IoT has connected things never previously connected to the internet, such as car engines, resulting in the generation of a large amount of continuous data streams. The following screenshot shows explorative data by IHS of the number of connected devices in billions in future years. Their estimate shows that the number of IoT devices will reach 75.44 billion by 2025:
The reduction in sensor cost, efficient power consumption techniques, a large range of connectivity (infrared, NFC, Bluetooth, Wi-Fi, and so on), and the availability of cloud platforms that support IoT deployment and development are the major reasons for this pervasion of IoT in our homes, personal lives, and industry. This has also motivated companies to think about providing new services and developing new business models. Some examples include the following:
- Airbnb: It connects people so that they can rent out spare rooms and cottages to one another, and it earns the commission.
- Uber: It connects cab drivers with travelers. The location of the traveler is used to assign them to the nearest driver.
The amount of data generated in the process is both voluminous and complex, necessitating a big data. Big data approach and IoT are almost made for each other; the two work in conjunction.
Things are continuously generating an enormous amount of data streams that provide their statuses such as temperature, pollution level, geolocation, and proximity. The data generated is in time series format and is autocorrelated. The task becomes challenging because the data is dynamic in nature. Also, the data generated can be analyzed at the edge (sensor or gateway) or cloud. Before sending the data to the cloud, some form of IoT data transformation is performed. This may involve the following:
- Temporal or spatial analysis
- Summarizing the data at the edge
- Aggregation of data
- Correlating data in multiple IoT streams
- Cleaning data
- Filling in the missing values
- Normalizing the data
- Transforming it into different formats acceptable to the cloud
At the edge, complex event processing (CEP) is used to combine data from multiple sources and infer events or patterns.
The data is analyzed using stream analytics, for example, applying analytical tools to the stream of data, but developing the insights and rules used externally in an offline mode. The model is built offline and then applied to the stream of data generated. The data may be handled in different manners:
- Atomic: Single data at a time is used
- Micro batching: Group of data per batch
- Windowing: Data within a timeframe per batch
The stream analytics can be combined with the CEP to combine events over a time frame and correlate patterns to detect special patterns (for example, anomaly or failure).
