How Data Science Can Help IoT Data Analysis?
With the development of connectivity on many objects and devices, the emergence of new communication protocols, the arrival on the cheap sensor in the market, and the inflation data that they emit, IoT (Internet of Things) now takes place not only in our daily lives, but also in many businesses.
When talking about the “IoT Analytics”, it seems that the discipline as something in itself. But is the IoT data analysis so different from other data analysis in the traditional sense? You can answer this question with the reconciliation of Data Science and IoT .
The characteristics of the IoT data
When talking about IoT data, we often speak of sensor data (e.g., temperature sensors, humidity, GPS, etc). These data have certain characteristics:
- They are often in the form of time series (e.g., temperature sensor data, humidity)
- They can come from heterogeneous sources (e.g., sound, video, temperature, etc.)
- There is no standard in the reception data. Indeed, similar devices from different manufacturers (e.g., thermometers) use completely different data formats and generate data at different frequencies.
- Sensor data often arrive in real time
- Sensor data are often geo-tagged
After reading this list, we see that the particularities of these data types are not exclusive domains of the IoT. They can be found indeed in other fields of application – Signal processing techniques have existed for centuries, and Deep Learning from recent advances allow to work wonders on pictures, videos or sound, etc. Therefore, methods of “classical” Data Science associated with these features will help us solve the IoT data analysis problems.
Successful integration of IoT data within the company
The value of the IoT lies mainly in the use of data from different sensors. Thus, a company that has thousands, even millions of available objects, will have to know how to exploit and process data to extract valuable insights.
Before extracting the value of this data, companies must first pass the integration phase of these new sources with the existing data, and connect these new data to their internal applications so you can trigger actions in real time . The challenge of connecting data with applications is the creation of value-added services – alerts, performance optimization or anomaly detection.
Once you integrate new IoT data sources with those existing data in the company, the next step is to add intelligence by integrating the analytical dimension and Machine Learning to devices. This will allow us to create new services such as providing advice to consumers, detect malfunctions and predict.
Some specifics related to the processing of IoT data
Regardless of the analytical dimension and the processing of the IoT data, the volume of these data is often considerable, and sometimes it is not necessary to send everything to the Cloud or the Data Lake.
Moreover, the sending of these data must be reliable in the sense that, for real-time processing, it is possible, for example, to avoid sending incorrect measures that could trigger erroneous decisions. Thus, some processing can be done at the Edge – some data can be deleted or summarized and aggregated or standardized at the Edge before being sent to the storage system.
The nature of the IoT data, often in time series, poses problems on the aggregations to be made in order to be able to analyze them. Thus, it is necessary to be able to align the data on the same temporal scale, and to perform the correct calculations during these aggregations.
We must also remember where the data comes from and what we measure, and therefore have new approaches to indexing. In this context, there are time-series oriented databases that respond to a more appropriate indexing logic by associating the time value with a particular class of metric (e.g. temperature measurement).
It should also be noted that the data from sensors are often auto-correlated, and therefore consecutive observations will very often be similar, which can make the modeling more complicated from a statistical point of view.
In addition, it may be complex to detect anomalies when measuring from several sources at the same time or requiring techniques for merging the sensor data. It then becomes necessary to think about new approaches to indexing the data, as well as the use of filtering techniques to denoise the data.
The IoT appears as a threatening case of application of Data Science techniques already known and proven in other fields of application, as it is the case for the hardware components and the means of communication. Indeed, by examining the type of available data and potential applications, we will find that it will be possible to draw on the knowledge and techniques already used in other fields in the past to help us. Some challenges remain, especially in terms of security, but the topic of Data Science and IoT is already launched and should increase in intensity very quickly.