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Comparison of MongoDB and Cassandra databases for Spectrum Monitoring as-a-Service

Publication typeJournal paper
Year of publicationIn press
AuthorsGiuseppe Baruffa, Mauro Femminella, Matteo Pergolesi, and Gianluca Reali
TitleComparison of MongoDB and Cassandra databases for Spectrum Monitoring as-a-Service
Journal titleIEEE Transactions on Network and Service Management
Volume
Issue
Pages
Editor
PublisherIEEE
Date In press
Place
ISSN number1932-4537
ISBN number
Key wordsDistributed spectrum sensing,Big Data,Lambda architecture,NoSQL,data model,MapReduce,data visualization
AbstractDue to the growing number of devices accessing the Internet through wireless networks, the radio spectrum has become a highly contended resource. The availability of low cost radio spectrum monitoring sensors enables a geographically distributed, real-time observation of the spectrum to spot inefficiencies and to develop new strategies for its utilization. The potentially large number of sensors to be deployed and the intrinsic nature of data make this task a Big Data problem. In this work we design, implement, and validate a hardware and software architecture for wideband radio spectrum monitoring inspired to the Lambda architecture. This system offers Spectrum Sensing as a Service to let end users easily access and process radio spectrum data. To minimize the latency of services offered by the platform, we fine tune the data processing chain. From the analysis of sensor data characteristics, we design the data models for MongoDB and Cassandra, two popular NoSQL databases. A MapReduce job for spectrum visualization has been developed to show the potential of our approach and to identify the challenges in processing spectrum sensor data. We experimentally evaluate and compare the performance of the two databases in terms of application processing time for different types of queries applied on data streams with heterogeneous generation rate. Our experiments show that Cassandra outperforms MongoDB in most cases, with some exceptions depending on data stream rate.
URLhttps://ieeexplore.ieee.org/document/8844790
DOIhttp://dx.doi.org/ 10.1109/TNSM.2019.2942475
Other information
Paper
Last update: 2015-10-12, 16:44:51