A Comprehensive Cross-Layer Framework for Optimization of Correlated Data Gathering in Wireless Sensor Networks

Author(s):  
Narsimha Neela ◽  
O.B.V. Ramanaiah
2017 ◽  
pp. 252
Author(s):  
Mohammed A. Abuhelaleh ◽  
Tahseen A. Al-Ramadin ◽  
Bassam A. Alqaralleh ◽  
Moha'med Al-Jaafereh ◽  
Khaled Almi'ani

2016 ◽  
pp. 221
Author(s):  
Mohammed A. Abuhelaleh ◽  
Tahseen A. Al-Ramadin ◽  
Khaled Almi'ani ◽  
Moha'med Al-Jaafereh ◽  
Bassam A. Alqaralleh

2012 ◽  
Vol 12 (5) ◽  
pp. 1147-1156 ◽  
Author(s):  
Hsien-Cheng Weng ◽  
Yu-Hsun Chen ◽  
Eric Hsiao-Kuang Wu ◽  
Gen-Huey Chen

Author(s):  
Utkarsha Sumedh Pacharaney ◽  
Ranjan Bala Jain ◽  
Rajiv Kumar Gupta

The chapter focuses on minimizing the amount of wireless transmission in sensory data gathering for correlated data field monitoring in wireless sensor networks (WSN), which is a major source of power consumption. Compressive sensing (CS) is a new in-node compression technique that is economically used for data gathering in an energy-constrained WSN. Among existing CS-based routing, cluster-based methods offer the most transmission-efficient architecture. Most CS-based clustering methods randomly choose nodes to form clusters, neglecting the topology structure. A novel base station (BS)-assisted cluster, spatially correlated cluster using compressive sensing (SCC_CS), is proposed to reduce number of transmissions in and form the cluster by exploiting spatial correlation based on geographical proximity. The proposed BS-assisted clustering scheme follows hexagonal deployment strategy. In SCC_CS, cluster heads are solely involved in data gathering and transmitting CS measurements to BS, saving intra-cluster communication cost, and thus, network life increases as proved by simulation.


Sign in / Sign up

Export Citation Format

Share Document