Introduction

Deep learning consists of supervised or unsupervised learning techniques. IoT is utilizing a broad range of sophisticated technologies, from embedded devices and communication technologies to data analytics. The number of IoT devices is increasing day by day. Due to this, there is a tremendous amount of data to be handled. Therefore data analytics becomes very important. In this blog, we’ll be mostly looking at data analytics. Deep Learning would play a vital role in creating smarter IoT as it has shown remarkable results in different fields including image recognition, information retrieval, speech recognition, natural language processing. Before we get into deep learning, let’s look at deep neural networks.

Deep Neural Networks

When we talk about deep learning, it becomes necessary to talk about deep neural networks. Deep Neural Network (DNN) comprises several processing layers. The working of deep neural networks was inspired by the functionalities of the human brain. The main aim is to construct an artificial neuron so as to mimic a human neuron. Each layer comprises several processing units called neurons. A neuron computes the weighted sum of the inputs and passes the resulting sum as an input to an activation function that produces the desired output. Therefore the activation function converts a given input into the desired output. Hence depending on a particular activation function, a particular output is produced. Given below are different activation functions and the basic structure of an artificial neuron.

Need for Deep Learning in IoT

DL consists of supervised or unsupervised learning techniques based on many layers of Artificial Neural Networks (ANNs). IoT devices continue to rise day after day. This means that a tremendous amount of data has to be handled. IoT has fast streaming data and big data. Therefore It becomes highly impossible to handle such data by humans. This gives way to deep learning in IoT.

IoT fast and streaming data

An IoT device continuously sends and receives data. So to do this efficiently, incremental processing is done. Incremental processing refers to fetching a small batch of data, processed quickly in a pipeline of tasks. Although these reduce the time latency to return a faster response time, this is not the best possible solution. It is an alternative to continuously streaming data so as to reduce latency.

IoT big data

IoT is well known as the major source of big data. It based on connecting a huge number of devices over the internet. Big data characterized by 6V’s:

• Volume: Data volume is a determining factor to consider a dataset as big data or traditional massive/ very large data.
• Velocity: The rate of IoT big data production and processing is high enough to support the availability of big data in real-time. This justifies the need for advanced tools and technologies for analytics to efficiently operate given this high rate of data production.
• Variety: Generally, big data comes in different forms and types. It may also consist of structured, semi-structured, and unstructured data. A wide variety of data types produced by IoT such as text, audio, video, sensory data, and so on.
• Veracity: Veracity refers to the quality, consistency of data. This property needs special attention to hold for IoT applications.
• Variability: This property refers to the different rates of data flow. Depending on the nature of IoT applications, different data generating components may have inconsistent data flows.
• Value: Value is the transformation of big data to useful information and insights that bring competitive advantage to the company. Data value is a measure of the treatment of data.

Challenges and future potential

Although Deep Learning models have got the immense potential for analyzing data produced by IoT devices. Moreover, the challenges include:

• Massive-scale for IoT data: The massive quantity of data creates a huge challenge for Deep Learning in terms of time and structure complexities.
• Data pre-processing: Data pre-processing in the case of IoT is a difficult task as the system deals with data from different sources and may contain noisy or missing data.
• High velocity: The rate of production of IoT data requires fast processing of data. By optimizing the existing algorithms, this can be done.
• The requirement of large IoT data sets: Training of deep learning models requires a large number of instances for generating accurate results.
• Heterogeneity: It refers to the variety of the data set. Managing conflicts from a variety of data sets is still a challenge. Therefore heterogeneity is a major challenge.

Conclusion

Deep Learning is quite effective for analyzing highly complex data generated by IoT applications. Moreover, Deep learning is very essential in analyzing large sets of data. The human brain is not capable of this. Deep learning models generate accurate prediction results. Deep learning also helps in analyzing complex data sets which is difficult to be understood by the human brain. In the end, there are still a lot of challenges to overcome to be fully reliable in deep learning. But in the near future deep learning will play a major role in IoT.