Abstract
The Internet of Things (IoT) has created new and challenging opportunities for data analytics. The IoT represents an infinitive source of massive and heterogeneous data, whose real-time processing is an increasingly important issue. IoT applications usually consist of multiple technological layers connecting ‘things’ to a remote cloud core. These layers are generally grouped into two macro levels: the edge level (consisting of the devices at the boundary of the network near the devices that produce the data) and the core level (consisting of the remote cloud components of the application). The aim of this work is to propose an adaptive microservices architecture for IoT platforms which provides real-time stream processing functionalities that can seamlessly both at the edge-level and cloud-level. More in detail, we introduce the notion of μ-service, a stream processing unit that can be indifferently allocated on the edge and core level, and a Reference Architecture that provides all necessary services (namely Proxy, Adapter and Data Processing μ-services) for dealing with real-time stream processing in a very flexible way. Furthermore, in order to abstract away from the underlying stream processing engine and IoT layers (edge/cloud), we propose: (1) a service definition language consisting of a configuration language based on JSON objects (interoperability), (2) a rule-based query language with basic filter operations that can be compiled to most of the existing stream processing engines (portability), and (3) a combinator language to build pipelines of filter definitions (compositionality). Although our proposal has been designed to extend the Senseioty platform, a proprietary IoT platform developed by FlairBit, it could be adapted to every platform based on similar technologies. As a proof of concept, we provide details of a preliminary prototype based on the Java OSGi framework.
Highlights
Nowadays, with the rise of the Internet of Things (IoT), we have at our disposal a wide variety of smart devices able to constantly produce large volumes of data at an unprecedented speed.Sensors, smartphones, and any other sorts of IoT devices are able to measure an incredible range of parameters, such as temperature, position, motion, health indicators, and so forth
At a very high level, a general IoT application consists of the following layers: the sensors/actuators layer, which includes the IoT devices; The edge layer, which includes all the devices near the sensors/actuator level, and these edge devices usually play the role of gateways, enabling the collection and the transmission of data; the core/cloud layer, which includes all the core functionalities and services of the application; the application/presentation layer, which includes all the client applications that have access to the core functionalities and services
In order to cope with all above mentioned challenges, in our work we propose a flexible and adaptive solution for integrating real-time stream processing functionalities into an IoT platforms able to satisfy the different requirements imposed by the edge level and the cloud level
Summary
With the rise of the Internet of Things (IoT), we have at our disposal a wide variety of smart devices able to constantly produce large volumes of data at an unprecedented speed. The aim of real-time stream processing is to query continuous data. The IoT world offers an infinite set of use cases where real-time stream processing functionalities can be applied, but IoT applications provide, at the same time a heterogeneous environment with respect to requirements, devices, and technologies. For these reasons, integrating real-time processing engines in IoT platforms becomes a challenging operation that requires special attention. When integrating real-time stream processing engines into IoT platforms, the main difficulties arise from the high heterogeneity and dynamicity of the requirements and technologies of common. At a very high level, a general IoT application consists of the following layers: the sensors/actuators layer, which includes the IoT devices; The edge layer, which includes all the devices near the sensors/actuator level, and these edge devices usually play the role of gateways, enabling the collection and the transmission of data; the core/cloud layer, which includes all the core functionalities and services of the application; the application/presentation layer, which includes all the client applications that have access to the core functionalities and services
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