The next era of information revolution will rely on aggregating big data from massive numbers of devices that are widely scattered in our environment. The majority 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 regards, this paper proposes aerial data aggregation 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, an UAV flies over the field and collects the required data. Particularly, the field is divided into several subregions over which the UAV hovers to collect samples from the underlying nodes. To this end, an optimization problem is formulated and solved to find the optimal number of subregions, the area of each subregion, the hovering locations, the hovering time at each location, and the trajectory traversed between hovering locations such that an average number of samples are collected from the field in minimal time. 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.
|Title of host publication
|2018 IEEE Global Communications Conference (GLOBECOM)
|Institute of Electrical and Electronics Engineers (IEEE)
|Published - Feb 21 2019
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: This work is supported by the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700.