An Efficient Route Failure Detection Mechanism with Energy Efficient Routing (EER) Protocol in MANET

Jayalakshmi, D. S.; Hemanand, D.; Kumar, G. Muthu; Rani, M. Madhu

doi:10.5815/ijcnis.2021.02.02

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…To achieve smart, sustainable energy management, Hemanand et al [19] suggested applying the existing Glow worm Swarm Optimization technique across IoT sensors to detect the devices in need of energy and distribute appropriate energy on a need basis. According to Jayalakshmi et al [20], the routing protocol should be one of the factors examined when gauging a network's efficacy. It was proposed by Gopalakrishnan et al that the security of the system may be enhanced by deploying highly secured cryptographic algorithms on each node in the network.…”

Section: Related Workmentioning

confidence: 99%

A Novel CNN Model with Dimensionality Reduction for WSN Intrusion Detection

Alhasan,

Hamza

2023

RIA

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Wireless sensor networks (WSNs) play a critical role in cyber-physical systems, enabling communication between autonomous sensors. When integrated with the Internet of Things (IoT), WSNs unfortunately become vulnerable to various attacks, such as blackhole, grayhole, flooding, and scheduling, thereby posing significant security threats. Existing methods for intrusion detection in WSNs often suffer from low detection rates, significant computational overhead, and false alarms, primarily due to resource constraints and data correlations. This study introduces IDS-CNN, a novel intrusion detection method leveraging Convolutional Neural Networks (CNNs). The proposed IDS-CNN model, designed to optimize efficiency and reduce processing time, comprises nine convolutional neural network layers and six Max-Pooling1D layers. To alleviate computational demands, dimensionality reduction techniques, specifically Principal Component Analysis and Singular Value Decomposition, are applied to raw traffic data. The IDS-CNN model is then employed to classify and categorize network threats. Experimental evaluations suggest that the IDS-CNN approach yields a high accuracy rate of 99% compared to existing methods, based on tests performed on two datasets, WSN-DS and UNSW-NB15. Notably, with the UNSW-NB15 dataset, accuracy rates were further improved to 99.99% and 100%. By leveraging deep learning techniques to enhance intrusion detection in WSNs, this study presents a significant contribution to the field. The IDS-CNN model advances our understanding of WSN security by exceeding the accuracy rates of prior models. As it addresses the limitations of existing methods, the implications of this research are substantial, offering a more reliable and efficient solution for WSN intrusion detection. The findings underscore the potential of IDS-CNN in safeguarding WSNs and IoT systems from sophisticated and evolving cyber threats.

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Section: Related Workmentioning

confidence: 99%

A Novel CNN Model with Dimensionality Reduction for WSN Intrusion Detection

Alhasan,

Hamza

2023

RIA

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“…Results from simulation and analysis demonstrate the viability of the suggested fixes. The Energy Productive Directing (EER) convention, which depends on compelling course disappointment identification, is proposed in [21]. This research provided an innovative routing method for MANET that minimizes failed communication.…”

Section: Literature Surveymentioning

confidence: 99%

Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET

R.,

Mohan,

Mahesh

2023

IJRITCC

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Portable impromptu organization is a self-putting together, major construction-less, independent remote versatile hub that exists without even a trace of a determined base station or government association. MANET requires no extraordinary foundation as the organization is unique. Multicasting is an urgent issue in correspondence organizations. Multicast is one of the effective methods in MANET. In multicasting, information parcels from one hub are communicated to a bunch of recipient hubs all at once, at a similar time. In this research work, Failure Node Detection and Efficient Node Localization in a MANET situation are proposed. Localization in MANET is a main area that attracts significant research interest. Localization is a method to determine the nodes’ location in the communication network. A novel routing algorithm, which is used for Predicting and Recovering Link Failure Localization using a Genetic Algorithm with Competitive Swarm Optimization (PRLFL-GACSO) Algorithm is proposed in this study to calculate and recover link failure in MANET. The process of link failure detection is accomplished using mathematical modelling of the genetic algorithm and the routing is attained using the Competitive Swarm optimization technique. The result proposed MANET method makes use of the CSO algorithm, which facilitates a well-organized packet transfer from the source node to the destination node and enhances DSR routing performance. Based on node movement, link value, and endwise delay, the optimal route is found. The main benefit of the PRLFL-GACSO Algorithm is it achieves multiple optimal solutions over global information. Further, premature convergence is avoided using Competitive Swarm Optimization (CSO). The suggested work is measured based on the Ns simulator. The presentation metrix are PDR, endwise delay, power consumption, hit ratio, etc. The presentation of the proposed method is almost 4% and 5% greater than the present TEA-MDRP, RSTA-AOMDV, and RMQS-ua methods. After, the suggested method attains greater performance for detecting and recovering link failure. In future work, the hybrid multiway routing protocols are presented to provide link failure and route breakages and liability tolerance at the time of node failure, and it also increases the worth of service aspects, respectively.

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“…Capability recognizes this in this hyper-competitive era, truly a competitive edge to instantly understand and react to hidden trends. Reports project violations for trends of interest in analyzing highway project bid data [8]. Data analysis revealed that bids with higher rates incur significant costs.…”

Section: Big Data Analyticsmentioning

confidence: 99%

Exploring the recent trends in Big Data Analysis

Suresh¹,

Ramachandran²,

Sivaji³

2022

Data Analytics and Artificial Intelligence

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Big Data is a collection of technologies developed to store, analyze and manage this data. It is a macro tool. Today it is used in fields as diverse as medicine, agriculture, gambling and environmental protection. Machine learning, forecasting Companies use big data to streamline their marketing campaigns and techniques. Modeling and other advanced analytics applications enable big data organizations to generate valuable insights. Companies use it in machine learning. Programs cannot balance large data in any particular database. When datasets are large in size, velocity, and volume, the following three distinct dimensions constitute "big data." Examples of big data analytics include stock markets, social media platforms, and jet engines. But it is growing exponentially with time. Traditional data management tools cannot store or process it efficiently. It is a large scale technology developed by Story, Analysis and Manoj, Macro-Tool. Find patterns of exploding confusion in information about smart design, however, implying that various big data structures are structured, whether this includes the amount of information, the speed with which it is generated and collected, or the variety or scope of related data points. In the past, data was collected only from spreadsheets and databases of large, disparate information that grew at an ever-increasing rate. Very large, complex data refers to large amounts of data for sets that are impossible to analyze with traditional data processing applications. Is software configuration used to handle the problem? Apache is an advanced application for storing and processing large, complex data sets and analyzing big data. Analytical techniques against very large, heterogeneous databases containing varying amounts of data, big data analytics help businesses gain insights from today's massive data sources. Defined as software tools for processing and extraction. People, companies and more and more machines are now developing technologies to analyze more complex and large databases that cannot be handled by traditional management tools. It is designed to discover patterns of chaos that explode in information in order to design smart solutions.

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An Efficient Route Failure Detection Mechanism with Energy Efficient Routing (EER) Protocol in MANET

Cited by 13 publications

References 13 publications

A Novel CNN Model with Dimensionality Reduction for WSN Intrusion Detection

A Novel CNN Model with Dimensionality Reduction for WSN Intrusion Detection

Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET

Exploring the recent trends in Big Data Analysis

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