The Internet of Things (IoT) is a foundational building block for the upcoming information revolution. Particularly, the IoT bridges the cyber domain to anything within our physical world which enables unprecedented monitoring, connectivity, and smart control. The utilization of Unmanned Aerial Vehicles (UAVs) can offer an extra level of flexibility which results in more advanced and efficient connectivity and data aggregation. In the first part of the thesis, we focus on the optimal IoT devices placement and, the spectral and energy budgets management for accurate source estimation. Practical aspects such as measurement accuracy, communication quality, and energy harvesting are considered. The problem is formed such that a set of cheap and expensive sensors are placed to minimize the estimation error under limited system cost. The IoT revolution relies on aggregating big data from massive numbers of devices that are widely scattered in our environment. These devices are expected to be of low- complexity, low-cost, and limited power supply, which impose stringent constraints on the network operation. Aerial data transmission offers strong line-of-sight links and flexible/instant deployment. The UAV-enabled IoT networks can, for instance, offer solutions to avoid and manage natural disasters such as forest fire. We investigate in this thesis the aerial data aggregation for field estimation, wildfire detection, and connection coverage enhancement via UAVs. To accomplish the network task, the field of interest is divided into several subregions over which the UAVs hover to collect samples from the underlying nodes. To this end, we formulate and solve optimization problems to minimize total hovering and traveling times. This goal is fulfilled by optimizing the UAV hovering locations, the hovering time at each location, and the trajectory traversed between hovering locations. Finally, we propose the utilization of the tethered UAV (T-UAV) to assist the terrestrial network, where the tether provides power supply and connects the T-UAV to the core network through a high capacity link. The T-UAV however has limited mobility due to the limited tether length. A stochastic geometry-based analysis is provided for the optimal coverage probability of T-UAV-assisted cellular networks.