All you need to know about real-time analytics and IoT



We live in an increasingly interconnected world. We now have the technological wherewithal to communicate with each other in near real-time from across two ends of the globe. In the digital age, even machines can now communicate with each other, process information, and analyze data without the intervention of humans. This system of interrelated computing devices is known as ‘Internet of Things’ (IoT).

IoT is a part of many devices now – from lightbulbs and human organ implants to cameras and self-driving vehicles. Data is captured by IoT sensors embedded in these devices and transmitted over the internet (through wired or wireless networks) for storage or further processing by other connected devices. Together with big data (extremely large data sets that can be mined for useful insights) and cutting-edge business intelligence tools, IoT is reshaping how our world operates.

Internet of things and big data

It is estimated that by 2020, there will be 50.1 billion devices that will be IoT enabled (refer to the image above). And, IDC, in its 2018 Worldwide Semiannual Internet of Things Spending Guide, estimates that spending on IoT will hit a whopping $1.2 trillion by 2022, at a compounded annual growth rate of 13.6 percent.

Real time analytics

The evolution of IoT, big data, artificial intelligence, and machine learning is so fast-paced that it demands better processing capabilities. In order to make this system sustainable, technology is slowly shifting from conventional CPU’s to much larger GPU’s which helps keep pace with these emerging technologies. The data collected from these devices will exceed gigabytes of size within a span of a few hours. It is crucial that we maintain and process these data without any complications, for which we use big data technologies.

With the explosion in the number of IoT devices across the globe from a few millions to billions in the past years, there arises a necessity to harness the power of data to produce meaningful insights. IoT is being widely used in industries such as e-commerce sites, autonomous driving vehicles, manufacturing industries, healthcare firms, transportation sectors and utilities.

One of the emerging sectors that is utilizing IoT data is the automotive industry. Companies such as Elon Musk’s Tesla and tech giant Google have made significant headway in using IoT to build automobiles of the future. So, what does the use of IoT in automotive offer? We shall explore in the following sections.

IoT data and real-time analysis of connected cars

The huge amount data obtained from these IoT sensors can then be analyzed using predictive modelling. This data can be used to:

  1. Understand driving styles
  2. Create driver segmentation
  3. Identify risky neighbors
  4. Create risk profiles
  5. Compare microsegments

Now, let’s try to understand these uses in more detail.

Understand driving styles
With the data from sensors, we can dive deeper into the driving profiles of every vehicle of in a particular segment. This is useful in finding unique driving behavior of users for a set of vehicles. Parallel Coordinates Visualization is an effective method to understand multiple metrics at the same time. For example, we can consider the sensor data from a set of taxi drivers and compare their driving variables to check for similarities in driving styles and to figure out common defects.

Create driver segmentation

Creating driver segmentation would require the application of clustering techniques. Clustering helps in determining the intrinsic grouping in a given data. This helps in grouping vehicles with similar driving profiles, thus enabling us to classify cars into different categories such as taxi drivers, police cars, ambulances, tourers, domestic cars, and so on.

Identify risky neighbors

Through Random Forest models we can find the closest neighbors to a group of vehicles based on their driving profiles. This is highly useful when it comes to finding out potential cars that may fall prey to a particular defect in the near future.

Create risk profiles

Risk profiling involves analyzing and comparing sensor data from a particular vehicle against the entire data universe to suggest customized extended warranty care packages based on driving styles and defect patterns. This helps target customers at a vehicle level and to minimize the cost incurred due to excessive warranty claims.

Compare microsegments

From the data available, we can gain meaningful insights by comparing the driving variables, top defects, global vehicle distributions, and so on. This enables us to create microsegments. For example, we can compare cars of the same series, of two countries to get broad insights on variations in their driving standards and defect patterns.

IoT data and autonomous driving cars

The wide scale implementation of driverless cars connected through IoT has the potential to drastically reduce human error, increase the efficiency of traffic regulation, and mitigate randomness in driving styles. Advantages of using IoT data in connected cars include proactive maintenance, real-time monitoring using OBD (on-board diagnostics) and remote diagnosis of potential problems. Sensor data obtained from the vehicle is collected and saved on a dedicated database in real-time.

The data flows through a real-time pipeline that constitutes Kafka and Spark streaming, Cassandra and Spark MLlib for on-the-go machine learning. Kafka is used to collect and stream the data from the respective IP’s assigned to a particular device from time to time. Spark provides the platform to process the data to obtain insights. Cassandra is our primary database for storing all IoT data. Various other frameworks are also available in the market to achieve the same architecture.

There are three major categories in autonomous driving cars. They are:

  1. Driver assistance
  2. Semi-automated driving
  3. Fully-automated driving

Driver assistance

In this category, driver assistance systems merely support the driver but do not take control of the car. These systems use natural language processing and voice-enabled interaction to provide on-the-go driving assistance. Examples of driver assistance systems are automatic braking, automatic parking, collision avoidance systems, driver drowsiness detection, global positioning system (GPS) navigation, and so on.

Semi-automated driving

Semi-automated driving is a combination of both human and machine interaction for efficient and relaxed driving. The driver can choose to auto pilot from a variety of modes (city roads, highways, etc.) in case they want to have a quick nap. The cruise control is one of the most common examples of this.

Fully-automated driving

In fully automated driving systems, artificial intelligence (AI) takes complete responsibility of locomotion with the help of IoT devices. The vehicle assumes all the driving functions and the people in the car are merely passengers.

Road ahead

IoT data has signaled a new era in computing, however, the technology comes with some potential concerns that include processing power required to process such high volumes of data and security threats.

Though onboard computer and other devices are available to obtain useful insights, it is not always possible to compute a massive amount of stacked data with the existing processors. For this we need to make use of powerful processing systems. Security poses a great threat to IoT as this is an emerging technology and doesn’t have any kind of security regulations. Tech companies around the world are continuously working on functional development of their IoT model to secure the devices and the network.

It is evident that IoT and real time analytics offer an opportunity to scale new frontiers – with limitless possibilities for technological and human evolution.

Help your business realise the promise of advanced analytics

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