Maximum Likelihood Topology Maps for Wireless Sensor Networks Using an Automated Robot

Abstract

Topology maps represent the layout arrangement of nodes while maintaining the connectivity. As it is extracted using connectivity information only, it does not accurately represent the physical layout such as physical voids, shape, and relative distances among physical positions of sensor nodes. A novel concept Maximum Likelihood-Topology Maps for Wireless Sensor Networks is presented. As it is based on a packet reception probability function, which is sensitive to the distance, it represents the physical layout more accurately. In this paper, we use a binary matrix recorded by a mobile robot representing the reception of packets from sensor nodes by the mobile robot at different locations along the robots trajectory. Maximum likelihood topology coordinates are then extracted from the binary matrix by using a packet receiving probability function. Also, the robot trajectory is automated to avoid the obstacles and cover the entire network within least possible amount of time. The result shows that our algorithm generates topology maps for various network shapes under different environmental conditions accurately, and that it outperforms the existing algorithms by representing the physical layout of the network more accurately.