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Performance Evaluation of RPL Protocol Under Decreased and Increased Rank Attacks: A Focus on Smart Home Use-Case

SN computer science 2023年第4期4卷 329页

作者： Bang, Ankur Rao, Udai Pratap Knowledge Associate Centre for Development of Advanced Computing (C-DAC) Maharashtra Mumbai 400049 India Computer Science and Engineering Department National Institute of Technology Bihar Patna 800005 India

The rank value of a node in Routing Protocol for Low Power and Lossy Networks (RPL) plays a significant role in preventing loops and route optimization. Advertising a fake rank value creates a false topology and can degrade network performance to a great extent. This manuscript aims to study the effects of increased and decreased variants of the rank attack on the RPL performance. We consider a smart home network topology to evaluate the RPL performance, and the simulation experiments are performed using the Cooja simulator. Through this study, we portray a rational analysis and discuss how the network performance can be degraded by each variant of the rank attack and the position of the attacker node in the network topology. The simulation results obtained in our work demonstrate how each variant of the rank attack can disrupt the default RPL working. We notice significant effects of both the studied rank attacks on the crucial parameters like packet overhead, average inter-packet time, packet delivery ratio and average power consumption. Researchers working in this field can take advantage of our research findings and extend their study to develop new defence mechanisms against these attacks. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： IoT Performance evaluation Rank attack RPL Smart home

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Augmented Reality based Smart Surgical Glass for Medical Surgery 14

Augmented Reality based Smart Surgical Glass for Medical Sur...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sudha, S. Ponmurugan, P. Kamatchi Kannan, V. Russia, S. Rohith Bhat, C. Kavin Kumar, K. Velalar College of Engineering and Technology Department of BiomedicalEngineering Tamilnadu Erode India Chennai600078 India Knowledge Institute of Technology Department of Electrical and Electronics Engineering Salem India Velalar College of Engineering and Technology Department of Computer Science and Engineering Erode Thindal India Simats Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India

ISBN: (纸本)9798350335095

Surgeons are always looking for technologies to make their working environment better. They frequently take the lead in implementing innovations that help their industry deliver better surgical and patient experiences. The ongoing advancement of the surgical environment in the digital era has resulted in several breakthroughs being observed as possible disruptive technologies in the surgical work place As augmented reality (AR) grows in popularity, availability, and cost, it is critical to evaluate how technology might be leveraged to better utilize medical data in healthcare. Applications in surgical anatomy, surgical processes, and surgical recovery are being researched all the time. The use of computer-generated sensory input to improve performance on a specific task is referred to as "augmented reality". A technique that combines the view of the real world with important information for doctors displayed on semi-transparent glasses attached to an augmented reality headset. For this investigation, sensors are being utilized to monitor patients in a hospital and gather data in real time. The data is then processed by a computer and delivered immediately to the augmented reality glasses used by doctors. The alarm on these spectacles notifies doctors if something is wrong. Taking the patient's current state of health into account, the physician is able to make wise decisions and carry out the necessary procedures. © 2023 IEEE.

关键词： Augmented reality

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AI-Driven Privacy Analysis of IoT Services in Edge Computing 2

AI-Driven Privacy Analysis of IoT Services in Edge Computing

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Abdulhasan, Maki Mahdi Hussain, Ahmed Ali, Mariam Fadel Al-Dabagh, Mustafa Zuhaer Nayef Rustum Al-Assal, Jawad Radhi Al-Nisour University College Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Uruk University Department of Radiology Techniques Baghdad Iraq Knowledge University College of Science Department of Computer Science Erbil Iraq Al Kitab University Department of Petroleum Engineering Kirkuk Iraq

ISBN: (纸本)9798331534950

Inevitably, IoT equipment generates more data, but fog computing's concept of transferring all data to the cloud for computation has failed to meet real-time needs. Cutting-edge edge computing delivers all data from the cloud to edge devices, enhancing service quality for IoT applications that need delay. However, Edge devices are more vulnerable to attacks than endpoint technologies due to low processing power and storage. Edge computing IoT services face growing dangers, making security and privacy solutions crucial. This article proposes AI-SPM to optimally solve IoT security and privacy issues in edge computing devices. AI's better learning skills help the proposed system detect harmful intrusions more precisely. The proposed solution was compared to other IoT security and privacy management methods to prove its efficacy. The proposed framework paradigm updates security and gives edge computing devices the most robust defences against IoT service cyberattacks. The AI-SPM achieved 99.54% consumer fulfilment, 99.88% security, 99.98% system reliability, and 5.34% latency compared to other techniques. © 2024 IEEE.

关键词： Differential privacy

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Deep Hierarchical Learning for Estimating High Penetration of Renewable Energy Sources in Smart Grid Environments 2

Deep Hierarchical Learning for Estimating High Penetration o...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Abed Jawad, Mohammed Al-Azzawi, Waleed Ali, Mariam Fadel Hamid, Noor Kaylan Mohammed, Marwan Aziz Al-Nisour University College Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Uruk University Department of Radiology Techniques Baghdad Iraq Al Kitab University Department of Computer Education Kirkuk Iraq Knowledge University Erbil Iraq College of Science Department of Computer Science Erbil Iraq

ISBN: (纸本)9798331534950

A prototype model for deep hierarchical learning and information for highly penetrating renewable energy resources (HPRES) is presented in this study. Because the stochastic nature of renewable energy increases the potential for hazardous situations, the growing deployment of renewable generators has posed a significant challenge to power system stability. As a result, a learning algorithm is used based on labelled databases and other complex data to learn how to reliably predict HPRES in real time without knowing the incoming renewable power data. The suggested method uses various historical data, such as wind direction, sunshine duration, and power distribution operations data, to produce modest, real-time results on the predictability of the data. MA does a rudimentary spectral analysis before starting to build the training dataset. The strategy's effectiveness is evaluated using a typical 16-machine, 64-bus testing setup. © 2024 IEEE.

关键词： Clean energy

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A Novel system for Tracking and Screening COVID Patients

A Novel system for Tracking and Screening COVID Patients

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2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022

作者： Sasikala Devi, S. Arulkumar, V. Kumar, Saroj Priyanka, D. PPG College of Arts and Science College Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu India Chandigarh University Computer Science and Engineering Gharuan India Institute of IT Saveetha School of Engineering Department of Knowledge Engineering Chennai India

ISBN: (纸本)9781665495158

The wandering of older persons with Covid is one of the several behavioral difficulties that they experience, and it is the source of the most anxiety for their caregivers. Using a novel mobile phone-based safety assistance system, we have been able to relay information about a wandering individual's whereabouts to others who are near that person. The wearable sensor is made up of a GSM module, an Arduino UNO, and a GPS module. It is possible to determine the position of the wandering individual after they have beyond a particular meter. When the old person leaves the area, the GSM transmits the position of the wandering elderly person, which is determined with the aid of GPS. The server computer sends an SMS to the caregiver to notify him or her of the situation. The caregiver may keep track of the wandering person's whereabouts on a map by connecting to the Internet. © 2022 IEEE.

关键词： Global system for mobile communications

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Altered Cloud Service Composition Techniques for Enhanced Integration

Altered Cloud Service Composition Techniques for Enhanced In...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Arya, Nancy Gupta, Priyanka Rajalakshmi, B. Nijhawan, Ginni Praveen Alrikabi, Hareth A. Shree Guru Gobind Singh Tricentenary University Department of Computer Science and Engineering Gurugram India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Science Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology Collage of Dentist Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Create dynamic, dependable, and cost-effective cloud service combinations using the Dynamic Cloud Service Composition (DCSC) approach. Service Ranking (SR), Adaptive Composition (AC), and Cost Optimization algorithm are used in this strategy. Their combined efforts classify services by QoS, generate flexible compositions, and minimize prices. The most important factors for ranking service quality can be found using weighted numbers. It is possible to achieve dynamic flexibility by keeping an eye on service traits and using Adaptive Composition to start reconfigurations. Cutting costs without lowering the standard of service is what cost optimization is all about. Economically beneficial compositions result. Performance Score (PS), Adaptation Rate (AR), Cost Efficiency (CE), Resilience Index (RI), Resource Utilization (RU), and (ACost) are evaluated. © 2024 IEEE.

关键词： Adaptability Cloud Service Composition Cost Optimization Dynamic Composition QoS Parameters Quality of Service Resilience Resource Utilization Service Ranking

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Ensemble methods-based comparative study of Landsat 8 operational land imager (OLI) and sentinel 2 multi-spectral images (MSI) for smart farming crop classification

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Multimedia Tools and Applications 2024年 1-44页

作者： Gupta, Priyanka Gupta, Prateek Singh, Suraj Kumar Thakur, Bhavna Kumar, Manoj Suresh Gyan Vihar University Jaipur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Centre for Sustainable Development Suresh Gyan Vihar University Jaipur India School of Agriculture & amp Allied Science DBS Global University Dehradun India School of Computer Science FEIS University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates MEU Research Unit Middle East University Amman Jordan

Agriculture has a significant worldwide impact in terms of generating employment opportunities and stimulating economic growth. In India, agriculture plays a crucial role in development of food sources, boost up individual income of farmer and employment of the whole country. In the agriculture domain, meticulous machine learning-enabled crop classification is essential for production. Nowadays, for meticulous crop classification satellite images are used together with Geographical Information System (GIS) and remote sensing. Proposed ensemble methods are commonly used to estimate and examine the accomplishment of advanced machine learning concepts in the remote sensing community. In this paper, authors describe a framework with the combination of Support vector machine (SVM), Decision tree (DT), K-nearest neighbor (KNN), and Random forest (RF) and proposed ensemble method named as "SVM-DT-KNN-RF" that investigates the potential of crop mapping used to compare different architectures of machine learning as logistic regression (LR), RF, DT, KNN, SVM, bagging as well as boosting. The classifiers are tested on two remotely sensed datasets, one is Landsat 8 OLI (L8) and another is Sentinel 2 MSI (S2). The three primary findings based on our experiments are first, voting model proved to be the most accurate classifier. Second, bagging and boosting offer good accuracy in classification results. Finally, the results with two satellites i.e., S2 as well as L8 are compared. In the S2 dataset, the proposed ensemble method with a combination of two models i.e., SVM-DT, SVM-KNN and SVM-RF gave 89.1%, 89.8% and 89.7% results respectively, and the combination of four models i.e., SVM-DT-KNN-RF (for voting) is the better performer with 91.10% accuracy. In the L8 dataset, the proposed ensemble method which combines all four techniques SVM-DT-KNN-RF gave better results with a score of 90% while the worst performance was achieved by the decision tree with 79.58% accuracy. For KNN, th

关键词： Landsat

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RPR-Net: A Point Cloud-Based Rotation-Aware Large Scale Place Recognition Network 17th

RPR-Net: A Point Cloud-Based Rotation-Aware Large Scale Plac...

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17th European Conference on computer Vision, ECCV 2022

作者： Fan, Zhaoxin Song, Zhenbo Zhang, Wenping Liu, Hongyan He, Jun Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China Department of Management Science and Engineering Tsinghua University Beijing100084 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783031250552

Point cloud-based large scale place recognition is an important but challenging task for many applications such as Simultaneous Localization and Mapping (SLAM). Taking the task as a point cloud retrieval problem, previous methods have made delightful achievements. However, how to deal with catastrophic collapse caused by rotation problems is still under-explored. In this paper, to tackle the issue, we propose a novel Point Cloud-based Rotation-aware Large Scale Place Recognition Network (RPR-Net). In particular, to solve the problem, we propose to learn rotation-invariant features in three steps. First, we design three kinds of novel Rotation-Invariant Features (RIFs), which are low-level features that can hold the rotation-invariant property. Second, using these RIFs, we design an attentive module to learn rotation-invariant kernels. Third, we apply these kernels to previous point cloud features to generate new features, which is the well-known SO(3) mapping process. By doing so, high-level scene-specific rotation-invariant features can be learned. We call the above process an Attentive Rotation-Invariant Convolution (ARIConv). To achieve the place recognition goal, we build RPR-Net, which takes ARIConv as a basic unit to construct a dense network architecture. Then, powerful global descriptors used for retrieval-based place recognition can be sufficiently extracted from RPR-Net. Experimental results on prevalent datasets show that our method achieves comparable results to existing state-of-the-art place recognition models and significantly outperforms other rotation-invariant baseline models when solving rotation problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network architecture

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Revolutionizing Internet of Things with Blockchain for Enhanced Security and Data Management 7

Revolutionizing Internet of Things with Blockchain for Enhan...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Anitha, G. Gopal, K. Jain, Alok Emad, Rafal Thandra, Nagarjuna Tewari, Preeti Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Mechanical Engineeieng Bangalore India Lovely Professional University Phagwara India Hilla University College Babylon Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201310 India

ISBN: (纸本)9798350350067

This paper introduces a paradigm that might transform the Internet of Things. Blockchain technology makes this system workable. Five approaches comprise the framework. Each is crucial for a powerful, automated, and scalable Internet of Things ecosystem. The framework has five techniques. Making a permanent record is the first step. This record is unchangeable. The second program handles steps. The third way secures messages. The fourth program prioritizes customer privacy. The fifth technique addresses expansion and interoperability. Comparison testing and graphic presentations reveal that the technique outperforms all key qualities. The reason is: Since reviews are detailed, the technology is supposed to transform data management and security in the fast-growing Internet of Things. This modification will likely be major. © 2024 IEEE.

关键词： Smart contract

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Predicting Cyber Attacks on Integrated Energy and Gasoline Resources 2

Predicting Cyber Attacks on Integrated Energy and Gasoline R...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Jasim, Jasim Ahmede Majed, Aaied Eqab Muraay Mahmoud, Mina Ismail Al-Azzawi, Waleed Al-Dabagh, Mustafa Zuhaer Nayef Al-Nisour University College Baghdad Iraq Al Kitab University Medical Engineering Kirkuk Iraq University: Uruk University Department of Radiology Techniques Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Knowledge University College of Science Department of Computer Science Erbil Iraq

ISBN: (纸本)9798331534950

Connecting gas and electricity networks has recently been proposed as a viable solution to manage the unpredictability of renewable energy sources and increase the versatility of power operations. When critical operational communication and control inputs from both systems must be shared, the likelihood of cybercrime increases. This study introduces a novel approach, the Energy and Gasoline Integration for Cyber-Attacks (EGICA) model, which addresses cybersecurity in interconnected gas and electricity networks - a solution to manage renewable energy unpredictability and enhance operational flexibility. The model illustrates how undetected cyberattacks can compromise network operations by using power-to-gas (PtG) and gas-to-generation (GtG) methods. Two innovative detection techniques are proposed for identifying hazardous false data in PtG/GtG schedulers. The first employs fully convolutional and wavelet transformations to detect attacks in facilities planning data. In contrast, the second utilizes a hybrid neuron with unsupervised learning to detect attacks on scheduler outputs without labelled training data. These methods support a robust cyber-attack scheduling framework within integrated gas and electricity networks. Simulation tests on a bus energy system connected to a utility grid validate the model's effectiveness using historical data for evaluation. Results show that EGICA achieves high accuracy (86.3%), precision (89.1%), recall (87.2%), F-measure (90.2%), and efficiency (78.7%) compared to other methods, confirming its reliability in detecting cyber threats in complex energy networks. © 2024 IEEE.

关键词： Cyber attacks

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