25 Datasets for Deep Learning in IoT

IoT and Big Data: The relationship

IoT and Big data have a two-way relationship. IoT is the main producer of big data, and as such an important target for big data analytics to improve the processes and services of IoT. However, there is a difference between the two.

• Large-Scale Streaming data: IoT data is a large-scale streaming data. This is because a large number of IoT devices generate streams of data continuously. Big data, on the other hand, lack real-time processing.
• Heterogeneity: IoT data is heterogeneous as various IoT data acquisition devices gather different information. Big data devices are generally homogeneous in nature.
• Time and space correlation: IoT sensor devices are also attached to a specific location, and thus have a location and time-stamp for each of the data items. Big data sensors lack time-stamp resolution.
• High noise data: IoT data is highly noisy, owing to the tiny pieces of data in IoT applications, which are prone to errors and noise during acquisition and transmission. Big data, in contrast, is generally less noisy.

Big data, on the other hand, is classified according to conventional 3V’s, Volume, Velocity, and Variety. As such techniques used for Big data analytics are not sufficient to analyze the kind of data, that is being generated by IoT devices. For instance, autonomous cars need to make fast decisions on driving actions such as lane or speed change. These decisions should be supported by fast analytics with data streaming from multiple sources (e.g., cameras, radars, left/right signals, traffic light etc.). This changes the definition of IoT big data classification to 6V’s.

• Volume: The quantity of generated data using IoT devices is much more than before and clearly fits this feature.
• Velocity: Advanced tools and technologies for analytics are needed to efficiently operate the high rate of data production.
• Variety: Big data may be structured, semi-structured, and unstructured data. The data types produced by IoT include text, audio, video, sensory data and so on.
• Veracity: Veracity refers to the quality, consistency, and trustworthiness of the data, which in turn leads to accurate analytics.
• Variability: This property refers to the different rates of data flow.
• Value: Value is the transformation of big data to useful information and insights that bring competitive advantage to organizations.

Despite the recent advancement in DL for big data, there are still significant challenges that need to be addressed to mature this technology. Every 6 characteristics of IoT big data imposes a challenge for DL techniques. One common denominator for all is the lack of availability of IoT big data datasets.  

IoT datasets and why are they needed

Deep learning methods have been promising with state-of-the-art results in several areas, such as signal processing, natural language processing, and image recognition. The trend is going up in IoT verticals as well. IoT datasets play a major role in improving the IoT analytics. Real-world IoT datasets generate more data which in turn improve the accuracy of DL algorithms. However, the lack of availability of large real-world datasets for IoT applications is a major hurdle for incorporating DL models in IoT. The shortage of these datasets acts as a barrier to deployment and acceptance of IoT analytics based on DL since the empirical validation and evaluation of the system should be shown promising in the natural world. The lack of availability is mainly because:

• Most IoT datasets are available with large organizations who are unwilling to share it so easily.
• Access to the copyrighted datasets or privacy considerations. These are more common in domains with human data such as healthcare and education.

While there is a lot of ground to be covered in terms of making datasets for IoT available, here is a list of commonly used datasets suitable for building deep learning applications in IoT.