The upcoming digitalization in the context of Cyber-physical Systems (CPS), enabled through Internet-of-Things (IoT) infrastructures, require efficient methods for distributed processing of the data, that is generated by multiple sources. We address the problem of obstacle detection and localization through data clustering, which is a common component for data processing in the fusion of multiple point clouds, each obtained by a LIDAR sensor. Such sensors generate data at high rates and can rapidly exhaust traditional methods that centrally gather and process the global data. To that end, we propose MAD-C, an approximate method for distributed data summarization through clustering, that can orthogonally build on known methods for fine-grained point-cloud clustering, and synthesize a decentralized approach, which exploits the distributed processing capacity efficiently and prevents saturation of the communication network. In MAD-C, corresponding to the point-cloud gathered by each LIDAR sensor, local clusters are first identified, each corresponding to an object in the sensed environment from the perspective of the respective sensor. Afterwards, the information about each locally detected object is transformed into a data-summary, computable in a continuous manner, with constant overhead in time and space. The summaries are then combined, in an order-insensitive, concurrent fashion, to produce approximate volumetric representations of the objects in the fused data. We show that the combined summaries, in addition to localizing objects and approximating their volumetric representations, can be used to answer relevant queries regarding the relative position of the objects in environment and a geofence. We evaluate the performance of MAD-C extensively, both analytically and empirically. The empirical evaluation is performed on an IoT test-bed as well as in simulation. Our results show that MAD-C leads to (i) communication savings proportional to the number of points, (ii) multiplicative decrease in the dominating component of the processing complexity and, at the same time, (iii) high accuracy (with RandIndex $>0.95$), in comparison to its baseline counterpart for obstacle detection and localization, as well as (iv) linear computational complexity in terms of the number of objects, for the geofence related queries.

通过物联网（IoT）基础架构实现的即将在网络物理系统（CPS）范围内进行的数字化，需要高效的方法来分布式处理由多个源生成的数据。我们通过数据聚类解决了障碍物检测和定位的问题，数据聚类是多点云融合中数据处理的通用组件，每个点云都是由激光雷达传感器获得的。这样的传感器可以高速生成数据，并且可以迅速用尽传统方法集中收集和处理全局数据。为此，我们提出了MAD-C，这是一种通过聚类进行分布式数据汇总的近似方法，它可以在已知的细粒度点云聚类方法的基础上正交构建，并综合一种去中心化方法，这有效地利用了分布式处理能力并防止了通信网络的饱和。在MAD-C中，对应于每个LIDAR传感器收集的点云，首先识别局部簇，每个局部簇从相应传感器的角度对应于感测环境中的对象。然后，将有关每个本地检测到的对象的信息转换为数据摘要，以连续方式可计算，并且在时间和空间上具有恒定的开销。然后将这些摘要以顺序不敏感的并发方式进行组合，以生成融合数据中对象的近似体积表示。我们展示了组合的摘要，除了对对象进行定位并逼近其体积表示之外，可用于回答有关对象在环境和地理围栏中的相对位置的相关查询。我们通过分析和经验广泛地评估MAD-C的性能。在IoT测试平台以及仿真中进行实证评估。我们的结果表明，MAD-C导致（i）与点数成正比的通信节省;（ii）处理复杂性的主要组成部分成倍减少;同时（iii）高精度（使用RandIndex$>0。95$）（相比于用于障碍物检测和定位的基线对应对象，以及（iv）在对象数量方面的线性计算复杂度）进行地理围栏相关查询。