A Source Coding Perspective on Node Deployment in Two-Tier Networks

Multi-tier networks have many applications in different fields. We define a novel two-tier quantizer that can be applied to different node deployment problems including the energy conservation in two-tier wireless sensor networks consisting of N access points (APs) and M fusion centers (FCs). We...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on communications 2018-07, Vol.66 (7), p.3035-3049
Main Authors: Guo, Jun, Koyuncu, Erdem, Jafarkhani, Hamid
Format: Article
Language:English
Subjects:
Algorithms
Cellular networks
Computer simulation
Convexity
Distortion
Distortion measurement
Energy conservation
energy consumption
Linear transformations
Optimization
Partitioning algorithms
Quantization (signal)
Remote sensors
Sensors
Source coding
vector quantization
Wireless sensor networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Multi-tier networks have many applications in different fields. We define a novel two-tier quantizer that can be applied to different node deployment problems including the energy conservation in two-tier wireless sensor networks consisting of N access points (APs) and M fusion centers (FCs). We aim at finding an optimal deployment of APs and FCs to minimize the average weighted total, or Lagrangian, of sensor and AP powers. For one FC, M=1 , we show that the optimal deployment of APs is simply a linear transformation of the optimal N -level quantizer for density f , and the sole FC should be located at the geometric centroid of the sensing field. We also provide the exact expression of the AP-Sensor power function and prove its convexity. For more than one FC, M>1 , we provide a necessary condition for the optimal deployment. Furthermore, to numerically optimize the AP and FC deployment, we propose three Lloyd-like algorithms and analyze their convergence. Simulation results show that our algorithms outperform the existing algorithms.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2018.2809737