. This study investigates the application of edge computing, Internet of Things (IoT), and deep learning to improve the performance of Intrusion Detection systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. The results demonstrate better performance, showing their suitability for real-time security applications. These findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments. © 2025 ieee.

关键词： Edge computing

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Smart Helmet And Monitoring For Miners With Enhanced Protection 2

Smart Helmet And Monitoring For Miners With Enhanced Protect...

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2nd international conference on Edge computing and applications, ICECAA 2023

作者： Banik, Arnob Mishra, Divakar Manikandan, N. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Tamil Nadu Vellore India

ISBN: (纸本)9798350347579

Internet of Things (IoT) has lately been expanded across various applications, drawing significant attention to its design, where a loud industrial atmosphere can exist in the mining area. The central essence of this project was to detect hazardous events and to design a perfect, smart helmet that ensures the safety of workers. We have decided to implement a system that could be embedded in the helmets of the mine workers. Workers would be alerted via a helmet buzzer, so necessary action was taken on time. The critical feature of this project was that the model presented uses an application interface to detect real-time data and display it. The administrators outside may monitor the dynamic data from the system using a website and a mobile application. In contrast, the Android app displays or informs the data or alerts. The miners and the administrators can monitor real-time data that constantly changes according to the changes in the environment. They could also monitor the changes in temperature and humidity through graphs continuously updated on the applications. 'Rescue with Flexibility' was the main motto of our project. Thus, this project offers a directional platform that would bring a revolutionary change in this industry. © 2023 ieee.

关键词： Internet of things

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Multi-Accelerator Neural Network Inference via TensorRT in Heterogeneous embedded systems 48

Multi-Accelerator Neural Network Inference via TensorRT in H...

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48th Annual ieee international Computers, Software, and applications conference (COMPSAC) - Digital Development for a Better Future

作者： Zhou, Yuxiao Guo, Zhishan Dong, Zheng Yang, Kecheng Texas State Univ San Marcos TX 78666 USA North Carolina State Univ Raleigh NC USA Wayne State Univ Detroit MI USA

ISBN: (纸本)9798350376975;9798350376968

Neural Network Inference (NNI) has become a critical element in mobile and autonomous systems, particularly for time-sensitive operations like obstacle detection and avoidance. Alongside execution time, energy consumption holds significant importance in such workloads, given that power is a limited resource in these systems. Modern System-on-Chips (SoCs) in mobile and autonomous devices are equipped with a diverse range of accelerators, each characterized by distinct power and performance features. Adapting to dynamically changing physical conditions, the execution flow of these crucial workloads can be optimized to utilize multiple accelerators, allowing for a flexible trade-off between performance and energy consumption. In this study, we leverage multiple accelerators within an SoC to execute NNI using NVIDIA TensorRT. Our primary goal is to enable an energy-performance trade-off by intelligently distributing layers of a neural network between accelerators that prioritize performance and those that emphasize power efficiency. Initially, we analyze the execution time and energy characteristics of neural network layer execution on various accelerators. Subsequently, we examine various factors influencing layer execution. Finally, we propose two algorithms to determine the mapping of layers to accelerators, minimizing energy consumption while adhering to a predetermined target NN inference execution time. We evaluate our approaches on the NVIDIA AGX Orin SoC using the commonly used ResNet50 model. According to the experiment results, we suggest adopting a coarse-grained layer grouping strategy. For applications with stringent realtime requirements, it is recommended to utilize the proposed LTN approach to better achieve the target execution time. Alternatively, in other scenarios, the Knapsack approach may be chosen for potential improvements in energy consumption.

关键词： deep neural networks heterogeneous systems neural networks inference autonomous systems SoC TensorRT PyTorch DLA edge device

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Attention-Based real-time Defenses for Physical Adversarial Attacks in Vision applications 15

Attention-Based Real-Time Defenses for Physical Adversarial ...

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15th ACM/ieee international conference on Cyber-Physical systems (ICCPS)

作者： Rossolini, Giulio Biondi, Alessandro Buttazzo, Giorgio Scuola Superiore Sant Anna Dept Excellence Robot & AI Pisa Italy

ISBN: (纸本)9798350369274;9798350369281

Deep neural networks exhibit excellent performance in computer vision tasks, but their vulnerability to real-world adversarial attacks, achieved through physical objects that can corrupt their predictions, raises serious security concerns for their application in safety-critical domains. Existing defense methods focus on single-frame analysis and are characterized by high computational costs that limit their applicability in multi-frame scenarios, where real-time decisions are crucial. To address this problem, this paper proposes an efficient attention-based defense mechanism that exploits adversarial channel-attention to quickly identify and track malicious objects in shallow network layers and mask their adversarial effects in a multi-frame setting. This work advances the state of the art by enhancing existing over-activation techniques for real-world adversarial attacks to make them usable in real-time applications. It also introduces an efficient multi-frame defense framework, validating its efficacy through extensive experiments aimed at evaluating both defense performance and computational cost.

关键词： adversarial attacks real-world adversarial defense neural network monitoring robust and secure AI

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Should I Stay or Should I Go: A Learning Approach for Drone-based Sensing applications 20

Should I Stay or Should I Go: A Learning Approach for Drone-...

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20th Annual international conference on Distributed computing in Smart Sensor systems and the Internet of Things (DCOSS-IoT)

作者： Polychronis, Giorgos Koutsoubelias, Manos Lalis, Spyros Univ Thessaly Dept Elect & Comp Engn Volos Greece

ISBN: (纸本)9798350369458;9798350369441

Multicopter drones are becoming a key platform in several application domains, enabling precise on-the-spot sensing and/or actuation. We focus on the case where the drone must process the sensor data in order to decide, depending on the outcome, whether it needs to perform some additional action, e.g., more accurate sensing or some form of actuation. On the one hand, waiting for the computation to complete may waste time, if it turns out that no further action is needed. On the other hand, if the drone starts moving toward the next point of interest before the computation ends, it may need to return back to the previous point, if some action needs to be taken. In this paper, we propose a learning approach that enables the drone to take informed decisions about whether to wait for the result of the computation (or not), based on past experience gathered from previous missions. Through an extensive evaluation, we show that the proposed approach, when properly configured, outperforms several static policies, up to 25.8%, over a wide variety of different scenarios where the probability of some action being required at a given point of interest remains stable as well as for scenarios where this probability varies in time.

关键词： drones autonomous systems adaptive systems machine learning regression

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Intelligent Water-Efficient Street Cleaning Vehicle for Road Pollution Identification applications 11

Intelligent Water-Efficient Street Cleaning Vehicle for Road...

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11th ieee international conference on Consumer Electronics - Taiwan (ICCE-Taiwan) - Empower of Innovative Consumer Technology

作者： Wang, Szu-Hong Liao, Yan-Xin Zhang, Liang-Xu Yunlin Univ Sci & Technol Dept Elect Engn Yunlin Taiwan

ISBN: (纸本)9798350386851;9798350386844

The phenomenon of road dust during vehicle operation poses threats to visibility and road safety, while also negatively impacting respiratory health and air quality, becoming an environmental concern. In order to address this issue, this study proposes an AI-based street cleaning vehicle system aimed at reducing dust emissions and providing a clean and safe driving environment, while also preventing driver distraction leading to traffic accidents. The system utilizes two cameras on the street cleaning vehicle to capture images of road conditions and surface pollution, which are then input into an embedded system for real-time image analysis. This technology requires classification of 8 categories, and leveraging deep learning technology like YolactEdge, real-time detection of road pollution is achieved. AI automatically adjusts water pressure for road cleaning and categorizes road pollution into four levels, adjusting water pressure accordingly. This method achieves good results in road pollution identification and adjusting the amount of water for road cleaning. Firstly, in terms of road pollution identification, the average accuracy is 99.85%, and the frame rate on the NVIDIA Jetson Xavier NX embedded system is 13 frames per second. Secondly, in adjusting the amount of water for road cleaning, after measurement, it is found to reduce water consumption by over 40% compared to traditional methods. This system achieves efficient water usage by accurately identifying road surface dirt and debris for application in low-speed street cleaning vehicles traveling at 20 km/hr.

关键词： Image recognition processing road pollution recognition YolactEdge street cleaning vehicle embedded system

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SMT-based Control Safety Property Checking in Cyber-Physical systems under Timing Uncertainties 37

SMT-based Control Safety Property Checking in Cyber-Physical...

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37th international conference on VLSI Design (VLSID) / 23rd international conference on embedded systems (ES)

作者： Yeolekar, Anand Metta, Ravindra Chakraborty, Samarjit Tata Consultancy Serv TCS Res Pune Maharashtra India Tech Univ Munich Munich Germany Univ N Carolina Chapel Hill NC 27515 USA

ISBN: (纸本)9798350384406

This paper outlines formal methods and design automation techniques for exact checking of control safety and reachability properties of cyber-physical systems (CPS), under timing uncertainties (common deadline miss handling and control update policies). While such checking is often fraught with state-space explosion problems and is hence not scalable. This paper discusses a new joint encoding of control and scheduling behaviors as a satisfiability-modulo-theory (SMT) formulation and a novel abstraction-refinement procedure with incremental solving, to scale the analysis. In addition, we also outline empirical performance results of multiple well-known SMT solvers for this problem. These results can inform practical decision making for large scale control safety verification in the industry.

关键词： Cyber-Physical systems real-time systems Formal Verification SMT Solvers

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