Therefore, PLCs are more suitable for discrete processes’ automation, such as an automobile assembly line in which there are lots of digital signals and a few analog signals. Programmable logic controllers (PLCs) use a single CPU capable of controlling the whole process. DCS is more appropriate for continuous processes, including many analog/digital sensors/signals and PID (proportional, integral, derivative) control loops. If one fails, the other CPUs will continue executing their function (exhibiting fault tolerance). A decentralized control system (DCS) is a control method with some independent CPUs. Two main concepts exist in the industry, DCS and PLCs. Gathering the measurements of a set of IIoT sensors requires an appropriate control system. Finally, the relational PostgreSQL outperformed MongoDB and PostgreSQL JSON significantly in all correlation function experiments, with performance improvements from MongoDB, closing the gap with PostgreSQL towards minimizing response time to 26% and 3% for six and eight shards, respectively, and achieving significant gains towards average achieved throughput. At the same time, MongoDB achieved 19–31% faster responses and 44–63% higher throughput than PostgreSQL in the four tested sharding subcases (two, four, six, eight shards), accordingly. Relational PostgreSQL was × 2 times faster than MongoDB in its standalone implementation for selection queries. Furthermore, for the data insertion experimental cases of six and eight shards, MongoDB performed 13–20% more than Postgres in response time, achieving × 2 times higher throughput. The experimental results concluded that PostgreSQL with JSON achieves a 5–57% better response than MongoDB for the insert queries (cases of native, two, and four shards implementations), while, on the contrary, MongoDB achieved 56–91% higher throughput than PostgreSQL for the same set up. Three distinct scenarios have been thoroughly tested, the most common but widely used: (i) data insertions, (ii) select/find queries, and (iii) queries related to aggregate correlation functions. Several experimental cases have been performed to measure database queries’ response time, achieved throughput, and corresponding failures. Then, it focuses on evaluating industrial cloud storage engines for sensory functions, experimenting with three open-source types of distributed Database Management Systems (DBMS) MongoDB and PostgreSQL, with two forms of PostgreSQL schemes (Javascript Object Notation (JSON)-based and relational), against their respective horizontal scaling strategies. This paper presents the authors’ proposition for cloudcentric sensory measurements and measurements acquisition. Due to its enormous size, it may be stored in the cloud. For example, applications using Internet of Things (IoT) sensory data, such as in Industry 4.0, are a classic example of an organized storage system. Databases are an integral part of almost every application nowadays.
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