Analysis of deployment and movement policies in wireless sensor and robot networks

Mobile robots have been used to collect data in the network in order to reduce the energy consumed when forwarding data. In order to be energy efficient, mobile robots should be deployed and controlled in such a way as to minimize travel distance. To do this, we examined multiple deployment and movement policies and their effects on the performance of a network. We tested three different deployment schemes and analyzed the results. We also analyzed the expected distance to each event to discover what, if any, effects the initial deployment has on that metric and in turn compared this to our results. We then tested three different collection methods and analyzed their performance. Next, we added service times to our model and lastly we changed the distribution of events to examine how these changes affected the network performance. Through our extensive simulations and mathematical analysis, we found a random deployment to be the most effective deployment policy with respect to computation time and network performance. Our proposed location prediction policy was also able to improve network performance, even over the collect and stay policy as shown through our simulations.