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Utilizing internet of Things in Intelligent City, and Medical Sharing technology

Utilizing Internet of Things in Intelligent City, and Medica...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Ankita Gandhi Padmaja Tripathy Department of CSE PIET Parul University Department of Engg. & IT ARKA JAIN University

Due to the unsatisfactory national public hospital management system, excessive medical costs, a lack of channels, and some other issues affecting people’s ability to support themselves. The focus of public concern is on medical issues, namely those represented by "an inadequate medical system, subpar medical services, healthcare challenges, and exorbitant expenses." The main objective of this article is to develop a set of smart health data network platform technologies that will enable patients to receive elevated treatment and diagnosis options with low waiting times and expenses.. To properly accomplish "everyone’s heath, everyone’s health," that issue of "difficult and costly medical treatment" must be resolved. This project develops and designs a system for distributed health information sharing and integration. Then, a few premier local hospitals adopt it. These results of the poll and interviews research show that the healthcare field has substantially improved and that smart medicine distribution has increased by roughly 79 percent among general population compared to earlier medical treatments.

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Adaptive Task Offloading Framework for Serverless Edge computing

Adaptive Task Offloading Framework for Serverless Edge Compu...

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Disruptive Technologies (ICDT), international conference on

作者： Sana Bharti Rupali Gill Shilpi Harnal Chitkara University Institute of Engineering and Technology Chitkara University Punjab Chitkara University Rajpura India Chandigarh university Mohali Punjab Chandigarh university Mohali India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Serverless Edge computing (SEC) has emerged as a transformative paradigm to meet the computational demands of latency-sensitive applications in the internet of Things (IoT). By processing tasks closer to data sources., SEC minimizes latency and improves system responsiveness. However., the heterogeneity of edge resources., dynamic workloads., and the need to prioritize high-urgency tasks introduce significant challenges in resource allocation and scheduling. This paper presents a hybrid task offloading framework tailored for SEC environments., integrating centralized task classification., task-specific server clustering., and multiagent Proximal Policy Optimization (PPO) based decision-making to manage resource allocation and task execution dynamically. By balancing deterministic strategies for task specialization and adaptive learning for real-time decision making., the proposed framework achieves significant reductions in latency while enhancing task completion rates. Experimental results validate the framework's superiority over traditional deep reinforcement learning (DRL) approaches., by achieving latency reduction of about 15% and improving average completion time by 29.46% demonstrating its effectiveness and scalability in complex., distributed computing environments.

关键词： Processor scheduling Soft sensors Scalability Decision making Dynamic scheduling Real-time systems Resource management internet of Things Servers Edge computing

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Real-time Vehicular Pollution Detection Model using IoT and distributed Streaming Techniques

Real-time Vehicular Pollution Detection Model using IoT and ...

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Advanced Research in computing (ICARC), international conference on

作者： S. T. Rakkesh A. R. Weerasinghe R. A. C. Ranasinghe Department of Computer Science University of Colombo School of Computing (UCSC) Colombo Sri Lanka

Since the primordial days of human settlements emergence, metropolitan regions and traffic congestion have developed concomitantly. The factors that cause commuters to congregate in modern cities also contribute to often intolerable levels of traffic congestion. Air quality gets severely deteriorated as a result of rising urbanization and traffic congestion. When poor emission standards are instigated by existing older vehicles, this makes air quality even worse. In this paper we propose a real-time pollution detection solution model using cost efficient internet of Things (IoT) devices and distributed streaming techniques which can detect infringing vehicles instantaneously and calculate the present air quality levels on specific regions to reflect updates regularly. For our study, we have used a four-wheel prototype robot car, radio frequency identification (RFID) tagging unit, NodeMCU V3 Lua board, ATmega328P Arduino board, few electro-chemical toxic gas sensors like MQ-2, MQ-7, MQ-135, etc and a wireless sensor network (WSN) to connect the devices. Also, in this paper we propose a real time complex event processing (CEP) monitoring dashboard which receives feeds from the devices network using Apache Flink distributed streaming technology. Especially in urban areas our proposed solution model can be used cost efficiently to detect and control emission violations.

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An IoT Device Recognition Method based on Convolutional Neural Network

An IoT Device Recognition Method based on Convolutional Neur...

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Neural Networks, Information and Communication Engineering (NNICE), international conference on

作者： Minghao Lu Linghui Li Yali Gao Xiaoyong Li Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

Device recognition is the primary step toward a secure IoT system. However, the existing equipment recognition technology often faces the problems of unobvious data characteristics and insufficient training samples, resulting in low recognition rate. To address this problem, a convolutional neural network-based IoT device recognition method is proposed. We first extract the background icons of various IoT devices through the internet, and then use the ResNet50 neural network to extract icon feature vectors to build an IoT icon library, and realize accurate identification of device types through image retrieval. The experimental results show that the accuracy rate of sampling retrieval in the icon library can reach 98.5%, and the recognition accuracy rate outside the library can reach 83.3%, which can effectively identify the type of IoT devices.

关键词： Training Face recognition Image retrieval Feature extraction Libraries internet of Things Convolutional neural networks

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Boosting Algorithms to Identify distributed Denial-of-Service Attacks 3

Boosting Algorithms to Identify Distributed Denial-of-Servic...

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3rd international conference on Emerging Electrical Energy, Electronics and computing Technologies 2021, ICE4CT 2021

作者： Kumar, V. Kumar, A. Garg, S. Payyavula, S.R. Birla Institute of Technology Mesra Ranchi India Samsung Research India Bangalore India

In the current pandemic situation, much work became automated using internet of Things (IoT) devices. The security of IoT devices is a major issue because they can easily be hacked by third parties. Attackers cause interruptions in vital ongoing operations through these hacked devices. Thus, the demand for an efficient attack identification system has increased in the last few years. The present research aims to identify modern distributed denial-of-service (DDoS) attacks. To provide a solution to the problem of DDoS attacks, an openly available dataset (CICDDoS 2019) has recently been introduced and implemented. The attacks currently occurring in the dataset were identified using two machine learning methods, i.e. the light gradient boosting method (LGBM) and extreme gradient boosting (XGBoost). These methods have been selected because of their superior prediction ability in high volumes of data in less time than other methods require. The accuracy achieved by LGBM and XGBoost were 94.88% and 94.89% in 30 and 229 seconds(s), respectively. © Published under licence by IOP Publishing Ltd.

关键词： Denial-of-service attack

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Use of Block-chain and internet of Things (IoT) for Ensuring Privacy and Reliability of Well Being Records in Medical Services

Use of Block-chain and Internet of Things (IoT) for Ensuring...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Sunny Verma Arvind Kumar Pandey Deepak Kumar Sinha Chitra Krishnan Mudasir M Kirmani Babita Singla Department of ECE Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India Department of Computer Science ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science and Engineering Jain (Deemed-to-be University) Bangalore India Symbiosis Center for Management Studies Noida Symbiosis International Deemed University Pune India Division of Social Science Computer Science SKUAST-Kashmir India Chitkara Business School Chitkara University India

The medical services framework has key reliability and protection necessities when looked at like as a venture, for example, shielding patients' clinical accounts from undesirable access, safeguarded medication following, protected association with conveyance like ambulances, as well as secure and savvy e-wellbeing observation. Through reasonable safety efforts, distributed ledger technology provides novel ideas reliability and wellbeing of clinical details, and this might accommodate the error among distribution of details and classification. We consolidate qualities of both distributed ledger technology as well as distributed computation in this exploration to give a classification strategy to obstruct chain as well as IoT. This methodology consolidates IoT and conveys IoT administrations to hinder chain hubs; meanwhile, it assembles, analyses, works, and jam in the personality approval for wellbeing material. Cooperation and addresses the deficient registering abilities of some block tie hubs to affirm details legitimacy and *** proposed approach is productive, as exhibited by the reproduction try. It can safeguard and check the honesty of clinical details while additionally resolving issues, for example, high PC intricacy, details trade, and reliability insurance.

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Enhancing Task Scheduling in Cloud computing: A Multi-Objective Cuckoo Search Algorithm Approach

Enhancing Task Scheduling in Cloud Computing: A Multi-Object...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Pallavi Shetty Vivek Veeraiah Swati Vitthal Khidse Mritunjay Rai Ankur Gupta Dharmesh Dhabliya Dept. of MCA NMAM Institute of Technology Nitte (Deemed to be University) Udupi Karnataka India Dept. of Computer Science Sri Siddhartha Academy of Higher Education Tumkur Karnataka India Dr. Babasaheb Ambedkar Marathwada University Aurangabad Maharashtra India Dept. of ECE Shri Ramswaroop Memorial University Barabanki UP India Dept. of CSE Vaish College of Engineering Rohtak Haryana India Dept. of IT Vishwakarma Institute of Information Technology Pune Maharashtra India

Services that are delivered and managed online employ cloud computing. Without taking into account the end user's actual location, cloud computing offers data access and storage devices. Clouds are quickly becoming as a crucial application platform for science. Due to the limitless number of nodes in the cloud environment, resources are inherently unstable, which has a significant impact on task execution and scheduling. Hardware, virtualization, distributed computing, and internet-based service delivery improvements in recent years have all contributed to the evolution of cloud computing. Cloud computing may not entail many cutting-edge technology, but it does offer a novel approach to IT management. An essential and crucial issue in cloud systems is the proper scheduling of jobs that have been received across service providers. In cloud settings, the scheduling problem is NP-hard. The current system offers a scheduling algorithm that takes care of the main problems with job scheduling in the cloud. To plan the jobs in cloud computing for this project, the system suggested a method called Multi Objective Cuckoo Search.

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Design and Application of distributed computing Environment Based on Data Drive 3

Design and Application of Distributed Computing Environment ...

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2020 3rd international conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Zhu, Zhenguo Bi, Chao Zheng, Rong Zheng, Song College of Electrical Engineering and Automation Fuzhou University Fuzhou China Research Institute of Fujian Histron Group Co. Ltd. Fuzhou China Technology Co. Ltd. Fuzhou China

This paper proposes a distributed computing environment based on data-driven mechanism, which can be deployed on different levels of Industrial internet, designs and implements an Industrial internet architecture with high reusability and rapid deployment. This architecture has the consistency of the computing environment in the cloud and terminal control layer, which is beneficial to the realization of remote management and collaborative control of Industrial internet. Data driven technology enables heterogeneous industrial equipment to achieve interconnection and interworking at a low cost. The multi-engine computing environment constructed by it is not only an effective method to solve the application design and development of complex systems, but also a key technology to realize edge computing and fog computing. This paper designed a corresponding test bed platform, test results show that the technology can not only provide connectivity of heterogeneous data source and efficient technical support, and to ensure that the distributed industrial equipment real-Time and synchronization of data transmission, but also has the strong edge computing capabilities to cope with the constantly generated data stream, and reducing the pressure of the cloud server storage, computing. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

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Towards finding Hybrid Machine Learning Models for detection of IoT Botnets

Towards finding Hybrid Machine Learning Models for detection...

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international Symposium on Advanced computing and Communication (ISACC)

作者： Richy Laskar Rakesh Das Rahnak Laskar Samarjit Das Meghna Dasgupta Bachelor of Technology in Computer Science and Engineering The Assam Royal Global University Guwahati Assam Royal School of Information Technology The Assam Royal Global University Guwahati Assam Royal School of Engineering Technology The Assam Royal Global University Guwahati Assam

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The rapid expansion of internet of Things (IoT) devices has significantly increased their vulnerability to cyber-attacks, with IoT botnets posing a critical threat by enabling distributed denial-of-service (DDoS) attacks, data exfiltration, and other malicious activities. Traditional security measures often fall short of effectively mitigating these threats due to the resource limitations of IoT devices. This research explores the use of machine learning (ML) models for IoT botnet detection, focusing on Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) techniques. Furthermore, two hybrid models are proposed: Isolation Forest (IF) combined with SVM for anomaly detection and classification, and K-Means clustering integrated with Random Forest to enhance detection accuracy. These models are evaluated based on key performance metrics, including accuracy, precision, recall, and F1 score, to identify their effectiveness in detecting IoT botnets. The study demonstrates the potential of ML-based approaches, particularly hybrid models, to strengthen IoT security and overcome the limitations of traditional methods in resource-constrained environments.

关键词： Support vector machines Radio frequency Accuracy Biological system modeling Botnet Forestry internet of Things Decision trees Random forests Anomaly detection

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Analyzing Resource Allocation Methods in Fog computing for Task Scehduling: A Study of Heuristic and Meta-Heuristic Approaches

Analyzing Resource Allocation Methods in Fog Computing for T...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Himanshu Neeraj Mangla Department of Computer Science and Engineering Maharishi Markandeshwar (Deemeed to be) University Mullana Ambala India

With the remarkable improvement of the internet, there has been an important flow in technologies like cloud and fog computing. Moreover, it is important to identify the associated issues related to resource allocation and deployment in these technologies. Despite the rapid development of cloud computing (CC), several limitations persist due to inherent features like unreliable delays, limited mobility, and limited support. Fog computing (FC), on the other hand, includes taking the central CC resources nearby to the network edge. It aims to overcome the limitations faced by CC by providing a more distributed method compared to its centralized behavior. The disparities between compute-intensive uses and resource-constrained devices challenge the complete system’s performance. Task scheduling (TS) is important in identifying logical inconsistencies in fog technology. With the proliferation of the IoT, efficiently allocating resources to clients upon request becomes a highly challenging task in fog technology. Different authors survey several methods, which are analyzed in brief. Lastly, a study is made by comparing the demerits of each technique, and recommendations are defined for improved TS and RA in fog. In this article, some heuristic and metaheuristic methods are studied, and the hyper-heuristic scheduling (HHS) method is defined to explore the best distribution with minimum execution time. The implemented technique has enhancements in energy, execution time, etc.

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