Centralised Domain Based Faulty Node and Link Elimination in Hexagonal Node Based NoC

2017 ◽  
Author(s):  
Shilpa K. Gowda ◽  
K. R. Rekha ◽  
K. R. Nataraj
Keyword(s):  
Faulty Node

scholarly journals Distributed faulty node detection in DTNs in presence of Byzantine attack

2017 ◽  
Author(s):  
Wenjie Li ◽  
Francesca Bassi ◽  
Michel Kieffer ◽  
Alex Calisti ◽  
Gianni Pasolini ◽  
...  
Keyword(s):  
Faulty Node

Efficient Energy Conservation and Faulty Node Detection on Machine Learning-Based Wireless Sensor Networks

2021 ◽  
Vol 13 (2) ◽  
pp. 1-20
Author(s):  
Amarasimha T. ◽  
V. Srinivasa Rao
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Communication ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector ◽  
Faulty Node

Wireless sensor networks are used in machine learning for data communication and classification. Sensor nodes in network suffer from low battery power, so it is necessary to reduce energy consumption. One way of decreasing energy utilization is reducing the information transmitted by an advanced machine learning process called support vector machine. Further, nodes in WSN malfunction upon the occurrence of malicious activities. To overcome these issues, energy conserving and faulty node detection WSN is proposed. SVM optimizes data to be transmitted via one-hop transmission. It sends only the extreme points of data instead of transmitting whole information. This will reduce transmitting energy and accumulate excess energy for future purpose. Moreover, malfunction nodes are identified to overcome difficulties on data processing. Since each node transmits data to nearby nodes, the misbehaving nodes are detected based on transmission speed. The experimental results show that proposed algorithm provides better results in terms of reduced energy consumption and faulty node detection.

Peer-Comparison Based Fault Diagnosis for Hadoop Systems

2014 ◽  
Vol 621 ◽  
pp. 235-240
Author(s):  
Yue Gao Tang ◽  
Li Miao ◽  
Feng Ping Chen
Keyword(s):  
Big Data ◽  
Fault Diagnosis ◽  
Open Source ◽  
Performance Metrics ◽  
Massive Datasets ◽  
Faulty Node ◽  
Comparison Approach ◽  
Hadoop Clusters ◽  
Hadoop Cluster ◽  
Level Performance

In the age of big data, MapReduce is developed as an important tool to process massive datasets in a parallel way on cluster and Hadoop is an open-source implementation of it. However, with the increasing size of clusters, it becomes more and more difficult to identify and diagnose faulty nodes, especially those continuing running but with degraded performance. Then, based on an observation that the behaviors of all nodes in the cluster are relatively similar, we propose a peer-comparison approach that can automatically diagnose performance problems in Hadoop cluster through extracting, analyzing both Hadoop logs and OS-level performance metrics on each node. Compared with previous works, our approach is more scalable and effective and can pinpoint the underlying bug of faulty node in Hadoop clusters.

scholarly journals Data Mining Technique to Data Collection and Analysis for Cyber Forensic

2020 ◽  
Vol 8 (5) ◽  
pp. 2786-2789
Keyword(s):  
Law Enforcement ◽  
Data Collection ◽  
Crime Scene ◽  
Forensic Analysis ◽  
Digital Data ◽  
Data Mining Technique ◽  
Faulty Node ◽  
Digital Forensic ◽  
Data Collection And Analysis ◽  
Data Investigation

In the world of Digital forensic the uncovered digital may contain vital information for digital data investigation for investigator. Digital data collected from the crime scene leads to find out the clue after performing analysis by the examiner. This process of data examination data collection and analysis plays important role in cyber world for the forensic investigator. The cybercrime is a part of computer forensics where the digital evidences are analyze by the investigator and to perform analysis special measurements and techniques are required in order to use this details that has to be accepted in court of law for law enforcement. The data collection of evidence is a key aspect for the investigator, such kind of digital data has to be collected from different sources at the crime scene and this process involves to collect each and every evidence of digital crime scene and later this gather data will be analyze by the experts to reach to the conclusion. In this paper the proposed method collected the data from the crime scene efficiently which includes log data, transactional data, physical drive data, and network data; later this collected data analyzed to find out the theft node in the network. In this paper FTK 4.0 digital forensic tool used to reduce plenty of time for data processing and later report will be produce that will be accepted tin the court of law. This paper also focuses the data collection method with in the network and reach to the faulty node and later this faulty node analyzed with all collected data for forensic analysis. For this standard algorithm used to analyze the performance of distinct features used for network attacks. Kmeans clustering methodology is used to create cluster of victim node and represent victim data in systematic manner for the ease of law enforcement.

A Faulty Node Detection Method in Wireless Sensor Network in Seedling for Hydroponics

2021 ◽  
Author(s):  
Dong-Hee Noh ◽  
Tae-Hwan Ko ◽  
Ahhyeon Hong ◽  
Kyeong-Hun Kim ◽  
Seok-Bong Noh
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Detection Method ◽  
Wireless Sensor ◽  
Faulty Node
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