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Role of RPA & AI in Optimizing Network Field Services 7

Role of RPA & AI in Optimizing Network Field Services

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Yendluri, Dileep Kumar Ponnala, Jainath Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. University of the Cumberlands Computer Science United States Jawaharlal Technological University Ece Hyderbad India University of the Central Missouri Computer Information Systems Warrensburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Cumberlands Information Technology Williamsburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350343144

The paper investigates incorporating and implementing RPA and AI technologies within NFS to improve efficiency and boost service quality. Robotic Process Automation enables the streamlining of repetitive processes. It simplifies work processes and releases personnel for more critical projects. On the contrary, AI strengthens NFS providers via data-informed decision-making. Additionally, it facilitates anticipatory maintenance and preemptive network administration. The document explores particular RPA and cognitive computing instances, including AI-powered network issue resolution and AI-driven preventive maintenance for the network hardware. Furthermore, it emphasizes the advantages and constraints of adopting robotic process automation and artificial intelligence within the NFS framework. It considers aspects including data handling, growth potential, and moral implications. Prospects for the future related to RPA and AI within the NFS domain are also analyzed. It foresees self-governing network management, sophisticated predictive analytics, and merging with IoT and edge computing technologies. The article highlights the revolutionary capability of robotic process automation and artificial intelligence in transforming the field service sector, aiming for increased efficiency, reliability, and a customer-centric approach. It emphasizes embracing these technologies to keep up in the dynamic business sector. © 2023 IEEE.

关键词： Decision making

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Design of Cloud-Powered Type of Strategies for Management System Used in Drones

Design of Cloud-Powered Type of Strategies for Management Sy...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： A S Valarmathy Tushar Bhavsar E. Arunkumar Saloni Bansal R. Velmurugan A. Vijayaprabhu Department of Electrical and Electronics Engineering (EEE) Prince Shri Venkateshwara Padmavathy Engineering College TN Department of Mechanical Engineering (me) IES College of Technology Bhopal M.P India Department of Electronics and Communication Engineering (ECE) Karpagam College of Engineering TN Department of Computer Engineering and Applications (CEA) GLA University Mathura Karpagam Academy of Higher Education Eachanari Coimbatore India Department of ECE Siddharth institute of Engineering and Technology Puttur Andhrapradesh India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Drones, sometimes referred to as drones that fly (UAVs), are becoming more and more popular as affordable solutions that may support creative business and civic applications in the Internet connected Things (IoT). Even while there is increasing excitement about their potential, there are still intrinsic difficulties that prevent their real-world use. The most significant of these difficulties is the restricted wireless communication range that drones have with ground stations, which limits their ability to effectively control across long distances. Furthermore, low-cost aerial platforms’ limited computing and energy resources make it difficult for them to run resource-intensive programs aboard. Our solution to these limitations is the Dronemap Organizer (DP), a cloud-based drone management platform that is service-oriented and intended to monitor, operate, and interact with drones over the Internet in a smooth and efficient manner. Numerous applications and proof-of-concepts created using DP’s architecture serve as examples of its adaptability. A thorough performance assessment research was carried out using an actual drone in a live monitoring application in order to gauge the effectiveness of DP. The study’s findings support DP’s promise for practical applications by highlighting how well it works to overcome issues with constrained processing power and communication range. In summary, the Dronemap Planner stands out as a ground-breaking solution in the field of UAV administration, especially when it comes to resolving the issues preventing the broad use of inexpensive drones. DP is a monument to the way drone technology is developing and how it is being incorporated into the wider Web of Things ecosystem by smoothly integrating computing capabilities and facilitating communications over the Internet without being limited by geography. By using this creative approach, DP not only demonstrates that it is possible to overcome current obstacles, but it also creates n

关键词： Wireless communication Cloud computing Transportation Autonomous aerial vehicles Safety Security Internet of Things

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Global Identification Passport: A Unique Cloud based Passport Model

Global Identification Passport: A Unique Cloud based Passpor...

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International Conference on Smart Systems and Inventive Technology (ICSSIT)

作者： A Ravi Raja T Anuradha Sankararao Majji Tulasi Radhika Patnala C Sushama Dhiraj Kapila ECE department Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Department of Electrical and Electronics Engineering KCG College of Technology Chennai India Department of ECE GRIET Hyderabad India Dept of ECE GITAM Universirty Hyderbad India Department of Computer Science and Engineering School of Computing Mohan Babu University Vijayawada India Department of Computer Science and Engineering Lovely Professional University Phagwara India

DDoS attacks pose a serious risk to cloud networks because of the money they might cost if they succeed. For this reason, the Security Research Group has been hard at work creating various detection methods to lessen the blow of any future cyberattacks. Cloud computing, often known as internet computing, is a model of delivering computing as a service, in which shared, elastically scalable and virtually hosted servers are made available to users anywhere there is an internet connection. Numerous companies provide cloud services online to meet the needs of various clientele. Since most cloud users lack their own computing capabilities, they must save their data, documents, and applications in the cloud in order to perform their work efficiently. The focus of this work is on the worldwide identification procedure utilising a digital passport in the cloud. There has been and will continue to be significant growth in cloud-based innovation and advancement in the coming years. Users can benefit greatly from cloud computing by delegating complex activities to remote data centers. Whether or not consumers can access the cloud’s services is determined by the terms of the Service Level Agreement (SLA) in place between the cloud’s suppliers and the users. By detecting when a service level agreement (SLA) has been broken, cloud service providers may boost customer confidence, decrease the likelihood of incurring costly fines, and boost their bottom line. This study makes two significant contributions to the fields of service provider selection for virtual machine (VM) migrations and SLA (service level agreement) violation detection in public cloud computing. The pros and cons of a passport-free international travel system were examined. This study has compared various research methodologies and frameworks for implementing new technologies. The present study is predicated on a Cloud Passport adoption model that was developed for previous work.

关键词： Cloud computing Technological innovation Computational modeling Terrorism Logic gates Virtual machining Safety

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Secure Smart Healthcare Surveillance Framework Using Fuzzy C-Means Clustering with Effective Ant Colony Optimization in Internet of Things

Secure Smart Healthcare Surveillance Framework Using Fuzzy C...

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2022 IEEE International Conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Karuppaiah, Jayakumar Aarthi, C. Girirajan Jananai, M. Parameshachari, D.B. Biomedical Engineering Kalaigar Karunanidhi Institute of Technology Coimbatore India Sengunthar Engineering College Thiruchengode India Sr University Department of ECE Warangal India Kpr Instiute of Engineering and Technology Computer Science and Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9781665456371

The emergence of IoT device networks is not limited to a specific industry, application, or environment;instead, they are assisting in the development of the country in a number of sectors, including industries, cities, agriculture, and defense. But IoT device networks have some restrictions, including those related to bandwidth, energy resources, and other resources, as well as restrictions based on the environment. The fast growth of the Internet of Things (IoT) has brought to light significant privacy protection problems. This has a tremendous impact on the large-scale IoT applications. In this study, a ground-breaking IOT computing platform for secure and knowledgeable healthcare monitoring services is introduced. Within an IoT architecture, fully homomorphism encryption (FHE), which protects data privacy, is handled and kept. For the proposed IoT framework, a distributed method for clustering-based approaches is created with the scalability to gather and independently analyse massive amounts of hetrogeneous data in distributed IoT devices before it is transferred to the cloud. In this study, the Cluster Heads (CHs) were chosen from the available IoT devices using a brand-new Efficient Ant Colony Optimization (EACO) technique. For healthcare surveillance, fuzzy C-Means clustering (FCMC) is employed. FCM allows us to recognise regular (typical) bio signal patterns. © 2022 IEEE.

关键词： Internet of things

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Multi-Objective Modified Glowworm Swarm Optimization Algorithm for Efficient Spectrum Sensing in the Cognitive Radio Network

Multi-Objective Modified Glowworm Swarm Optimization Algorit...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Babiyola, A. Kumar, Gotte Ranjith Shridhar, V. Hussein, Abbas Hameed Abdul Alzubaidi, Laith H. Meenakshi Sundararajan Engineering College Dept of Ece Chennai India SR University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Ahl Al Bayt University College of Pharmacy Karbala Iraq The Islamic University Najaf Iraq

ISBN: (纸本)9798350315455

The combination of machine learning and Cognitive Radio (CR) technology has generated a lot of interest in research, especially in the field of spectrum management. Even with the possible advantages, high spectral efficiency, low latency, high network efficiency, and enhanced energy economy are only a few of the performance qualities that need to be addressed in order to prepare technology for spectrum management. But accomplishing these goals might frequently provide opposite difficulties. In the context of spectrum management, a new Multi-Objective Modified Glowworm Swarm Optimization (MOMGSO) approach is developed to handle multi-objective optimization issues. In order to achieve global optima, the Glowworm Swarm Optimization (GSO) modification seeks to achieve a balance between the exploration and exploitation processes. This tweak modifies the mutation coefficient, leader selection, and discrimination weight. An analysis is conducted between the suggested technique and current methods in terms of energy efficiency, interference, and throughput. The results of the simulation show that the MOMGWO algorithm outperforms existing techniques in terms of the Pareto front quality. © 2023 IEEE.

关键词： Cognitive Radio Multi-Objective Modified Glowworm Swarm Optimization Multi-Objective Optimization Spectrum Management Spectrum Sensing

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Efficient Model Training in Decentralized Systems with Federated Learning 2

Efficient Model Training in Decentralized Systems with Feder...

引用

2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Rao, M.S. Srinivasa Saraswathy, S. Neela, V. Arun, M.R. Gupta, Sandeep Taqui, Syed Noeman Vignana Jyothi Institute of Engineering & Technology Vnr Department of Mechanical Engineering Telangana Hyderabad India Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India School of Computing and Information Technology Reva University Karnataka Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Department of Electrical Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

In the fast-changing field of machine learning, data privacy and model training efficiency are crucial. Federated Learning (FL) is a breakthrough method for training models on several decentralized devices or servers without centralizing raw data. This study examines Federated Learning's challenges in optimizing model training in dispersed systems. The first stage is to examine the Federated Learning (FL) framework and its merits, particularly in protecting data privacy and reducing server dependence. The next section will discuss communication overhead, stragglers, and non-IID (independent and identically distributed) data issues in a decentralized training model. We developed novel optimization methodologies and adaptive algorithms to improve Federated Learning (FL) efficiency. This includes updating asynchronous models, adaptability-based communication frequency, and non-IID data management. We also present a resilient aggregation method that mitigates Byzantine failures in participating nodes. We demonstrate that our methods improve convergence rate, communication costs, and model performance trained with Federated Learning (FL) by conducting a series of comprehensive experiments and analyzing real-world case studies. We show that these enhancements do not compromise data privacy, preserving Federated Learning (FL) principles. In conclusion, this study advances Federated Learning in decentralized systems. In an age of data sensitivity, this approach could help implement efficient, scalable, and privacy-preserving machine-learning methods. © 2023 IEEE.

关键词： Byzantine Failures Communication Efficiency Data Privacy Decentralized Systems Federated Learning Model Training Non-IID Data

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Improving the Performance of Network-on-Chip by Utilizing Fault Zone Tiling to Reuse Trace Buffers

Improving the Performance of Network-on-Chip by Utilizing Fa...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： R. Valli Suseela Neethu Anna Sabu S.Kevin Andrews S. Revathy L. Revathy K.Lakshmi Narayanan Department of ECE Francis Xavier Engineering College Tirunelveli Tamil Nadu India Department of Electronics and Telecommunication K J Somaiya College of Engineering Mumbai Department of Computer Applications Dr. MGR Educational Educational and Research institute Chennai-95 ECE Department Francis Xavier Engineering College Tirunelveli Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Network-on-Chip (NoC) is employed in multi-core systems to enhance the data transfer capacity and adaptability. The Network-on-Chips (NoCs) experience dependability issues due to software faults. Tiling is a procedure used to enhance the accuracy of data in NoCs (Networks-on-Chip). The fault-masking technique prevents the need for rechecking by the block. At this location, the presence of X tracer and the process of tiling are ongoing. The proposed work is implemented by simulation using Xilinx 12.1.

关键词： Green communications Termination of employment Network-on-chip Debugging Data transfer Software Silicon Energy efficiency Table lookup Testing

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Deep Learning Approaches for Intrusion Detection with Emerging Cybersecurity Challenges

Deep Learning Approaches for Intrusion Detection with Emergi...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Hemalatha, S. Mahalakshmi, M. Vignesh, V. Geethalakshmi, M. Balasubramanian, D. Jose, Anand A. St. Joseph's Institute of Technology Department of Electrical and Electronics Engineering Chennai600119 India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai603203 India Karpagam College of Engineering Department of Computer Science and Engineering Coimbatore641032 India Kcg College of Technology Department of Mathematics Chennai600097 India Jaya Engineering College Department of Mca Chennai602024 India Kcg College of Technology Department of Ece Tamil Nadu Chennai600097 India

ISBN: (纸本)9798350313987

Intrusion Detection Systems (IDS) play a pivotal role in preventing computer networks from an ever-evolving landscape of cybersecurity threats. As cyberattacks become increasingly sophisticated and diversified, the need for robust and adaptable intrusion detection models has never been more critical. This study explores the application of Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) in the context of intrusion detection, particularly in addressing emerging cybersecurity challenges. The performance of deep learning (DL)-based IDSs is highly dependent on the dataset used and no benchmark models for intrusion detection have been found. In this work, a recent dataset CIC-DDoS-2019 is used for the detection of DDOS (distributed deny of services). This work underscores the importance of adopting DL techniques, including DNNs, CNNs, and RNNs, for intrusion detection in the face of emerging cybersecurity challenges. It highlights the potential of these neural network architectures to enhance network security, adapt to evolving threats, and mitigate the risks posed by modern cyber adversaries. Moreover, it encourages further research and development in this field to stay ahead of the ever-changing cybersecurity landscape. © 2023 IEEE.

关键词： DDoS attacks Deep learning Distributed denial-of-service (DDoS) Intrusion detection system Machine Learning

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Enhance Path Selection with Multi-Hop Data Dissemination to Achieve High Quality-of-Service in Vehicular Communication

Enhance Path Selection with Multi-Hop Data Dissemination to ...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Y Shasikala V. Chinnammal Maria Rubiston. M Imad Shalout Prashant D. Kamble M. Dinesh Department of Computer Applications Aditya University Surampalem Andhra Pradesh Department of ECE Rajalakshmi Institute of Technology Chennai Department of ECE S.A. Engineering College Chennai Department of Civil Engineering faculty of engineering Zarqa university Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mech Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

VANETs through the proposed EPMHQ approach. It discusses the challenges of VANETs, and related works, and introduces the EPMHQ approach integrating DSRC, IEEE 802.11p, AODV routing, and Whale Optimization Algorithm (WOA) based path selection. The methodology and objectives of the approach are outlined, emphasizing its aim to enhance network performance and efficiency. Results from experiments utilizing NS-2 simulation are shown, indicating better performance in packet delivery ratio, network throughput, average delay, energy efficiency, and routing overhead when compared to current techniques. The abstract concludes by highlighting the potential of the EPMHQ approach in improving VANETs for intelligent transportation systems.

关键词： Data dissemination Spread spectrum communication Quality of service Routing Throughput Whale optimization algorithms Energy efficiency Delays Intelligent transportation systems Global Positioning System

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Cluster, Cloud, Grid Computing via Network Communication Using Control Communication and Monitoring of Smart Grid 2

Cluster, Cloud, Grid Computing via Network Communication Usi...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Kumar, Santosh Kumar, N M G Geetha, B.T. Sangeetha, M. Chakravarthi, M. Kalyan Tripathi, Vikas Chandigarh University Department of CSE India Sree Vidyanikethan Engineering College Department of Electrical and Electronics Engineering Tirupathi India Saveetha School of Engineering Simats Department of ECE Tamil Nadu India School of Computing and Information Technology Reva University Bengaluru India School of Electronics Engineering VIT-AP University Amaravathi India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

Traditional power consumption management systems are not showing enough reliability and thus, smart grid technology has been introduced to reduce the excess power wastages. In the context of smart grid systems, network communication is another term that is used for developing the network between the users and the load profiles. Cloud computing and clustering are also executed for efficient power management. Based on the facts, this research is going to identify wireless network communication systems to monitor and control smart grid power consumption. Primary survey-based research has been carried out with 62 individuals who worked in the smart grid system, tracked, monitored and controlled the power consumptions using WSN technology. The survey was conducted online where the respondents provided their opinions via a google survey form. The responses were collected and analyzed on Microsoft Excel. Results show that hybrid commuting of cloud and edge computing technology is more advantageous than individual computing. Respondents agreed that deep learning techniques will be more beneficial to analyze load profiles than machine learning techniques. Lastly, the study has explained the advantages and challenges of using smart grid network communication systems. Apart from the findings from primary research, secondary journal articles were also observed to emphasise the research findings. © 2022 IEEE.

关键词： Wireless sensor networks

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