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Journal ArticleDOI

Self-maintenance model for Wireless Sensor Networks

TLDR
A distributed self-healing approach for both node and cluster head levels for wireless Sensor Networks, where, at node level, battery, sensor and receiver faults can be diagnosed while, at cluster head level, transmitter and mal-functional nodes can be detected and recovered.
About
This article is published in Computers & Electrical Engineering.The article was published on 2017-12-01. It has received 107 citations till now. The article focuses on the topics: Fault detection and isolation & Wireless sensor network.

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Citations
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Journal ArticleDOI

Trust-based secure clustering in WSN-based intelligent transportation systems

TL;DR: A bio-inspired and trust-based cluster head selection approach for WSN adopted in ITS applications and the results demonstrated that the proposed model achieved longer network lifetime, i.e., nodes are kept alive longer than what LEACH, SEP and DEEC can achieve.
Journal ArticleDOI

Anomaly detection in wireless sensor network using machine learning algorithm

TL;DR: This paper forms an Online Locally Weighted Projection Regression (OLWPR) for anomaly detection in Wireless Sensor Network and attains the detection rate of 86 percentage and very low error rate of only 16%.
Book ChapterDOI

Energy Efficient Optimal Routing for Communication in VANETs via Clustering Model

TL;DR: This paper presents K-Medoid Clustering model to cluster the vehicle nodes and after that, energy efficient nodes are recognized for compelling communication and the V2V communication accomplishes less execution time contrasted with existing algorithms.
Journal ArticleDOI

Energy efficient collaborative proactive routing protocol for Wireless Sensor Network

TL;DR: A Collaborative Distributed Antenna (CDA) routing protocol is proposed that is based on DCT with optimal node degree and is designed for periodic data monitoring in WSN applications and is proved to double the network stability period and reduce the ratio between instability period and the network lifetime to its half.
Journal ArticleDOI

Parallel and incremental credit card fraud detection model to handle concept drift and data imbalance

TL;DR: A transaction window bagging model, a parallel and incremental learning ensemble, is proposed as a solution to handle the issues in credit card transaction data to effectively handle concept drift and data imbalance.
References
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Journal ArticleDOI

A Genetic Algorithm-Based, Dynamic Clustering Method Towards Improved WSN Longevity

TL;DR: A genetic algorithm-based, self-organizing network clustering (GASONeC) method that provides a framework to dynamically optimize wireless sensor node clusters and greatly extends the network life and the improvement up to 43.44 %.
Journal ArticleDOI

Balancing Energy Consumption in Heterogeneous Wireless Sensor Networks Using Genetic Algorithm

TL;DR: A Genetic Algorithm based method that optimizes heterogeneous sensor node clustering and greatly extends the network life, and the average improvement with respect to the second best performance based on the first-node-die and the last- node-die is 33.8% and 13%, respectively.
Proceedings ArticleDOI

FIND: faulty node detection for wireless sensor networks

TL;DR: FIND is proposed, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections and shows that average ranking difference is a provable indicator of possible data faults.
Journal ArticleDOI

Optimizing K-coverage of mobile WSNs

TL;DR: A new model that uses the Genetic Algorithm (GA) to optimize the coverage requirements in WSNs to provide continuous monitoring of specified targets for longest possible time with limited energy resources is proposed.
Journal ArticleDOI

An energy efficient encryption method for secure dynamic WSN

TL;DR: Simulation results demonstrated that the proposed method exhibited much improved network lifetime and reduced the energy consumption most evenly among all sensor nodes, and overcame many security attacks including brute-force attack, HELLO flood attack, selective forwarding attack, and compromised cluster head attack.
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