So, consider a dynamic wireless sensor network. We wish to minimize the average wait time for each of the nodes in the network to be serviced by new information. We however do not want to increase the By using the bounty hunting algorithm we can do this. I might want to look into routing algorithms.

Consider poison point processes with holes. When we have a single neighborhood we have a poisson point process, when we have multiple neighborhoods we will have non-overlapping regions where no tasks are generated. This is where the “holes” are. **Stochastic Geometry** is the area of mathmatics which is interested in this.

But, I’ve not really been focused on wireless sensor networks, and it is a bit of a stretch to fit bounty hunting to it (at least as far as I can tell. My first papers might suggest otherwise). But with my current direction I have more interest with spatial queues, I have queues rather than wireless sensor networks. So, there is spatial queuing theory, but there is not a spatial queuing theory with holes! The paper “Risk and Reward in Spatial Queuing Theory” deals with spatial queuing theory for the dynamic traveling repairman problem. All of these systems assume a region without holes or space where no tasks will be generated. This is an important thing in the real world as there are generally spaces where there won’t actually be tasks. Therefore, I think I need to incorporate the concept of Poisson Point Processes with Holes. Then build from that what to expect based on the size of the holes and locations. The holes matter because the distance the servers must travel between the next task is dependent on the size of these holes!

So, I think this is important. Actually I think that holes might not be general enough. It would be better if I could generalize to any space.