What is the Role of Machine Learning in IOT?

With the advent of Internet of Things (IoT), companies can easily gain access to large volumes of customer data on a regular basis. If effectively analyzed, it can help them gain useful insights, enabling them to make decisions regarding consumer behavior, business policies, market trends and more.

However, regularly assessing such high data volumes can be very hectic and lead to human errors. Thus, organizations are actively implementing machine learning for IoT?models in order to fulfill this need.

So, if you are thinking of using these solutions in your business, keep reading this blog. You will get a comprehensive idea of the use cases of ML models in IoT applications.

Convergence of IoT and Machine Learning

The need for analyzing high data volumes and automating these tasks to increase their speed and efficiency has led to the convergence of IoT and machine learning. Companies are actively training machine learning models to search patterns from IoT devices and make forecasts in several fields like:

• Data quality analysis
• Behavioral analysis
• Service quality
• Edge computing
• Smart Healthcare
• Resource consumption
• Neural networks
• Attack detection and prediction
• Distributed deep learning, etc.?

After successful training, these ML models can automatically categorize data, identify patterns and provide useful insights. Moreover, they can work 24/7, enabling companies to allocate their human resources to other parts of their operations.?

Machine Learning for IoT Devices

Here are some of the commonly used machine learning for IoT?applications:

1. On-device Machine Learning

On-device machine learning refers to applications that are present on devices (web browsers and applications) rather than sending data to a server for data processing.

Innovative frameworks like TensorFlow Lite allow companies to run on-device ML models on desktops, Android, iOS and other devices. They have low latency, added privacy features and can even work offline. Additionally, these software reduce costs as they utilize the device’s processing power, rather than being dependent on additional servers.?

2. Model Optimization for Resource-constrained Devices

Now, all devices may not have the high computing power required to run machine learning models. For them, model optimization can be a viable solution. Companies need to modify their machine learning models, architecture and initial parameters in such a way that it is implementable on lightweight devices and consumes less power while improving performance.

Internet of Things and machine learning model optimization for resource-constrained devices can be used for developing the following:

• Single-shot detector (SSD) and “You Only Look Once” models.
• IoT and edge applications
• Compressed deep learning models for explainable AI applications
• General light-weight architecture for deep learning problems
• ML models and architectures which can operate with less training data on remote applications, etc.

3. Anomaly Detection on IoT Devices

Internet of things machine learning?models can be used to detect abnormal data patterns. Usually, they are a result of external factors like cyberattacks or sensor failures. Earlier, manual data analysis was used for anomaly detection.

However, with the advent of machine learning models for IoT, such anomalies can be monitored round the clock and detected faster than human operators. Now, these frameworks can be used for various applications. Some of them are as follows:

1. Health monitoring applications for detecting diseases.
2. Incoming threats in battlefields.
3. Accident prevention mechanisms in transportation systems.
4. Detecting pollution levels, weather and climate changes and incoming disasters like tsunamis and earthquakes.

4. Predictive Maintenance in Edge Devices

ML models for predictive maintenance on Edge devices can reduce potential latency issues which come with operating these applications on remote servers. Such solutions are all the more necessary for industries where a slight delay in data transmission can lead to major problems.

IoT devices and sensors can collect data every second, while ML models can detect potential failures and notify the operators of the same. These solutions can also help organizations reduce data transfer and as a result their cloud storage costs.?

Security and Privacy in IoT and ML

IoT security and privacy encompasses systems, methods, tools, and processes which are responsible for protecting a company's Internet of things components. Take a look at its various aspects:

1. IoT Security Challenges: Although machine learning for IoT?can come in handy in several use cases, they are prone to several security flaws. Some of them are as follows:
2. Inadequate Visibility:?IoT devices are often deployed by users without the knowledge of the IT department. Thus, it becomes very difficult for the latter to maintain a list of which components need to be monitored and protected.?
3. Open-source code:?Open-source codes make ML IoT?a lot more customizable as per the company’s needs. However, it makes the software highly prone to bugs and several other vulnerabilities.?
4. High data volume:?The high volumes of data that IoT systems handle on a daily basis can make implementing data protection, management and oversight measures very difficult.
5. Difficulty in security system integrations:?Given the wide variety of IoT devices and their scaling capabilities, integrating security systems into this software can be a very hectic task.?
6. Lack of proper testing:?Developers usually do not pay much attention to security measures when designing IoT systems. Thus, they do not perform adequate vulnerability testing to identify the system’s weak points. For instance, APIs are a common entry point for cybercriminals. They use them as control centers in order to breach networks and launch attacks like man-in-the-middle (MITM), distributed denial of service (DDoS) and SQL injection.?
7. Lack of strong passwords:?IoT devices generally come with default passwords which can be difficult for new users to change on their own. Additionally, companies often use passwords which are very easy to crack. These factors make ML for IoT models an easy target for cybercriminals.
8. Unpatched vulnerabilities:?It is very common to find unpatched vulnerabilities in IoT devices. It may be due to the unavailability of software patches or difficulty in installing and assessing them.
9. A. Data Privacy and Compliance

IoT applications deal with high data volumes, most of which are private data. Thus, companies must obtain appropriate consent from users when storing, processing, and collecting data from IoT devices.

In this regard, organizations should implement transparent policies which inform customers of their purpose, retention and scope of data collection. Apart from this, based on geographical location and industry, IoT devices may be subject to various regulatory compliances.

Thus, companies should be aware of regulations like the California Consumer Privacy Act (CCPA), General Data Protection Regulation (GDPR), etc. Furthermore, they should ensure strict adherence to these policies while launching their IoT and ML?models.?

B. ML Model Security in IoT

Machine learning for IoT?security models is a set of security protocols and measures which are implemented to provide security from data breaches and cyber-attacks. They ensure data availability, confidentiality and integrity between devices, along with facilitating end-user privacy and security

IoT security models usually have the following components:

• Cloud Security:?As the name suggests, cloud security refers to the security measures implemented in order to safeguard the data stored on the cloud. It includes access control, encryption, data backup and recovery.
• Network Security:?Network security includes those security measures which are responsible for protecting the communication between systems and IoT devices. Security protocols like usage of TLS, SSL and HTTPS usually fall under this part, along with the usage of access control, firewalls and intrusion detection systems.?
• Device Security:?Under device security falls measures which provide device-level security from cyber threats and data breaches. It includes secure firmware updates, secure boot processes, high-level encryption and usage of strong passwords.
• Application Security:?Application security involves security processes and measures which protect the applications running on devices. Solutions in this regard involve providing regular updates for protection against malware and other security vulnerabilities.
• End-to-End Security:?End-to-end security measures involve securing the entire IoT system, including the cloud, devices, applications and everything in between. Companies can implement such solutions via regular security audits, stringent monitoring procedures, using VPNs, firewalls, etc.?

IoT Cloud Platforms and ML Integration

Now, one of the most common ways of managing ML models for data analysis is by using cloud computing platforms. Here are some points that you need to know

A. Cloud Services for IoT Data Management

The usage of cloud services for IoT data management can vary across companies. Some may use them for storing user data and information collected by sensors. Additionally, others may store their ML models for analyzing their data in real-time, generating reports and other tasks.

Some of the most popular cloud services in this regard are:

• AWS IoT Platform:?The Amazon Web Services IoT platform provides solutions for data collection from IoT devices and sensors. They also assist companies in collecting these data and sending them to the cloud for analysis, along with device management options.
• Thingworx 8 IoT Platform:?This platform is specifically designed for IoT app development. It features integrated machine learning, pre-built dashboard widgets and interconnection with RFIDs and sensors. Thus, companies can benefit from the reduced project complexity and facilitate quicker enterprise-level app development.
• Google Cloud’s IoT Platform:?Due to the presence of its top-notch machine intelligence, analytics and web-scale computing services, Google Cloud IoT is one of the best places to operate machine learning for IoT data management frameworks. It is scalable, efficient, has low access time and integrates with a wide array of Google services.?
• Microsoft Azure IoT Suite:?The Microsoft Azure IoT Suite features several pre-built connected solutions which allow users to improve their productivity and profitability. This platform has its own operating system for IoT devices and facilitates integrations with several third-party platforms like Oracle, WebSphere, Salesforce, etc.

B. Integrating ML Services with IoT Platforms

Now, you can integrate your own ML models into cloud IoT platforms. To do so, users need to install the neural network library software, which they used for training their model, in the IoT device.

Then, they can copy and paste the training model file and write the necessary code to load the model. To test whether the system is working, individuals can use the system to make a prediction.

However, to deploy ML models on IoT devices on Edge computers, companies need to write custom codes in C/C++.

C. Cloud-based Machine Learning for IoT

Many organizations may not have adequate on-premise resources to construct machine learning models. In this regard, they can use cloud computing resources like storage, compute and other services to train their ML models.

Such solutions enable companies to scale models as per their real-life workloads without the need for investing in additional hardware. Furthermore, thanks to the ease of operation, companies can utilize several ML capabilities without the need for hiring AI and data science experts.?

Machine Learning for IoT Real-world Applications

Following are some of the real-world applications of machine learning for IoT:

1. Smart Cities and Urban Planning

For building smart cities and efficient urban planning, ML for IoT applications can play a major role. They can analyze data on a large scale, understand the requirements of consumers in a particular area and help property developers make fruitful decisions regarding the same.?

For instance, in areas with no natural water sources, companies can develop a smart irrigation system using machine learning. Furthermore, for areas with no electricity supply, ML models can help develop smart solar panels and solve their energy needs.?

2. Precision Agriculture

Precision agriculture using ML models for internet of things devices can help farmers effectively improve their agricultural yield. They can use these systems to select crops with better yields, collect and process real-time farming data, forecast weather patterns and more.

Apart from this, they can also divide the fields into different zones in order to facilitate efficient fuel usage, pest control, fertilization and irrigation. As per reports, precision agriculture has increased agricultural output by 4% globally. It has also cut down herbicide and fertilizer usage by 9% and 7% respectively, along with actively reducing fossil fuel consumption.

3. Healthcare and Remote Monitoring

Today, thanks to IoT and ML models, healthcare is available to individuals living even in remote areas. Healthcare providers are offering telemedicine, which leverages telecommunication and machine learning to analyze patient data and provide medical consultations to patients remotely.

Moreover, healthcare experts can continuously monitor the health status of patients via remote monitoring applications. Such solutions enable them to assess the health status of individuals via mobile apps or wearables and take timely medical action. ?

4. Connected Vehicles and Transportation

AI ML IoT?applications can come in really handy in revolutionizing the transportation sector. Connected vehicles can process real-time traffic data via ML IoT models and make travels faster and safer.

Additionally, they can provide real-time mapping and access to remote emergency services to passengers making such vehicles usable even in case of long journeys.?

Future Trends of Machine Learning for IoT

Below are some of the future trends of machine learning for IoT:

1. Scalability and Interoperability

ML models can play a major role in facilitating scalability and interoperability in IoT systems. They can analyze data volume and based on them, notify the operators to accordingly scale their operations.

Coming to interoperability, machine learning systems can manage data from multiple sources. They can categorize and store them in various formats, which allow them to be interpreted by several types of devices, software and other technologies.?

2. Federated Learning in IoT

Federated learning refers to a distributed machine-learning technique which allows numerous IoT devices to jointly train as a machine learning model. It is used to construct ML models which are based on distributed data sets.

This technique facilitates local data preservation, data ownership, user data protection and In-Edge AI. Thus, it can be an effective solution for IoT cyber security. These ML models can analyze high-frequency data generated by time-series sensors without storage overheads and at the same time preserve user data privacy.?

3. Explainable AI for IoT Applications

Explainable AI for IoT applications can help developers understand how ML-powered IoT applications are arriving at their outcomes. They can help in characterizing models according to their accuracy, transparency, outputs and fairness, thereby assessing their overall reliability and efficiency.

4. 6G and Next-gen IoT

In the upcoming years, 6G will take high-speed internet connectivity to a new level. It will chiefly focus on converging the physical, human and digital worlds, along with filling the connectivity void in rural areas.

With speeds of around 1 Tbps and sub-millisecond latency, 6G will come in really handy in connecting next-gen IoT solutions like smart wearables, self-driving cars, remote-controlled factories, etc.

Conclusion

Now that you know how machine learning for IoT can impact businesses, you are ready to implement these solutions in your operations. But, before you start, you and your employees must have the necessary skills for operating the same.