Wireless sensor networks (WSNs) are an important means of collecting data in a variety of situations, such as the monitoring of large or hazardous areas. The retrieval of WSN data can yield better results through the use of unmanned aerial vehicles (UAVs), for example, concerning the increase in the amount of data collected and the decrease in the time between the collection and use of the data. In particular, disaster areas may be left without communication resources and with high residual risk to humans, at which point a WSN can be quickly launched by air to collect relevant data until other measures can be established. The set of rules of each problem’s component (e.g., number of UAVs, UAVs dislocation control, sensors, communication) is considered the approaches to solve the problem. In this meaning, some studies present approaches for the use of UAVs for the collection of WSN data, focusing primarily on optimizing the path to be covered by a single UAV and relying on long-range communication that is always available; these studies do not explore the possibility of using several UAVs or the limitations on the range of communication. This work describes DADCA, a distributed scalable approach capable of coordinating groups of UAVs in WSN data collection with restricted communication range and without the use of optimization techniques. The results reveal that the amount of data collected by DADCA is similar or superior to path optimization approaches by up to 1%. In our proposed approach, the delay in receiving sensor messages is up to 46% shorter than in other approaches, and the required processing onboard UAVs can reach less than 75% of those using optimization-based algorithms. The results indicate that the DADCA can match and even surpass other presented approaches, since the path optimization is not a focus, while also incorporating the advantages of a distributed approach.

中文翻译：

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评估用于WSN数据收集的无人机小队的飞行协调方法，以扩大WSN数据收集的互联网范围

无线传感器网络（WSN）是在各种情况下（例如监视大型或危险区域）收集数据的重要手段。通过使用无人飞行器（UAV），WSN数据的检索可以产生更好的结果，例如，涉及到收集的数据量的增加和数据的收集与使用之间的时间的减少。特别是，灾区可能没有通信资源，对人类具有很高的残留风险，这时可以通过空中快速发射WSN以收集相关数据，直到可以采取其他措施为止。每个问题组成部分的规则集（例如，无人机数量，无人机位置控制，传感器，通信）被认为是解决问题的方法。在这个意义上，一些研究提出了使用无人飞行器收集WSN数据的方法，主要集中在优化单个无人飞行器所要覆盖的路径以及依赖始终可用的远程通信方面。这些研究没有探讨使用几种无人机的可能性或通信范围的限制。这项工作描述了DADCA，这是一种分布式可扩展方法，能够在受限的通信范围内且不使用优化技术的情况下协调WSN数据收集中的UAV组。结果表明，DADCA收集的数据量与路径优化方法相近或优于1％。在我们提出的方法中，接收传感器消息的延迟比其他方法缩短了46％，而且使用基于优化的算法所需要的机载无人机的处理能力不到其的75％。结果表明，DADCA可以匹配甚至超越其他提出的方法，因为路径优化不是重点，同时还结合了分布式方法的优点。