Cross Layer Protocol Suite for Correlated Data Gathering in Wireless Sensor Networks

Overview

[This project is in collaboration with Dr. M. Ingram, Dr. F. Fekri, and Dr. R. Sivakumar.]

Wireless Sensor Networks (WSNs) have garnered a considerable amount of attention over last half a decade, primarily due to the unique applications they enable. However, there is an important constraint on the operation of such networks - the energy source at sensors. Except for environments where an energy source can be harnessed in a low cost manner, the very survivability of WSNs depends upon how energy efficiently the sensors operate in performing their required functions.

In this project, we focus on the primary operation in any WSN: collection of sensor data from the sensors in the field to the sink for processing (the data gathering process), and aim to improve the energy efficiency of such a process. Since data gathering is the most important and frequent operation in a WSN, energy gain obtained through the optimization of this process can help extend the lifetime of sensor networks significantly.

We consider the problem of data gathering in environments where data from the different sensors are correlated. Performing energy-aware correlated data gathering through multiple correlated paths in WSNs is a unique challenging problem due to the distributed nature of correlated data, moderate or short size of data frames, low memory and processing power of the sensor devices. We consider a network topology where vast number of sensors are dispersed in a sensor field and many sensor nodes collect information about the physical phenomenon from these sensors. This information is then efficiently transferred to a central location, i.e., sink, through the multi-hop wireless sensor network. Accordingly, the following are investigated as a part of the cross-layer protocol suite for correlated data gathering in WSNs:

  • Cross-Layer Protocol Design, Error Control and Packet Size Optimization.
  • MAC-Free Reading of Correlated Sensor Networks.
  • Distributed Source Coding in Sensor Networks.
  • Energy-efficient Data Gathering in Wireless Sensor Networks.
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