Continuous and parallel LiDAR point-cloud clustering
Paper i proceeding, 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

Författare

Hannaneh Najdataei

Chalmers, Data- och informationsteknik, Nätverk och system

Ioannis Nikolakopoulos

Chalmers, Data- och informationsteknik, Nätverk och system

Vincenzo Massimiliano Gulisano

Chalmers, Data- och informationsteknik, Nätverk och system

Marina Papatriantafilou

Chalmers, Data- och informationsteknik, Nätverk och system

Proceedings - International Conference on Distributed Computing Systems

Vol. 2018-July 671-684

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

HAREN: Självdistribuerad och anpassningsbar dataströmningsanalys i dimman

Vetenskapsrådet (VR), 2017-01-01 -- 2020-12-31.

Molnbaserade produkter och produktion (FiC)

Stiftelsen för Strategisk forskning (SSF), 2016-01-01 -- 2020-12-31.

Ämneskategorier

Datorteknik

Annan data- och informationsvetenskap

Mediateknik

DOI

10.1109/ICDCS.2018.00071

Mer information

Senast uppdaterat

2019-01-09