Semantic Scholar Open Access 2017 27 sitasi

Collaborative neighbor discovery in directional wireless sensor networks: algorithm and analysis

Fernaz Narin Nur Selina Sharmin Md. Ahsan Habib M. Razzaque M. Islam +3 lainnya

Abstrak

In directional wireless sensor networks (DSNs), sensor nodes with directional antennas provide extended network lifetime and better coverage performance. However, one of the key challenges of directional nodes is to discover their neighbors due to difficulty in achieving synchronization among their directed transmissions and receptions. Existing solutions suffer from high discovery latency and poor percentage of neighbor discovery either due to lack of proper coordination or centralized management of the discovery operation. In this work, we develop a collaborative neighbor discovery (COND) mechanism for DSNs. Each COND node polls to directly discover its neighbors in a distributed way and collaborates with the already discovered nodes so as to allow indirect discovery. It helps to increase the neighbor discovery performance significantly. A Markov chain-based analytical model is developed to quantify theoretical performances of the proposed COND system. The performance of the COND system is evaluated in Network Simulator Version 3, and simulation results reveal that it greatly reduces the discovery latency and increases neighbor discovery ratio compared to state-of-the-art approaches.

Topik & Kata Kunci

Penulis (8)

F

Fernaz Narin Nur

S

Selina Sharmin

M

Md. Ahsan Habib

M

M. Razzaque

M

M. Islam

A

Ahmad S. Almogren

M

Mohammad Mehedi Hassan

A

Atif Alamri

Format Sitasi

Nur, F.N., Sharmin, S., Habib, M.A., Razzaque, M., Islam, M., Almogren, A.S. et al. (2017). Collaborative neighbor discovery in directional wireless sensor networks: algorithm and analysis. https://doi.org/10.1186/S13638-017-0903-6

Akses Cepat

Lihat di Sumber doi.org/10.1186/S13638-017-0903-6
Informasi Jurnal
Tahun Terbit
2017
Bahasa
en
Total Sitasi
27×
Sumber Database
Semantic Scholar
DOI
10.1186/S13638-017-0903-6
Akses
Open Access ✓