Edge computing constitutes a promising paradigm of managing and processing the massive amounts of data generated by Internet of Things (IoT) devices. Data and computation are moved closer to the client, thus enabling latency- and bandwidth-sensitive applications. However, the distributed and heterogeneous nature of the edge as well as its limited resource capabilities pose several challenges in implementing or choosing an efficient edge-enabled storage system. Therefore, it is imperative for the research community to contribute to the clarification of the purposes and highlight the advantages and disadvantages of various edge-enabled storage systems. This work aspires to contribute toward this direction by presenting a performance analysis of three different storage systems, namely MinIO, BigchainDB, and the IPFS. We selected these three systems as they have been proven to be valid candidates for edge computing infrastructures. In addition, as the three evaluated systems belong to different types of storage, we evaluated a wide range of storage systems, increasing the variability of the results. The performance evaluation is performed using a set of resource utilization and Quality of Service (QoS) metrics. Each storage system is deployed and installed on a Raspberry Pi (small single-board computers), which serves as an edge device, able to optimize the overall efficiency with minimum power and minimum cost. The experimental results revealed that MinIO has the best overall performance regarding query response times, RAM consumption, disk IO time, and transaction rate. The results presented in this paper are intended for researchers in the field of edge computing and database systems.

The sudden rise of GPS-enabled mobile devices has given birth to research related to the analysis and visualization of big mobility data that are stored in large spatio-temporal databases. Therefore, this research is focused on evaluating and comparing widely-used database systems that are employed in the analysis of spatio-temporal data. Specifically, three database systems are evaluated and compared with each other, namely PostGIS, MobilityDB, and MongoDB, in their ability to perform range, temporal aggregate, distance and nearest-neighbor queries. To this end, a subset of the BerlinMOD benchmark queries is employed for evaluation purposes over vessel tracking data. The experimental results presented in this paper are only preliminary in an attempt to drive future research in the field of industrial use case surveillance.