Fuzzy Based Unequal Clustering and Context-Aware Routing Based on Glow-Worm Swarm Optimization in Wireless Sensor Networks: Forest Fire Detection


Wireless sensor networks (WSNs) displays an encouraging outcome for forest fire (FF) identification. The most serious WSN investigation tasks is forest fire’s early prediction, which is used to save the ecosystem. For conveying the detected information to the BS (base station), the SNs (sensor nodes) are placed in remote forest region in WSN based FF discovery scheme that is manageable by the forest sector. Various studies have been finished in this field but they studied only few amount of constraints and the encountered situations influence has not discussed after the system positioning. In this work, fuzzy based unequal clustering and context aware routing (CAR) procedure with GSO (glow-worm swarm optimization) is developed in RWP (random way point) based dynamic WSNs. Based on FL (fuzzy logic) the unequal clustering is formed and the optimal CH (cluster head) is nominated to convey the information from CM (cluster member) to BS to increase the system lifespan and to decrease the energy consumption. Further, the routing process is performed by the CAR procedure with GSO process to enhance the efficiency of network. Lastly, a case study of FF identification is offered as a justification of the suggested method. The suggested work is executed in MATLAB. The simulation outcomes proved that the proposed approach provide the better outcomes in average energy consumption (0.025 J), PDR (99.4%), jitter (4.01 s), delay (0.0304 s), BER (15%), throughput (144.6Kbps), network lifetime (38.7 s) as related to other current protocols.

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Vinodhini, R., Gomathy, C. Fuzzy Based Unequal Clustering and Context-Aware Routing Based on Glow-Worm Swarm Optimization in Wireless Sensor Networks: Forest Fire Detection. Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-021-08191-y

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  • Sensor networks
  • Optimization algorithm
  • Routing protocol
  • Context aware
  • Fuzzy logic
  • Fire detection in forest