Backup node selection using particle swarm optimisation algorithm for cut node recovery in wireless sensor network

Devi, E. Anna; Manickam, J.

doi:10.1504/ijbis.2020.111422

Cited by 1 publication

(1 citation statement)

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“…Figures 3–11 show the simulation result of the FND‐RS using HO‐COA for LS‐WSN. Here The various evaluation metrics, like network delay, global energy, throughput and energy consumption, are analysed and compared with the existing methods, namely, FND‐RS for LS‐WSN in distributed intermittent fault diagnosis algorithm (NDR‐WSN‐DISFD), 33 FND‐RS for LS‐WSN in harmony search algorithm (FNDR‐WSN‐HAS),19 FND‐RS for LS‐WSN in t ‐distribution‐based satin bowerbird optimization (FNDR‐WSN‐ t ‐DSBO), 34 FND‐RS for LS‐WSN in optimal emperor penguin optimization (FNDR‐WSN‐OEPO) 35 and FND‐RS for LS‐WSN in particle swarm optimization algorithm (FNDR‐WSN‐PSO) 36 . Then, the proposed method is analysed by varying the number of nodes with a fixed data rate of 5000.…”

Section: Resultsmentioning

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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