Intelligent Wireless Sensor Network

In recent years, there has been significant increase in utilisation of embedded-microcontrollers in broad range of applications extending from commercial products to industrial process system monitoring. Furthermore, improvements in speed, size and power consumption of microcontrollers with added wireless capabilities has provided new generation of applications. These include versatile and
low cost solutions in wireless sensor networking applications such as wireless system monitoring and control.
In many applications, there are situations where multiple identical devices form a wireless network and work together towards achieving a common goal. Each individual device or node is controlled by a microcontroller and the whole network is controlled by an upper-level microcontroller to facilitate data distribution and supervisory tasks. In order to maximise the network performance the nodes are designed to control their local process in an intelligent manner so that they can adapt to their
environment and set-point changes. The knowledge gained by individual nodes is then made available
to other nodes on the network.
The aim of this research project is to implement Fuzzy Logic Controller (FLC) as an artificial intelligent technique to devise a microcontroller-based self-learning algorithm that enables real-time online adaptation to the new environment situations. The research to date has focused on machine-based FLC with extended recourses such as CPU power and storage in contrast with microcontrollers with limited resources.
In order to improve FLC performance, several techniques are available for defining its parameters such as scaling the universe of discourse (known as scaling gain), tuning membership functions and rules. Design simulations have been carried out and the latest results have been promising which indicate certain degree of improvement in system performance.