Project Name: Ad Hoc Public Safety network optimization allocation with GAs ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ Project Area: Ad Hoc Public Safety Network; GAs ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ Target: USS at Miami ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ People Needs and Allocation: Undergraduate Student; Co-advisor Gokhan Sahin ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ Skills: Programming ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ Description: For this Undergraduate Summer Scholars’ research project, I will work with Dr. Peter Jamieson in determining the effectiveness a genetic algorithm for finding a good dynamic placement for mobile base stations in the use of an ad hoc public safety network. Our work will expand upon the initial work and results reported by Shen, et. al. in their paper “Efficient Mobile Base Station Placement for First Responders in Public Safety Networks” (Future of Information and Communications Conference (FICC) 2019). In this work, they discussed the importance of finding an efficient algorithm for the dynamic placement of wireless communication relays in emergency/disaster situations. Emergencies are inherently unforeseeable and unpredictable, and the responses to these emergency situations need to be rapid and effective in meeting these different scenarios. During these emergencies, wireless communications relays, or mobile base stations (mBSs), need to be introduces to facilitate communication among first responders. These relays are limited in quantity and must be placed to allow connectivity and coverage for all first-responders, especially, “essential voice communications”. As the emergency response evolves, so too must the locations of the mBSs change to accommodate the movement of the personnel at the scene. Thus, an efficient algorithm is needed for the dynamic placement of mBSs is needed that can be utilized in ad hoc public safety networks. As a proof of concept, Shen et. al. identified three algorithms and scored them using a table of weighted priorities in a simulation environment. They conclude that an algorithm that is effective in narrow conditions, but that more work can be done in this area. In this USS research, we hope to expand upon this work by evaluating the efficacy of a genetic algorithm in finding a dynamic placement of mBSs in ad hoc public safety networks. By using a genetic algorithm approach, we hope to find better solutions to this problem that may take into consideration additional factors such as the cost associated with the movement of mBSs. A genetic algorithm is a method to finding solutions to optimization problems based on natural selection. We will construct a model space for our algorithms that includes sets of parameters to be varied in a simulation environment. Then, we will create an initial generation of algorithms with random characteristics, score each algorithm using a table of weighted priorities, and combine elements of the highest scoring algorithms to create the next generation of algorithms. By repeating this process, we will evolve a better solution, hopefully, improving on results previously reported. ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ References: [1] Efficient Mobile Base Station Placement for First Responders in Public Safety Networks” (Future of Information and Communications Conference (FICC) 2019) ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------ Resources: