An Obstacle-Aware Clustering Protocol for Wireless Sensor Networks with Irregular Terrain - Wired/Wireless Internet Communications
Conference Papers Year : 2018

An Obstacle-Aware Clustering Protocol for Wireless Sensor Networks with Irregular Terrain

Riham Elhabyan
  • Function : Author
  • PersonId : 1052601
Wei Shi
  • Function : Author
  • PersonId : 1052602
Marc St-Hilaire
  • Function : Author
  • PersonId : 1052603

Abstract

Clustering in Wireless Sensor Networks (WSNs) is considered an efficient technique to optimize the energy consumption and increase the Packet Delivery Rate (PDR). Most of the proposed clustering protocols assume that there is a Line of Sight (LOS) between all the sensors. In real situations, there are obstacles which could interfere this LOS. Moreover, most of the available WSNs simulators assume the use of optimistic path loss models that neglect the effect of the obstacles on the PDR. In this paper, we adopt an obstacle-aware path loss model to reflect the effect of the obstacles on the communication between any the sensors. The Castalia simulator is then adapted to use this the proposed path loss model. Moreover, we propose an obstacle-aware clustering protocol, the NSGA-based, Non-LOS Cluster Head selection (NSGA-NLOS-CH) protocol, to solve the CHs selection problem in WSNs with an irregular field. Simulation results have shown that the effect of the obstacles on the PDR cannot be neglected. Moreover, NSGA-NLOS-CH outperforms other competent protocols in terms of the PDR while maintaining an acceptable energy consumption at the same time.
Fichier principal
Vignette du fichier
470666_1_En_5_Chapter.pdf (1.07 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-02269728 , version 1 (23-08-2019)

Licence

Identifiers

Cite

Riham Elhabyan, Wei Shi, Marc St-Hilaire. An Obstacle-Aware Clustering Protocol for Wireless Sensor Networks with Irregular Terrain. International Conference on Wired/Wireless Internet Communication (WWIC), Jun 2018, Boston, MA, United States. pp.54-66, ⟨10.1007/978-3-030-02931-9_5⟩. ⟨hal-02269728⟩
53 View
101 Download

Altmetric

Share

More