Big Spatial Data Management for the Internet of Things: A Survey
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Big Spatial Data Management for the Internet of Things: A Survey Isam Mashhour Al Jawarneh1 · Paolo Bellavista1 · Antonio Corradi1 · Luca Foschini1 · Rebecca Montanari1 Received: 13 March 2020 / Revised: 9 June 2020 / Accepted: 23 June 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The high abundance of IoT devices have caused an unprecedented accumulation of avalanches of geo-referenced IoT spatial data that if could be analyzed correctly would unleash important information. This can feed decision support systems for better decision making and strategic planning regarding important aspects of our lives that depend heavily on location-based services. Several spatial data management systems for IoT data in Cloud has recently gained momentum. However, the literature is still missing a comprehensive survey that conceptualize a convenient framework that classify those frameworks under appropriate categories. In this survey paper, we focus on the management of big geospatial data that are generated by IoT data sources. We also define a conceptual framework and match the works of the recent literature with it. We then identify future research frontiers in the field depending on the surveyed works. Keywords Spatial data · Spark · Hadoop · HBase · MongoDB · Spatial partitioning · Internet of Things · Query optimizers Abbreviations BSO Boundary spatial objects BSP Binary space partition DB Database DBSCAN-MR Density-based spatial clustering of applications with noise—MapReduce GI Global indexing GIS Geographic information System * Paolo Bellavista [email protected] Isam Mashhour Al Jawarneh [email protected] 1
Department Computer Science and Engineering (DISI), University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy
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Vol.:(0123456789)
Journal of Network and Systems Management
IoT Internet of Things JSON JavaScript object notation kNN k nearest neighborhood MBR Minimum bounding rectangle MLI Multi-level index NoSQL Not-only SQL ODSI On demand spatial indexing OLI One-layer index QoS Quality of service RDD Resilient distributed datasets SAQP Spatial approximate query processing SCI Spatial coding index SDL Spatial data locality SDME Spatial data management engine SDMS Spatial data management system SFC Space-filling curves SLA Service level agreement SLR Systematic literature review SPE Spatial processing engine SRDD Spatial RDD STR Sort-tile recurse
1 Introduction In the last decade or so, the proliferation of ubiquitous positioning devices, and a massive spread of the Internet of Things (IoT) paradigm have caused an accumulation of an unprecedented huge mass of datasets, forming a phenomenon referred to as big data. Today, all kinds of businesses are data-driven, with data being mostly geocoded and real-time [1], making timely analysis a priority, and thus promoting the emergence of Geographic Information Systems (GISs), with wide spectrum of applications, including participatory healthcare [2], neurology analytics
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