Distributed Motion Coordination for Mobile Wireless Sensor Networks Using Vision

Justin T. C. Lee
Master’s thesis
Department of Computing
Curtin University of Technology
Perth, Australia
March 2003
URL: http://adt.curtin.edu.au/theses/available/adt-WCU20031201.132347/

Abstract

Mobile wireless sensor networks (MWSNs) will enable information systems to gather detailed information about the environment on an unprecedented scale. These self-organising, distributed networks of sensors, processors and actuators that are capable of movement have a broad range of potential applications, including military reconnaissance, surveillance, planetary exploration and geophysical mapping.

In many of the foreseen applications a certain geometric pattern will be required for the task. Hence, algorithms for maintaining the geometric pattern of an MWSN are investigated. In many tasks such as land mine detection, a group of nodes arranged in a line must provide continuous coverage between each end of the formation. Thus, we present algorithms for maintaining the geometric pattern of a group of nodes arranged in a line.

An MWSN may also need to form a geometric pattern without assistance from the user. In military reconnaissance, for example, the nodes will be dropped onto the battlefield from a plane and land at random positions. The nodes will be expected to arrange themselves into a predetermined formation in order to perform a specific task. Thus, we present algorithms for forming a circle and regular polygon from a given set of random positions.

The algorithms are distributed and use no communication between the nodes to minimise energy consumption. Unlike past studies of geometric problems where algorithms are either tested in simulations where each node has global knowledge of all the other nodes or implemented on a small number of robots, the robustness of our algorithms has been studied with simulations that model the sensor system in detail. The nodes locate their neighbours using simulated vision where a ray-tracer is used to generate images of a model of the scene that would be captured by each node's cameras. The simulations demonstrate that the algorithms are robust against random errors in the sensors and actuators. Even though the nodes had incomplete knowledge of the positions of other nodes due to occlusion, they were still able to perform the assigned tasks.