Abstract
Increasingly inexpensive unmanned aerial vehicles (UAVs) are helpful for searching and tracking moving objects in ground events. Previous works either have assumed that data about the targets are sufficiently available, or they solely rely on on-board electronics (e.g., camera and radar) to chase them. In a searching mission, path planning is essentially preprogrammed before taking off. Meanwhile, a large-scale wireless sensor network (WSN) is a promising means for monitoring events continuously over immense areas. Due to disadvantageous networking conditions, it is nevertheless hard to maintain a centralized database with sufficient data to instantly estimate target positions. In this paper, we therefore propose an online self-navigation strategy for a UAV-WSN integrated system to supervise moving objects. A UAV on duty exploits data collected on the move from ground sensors together with its own sensing information. The UAV autonomously executes edge processing on the available data to find the best direction toward a target. The designed system eliminates the need of any centralized database (fed continuously by ground sensors) in making navigation decisions. We employ a local bivariate regression to formulate acquired sensor data, which lets the UAV optimally adjust its flying direction, synchronously to reported data and object motion. In addition, we also construct a comprehensive searching and tracking framework in which the UAV flexibly sets its operation mode. As a result, least communication and computation overhead is actually induced. Numerical results obtained from NS-3 and Matlab cosimulations have shown that the designed framework is clearly promising in terms of accuracy and overhead costs.
Highlights
Combination of unmanned aerial vehicles (UAVs) and a wireless sensor network (WSN) has been attracting much attention from the research community
(4) We present a maneuvering framework in which the UAV flexibly changes its path adjustment policy depending on gathered measurement data
That the searching and tracking strategies have clearly been clarified, let us evaluate their complexity and data communication load. It can be inferred from the analytic model and algorithms presented in Sections 3 and 4 that the overhead is influenced by the UAV steering policy, operation mode, object mobility, data updating frequency, and regression parameters
Summary
Combination of unmanned aerial vehicles (UAVs) and a wireless sensor network (WSN) has been attracting much attention from the research community. With respect to usage of on-board sensing electronics, in [20, 21], the authors proposed image-based navigation algorithms This strategy obviously eases the work of users/operators, given that UAVs provide visual data of targets. Ontology-driven representation using semantic statements such as those in the TrackPOI schema [23, 24] generates high-level descriptions of the scene, which supports UAVs and human operators to visually detect, differentiate and track moving objects Note that this approach does not target at searching any object or navigating UAVs in real time to acquire those data. Preprogramming paths before departure, as mentioned earlier, potentially navigate the UAVs along nonoptimal trajectories This means that the model works efficiently against only such events as gas emission, where no highly dynamic object is targeted.
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