Evaluating a Flow-Based Programming Approach as an Alternative for Developing CEP Applications in IoT

Abstract

One of the main advantages brought by the Internet of Things (IoT) is the possibility of having large amounts of data from several sources that allow us, once analyzed, to make decisions in various domains in real time. This implies the need to be able to process large volumes of data in more or less limited processing times depending on the application domain. In this sense, complex event processing (CEP), used in conjunction with an enterprise service bus (ESB), has proven to be very efficient in multiple domains. In search for greater efficiency, some CEP engines offer the option of using flow-based programming (FBP) rather than their traditional programming using CEP together with an event bus. However, its use, while it may be more efficient, can lead to other limitations. In this article, we analyze and describe the performance and limitations of using a CEP engine with an ESB versus a CEP engine with FBP. This will allow developers to decide which option is more convenient for their IoT system depending on the application domain and its specific needs.Index Terms-Complex event processing (CEP), dataflow, dataflow computing, enterprise service bus (ESB), flow-based programming (FBP), Internet of Things (IoT).

Model-Driven Engineering for Complex Event Processing: A Survey.

“…CEP is one of the most powerful technologies in distributed real-time environments as it provides a fast and efficient way to correlate and infer conclusions about events occurring in real time (Barquero et al 2018). This technology can be applied in a multitude of areas such as logistics, monitoring critical infrastructure and financial applications (Hinze et al 2009), business processes (Soffer et al 2019), e-health (Caballero et al 2021), air quality monitoring (Boudriki Semlali et al 2021;, cybersecurity (Roldán et al 2020;Roldán-Gómez et al 2021), Internet of things (Ortiz, Castillo, et al 2022), autonomous unmanned aerial vehicles (Boubeta-Puig et al 2018), self-adaptive systems (Parra-Ullauri et al 2022;Romero-Garcés et al 2022), and intelligent transportation systems (Brazález et al 2022).…”