Continuous and parallel LiDAR point-cloud clustering
Paper in proceedings, 2018

In distributed digitalized environments in the context of the Internet of Things, we often need to do an analysis of big data originating at high rate-sensors at the edge of the infrastructure. A characteristic example is the light detection and ranging (LiDAR) technology, that allows sensing surrounding objects with fine-grained resolution in large areas. Their data (known as point clouds), generated continuously at very high rates, through appropriate analysis can provide information to support automated functionality in distributed cyber-physical? systems; clustering of point clouds is a key problem to extract this type of information. Methods for solving the problem in a continuous fashion can facilitate improved processing in fog architectures, through enabling low-latency, efficient continuous and streaming processing of data close to the sources; moreover, parallelism is a key requirement to exploit a variety of computing architectures in this context. We proposeLisco, a single-pass continuous Euclidean-distance-based clustering of LiDAR point clouds, that maximizes the granularity of the data processing pipeline and thus shows the potential for data-and pipeline-parallelism. We further present its parallel version, P-Lisco, that is architecture-independent and exploits the parallelism revealed byLisco'salgorithmic approach. Besides their algorithmic analysis, we provide a thorough experimental evaluation on architectures representative of high-end servers and of resource-constrained embedded devices and highlight the multiplicative improvements and scalability benefits of the proposed algorithms compared to the baseline, using both real-world datasets as well as synthetic ones to fully explore a wide spectrum of stress-levels for the algorithms.

Data Analysis

Edge Computing

Parallel

Lidar

Streaming

Fog Computing

Clustering

Point-Cloud

Author

Hannaneh Najdataei

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Ioannis Nikolakopoulos

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Vincenzo Massimiliano Gulisano

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Marina Papatriantafilou

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Proceedings - International Conference on Distributed Computing Systems

Vol. 2018-July 671-684

38th IEEE International Conference on Distributed Computing Systems, ICDCS 2018
Vienna, Austria,

HARE: Self-deploying and Adaptive Data Streaming Analytics in Fog Architectures

Swedish Research Council (VR), 2017-01-01 -- 2020-12-31.

Future factories in the Cloud (FiC)

Swedish Foundation for Strategic Research (SSF), 2016-01-01 -- 2020-12-31.

Subject Categories

Computer Engineering

Other Computer and Information Science

Media Engineering

DOI

10.1109/ICDCS.2018.00071

More information

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1/9/2019 3