User-Centric Analysis of Multimedia Data Group, TU Ilmenau, 98693 Ilmenau, Germany.
Chair of Communication Networks, University of Würzburg, 97070 Würzburg, Germany.
Sensors (Basel). 2021 Aug 31;21(17):5880. doi: 10.3390/s21175880.
Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems that use mobile users in conjunction with fixed sensors might help to overcome these limitations, as such systems allow assessing the quality of the submitted crowdsensed data and provide sensor values where no crowdsensing data are typically available. In this work, we first used a simulation study to analyze a simple crowdsensing system concerning the detection performance of spatial events to highlight the potential and limitations of a pure crowdsourcing system. The results indicate that even if only a small share of inhabitants participate in crowdsensing, events that have locations correlated with the population density can be easily and quickly detected using such a system. On the contrary, events with uniformly randomly distributed locations are much harder to detect using a simple crowdsensing-based approach. A second evaluation shows that hybrid systems improve the detection probability and time. Finally, we illustrate how to compute the minimum number of fixed sensors for the given detection time thresholds in our exemplary scenario.
众包感知为收集大量环境传感器数据提供了一种具有成本效益的方式；然而，由于参与者携带传感器，众包传感器的空间分布很难受到影响，此外，众包数据的质量可能会有很大差异。结合移动用户和固定传感器的混合系统可能有助于克服这些限制，因为此类系统可以评估提交的众包数据的质量，并在通常没有众包数据的情况下提供传感器值。在这项工作中，我们首先使用模拟研究来分析一个简单的众包感知系统，以突出纯众包系统的潜力和局限性。结果表明，即使只有一小部分居民参与众包感知，位置与人口密度相关的事件也可以使用这样的系统轻松快速地检测到。相反，使用简单的基于众包的方法很难检测到位置均匀随机分布的事件。第二项评估表明，混合系统可以提高检测概率和时间。最后，我们说明了如何在我们的示例场景中计算给定检测时间阈值的固定传感器的最小数量。
