IoT Machine Learning

How to tackle challenges in IoT using Machine Learning?

It is estimated that by 2020 number of connected devices are expected to exceed 50 billion. More and more companies are adopting to Machine Learning and IoT and this is the buzzword of the industry right now. Potential areas where Machine Learning & IoT implemented is home automation, self-driving cars, telecom, automobile industry, wearables, oil & gas, manufacturing. Perfomatix is a top Machine Learning Development Company that constantly focuses on implementing technological trends by analyzing the feasibility of solutions.

As more devices are connected, a significant amount of data is coming from different sources. Machine learning helps in analyzing this data, apply relevant Machine learning algorithms, create data models, predicts outcomes and identifies problems. In this blog post, let us explore some of the challenges in IoT.

Top 4 Challenges in IoT

IoT data growth is exponentially increasing regarding quality, quantity, and volume. The real challenge of IoT devices is analyzing this large volume of data and take action in real-time.

Security: IoT devices are generating tons of data, and to ensure the security and increase efficiency machine learning is a critical component for developing IoT security. IoT security analytics is required to determine the security attacks, and data flows in IoT connected devices as it cannot be identified using traditional security networks like firewalls.

Connectivity: IoT connected devices should have a reliable two-way signaling network as sometimes devices have to collect data from the server or the server has to receive data from devices or sometimes devices has to talk to each other. In all these cases sometimes there will be connectivity drop-offs and require reliable signaling to ensure that the data reaches the other end of time.

Data Mining: Exploring large volumes of data and extracting useful information for future predictions is a major challenge facing in IoT devices.

Big Data Analysis: As data curated from different sources in different formats analysis of big data is a real challenge in IoT devices. And, analyzing this data in real-time is also making trouble as the data is extensive.

Use cases of Machine Learning in IoT

Security and Big Data Analysis – IoT devices generate mountains of data that humans cannot analyze and make predictions. Machine Learning helps in analyzing this high volume data, determine the data flows in IoT devices, it makes use of algorithms and makes predictions on data.

Anomaly Detection – In IoT devices, it is required to detect devices that show anomalies in sensor data that indicate bad product usage. Machine Learning helps in identifying these anomalies in IoT devices. The data collected from sensors analyzed with previous historical data and anomalies detected in real-time.

Clustering of Data – Big data technologies and data clustering algorithms considered as an essential data analytics tool in IoT. Machine Learning algorithms help in classification of IoT data.

Predictions of Data– In IoT, as more and more devices get connected to each other for the devices to communicate with each other more efficiently, it becomes inevitable for predictive analytics. Enterprises are using Machine Learning based predictive analytics to gain an advantage in the business. Machine Learning algorithms discover hidden patterns in unstructured data sets and uncover new information.

Perfomatix | Machine Learning Development Company

Perfomatix is a top Machine Learning Development Company that constantly focuses on implementing technological trends by analyzing the feasibility of solutions.

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