IoT Device Onboarding, Monitoring, and Management: Approaches, Challenges, and Future

Introduction

The Internet of Things (IoT) is the interconnection of physical objects accessible via the Internet¹. All these objects interact with internal or external environments. The challenges exist in the environment during the object’s communication and sensing is to make the decision properly and control the environment in an efficient manner. It is predicted that the greater number of connected devices that rises to 50 billion in 2020 according to the report by Cisco. Each IoT devices generate an enormous amount of unstructured and structured data. So, there is a need for automatic data analysis to make the decision effectively.

With the help of IoT, the environment become smarter and makes transport, cities more intelligent. The main goal of IoT is to enable the device to be connected at any time, anyplace with anything using a network or service. IoT is better than machine-to-machine(M2M), Wireless sensor networks, GSM, GPRS, microcontroller, GPS, microprocessor, 2G/3G/4G, etc. IoT is a fusion of hardware and software. It has the ability and flexibility to adapt to the environment. These systems allow the user to achieve deeper automation, analysis and integration within a system. The technology makes the environment so modernized that improves the quality of life. Deep Learning helps to make such kind of decision. It is a kind of Neural Networks (NN) which is a part of machine learning and uses a neural network to simulate human-like decision making. There are many kinds of neural networks such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Deep Belief Networks (DBN).

Deep learning plays a major part in the development of industrial robotics and processes a large amount of data when compared to machine learning and it is suitable for IoT environment. Deep learning takes longer time to train up the data and when it comes to hardware dependency and it requires GPU to train the data. This can be tuned in different ways but in machine learning, it has limited tuning capabilities. Deep learning comprises three layers including an input layer, hidden layer, output layer. The input layer is used to take input data and hidden layer learns by itself so it was a good learning process and it tends to be more accurate and efficient for performing various computations on input data. These neural networks are used to predict the output and perform classification on the data.

Deep learning requires a minimum time to infer information than any other methods. IoT devices produce an enormous amount of data and involve real-time communication so it is efficient to use deep learning models which provide better results than other models. By leveraging deep learning for IoT applications, aims to improve the learning performance, user experience and enhance network traffic. All the deep learning models allows the storage of information. Enabling deep learning in IoT devices and it also provides an efficient way to analyse the unstructured data and act intelligently to both the user and the environment. It also provides a quality network connection in IoT devices. The integration of deep learning in IoT makes the system to capture and understand the environment easily and act accordingly. The next section provides detailed information about IoT characteristics, architecture, security requirements, challenges and applications, advantages and disadvantages.