Introduction to IoT Data Analytics
When the internet was first created, it was only accessible to a small group of private companies and government agencies. After the World Wide Web (WWW) was created in 1989 by Time Berners-Lee (who would later be knighted for his contributions), the internet became widely accessible and businesses around the world began coding their own websites and connecting with customers online.
The next major turning point in connected technologies was the mobile revolution of the early 2000s. As computer processors and data storage became smaller, faster, and cheaper, the world's leading electronics manufacturers released the first smartphones. In 2019, there are 5.1 billion unique mobile subscribers with nearly 9 billion total connected devices - that's more connected mobile devices than the entire population of the world.
The Internet of Things (IoT) represents the next major step in the evolution of connective information technology. With the IoT, connectivity has extended beyond mainframes, computers, and connected mobile devices to include physical objects of all kinds in a variety of commercial, industrial, and consumer applications. Any device that can be connected to the internet is an IoT device, and there are already billions of IoT devices deployed around the world today (and more on the way).
Big Data & IoT Analytics - A Perfect Fit
The development of IoT solutions required processors that were cheap to manufacture and consumed little power while being capable of wireless communication. In all, three major factors have made it possible for billions of IoT devices to exist today:
- RFID - The widespread adoption and integration of RFID tags into objects. An RFID tag is a smart barcode that can be identified and tracked using electromagnetic fields. They have small on-board batteries, use very little power and can be detected by an RFID reader or scanner from hundreds of yards away.
- IPv6 - The previous Internet Protocol Version 4 (IPv4) standard allotted just 32 bits of space for IP addresses, and as a result, there was a growing realization that the number of available IP addresses would be exhausted. The new IPv6 standard allocates 128 bits of space for IP addresses, making billions more IP addresses available for assignment. Without the new IPv6 standard, it would be impossible for each IoT device to have its own unique IP address.
- Enhanced Wireless Speed - The increased availability and speed of wireless networks have accelerated the adoption of IoT devices, since more businesses and consumers have existing wireless infrastructure that can be used or modified for use with IoT devices.
Image by The concept of Big Data has grown in relevance and importance along with the proliferation of the IoT. The key difference between data and "Big Data" comes down to three factors: volume, velocity and variety.
- With more connected devices (hardware) and applications (software) floating around, humans and machines are generating a greater volume of data than ever before.
- The velocity of that data is also increasing - it continues to be generated at faster and faster rates each day.
- There is also significant variety in the types of data being generated - textual, images, videos, audio, sensor data and metadata, just to name a few.
It is also useful to distinguish between human-generated data and machine data. Facebook users upload more than 900 million photos a day - that's human-generated data. Machine data includes things like sensor readings from IoT devices, computer-generated event logs from an application or operating system, telemetry logs, financial instrument trades, and others. The discipline of big data analytics has evolved to deal with the growing volume, velocity and variety of data that is being produced each day.
Big Data and IoT Data Analytics
IoT data analytics represents the implementation of Big Data management and processing techniques to IoT analytics applications. Companies or facilities that deploy a high number of IoT devices need big data analytics to collect data from across the network, clean and transform the data, aggregate it into a single system, and analyze the data in real time to make effective use of it.
It may be difficult for the uninitiated to fully appreciate the quantity of data that can be generated and captured using IoT devices, so let's look at a current example. The United Parcel Service (UPS) headquartered in Atlanta, Georgia recently undertook a project to improve its delivery service using the IoT. Each vehicle in the fleet has been fitted with a connected device that uses sensors to capture data from the environment.
Sensors are the key to data generation in the IoT. Different types of sensors can be programmed to collect information on temperature, humidity, other environmental factors, pressure, location, speed, acceleration, the orientation of a physical object in space, infrared, smoke, gas, chemicals, and many, many other things. In the case of the UPS trucks, sensors are used to collect data on roughly 200 different environmental and operational factors.
The UPS delivery fleet consists of roughly 110,000 vehicles. Each one records 200 different types of data. The data will also be combined with metadata (date, time, truck number, driver, etc.) to make it more relevant and useful. Even if the trucks only transmit sensor data from their RFID chips to a centralized RFID reader once per day (somewhat useful, but not very useful), that's still at least 22,000,000 individual data points per day.
Now, imagine that UPS wants real-time visibility into their operations using this sensor data so they decide to transmit data from the trucks every six seconds - that makes 14,400 uploads per day, generating over 300 billion unique data points. Big data processing and analytics is the major key to making sense of all of this data and extracting useful and actionable insights that can drive business results.
The Los Angeles MTA has also tried to implement this type of real-time monitoring on their city buses allowing them to optimize truck usage and monitor traffic flows. The Total Phase Komodo CAN Duo Interface was used to collect the CAN data from the city buses and transport it back to the MTA headquarters.
IoT Data Analytics Applications
IoT devices and big data analytics capabilities are becoming cheaper and more widely available, creating new opportunities for IoT analytics applications that drive innovation and business decision-making across industry verticals. While some companies choose to manage their own Internet of Things analytics, there are also IoT data services and IoT analytics services - companies that specialize in the effective processing of IoT data into business insights.
With that in mind, let's review three innovative new industrial IoT applications:
IoT Data Analytics in Agriculture
Farmers are using data from IoT devices to improve their crop yields, planning, and maintenance of agricultural operations. A company called The Climate Corporation is using IoT devices with sensors that measure soil quality and moisture, helping farmers determine how to rotate crops and when they should be watered. Farmers are also using IoT devices to collect data from farming vehicles and are using IoT drones for aerial imagery analytics.
IoT Data Analytics in Food Services
Restaurants and bars are using the IoT to help monitor their inventory and find more efficient ways to manage business. A company called I-TAPR2 technologies uses a wireless smart tap that monitors beer flow and helps food service managers determine which products are selling the most, when to order new inventory, which beverages it should focus on marketing and which ones it should stop carrying.
IoT Data Analytics in Logistics Management
Through our UPS example, we've already seen how IoT data analytics can impact logistics management. UPS claims on their website that IoT data analytics has helped them find savings of over $400 million annually. That's mostly on the transportation side, but on the warehousing side of logistics there are even more ways to leverage the IoT. Warehouses can use sensors and robotics to optimize their layout, reduce labor costs, track inventory and orders through the supply chain, and automate inventory management to reduce errors.
Total Phase Builds Tools and Products for IoT Engineers
If you're building an innovative new product that will change the world with IoT data analytics, Total Phase is here to help. Our range of development and diagnostic products can help you save time and resources in product development, testing, and debugging, helping you innovate faster and reduce your time-to-market.
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