TOSSIM simulation of wireless sensor network serving as hardware platform for Hopfield neural net configured for max independent set

摘要

This paper, the third one in a three-paper sequence, presents the result of TOSSIM simulation of a Hopfield neural network as a static optimizer and configured to solve the maximum independent set (MIS) problem using a wireless sensor network as a fully parallel and distributed computing hardware platform. TinyOS with its default protocol stack along with nesC were used to develop the simulation model. Simulations were realized for mote counts of 10, 50, 100, and 182; messaging complexity, memory and simulation time costs were measured. Results indicated, as the most prominent finding, that the neural optimization algorithm was able to compute solutions to the MIS problem. The memory footprint of the TOSSIM process in Windows XP environment was about 20 MB for the range of sensor networks considered. The messaging complexity as measured by the total number of messages transmitted and the simulation time increased rather quickly indicating a need to optimize and tune certain aspects of the simulation environment if wireless sensor networks with higher mote counts need to be simulated.