- Trending Categories
- Data Structure
- Operating System
- MS Excel
- C Programming
- Social Studies
- Fashion Studies
- Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Deep Learning and the Internet of Things
Deep Learning provides a new horizon to the Internet of Things. The availability of different IoT sensors helps us collect data that is so important for Deep Learning. There are many uses of Deep Learning in IoT, which makes IoT more powerful. Deep Learning and the Internet of Things together form a new era that is more advanced than Web 3.0.
The major key sub-divisions under IoT, like IoP (Internet of People), IoT (Internet of Everything), and IIoT (Industrial Internet of Things), are developing and highly dependent upon Deep Learning technology. There are many different Deep Learning Models which can handle multiple tasks along with different IoT sensor data. But what do the deep learning models do? Let us look at how Deep Learning architecture works and what it can provide to the future generation.
Deep Learning Architecture
As the name suggests, it is a highly dense Neural Network that allows the machine to learn new things. You may wonder how Tesla automatically decides the path lane and avoids crashing with some funky truck driver. We first need to learn Neural Network architecture to understand Deep Learning architecture. It is not very simple, but not complex. Assume a network is like a web, which has different layers. Each layer consists of nodes or neurons, interconnected with every neuron between two layers. So a combination of neurons in a specific arrangement will make a neural network.
A neuron takes input, say some data, sentence, word, pixel, binary, tokens, letters, etc., any data that can fit. Then it processes that data by matrix multiplication and sends that to the next neuron. Hence, all neural networks are just a Linear Combination of Vectors multiplied layer by layer. But it is still linear and similar to predicting a sine curve with a straight line. To bring the non-linearity, we use the Activation function, such as Sigmoid, ReLU, Softmax, Tanh, etc., which helps predict/recognize anything related to the given data.
Different Deep Learning Models
There are three parts of Deep Learning architecture: The Input layer, the Hidden Layer, and the Output layer. The main difference between a neural network and a deep neural network is that it has more than one hidden layer. There are many different Deep Learning models such as RNN (Used for text and audio recognition), CNN (mainly used for Image prediction), AE (Auto Encoder to process tokens and encode data), GAN (Generative autoencoder to generate AI images), etc.
Deep Learning and the Internet of Things together
Combining DL with IoT gives us what we have wanted for years. There are tons of use for IoT devices that use deep learning techniques. Such as smart IoT watches, traffic cameras, smart sensors, humidity and temperature sensors, weather prediction tools, etc. Even your smartphone is just a perfect example of IoT with DL. You watch Youtube on your smartphone with just the stuff you like to watch. Some broad areas of IoT with DL are discussed below.
Applications of IoT with Deep Learning
Healthcare − Robotics in the medical sector is a perfect example of IoT and Deep learning in healthcare. Monitoring every patient for continuous 24-hour was not possible. Still, with the help of smart IoT medical devices, it can measure critical changes to a patient's body and alerts in emergency situations. Deep Learning smart device is already trained to predict patient’s health using symptoms.
Smart Home − As you may be aware smart devices like amazon echo and smart lights, both connected using IoT and DL models, help to adjust the light settings according to weather or mood automatically. Smart vacuum robots clean your house with just one command. Moreover, energy demand prediction, behavior monitoring, human activity, preference recognition, etc., can be developed.
Smart Manufacturing − At the industry level, IoT and DL have a huge demand. Monitoring and maintaining machine condition and worker activity recognition is possible due to IoT and DL combined. Also, some Fault detection and Reuse of recyclable machine parts and products significantly can be achieved.
Smart Grid − Smart grid can be important in forecasting power demand and electricity price prediction using smart devices because it can raise the stocks. Imagine full control of electricity and its usage at your home using just a device can be possible. Electricity theft detection, anomaly detection, etc., can be achieved.
Smart Transportation − Traffic monitoring devices and sensors helps in many different ways. Some people may feel bad about traffic speed detection, number plate detection, and all those rules. But they are for aid. Obstacle detection and accident detection can be very helpful in preventing deaths caused by road accidents.
Smart Agriculture − IoT and agriculture have a special bond. It always helps to increase crop production through plant classification, disease recognition, crop production, fruit, and veggies counting, weed detection, soil health and tree plantation programs, etc.
Musical Instruments − Now, you can tune your guitar automatically using tuning sensors without even touching the tuners! There are many sensors and sound devices that are evolving day by day. Moreover, audio recognition, music suggestions based on your current mood, and many more features are available to use freely.
Smart Cities − All those small smart devices and Internet can make a whole area or sector or a city smart. With smart supply chain management, any city corporation can plan new ideologies to implement. Proper food and raw materials management, weather predictions and planning, flood areas, garbage management, etc., can all be achieved. Hence using those benefits, people of the city can live and enjoy the new era of the 21st century.
All these IoT devices available to us now are good enough to automate your whole home/area/city. These beautiful pillars of technology and new ideas can motivate anyone to contribute to IoT and AI technology one can. Ultimately it benefits both the individual and the surrounding organization. And many new devices are under research work that uses both Deep Learning and Internet of Things to make our lives far more advanced than ever.
- Related Articles
- What are the Prerequisites for Learning the Internet of Things (IoT)?
- Smart Government and the Internet of Things
- Difference between Deep Learning and Reinforcement Learning
- Relation between deep learning and machine learning
- Understanding Things in Internet of Things
- The Internet of Nano Things
- Deep Web: The Dark Side of Internet
- Edge Computing of the Internet of Things
- Difference between Deep Learning and NLP
- Internet of Robotic Things (IoRT)
- Understanding Internet of Robotic Things
- The Biggest Challenge of the Internet of Things
- Deep Belief Network (DBN) in Deep Learning
- Startups Focused on the Internet of Things
- Roadmap to study AI, Machine Learning, and Deep Machine Learning