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Energy efficient random forest classifier-based secure routing for opportunistic internet of things

International Journal of Ad Hoc and Ubiquitous Computing 2024年第2期46卷 80-89页

作者： Dhurandher, Anjay K. Kandhoul, Nisha Woungang, Isaac Chao, Han-Chieh Department of Information Technology Netaji Subhas University of Technology New Delhi India National Institute of Electronics and Information Technology New Delhi India Department of Computer Science Toronto Metropolitan University Toronto Canada Department of Artificial Intelligence Tamkang University Tamsui New Taipei Taiwan Department of Electrical Engineering National Dong Hwa University Hualien Taiwan Institute of Computer Science and Innovation UCSI University Kuala Lumpur Malaysia

Opportunistic internet of things (OppIoT) is a class of opportunistic networks, where the data are transmitted in a broadcast manner and shared among the nodes (i.e., IoT devices and human communities' devices) through opportunistic contact. In such networks, taking into consideration the energy level of each node while performing the routing of data is of utmost importance. This paper proposes an energy-efficient secure routing protocol that uses a random forest classifier (called ESRFCSec) for device behaviour predictions and for protecting the OppIoT network against packet collusion attacks. Through simulations, ESRFCSec is shown to achieve a prediction accuracy of 97.97%. It is also shown to be superior to three benchmark routing schemes in terms of node's residual energy, delivery probability, average latency, number of dead nodes, and number of dropped packets. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Internet of things

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Smart question answering system using vectorization approach and statistical scoring method

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Materials Today: Proceedings 2023年 80卷 3719-3725页

作者： Manjunath, T.N. Yogish, Deepa Mahalakshmi, S. Yogish, H.K. Information Science & Engineering B M S Institute of Technology &Management Karnataka Bangalore560064 India K S School of Engineering & Management Computer Science & Engineering Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of Master of Computer Applications M S Ramaiah Institute of Technology Karnataka Bangalore560054 India

In this digital era, internet users are increasing day by day to extract information related to various fields such as health, economic, educational, politics and daily needs from Information Retrieval Systems (IRS) such as Google, Ask, Yahoo etc. Information Retrieval (IR) is a conventional method which depends on metadata searching method that is searching of adocuments for information within documents and information about documents. Lot of research in the IR area improves accessing the quality of information. Most of the search engines such as Google, Yahoo, Ask Jeeves depends on keyword based query processing mechanism internally which can't handle natural language questions which results in returning long list of documents for a query. The task of users is to manually explore each document to retrieve correct answer which is a time consuming process. To overcome the problems of conventional search engines, smart question answering system comes to play. The objective of Smart Automated Answering system is to impart accurate and valid answers to user question in natural language rather than set of full documents. The Smart Question answering system which accepts user question in Natural language in specific domain and provide short and precise answer using Natural language processing technique, used for pre-processing, normalized term weighting-TF-IDF with cosine similarity approach for document retrieval and enhanced BM25 ranking function to extract answer. The main focus is on decreasing the response time, designing ranking function and giving the pertinent responses to the users’ intent. The system achieved an overall Precision of 93.2%, Recall of 84.3% and F-measure of 88.5% with 80% of Accuracy. © 2021

关键词： Information retrieval

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Solar Irradiance Forecasting Using MATLAB Platform

Solar Irradiance Forecasting Using MATLAB Platform

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International Conference on Green Energy and Sustainable technology, ICGEST 2023

作者： Roga, Sukanta Bardhan, Shawli Das, Soumyabrata Uppara, Abhijith Manikanta, Suragani Ashok Mechanical Engineering Department Visvesvaraya National Institute of Technology Nagpur Nagpur India Department of Computer Science and Engineering Techno College of Engineering Agartala Agartala India Electrical Engineering Department Visvesvaraya National Institute of Technology Nagpur Nagpur India

ISBN: (纸本)9789819610112

The solar power industry has emerged as a significant player in the growing energy market. In recent years, its grid penetration has increased correspondingly. Short-term solar forecasting accuracy is difficult to come by. It is crucial for solar park scheduling. Solar generation technologies have seen tremendous expansion in the energy market over the last decade or two, owing to increased renewable energy penetration in electrical grid systems. The solar resource is highly changeable, owing to a variety of factors. As a result of climate conditions, different grid and plant-level issues arise. As a result, forecasting methods are essential for various periods. With great accuracy, models for numerical weather prediction (NWP) are used in this article. This method applies to solar forecasts for the day-ahead forecasting and it is assessed by forecasting global outcomes. Irradiance was measured horizontally at five key solar power facility locations in the subcontinent of India. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Solar irradiance

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Implementation of a hybrid adaptive system for SLA violation prediction in cloud computing

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International Journal of Cloud Computing 2022年第4期11卷 389-406页

作者： Pandita, Archana Upadhyay, Prabhat Kumar Joshi, Nisheeth Department of Computer Science and Engineering Birla Institute of Technology Ras Al Khaimah United Arab Emirates Department of Electrical and Electronics Engineering Birla Institute of Technology Mesra India Department of Computer Science Banasthali University Rajasthan India

The cloud has to commit with service level agreements (SLA) which ensures a specific level of performance and sets a penalty if SLA is violated by the provider. These days, managing and applying penalties have become an essential and critical issue for cloud computing. In this research, adaptive neuro-fuzzy inference system (ANFIS) is used to develop proactive fault prediction model which has been designed by utilising the power of machine learning and tested on the datasets to highlight the accurate models for faults prediction in the cloud environment. The suggested algorithm has achieved a percentage accuracy of 99.3% in detecting violations. The performance of the proposed model has been compared with Bayesian regularisation and scaled conjugate gradient methods which reflect the facts as obtained from the results that effectiveness of this scheme in terms of predicting system's violation is more effective. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Quality of service

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ML Based Intrusion Detection Scheme for various types of attacks in a WSN using C4.5 and CART classifiers

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Materials Today: Proceedings 2023年 80卷 3769-3776页

作者： Gite, Pratik Chouhan, Kuldeep Murali Krishna, K. Kumar Nayak, Chinmaya Soni, Mukesh Shrivastava, Amit Department of Computer Science and Engineering IPSA-IES Indore AB Road MP Indore India Department of Computer Science and Engineering I.T.S Engineering College Greater Noida India Department of Electronics and Communication Engineering Vignan's Institute of Information Technology Duvvada Andhra Pradesh Visakhapatnam India Faculty of Emerging Technology Sri Sri University Odisha751001 India Department of Computer Science & Engineering Jagran Lakecity University Bhopal India Department of Computer Science Engineering Swami Vivekanand College of Engineering Khandwa Road Near Tolnaka Vivekanand Knowledge City Indore India

A key component for preserving a WSN's integrity is designing an intrusion detection system (IDS). This article covers several sorts of security threats that may occur in a WSN. It proposes a detection of malicious nodes in a WSN. This system identifies four common assaults (i.e., black hole, wormhole, gray hole, and DDoS attacks) and is based on a Base Station machine learning (ML) algorithm (BS). The suggested ML algorithm continually analyzes data patterns from each node. Based on this technique, BS recognizes the network's harmful behavior and provides notifications to neighbor nodes to prevent attacker. First, the various assaults are examined and their characteristics are derived in terms of network parameters. Furthermore, the gathered pattern trains an ML algorithm. Then, the attacker node is effectively and properly classified within the BS. The NS2 simulator simulated the needed secure WSN. The experimental findings revealed excellent accuracy of the suggested method for attack detection. This precision can enhance network performance in terms of energy usage and packet delivery (PDR). © 2021

关键词： Base stations

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Automated Energy Monitoring System for Smart grids and Microgrids

Automated Energy Monitoring System for Smart grids and Micro...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Revathi, R. Keerthi Ragavendran, S. Dhamodharan, B. Harish, P.S. Thaiyub A, Ahamed Kpr Institute of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350308167

The technical advancements in modernizing the world in every way possible have increased the usage of power and energy. Due to this, an increase in tariff, decrease in power quality, and frequent blackouts have increased every day because of traditional methods used in transmission lines. The proposed methodology addresses this drawback by offering the usage of advanced energy meters in smart grids and microgrids for efficient transmission of power and energy across every household and industry, improving power quality and reducing the chance of blackouts [1] significantly. This allows both the user and the utility to continuously monitor the energy usage, power factor, power quality, and load usage in real-time, to maintain the load balance in the grid. © 2023 IEEE.

关键词： Automated Energy Monitoring System (AEMOS) Facebook Prophet Micro Grid (MG) Smart Grid (SG)

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X-ray Image Analysis for Aided Detection of COVID-19

X-ray Image Analysis for Aided Detection of COVID-19

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Sharma, Avinash Barua, Tarkeshwar Garg, Prachi Saxsena, Rini Goyel, Parul Singh Parmar, Vivek Chandigarh Group of Colleges Department of Computer Science and Engineering Chandigarh Engineering 140307 India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Mmec Department of Computer Science and Engineering Mullana India M. M. Engineering College Maharishi Markandeshwar Mullana Computer Science & Engineering Haryana Ambala1307 India Chitkara University Department of Cse India

ISBN: (纸本)9798331508432

The COVID-19 epiphytotic highlighted the necessity for speedy and accessible identification instruments. Chest X-rays offer a readily available and relatively inexpensive Tomography proficiency. This stuff explores the potential of X-ray image analysis using Profound learning algorithmic program for the aided detection of COVID-19 contagions. While chest X-rays and CT scans are valuable instruments for COVID-19 diagnosis, manual analysis is time-consuming and error-prone. Profound learning, a powerful subset of artificial intelligence[10], offers promising solutions for automated and accurate COVID-19 detection from these Tomography modalities. This stuff reviews key investigation findings on Profound learning-based classification of COVID-19 using chest X-rays and CT scans. We explore the current state-of-the-art advancements, discuss remaining challenges, and outline potential future directions in this crucial field. Containing COVID-19 relies heavily on isolating infected individuals and widespread inoculation. While RT-PCR tests remain the gold standard for diagnosis, delays in processing limit their effectiveness. Additionally, post-recovery positive tests raise concerns about accuracy [7]. Vaccines offer protection, but breakthrough contagions occur. Developing highly effective, long-lasting vaccines and achieving planetary immunization face significant logistical hurdles. Faster and more reliable identification instruments are crucial to complement these ongoing efforts. © 2024 IEEE.

关键词： Vaccines

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Towards Autonomous Shooting Rover via Situation Aware Visual-Perception and Dynamic Action Execution

Towards Autonomous Shooting Rover via Situation Aware Visual...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Chikoti, Shreenaga Shubham Seelam, Yukkta Jain, Aditya Mehta, Nishi Watsa, Sidhartha Ranjan, Ayush Behera, L. Indian Institute of Technology Kanpur Department of Mathematics and Scientific Computing India Indian Institute of Technology Kanpur Department of Material Science and Engineering India Indian Institute of Technology Kanpur Department of Electrical Engineering India Indian Institute of Technology Kanpur Department of Computer Science and Engineering India Indian Institute of Technology Kanpur Department of Mechanical Engineering India

ISBN: (纸本)9798350312072

In this system paper, we present our approach to the DJI Robomaster AI Challenge, 2022 where our team ERA-IITK was ranked 3rd worldwide among 83 teams. We primarily focus on system hardware design and algorithmic components to achieve full autonomy. We first detail the hardware infrastructure, including intricate details on sensors and communication setup. Then we comprehensively describe our algorithmic pipeline and a state machine based on them to realize an autonomous system. Our major novelty is the high-speed and accurate visual perception module for target detection, and high-speed self-localization to maximize the likelihood of a target being shot. Our research endeavor in this paper can serve as a foundation for more complex systems and holds immense potential for various real-world applications. © 2024 IEEE.

关键词： Vision

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Study of Ocean Pattern's and Ocean Bed Using Applications of IOT 2

Study of Ocean Pattern's and Ocean Bed Using Applications of...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Vishwanath, K. Reddy, Rachit Roy, Sritama Vellore Institute of Technology Department of Computer Science Chennai600127 India School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798350328202

The ocean plays an integral role for influencing Earth's climate and sustaining diverse ecosystems as oceans covers almost 70 % of the earth's surface. It is important to monitor and control the ocean biodiversity through the advanced IoT technologies. The internet of Underwater things (IoUT) is an emerging technology which can connect the underwater objects as well as environment. IoUT is directly related with the navigation, disaster prediction, smart monitoring and intelligent system. Understanding the dynamics of the ocean is crucial for various applications, including weather forecasting, maritime operations, and environmental conservation. This research paper presents an in-depth analysis of an Ocean buoy Monitoring System that utilizes temperature, humidity, and geolocation data collected from buoys to predict ocean patterns, tides, and marine life. The system combines real-time sensor measurements with advanced data analysis techniques to provide valuable insights into oceanographic phenomena. © 2024 IEEE.

关键词： Buoys

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E-Certificate Verification Using Block Chain 2

E-Certificate Verification Using Block Chain

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Chitra, N. Thulasi Sreenivasulu, G. Subhashini, P. Mlr Institute of Technology Department of Computer Science and Information Technology India Vardhaman College of Engineering Department of Cse Shamshad India Mlr Institute of Technology Department of Csit Hyderabad India

ISBN: (纸本)9798350370188

This universe shows mood through facial expressions. Every expression conveys emotions. AI allows us differentiate different emotions, even though everyone has a unique face expression and emotional repertoire. Deep learning builds face emotion-recognition AIs. Train a CNN on the dataset for high prediction accuracy. The goal is to recognise seven fundamental face emotions from hundreds. Happy, sad, neutral, anger, surprise, fear, and contempt are the most common expressions, followed by neutral. Deep face algorithms, machine learning, AI, TensorFlow, etc. can identify facial expressions. Our model is 70-80% correct. The easiest and most effective technique may be this. © 2024 IEEE.

关键词： Network security

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