The internet of things (IoT) has led to an explosive increase in connected devices, generating a massive volume of data. Real-time analytics in IoT systems is crucial for timely decision-making, enhancing system efficiency and reliability. This involves processing discrete IoT data series within a bounded completion time, providing services like data classification, pattern analysis, and tendency prediction. However, the continuous and heterogeneous generation of IoT data poses significant technical challenges. Designing IoT systems to handle this data in a timely manner becomes critical. This chapter comprehensively explores real-time data analytics in IoT systems, elucidating its characteristics and analysing suitable architectures. A survey of existing applications highlights system design perspectives and performance shortcomings. Lastly, challenges in applying real-time analytics in IoT systems are identified, paving the way for future research directions.
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An eclectic assortment of applications, such as smart homes, connected healthcare, as well as intelligent transportation, have integrated Internet of Things (IoT) systems, as shown in Figure 1 (Gubbi et al., 2013). This is due to the rapid evolution for communication, computation as well as embedded systems technologies. The total number of active Internet of Things (IoT) devices has significantly increased as a result of this technological development; by 2022, it is predicted that there will be 18 billion devices worldwide (Qureshi, 2014). These IoT devices, dynamically interacting with the physical environment, continuously generate a prodigious volume of data, thereby introducing formidable challenges for data analytics within IoT systems. Notably, the challenges are accentuated when the completion time for data analytics becomes a critical requirement.
Given the imperative nature of timely data processing in the dynamically evolving landscape of IoT applications, real-time data analytics assumes paramount significance within IoT systems. This necessitates a conscientious consideration of the real-time dimension in the analytical processes of IoT systems to ensure the seamless extraction of actionable insights from the continuous and diverse streams of IoT data.
Delving into the intricacies of IoT systems, this section initiates with an introduction to the conceptual architecture. This architectural framework provides a comprehensive blueprint that delineates the interconnected elements, including sensors, communication networks, computing nodes, and actuators. It serves as a foundational guide, orchestrating seamless interactions between the physical and digital realms within the IoT ecosystem. The deployment of IoT systems spans a multitude of applications, each tailored to address specific challenges and enhance user experiences.
The challenges inherent in the domain of IoT data analytics stem from the continuous generation of heterogeneous and voluminous data. Traditional approaches find themselves inadequate in coping with the dynamic and real-time nature of this data. Consequently, real-time data analytics emerges as a strategic imperative, facilitating the expeditious processing and interpretation of dynamic IoT data streams. In scenarios where IoT devices directly engage in real-time monitoring, such as in healthcare or industrial automation, the significance of real-time analytics becomes increasingly evident.
Understanding the characteristics of IoT data is pivotal in devising effective strategies for real-time analytics. IoT data exhibits traits such as high velocity, diverse formats, and staggering volume, necessitating advanced analytics techniques capable of handling the intricacies of real-time IoT data. A taxonomy of IoT data analytics is developed, encompassing dimensions like the nature of data, analytics objectives, and methodologies. Within this taxonomy, real-time analytics is distinguished as a critical subset, focusing on the expeditious processing of data within stringent time constraints.