Faulty Node Detection Using Vertex Magic Total Labelling in Distributed System

Puthussery, Antony; Muneeswari, G.

doi:10.1007/978-981-15-8677-4_50

Cited by 2 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Puthussery and Muneeswari 26 have suggested a faulty node detection utilizing vertex magic total labelling (VMTL) in distributed scheme, where an innovative approach was presented depending on VMTL value for every node on the network. After getting a message from the sender node, the receiver node can rapidly identify the faulty node by comparing the VMTL pivot value (Pv).…”

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

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Surveymentioning

confidence: 99%