The next era of information revolution will rely on aggregating big data from massive numbers of devices that are widely scattered in our environment. Most of these devices are expected to be of low-complexity, low-cost, and limited power supply, which impose stringent constraints on the network operation. In this regard, this paper investigates aerial data aggregation and field estimation from a finite spatial field via an unmanned aerial vehicle (UAV). Instead of fusing, relaying, and routing the data across the wireless nodes to fixed locations access points, a UAV flies over the field and collects the required data for two prominent missions; data aggregation and field estimation. To accomplish these tasks, the field of interest is divided into several subregions over which the UAV hovers to collect samples from the underlying nodes. To this end, we formulate and solve an optimization problem to minimize total hovering and traveling time of each mission. While the former requires the collection of a prescribed average number of samples from the field, the latter ensures for a given field spatial correlation model that the average mean-squared estimation error of the field value is no more than a predetermined threshold at any point. These goals are fulfilled by optimizing the number of subregions, the area of each subregion, the hovering locations, the hovering time at each location, and the trajectory traversed between hovering locations. The proposed formulation is shown to be NP-hard mixed integer problem, and hence, a decoupled heuristic solution is proposed. The results show that there exists an optimal number of subregions that balance the tradeoff between hovering and traveling times such that the total time for collecting the required samples is minimized.

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<div>Unmanned aerial vehicles (UAVs) have recently received a significant interest to assist terrestrial wireless</div><div>networks thanks to their strong line-of-sight links and flexible/instant deployment. However, UAVs’ assistance</div><div>is limited by their battery lifetime and wireless backhaul link capacity. At the expense of limited mobility,</div><div>tethered UAVs (T-UAVs) can be a viable alternative to provide seamless service over a cable that simultaneously</div><div>supplies power and data from a ground station (GS). Accordingly, this paper presents a comparative performance</div><div>analysis of T-UAV and regular/untethered UAV (U-UAV)-assisted cellular traffic offloading from a geographical</div><div>area that undergoes heavy traffic conditions. By using stochastic geometry tools, we first derive joint distance</div><div>distributions between the hot-spot users, the terrestrial base station (TBS), and the UAV. To maximize the endto-</div><div>end signal-to-noise ratio, a user association policy is developed, and corresponding association regions are</div><div>analytically identified. Then, the overall coverage probability of the U-UAV/T-UAV-assisted system is derived for</div><div>given locations of the TBS and the U-UAV/T-UAV. Moreover, we analytically prove that optimal UAV location</div><div>falls within a partial surface of the spherical cone centered at the GS. Numerical results show that T-UAV</div><div>outperforms U-UAV given that sufficient GS locations accessibility and tether length are provided.</div>

Unmanned aerial vehicles (UAVs) have recently received a significant interest to assist terrestrial wireless networks thanks to their strong line-of-sight links and flexible/instant deployment. However, UAVs' assistance is limited by their battery lifetime and wireless backhaul link capacity. At the expense of limited mobility, tethered UAVs (T-UAVs) can be a viable alternative to provide seamless service over a cable that simultaneously supplies power and data from a ground station (GS). Accordingly, this paper presents a comparative performance analysis of T-UAV and regular/untethered UAV (U-UAV)-assisted cellular traffic offloading from a geographical area that undergoes heavy traffic conditions. By using stochastic geometry tools, we first derive joint distance distributions between the hot-spot users, the terrestrial base station (TBS), and the UAV. To maximize the endto-end signal-to-noise ratio, a user association policy is developed, and corresponding association regions are analytically identified. Then, the overall coverage probability of the U-UAV/T-UAV-assisted system is derived for given locations of the TBS and the U-UAV/T-UAV. Moreover, we analytically prove that optimal UAV location falls within a partial surface of the spherical cone centered at the GS. Numerical results show that T-UAV outperforms U-UAV given that sufficient GS locations accessibility and tether length are provided.