Abstract & Overview
Task allocation is really important for how unmanned aerial vehicle-software-defined networking networks work and how much energy they use. This is especially true when the network is big or small and when things around it change. We looked at three ways to assign tasks: giving them out randomly giving them out in a manner that maintains load balance, and using something called ``particle swarm optimization'' We tried these methods in two situations: when the unmanned aerial vehicle-software-defined networking network is in open space and when it has to deal with obstacles. We used several unmanned aerial vehicles and tasks: 10 unmanned aerial vehicles with 20 tasks 50 unmanned aerial vehicles with 100 tasks and 250 unmanned aerial vehicles with 500 .tasksWe did each experiment multiple times to make sure the results were reliable. We measured how well the unmanned aerial vehicle-software-defined networking network worked by looking at how long it took for packets to travel how many packets were lost how many packets were delivered and how much data was transferred. We also examined the amount of energy the unmanned aerial vehicle-software-defined networking network used. What we found out is that particle swarm optimization usually does a job of keeping the duration of packet transmission is low and stable. It also uses energy efficiently compared to the other two methods. On the other hand, giving tasks out randomly or in a load-balanced way can be kind of unpredictable. So what we learned is that assigning tasks in a way that saves energy can make unmanned aerial vehicle-software-defined networking networks work better and more smoothly. How well it works can depend on the situation and how big the network is.