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In this paper, we analyze the peak age of information (PAoI) in UAV-assistedinternet of thing (IoT) networks, in which the locations of IoT devices aremodeled by a Mat\'{e}rn cluster process (MCP) and UAVs are deployed at thecluster centers to collect the status updates from the devices. Specifically,we consider that IoT devices can either monitor the same physical process ordifferent physical processes and UAVs split their resources, time or bandwidth,to serve the devices to avoid inter-cluster interference. Using tools fromstochastic geometry, we are able to compute the mean activity probability ofIoT devices and the conditional success probability of an individual device. Wethen use tools from queuing theory to compute the PAoI under two load modelsand two scenarios for devices, respectively. Our numerical results showinteresting system insights. We first show that for a low data arrival rate,increasing the number of correlated devices can improve the PAoI for both loadmodels. Next, we show that even though the time-splitting technique causeshigher interference, it has a limited impact on the mean PAoI, and the meanPAoI benefits more from the time-splitting technique. This is because of thenature of UAV communication, especially at places where devices (users) arespatially-clustered: shorter transmission distances and better communicationchannels, comparing the links established by the cluster UAV and servingdevices (users) to links established by interferers.

On the Peak AoI of UAV-assisted IoT Networks: A Stochastic Geometry Approach