November 24, 2020
Rapid advances in the telematics industry have dramatically boosted the efficiency of vehicle fleets and have found wide ranging applications from long haul transport to usage-based insurance. Incoming telemetry from a large fleet of vehicles provides a wealth of information that can help streamline operations and maximize productivity. However, telematics architectures face challenges in responding to telemetry in real time. Competitive pressures should spark innovation in this area, and real-time digital twins can help.
The volume of incoming telemetry challenges current telematics systems to keep up and quickly make sense of all the data. Here’s a typical telematics architecture for processing telemetry from a fleet of trucks:
Each truck today has a microprocessor-based sensor hub which collects key telemetry, such as vehicle speed and acceleration, engine parameters, trailer parameters, and more. It sends messages over the cell network to the telematics system, which uses its compute servers (that is, web and application servers) to store incoming messages as snapshots in an in-memory data grid, also known as a distributed cache. Every few seconds, the application servers collect batches of snapshots and write them to the database where they can be queried by dispatchers managing the fleet. At the same time, telemetry snapshots are stored in a data lake, such as HDFS, for offline batch analysis and visualization using big data tools like Spark. The results of batch analysis are typically produced after an hour’s delay or more. Lastly, all telemetry is archived for future use (not shown here).
This telematics architecture has evolved to handle ever increasing message rates (often reaching 2K messages per second), make up-to-the-minute information available to dispatchers, and feed offline analytics. Using a database, dispatchers can query raw telemetry to determine the information they need to manage the fleet in real time. This enables them to answer questions such as:
Offline analytics can mine the telemetry for longer term statistics that help managers assess the fleet’s overall performance, such as the average length of delivery or routing delays, the fleet’s change in fuel efficiency, the number of drivers exceeding their allowed shift times, and the number and type of mechanical issues. These statistics help pinpoint areas where dispatchers and other personnel can make strategic improvements.
There are three key limitations in this telematics architecture which impact its ability to provide managers with the best possible situational awareness. First, incoming telemetry from trucks in the fleet arrives too fast to be analyzed immediately. The architecture collects messages in snapshots but leaves it to human dispatchers to digest this raw information by querying a database. What if the system could separately track incoming telemetry for each truck, look for changes based on contextual information, and then alert dispatchers when problems were identified? For example, the system could perform continuous predictive analytics on the engine’s parameters with knowledge of the engine’s maintenance history and signal if an impending failure was detected. Likewise, it could watch for hazardous driving with information about the driver’s record and medical condition. Having the system continuously introspect on the telemetry for each truck would enable the dispatcher to spot problems and intervene more quickly and effectively.
A second key limitation is the lack of real-time aggregate analysis. Since this analysis must be performed offline in batch jobs, it cannot respond to immediate issues and is restricted to assessing overall fleet performance. What if the real-time telemetry tracking for each truck could be aggregated within seconds to spot emerging issues that affect many trucks and require a strategic response? These issues could include:
The current telematics architecture also has inherent scalability issues in the form of network bottlenecks. Because all telemetry is stored in the in-memory data grid and accessed by a separate farm of compute servers, the network between the grid and the server farm can quickly bottleneck as the incoming message rate increases. As the fleet size grows and the message rate per truck increases from once per minute to once per second, the telematics system may not be able to handle the additional incoming telemetry.
A new software architecture for streaming analytics based on the concept of real-time digital twins can address these challenges and add significant capabilities to telematics systems. This new, object-oriented software technique provides a memory-based orchestration framework for tracking and analyzing telemetry from each data source. It comprises message-processing code and state variables which host dynamically evolving contextual information about the data source. For example, the real-time digital twin for a truck could look like this:
Instead of just snapshotting incoming telemetry, real-time digital twins for every data source immediately analyze it, update their state information about the truck’s condition, and send out alerts or commands to the truck or to managers as necessary. For example, they can track engine telemetry with knowledge of the engine’s known issues and maintenance history. They can track position, speed, and acceleration with knowledge of the route, schedule, and driver (allowed time left, driving record, etc.). Message-processing code can incorporate a rules engine or machine learning to amplify their capabilities.
Real-time digital twins digest raw telemetry and enable intelligent alerting in the moment that assists both drivers and dispatchers in surfacing issues that need immediate attention. They are much easier to develop than typical streaming analytics applications, which have to sift through the telemetry from all data sources to pick out patterns of interest and which lack contextual information to guide them. Because they are implemented using in-memory computing techniques, real-time digital twins are fast (typically responding to messages in a few milliseconds) and transparently scalable to handle hundreds of thousands of data sources and message rates exceeding 100K messages/second.
Here’s a depiction of real-time digital twins running within an in-memory data grid in a telematics architecture:
In addition to fitting within an overall architecture that includes database query and offline analytics, real-time digital twins enable built-in aggregate analytics and visualization. They provide curated state information derived from incoming telemetry that can be continuously aggregated and visualized to boost situational awareness for managers, as illustrated below. This opens up an important new opportunity to aggregate performance indicators needed in real time, such as emerging road delays by region or impending scheduling issues due to timed out drivers, that can be acted upon while new problems are still nascent. Real-time aggregate analytics add significant new capabilities to telematics systems.
While telematics systems currently provide a comprehensive feature set for managing fleets, they lack the important ability to track and analyze telemetry from each vehicle in real time and then aggregate derived information to maintain continuous situational awareness for the fleet. Real-time digital twins can address these shortcomings with a powerful, fast, easy to develop, and highly scalable software architecture. This new software technique has the potential to make a major impact on the telematics industry.
To learn more about real-time digital twins in action, take a look at ScaleOut Software’s streaming service for hosting real-time digital twins in the cloud or on-premises here.
.jpg)