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Node localization and data aggregation scheme using cuckoo search and neural network
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DOI：10.1111/exsy.13033.png)
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Abstract
En 中文
Among multi-hop technology, wireless sensor network (WSN) has been extensively investigated owing to its potential application in vivid fields. However, a key issue probing WSN is node location that is also the major area of interest in the present paper. The paper takes advantage of cuckoo search (CS) as the swarm intelligence technique used to address the issues of identification of malicious or unknown nodes within the network. The distance vector (DV)-hop is used to determine the distance between the anchor sensor node and the unknown or the node with compromised nature. Then, artificial neural network architecture is used to distinguish the nodes based on the characteristics. This is followed by the evaluation of the proposed scheme to offer reliable data transmission using CS optimized data aggregation scheme. The simulation analysis over 1000 deployed nodes shows that CS significantly decreases the localization error to 0.494 and localization time to 0.058 s along with 15%-20% improvement in the throughput and packet delivery ratio. This shows that the proposed CS optimized architecture is successful in identifying the position of unknown nodes as well as compromised nodes that significantly improved the reliability of the data transmission.
Keywords:
artificial neural network
cuckoo search
data aggregation
node localization
wireless sensor network
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