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Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybrid Edge-Cloud Computing

Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybr...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Haitao Yuan Shen Wang Yaofei Ma Jing Bi Jinhong Yang Jia Zhang MengChu Zhou School of Automation Science and Electrical Engineering Beihang University Beijing China College of Computer Beijing University of Technology Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization.

关键词： Energy consumption Federated learning Collaboration Simulated annealing Programming Genetics Servers Resource management Particle swarm optimization Optimization

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Ai-Driven Video Surveillance for Safety Using Yolo with Iot Integration and Intelligent Alerts

Ai-Driven Video Surveillance for Safety Using Yolo with Iot ...

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Data Science and Business Systems (ICDSBS), International Conference on

作者： S.M. Latha Asan Saheb Fasiha Janusri M Dept. of Computer Science & Engg. B.S.Abdur Rahaman Crescent Institute of Science & Technology Chennai India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

The operational role of ensuring safety in dynamic and high-risk scenarios is an important task in the area of security threats or emergencies, particularly in areas with high risk of human crime. The proposed system for surveillance uses AI-based and Internet of Things (IoT) technologies to continuously monitor human behaviors through camera and sensor networks and uses it for detection of dangerous objects and recognition of aggressive or hostile physical behavior normally associated with violent confrontation or security risk. Thus the solution can be automatically designed to address threats promptly. One of the main functionality of this system is the application of a high-performance object detection model with high accuracy and low latency processing live video data. The training process has been carried out on this model to reliably detect unsafe objects and suspicious human behavior. These traits help to make immediate threat identification easy. When detecting danger, the system automatically initiates a multitiered emergency response strategy including alarming the situation and sending instantaneous alerts to designated security officials. The solution also uses IoT-based devices such as smart sirens that emit high-pitched auditory signals to alert surrounding people to possible danger. A specific novel feature in the proposed system is its dual-alert mechanism whereby auditory signals are deployed simultaneously at the incident site and at a second remote monitoring station. This way security personnel are better aware of the situation and reduced response time. To extend emergency preparedness, the system also enables automated email notifications to designated medical emergency responders, including ambulance services.

关键词： YOLO Training Accuracy Video surveillance Behavioral sciences Security Internet of Things Time factors Artificial intelligence Remote monitoring

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Dynamic Healthcare System using Cloud Computing

Dynamic Healthcare System using Cloud Computing

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： C.A Subasini Sarah Swetha Peter N Sneha Ravishankar Adlin Sheeba Dept. of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai

Dynamic healthcare system that facilitates communication between patients and physicians through a single platform based on their mutual proximity. The user can access the system to find outwhat type of disease he has and make appointments with the local doctor. The use renters their health concern, after which the program uses machine learning algorithms togather different parts of the information presented and identify what diseases arepresent based on symptoms or natural language. Our system offers features such as live doctor chat, appointment scheduling, and suggestions for medications, specialists, and diseases that can be predicted using machine learning. The user can selectany specialized doctor and get advice from them once the doctor approves the appointment. We also introduced the concept of an administrator on the website who has the authority to contact the patientdirectly before the doctor, with the database housed in the 9 Google Cloud. Healthcare is vital.

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IoT Intrusion Detection: Evaluating ML-Based IDS on Image and Network Traffic Datasets

IoT Intrusion Detection: Evaluating ML-Based IDS on Image an...

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IEEE International Symposium on Smart Electronic Systems (iSES)

作者： Manan Pathak Anuj Patel Bela Shah Dept. of Computer Science and Engineering CSPIT Charotar University of Science and Technology Anand India

With the increasing pervasiveness of Internet of Things (IoT) devices, ensuring their security has become a critical concern. Advanced intrusion detection systems (IDS) leveraging machine learning (ML) and Deep learning(DL) techniques offer promising solutions for protecting IoT networks. However, the success of ML models depends heavily on preprocessing and feature extraction processes. This research presents a survey on ML-based IDS for IoT, focusing on ML models applied to both image(IEEE Dataport) and UNSW-NB15 datasets. The survey evaluates several feature extraction methods, including image filters and Machine learning models for the image dataset. For the UNSW-NB15 dataset, a range of preprocessing techniques specific to network traffic analysis, such as statistical features and protocol-specific features, are considered. In addition to feature extraction, various ML algorithms are assessed on both datasets. The evaluation is based on accuracy, precision, recall, and F1 score performance metrics, providing a comprehensive analysis of the ML model's effectiveness in detecting intrusions. This research contributes to the field of IoT security by providing insights into the performance of feature extraction algorithms and ML models on both image and network traffic datasets. The findings offer guidance for developing effective IDS solutions that can accurately detect and mitigate intrusions in IoT networks, enhancing the overall security of IoT devices and systems.

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Crop Price Prediction for Coconut in Tamil Nadu Using Machine Learning

Crop Price Prediction for Coconut in Tamil Nadu Using Machin...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Mithuna R. J. Sebastian Terence Dept. of Computer Science and Engg. Karunya Institute of Technology and Sciences Coimbatore India

Agriculture is one of the most important sectors of the Indian economy, with a major contribution towards the country's GDP. The agricultural sector in India is still predominantly based on traditional farming methods, which makes it challenging for farmers to keep up with the ever-changing market prices of crops. In recent years, data-driven models have emerged as a powerful tool for crop price prediction. This paper aims to predict the prices of coconuts in India using Decision tree regression, gradient Boost regressor, random forest regressor and Extreme gradient boosting (XGBOOST) models. The study utilizes monthly coconut price data from 2010 to 2022. The results show that the xgboost model has the best performance, followed by the Gradient boosting and regression models. The findings of this study can help policymakers, farmers, and traders make informed decisions regarding coconut farming and trading.

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Image Encryption using Modified RC4 Algorithm

Image Encryption using Modified RC4 Algorithm

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Sciences and Techniques of Automatic Control and computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mahmoud H. S. Hasan Dept. Computer Science College of Information Technology Alasmarya Islamic University Zliten Libya

In this paper, we propose a new method for image encryption based on a modified RC4 stream cipher algorithm that scrambles image pixels before applying the XOR encryption function. We compare the performance of the proposed modified RC4 algorithm with that of the standard RC4 algorithm by analyzing histograms, calculating the correlation coefficient between adjacent pixels, and measuring information entropy. The results demonstrate that the proposed algorithm outperforms the standard RC4 algorithm in terms of resistance to statistical attacks.

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College Bus Number Plate Registration Detection Using YOLO-V8

College Bus Number Plate Registration Detection Using YOLO-V...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Ayesha Naureen K.Chaithanya Krishna N.Sai Teja L. Mahesh K. Vamshi Dept. of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana

There is a need for automation of vehicle entering system and to reduce the time of verification. We propose a solution for this problem considering college buses o BVRIT as organization. College bus number plate Registration Detection is crucial part of smart BVRIT planning and BVRIT transport management. This paper presents YOLO V8 algorithm for Detecting college bus using number plate registration detection. YOLO - You Only Look Once. This YOLO-V8 algorithm is state-of-the-art (SOA) model or algorithm. It's real-time object detection algorithm that can detect objects in any image, that has many classes in it with high accuracy and speed. A dataset of College bus images is collected manually and finetuned using transfer learning. Model is trained on powerful GPU (graphics Processing Unit), significantly reducing training time and taking less time for Character recognition. The model's accuracy is computed by average, precision, recall, and F1 score as well curves. Our results show that Yolo V8, model can accurately detect the bus with high accuracy and speed in any weather condition. The text from detected number plate is recognized using Easy-OCR. This number plate text is used to infer whether bus is of COLLEGE BUS or NON-COLLEGE BUS. Model's overall performance is remarkable. While maintaining recall of 95 percent, which improves its capacity to collect a substantial number of pertinent things, it attained a precision of 99.2 percent, suggesting a high accuracy in properly identifying objects. The model's ability to detect items with a high degree of confidence is demonstrated by its mAP50 score of 99 Percent. This is one of the optimal solutions to minimize human work and time, making automation. This research has been done with the purpose to find the registered college bus is supposed to enter the college campus and Non-college bus is not supposed to enter the campus.

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Transformer based multimodal similarity search method for E-Commerce platforms

Transformer based multimodal similarity search method for E-...

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Guwahati Subsection Conference (GCON), IEEE

作者： Chandan Charchit Sahoo Deepak Singh Tomar Jyoti Bharti Dept. of Computer Science and Engineering Maulana Azad National Institute of Technology Bhopal India

Multimodal similarity search has been attracting much attention from scholars in recent times. The intensification of research into Multimodal search lies in the motive to understand the context of the multiple modes associated with the data and the interdependency of these modes. Multimodal data can be in the form of audio, video, images, texts, and any combination of them. For relevant extraction of features from them can take too much time and thus it may cause the whole process of learning the features and searching for them time-consuming. In this work, a deep neural network uses Convolutional neural network and Bidirectional Encoder Representations from Transformers to learn features from text and image data. A K nearest neighbor a long with KD Tree has been used to search for similar image-text pairs. Evaluations based on several image-text multimodal retrieval procedures show the effectiveness of the proposed multimodal search method and its higher speed performance compared to existing algorithms.

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Design and Implementation of a Distributed Network Management System for a Multi-Branch University using GNS3

Design and Implementation of a Distributed Network Managemen...

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International Conference on Computing and Networking technology (ICCNT)

作者： Md Apu Hosen Shahadat Hoshen Moz Md. Ali Akber Dept. of Computer Science and Engineering Northern University of Business and Technology Khulna Khulna Bangladesh

The rapid digitalization of communication necessitates continuous skill updates to keep pace with evolving technology. Network management involves multiple functionalities that play a crucial role in optimizing network performance and ensuring continuous availability. It is very important to ensure a proper network management system for a university that has multiple branches to effectively manage and monitor their network infrastructure. A proper network management system provides centralized control and visibility over the entire network infrastructure, allowing administrators to efficiently manage and monitor network devices, configurations, and performance from a single interface. this research has developed a network management system that facilitates real-time network monitoring and troubleshooting, enabling proactive identification and resolution of issues to ensure smooth operations across all branches. It simplifies configuration management, ensuring consistency and compliance in network settings and updates. Resource optimization is achieved through insights into network utilization and performance metrics, allowing administrators to allocate resources efficiently and plan for future expansion. Security and compliance measures can be enforced, protecting sensitive data and ensuring adherence to regulatory requirements. This study utilized GNS3 to create a network layout for a university with multiple branches, along with essential security configurations for the network design. A diverse range of protocols was implemented to safeguard and cater to the users of the secure university network. Furthermore, Switches, Data Routers, firewalls, and BGP Routers were incorporated into the network to enhance communication efficiency and streamline operations.

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Task-Oriented Communication with Out-of-Distribution Detection: An Information Bottleneck Framework

Task-Oriented Communication with Out-of-Distribution Detecti...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Hongru Li Wentao Yu Hengtao He Jiawei Shao Shenghui Song Jun Zhang Khaled B. Letaief Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for downstream applications. Most existing deep learning (DL)-based task-oriented communication systems adopt a closed-world assumption, assuming either the same data distribution for training and testing, or the system could have access to a large out-of-distribution (OoD) dataset for retraining. However, in practical open-world scenarios, task-oriented communication systems will be exposed to unknown OoD data. The powerful approximation ability of learning methods may force the task-oriented communication systems to overfit the training data (i.e., in-distribution data). Therefore, these systems tend to provide overconfident judgments when encountering OoD data. Based on the information bottleneck (IB) framework, we propose a class conditional IB (CCIB) approach to address this problem, supported by information-theoretical insights. The idea is to extract distinguishable features from in-distribution data while keeping their compactness and informativeness. It is achieved by imposing the class conditional latent prior distribution and enforcing the latent of different classes to be far away from each other. Simulation results shall demonstrate that the proposed approach detects OoD data more efficiently than the baselines and state-of-the-art approaches, without compromising the rate-distortion tradeoff.

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