ZipWeave: Towards efficient and reliable measurement based mobile coverage maps
ZipWeave: Towards efficient and reliable measurement based mobile coverage maps
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DOI：
10.1109/infocom.2017.8057098
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发表时间：
2017-05
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影响因子：
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通讯作者：
Mah-Rukh Fida;Andra Lutu;M. Marina;Özgü Alay
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文献类型：
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作者：
Mah-Rukh Fida;Andra Lutu;M. Marina;Özgü Alay
测量驱动的移动覆盖图的精度取决于信号强度观测的质量、密度和模式。因此，确定有效的测量数据收集方法是至关重要的，特别是在考虑与测量收集方法(例如，路测、人群方法)相关的成本时。我们提出了ZipWeave，一个新的测量数据收集和融合框架，用于构建高效可靠的基于测量的移动覆盖图。ZipWeave采用了一种新颖的非均匀采样策略，以减少样本量获得可靠的覆盖图。假设已知感兴趣区域的传播特性，我们首先通过基于测量的统计分析方法来检查这种非均匀采样策略在不同情况下的潜在增益；这涉及将感兴趣区域不规则地空间分割成具有内部相似无线电传播特性的子区域，并基于这些子区域进行采样。然后，我们提出了一种实用的ZipWeave非均匀抽样策略，即使在没有任何先验信息的情况下也可以使用。在我们的所有评估中，我们表明，ZipWeave非均匀抽样方法比普通的系统-随机抽样方法减少了一半的样本，同时保持了相似的精度。此外，我们还展示了ZipWeave的另一个关键功能，它将高质量的受控测量(呈现类似于路测的有限地理足迹)与众包测量(覆盖更广泛的足迹)相结合，从而在总体上产生更可靠的移动覆盖地图。
The accuracy of measurement-driven mobile coverage maps depends on the quality, density and pattern of the signal strength observations. Thus, identifying an efficient measurement data collection methodology is essential, especially when considering the cost associated with the measurement collection approaches (e.g., drive tests, crowd approaches). We propose ZipWeave, a novel measurement data collection and fusion framework for building efficient and reliable measurement-based mobile coverage maps. ZipWeave incorporates a novel nonuniform sampling strategy to achieve reliable coverage maps with reduced sample size. Assuming prior knowledge of the propagation characteristics of the region of interest, we first examine the potential gains of this non-uniform sampling strategy in different cases via a measurement-based statistical analysis methodology; this involves irregular spatial tessellation of the region of interest into sub-regions with internally similar radio propagation characteristics and sampling based on these sub-regions. We then present a practical form of ZipWeave nonuniform sampling strategy that can be used even without any prior information. In all our evaluations, we show that the ZipWeave non-uniform sampling approach reduces the samples by half compared to the common systematic-random sampling, while maintaining similar accuracy. Moreover, we show that the other key feature of ZipWeave to combine high-quality controlled measurements (that present limited geographic footprint similar to drive tests) with crowdsourced measurements (that cover a wider footprint) leads to more reliable mobile coverage maps overall.