Data Gathering Scheme Using Chaotic Pulse-Coupled Neural Networks for Wireless Sensor Networks
Wireless sensor networks (WSNs) have attracted a significant amount of interest from many researchers because they have great potential as a means of obtaining information of various environments remotely. WSNs have a wide range of applications, such as natural environmental monitoring in forest regions and environmental control in office buildings. In WSNs, hundreds or thousands of micro-sensor nodes with such resource limitations as battery capacity, memory, CPU, and communication capacity are deployed without control in a region and used to monitor and gather sensor information of environments. Therefore, a scalable and efficient network control and/or data gathering scheme for saving energy consumption of each sensor node is needed to prolong WSN lifetime. In this paper, assuming that sensor nodes synchronize to intermittently communicate with each other only when they are active for realizing the long-term employment of WSNs, we propose a new synchronization scheme for gathering sensor information using chaotic pulse-coupled neural networks (CPCNN). We evaluate the proposed scheme using computer simulations and discuss its development potential. In simulation experiments, the proposed scheme is compared with a previous synchronization scheme based on a pulse-coupled oscillator model to verify its effectiveness.
wireless sensor networks energy consumption savings synchronization data gathering pulse coupled neural networks