Project 5 : Distributed Classification and Data Fusion in Wireless Sensor Networks
By : Mahmoud Faraj
Wireless sensor networks (WSNs) are a recently emerging technology consisting of a large number of battery powered sensor nodes interconnected wirelessly and capable of monitoring environments, tracking targets, and performing many other critical applications. The design and deployment of such type of network are challenging tasks due to the imperfect nature of the communicated nodes (i.e., sensors) in the WSNs. The dramatic depletion of the sensor’s energy while performing the regular tasks (e.g. sensing, processing, receiving and transmitting information) constitutes a major threat of shortening the lifetime of the network. That is due to the limited amount of energy in the sensor which is constrained by the dimensions of these sensors. The lack of energy makes the lifetime of the network shorter. Also, the death of some nodes causes partitioning the network. As a result, some nodes become not able to communicate with others to accomplish the ultimate goal of the remotely deployed network.
In our research work, we propose one of the techniques learned in the course to be used for performing distributed classification of a moving target (e.g. vehicle, animal, or person). Each sensor node will be able to classify the moving target and then track it in the WSN field. In order to conserve power and extend the lifetime of the network, we also propose distributed (in-network) data fusion by using Distributed Kalman Filter where the data are fused in the network instead of having all the data transmitted to the fusion center (sink). Each node processes the data from its own set of sensor, communicating with neighbouring nodes to improve the classification and location estimates of the moving target. Simulation results will be provided to demonstrate the significant advantages of using distributed classification and data fusion and also to show the improvement of the WSN as a whole.