A route selection approach for variable data transmission in wireless sensor networks
Autor:
Jain, Aarti
; Khari, Manju
; Verdú, Elena
; Omatsu, Shigeru
; González-Crespo, Rubén
Fecha:
09/2020Palabra clave:
Revista / editorial:
Cluster ComputingTipo de Ítem:
Articulo Revista IndexadaDirección web:
https://link.springer.com/article/10.1007/s10586-020-03115-0Resumen:
The nodes in wireless sensor networks (WSNs) are responsible for communicating data which is primarily of three types viz. video, audio and text. In literature, a large number of energy aware and shortest path based route selection approaches have been proposed to enhance the routing efficiency in WSNs. Most of these route selection approaches are designed by assuming fixed type and size of data packets and results in the same path selection for all types of data. This path selection, does not consider data size and data priority, results in high contention losses, topology failures and non-uniformity in energy depletion of nodes. In this paper, a route selection approach which is based on the preservation of network connectivity for improving overall network lifetime has been proposed for applications where sensor nodes are required to transmit different types of data. The proposed approach preserve highly connected edges at the initial rounds of data communication in an energy efficient manner, such that network connectivity would be maintained even at later rounds of data communications. The proposed route selection approach, VDR, (Variable Data rate Routing) has been simulated with both proactive and reactive route maintenance strategies. The simulation results show that the proposed algorithm with both proactive and reactive route maintenance strategies result in better network lifetime, energy consumption, overall network connectivity, packet delivery ratio as compared to the existing State-of-the-Art energy efficient route selection approaches with proactive and reactive route maintenance strategies. The results also show that the proposed algorithm is scalable and performs better than the compared algorithms both at low node density and high node density.
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