Diagnosis of fault node in wireless sensor networks using adaptive neuro-fuzzy inference system

Rajan, M. Sundar; Dilip, Golda; Kannan, Nithiyananthan; Namratha, M; Majji, Sankararao; Mohapatra, Srikanta; Patnala, Tulasi Radhika; Karanam, Santoshachandra Rao

doi:10.1007/s13204-021-01934-0

Cited by 41 publications

(14 citation statements)

References 18 publications

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“…Many researchers have proposed various methods on detecting the sensors nodes failure and provide efficient localization among them. In WSN, the authors suggested an ANFIS, a decentralized faulty node detection and categorization approach [11]. The proposed scheme categorizes sensor node faults based on the sensor nodes' crisper performance measure.…”

Section: Related Workmentioning

confidence: 99%

A Poisson Hidden Markov Model and Fuzzy based Chicken Swarm Optimization algorithm for efficient fault node detection in wireless sensor network

Nagarajan

S.V.N

2023

Preprint

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Wireless Sensor Networks (WSN) is built with miniature sensor nodes (SN) which are deployed into the geographical location being sensed to monitor environmental condition which transfer the sensed physical information to the base station for further processing. The sensor nodes frequently experience node failure as a result of their hostile deployment and resource limitations. In WSN, node failure can cause a number of issues, namely Wireless Sensor Networks topology changes, broken communications links, disconnected portions of the network, and data transmission errors. An important concern of WSN is the detecting, diagnosing and recovering of sensor node failures. In the course of this effort, an effective strategy for sensor node failure detection algorithm using Poisson Hidden Markov Model (PHMM) and the Fuzzy based Chicken Swarm Optimization (F-CSO) is proposed for efficient detection of sensor nodes fault in the WSN. The proposed work offers optimal false alarm, false positive, energy consumption, detection accuracy, network lifetime, and least delay rates. Moreover, the F-CSO provides improved localization to locate the defective sensor nodes which are present in the WSN. The proposed work is implemented in the NS2 simulator with realistic simulation parameters and the simulation result demonstrate that the proposed work is more effective in terms of false alarm rate, false positive rate, detection accuracy, delay, energy consumption and network lifetime when it is compared with other existing state of art systems.

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Section: Related Workmentioning

confidence: 99%

A Poisson Hidden Markov Model and Fuzzy based Chicken Swarm Optimization algorithm for efficient fault node detection in wireless sensor network

Nagarajan

S.V.N

2023

Preprint

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“…Rajan et al, 28 have presented the identification of fault node in WSNs using adaptive neuro‐fuzzy inference system. ANFIS method was presented for the summation with the help of neuro‐fuzzy optimization mode estimator of defect‐tolerant WSNs.…”

Section: Literature Surveymentioning

confidence: 99%

Faulty node detection and recovery scheme for large‐scale wireless sensor network using hosted cuckoo optimization algorithm

Palanikumar¹,

Ramasamy²,

Ragavan³

2022

Int J Communication

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Summary Large‐scale wireless sensor networks (LS‐WSNs) are used for collecting and monitoring the physical state of the environment. These networks are first sending information to the base station and then the recipient. LS‐WSNs consist of several components, such as battery, sensor, transmitter, receiver and microcontroller circuits. If any one of these hardware components does not work properly, then the entire system will become faulty condition, resulting reducing network life and accuracy. To overcome these hardware node faults, this manuscript proposes an effective deep reinforcement learning (DRL)‐adopted faulty node detection and recovery scheme (FND‐RS) integrated with the hosted cuckoo‐based optimal routing scheme for LS‐WSNs. Here, the DRL process is utilized for noticing and recovering the hardware node fault on sensor. The major objective of this system is ‘to enhance the network life with least energy consumption and also enhance the accuracy of Large Scale Wireless Sensor Networks using hosted cuckoo optimization (HO‐COA) algorithms’. The simulation is done in MATLAB. The experimental results shows that the proposed method attains lower overhead 15.65%, energy loss 21.62%, average packet delivery ratio 13.13%, energy efficiency 23.25%, network lifetime 19.12%, throughput 13.5%, time complexity 19.35% and lower delay 17.55% when compared to the existing methods, like FND‐RS for LS‐WSN in distributed intermittent fault diagnosis algorithm (NDR‐WSN‐DISFD), FND‐RS for LS‐WSN in harmony search algorithm (FNDR‐WSN‐HAS), FND‐RS for LS‐WSN in t‐distribution‐based satin bowerbird optimization (FNDR‐WSN‐t‐DSBO), FND‐RS for LS‐WSN in optimal emperor penguin optimization (FNDR‐WSN‐OEPO) and FND‐RS for LS‐WSN in particle swarm optimization algorithm (FNDR‐WSN‐PSO) respectively.

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“…For the identification and classification of osteosarcoma, the ANFIS model is exploited. Soft computing techniques like neural networks and fuzzy set concepts are instances of instruments that might be exploited for establishing smart systems [ 21 ]. This theory provides a new methodology to resolve the problems that probability theory was incapable of shedding light on.…”

Section: The Proposed Modelmentioning

confidence: 99%

Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model

Vaiyapuri

Jothi

Narayanasamy

et al. 2022

Cancers

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Osteosarcoma is one of the aggressive bone tumors with numerous histologic patterns. Histopathological inspection is a crucial criterion in the medical diagnosis of Osteosarcoma. Due to the advancement of computing power and hardware technology, pathological image analysis system based on artificial intelligence (AI) were more commonly used. But classifying many intricate pathology images by hand will be challenging for pathologists. The lack of labeling data makes the system difficult to build and costly. This article designs a Honey Badger Optimization with Deep Learning based Automated Osteosarcoma Classification (HBODL-AOC) model. The HBODL-AOC technique’s goal is to identify osteosarcoma’s existence using medical images. In the presented HBODL-AOC technique, image preprocessing is initially performed by contrast enhancement technique. For feature extraction, the HBODL-AOC technique employs a deep convolutional neural network-based Mobile networks (MobileNet) model with an Adam optimizer for hyperparameter tuning. Finally, the adaptive neuro-fuzzy inference system (ANFIS) approach is implemented for the HBO (Honey Badger Optimization) algorithm can tune osteosarcoma classification and the membership function (MF). To demonstrate the enhanced classification performance of the HBODL-AOC approach, a sequence of simulations was performed. The extensive simulation analysis portrayed the improved performance of the HBODL-AOC technique over existing DL models.

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Diagnosis of fault node in wireless sensor networks using adaptive neuro-fuzzy inference system

Cited by 41 publications

References 18 publications

A Poisson Hidden Markov Model and Fuzzy based Chicken Swarm Optimization algorithm for efficient fault node detection in wireless sensor network

A Poisson Hidden Markov Model and Fuzzy based Chicken Swarm Optimization algorithm for efficient fault node detection in wireless sensor network

Faulty node detection and recovery scheme for large‐scale wireless sensor network using hosted cuckoo optimization algorithm

Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model

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